program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
    func main<ios18>(tensor<fp32, [1, 96000]> audio) {
            int32 var_18 = const()[name = string("op_18"), val = int32(192)];
            int32 var_19 = const()[name = string("op_19"), val = int32(72)];
            int32 var_23 = const()[name = string("op_23"), val = int32(96)];
            int32 var_27 = const()[name = string("op_27"), val = int32(144)];
            fp32 var_36 = const()[name = string("op_36"), val = fp32(0x1.5798eep-27)];
            fp32 var_37 = const()[name = string("op_37"), val = fp32(0x1.7d784p+26)];
            int32 var_40 = const()[name = string("op_40"), val = int32(1)];
            int32 var_41 = const()[name = string("op_41"), val = int32(2)];
            int32 var_43 = const()[name = string("op_43"), val = int32(-1)];
            tensor<int32, [1]> x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor<int32, [1]>([1])];
            string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1, 96000]> audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_87")];
            tensor<fp16, [1, 1, 96000]> x_3_cast_fp16 = expand_dims(axes = x_3_axes_0, x = audio_to_fp16)[name = string("x_3_cast_fp16")];
            tensor<int32, [1]> var_55_axes_0 = const()[name = string("op_55_axes_0"), val = tensor<int32, [1]>([2])];
            bool var_55_keep_dims_0 = const()[name = string("op_55_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 1]> var_55_cast_fp16 = reduce_mean(axes = var_55_axes_0, keep_dims = var_55_keep_dims_0, x = x_3_cast_fp16)[name = string("op_55_cast_fp16")];
            tensor<fp16, [1, 1, 96000]> var_56_cast_fp16 = sub(x = x_3_cast_fp16, y = var_55_cast_fp16)[name = string("op_56_cast_fp16")];
            tensor<fp16, [1, 1, 96000]> square_0_cast_fp16 = square(x = var_56_cast_fp16)[name = string("square_0_cast_fp16")];
            tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = string("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([2])];
            bool reduce_mean_1_keep_dims_0 = const()[name = string("reduce_mean_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 1]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = string("reduce_mean_1_cast_fp16")];
            tensor<fp16, [1, 1, 1]> sqrt_0_cast_fp16 = sqrt(x = reduce_mean_1_cast_fp16)[name = string("sqrt_0_cast_fp16")];
            fp16 var_59_to_fp16 = const()[name = string("op_59_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 1]> var_60_cast_fp16 = add(x = sqrt_0_cast_fp16, y = var_59_to_fp16)[name = string("op_60_cast_fp16")];
            tensor<fp16, [1, 1, 96000]> var_61_cast_fp16 = real_div(x = var_56_cast_fp16, y = var_60_cast_fp16)[name = string("op_61_cast_fp16")];
            tensor<int32, [6]> x_7_pad_0 = const()[name = string("x_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
            string x_7_mode_0 = const()[name = string("x_7_mode_0"), val = string("reflect")];
            fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 96001]> x_7_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = x_7_mode_0, pad = x_7_pad_0, x = var_61_cast_fp16)[name = string("x_7_cast_fp16")];
            string var_71_pad_type_0 = const()[name = string("op_71_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_71_strides_0 = const()[name = string("op_71_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_71_pad_0 = const()[name = string("op_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_71_dilations_0 = const()[name = string("op_71_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_71_groups_0 = const()[name = string("op_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2]> model_spec_torchfbank_1_flipped_filter_to_fp16 = const()[name = string("model_spec_torchfbank_1_flipped_filter_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1.f0cp-1, 0x1p+0]]])];
            tensor<fp16, [1, 1, 96000]> var_71_cast_fp16 = conv(dilations = var_71_dilations_0, groups = var_71_groups_0, pad = var_71_pad_0, pad_type = var_71_pad_type_0, strides = var_71_strides_0, weight = model_spec_torchfbank_1_flipped_filter_to_fp16, x = x_7_cast_fp16)[name = string("op_71_cast_fp16")];
            string real_part_pad_type_0 = const()[name = string("real_part_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> real_part_pad_0 = const()[name = string("real_part_pad_0"), val = tensor<int32, [2]>([80, 80])];
            tensor<int32, [1]> real_part_strides_0 = const()[name = string("real_part_strides_0"), val = tensor<int32, [1]>([160])];
            tensor<int32, [1]> real_part_dilations_0 = const()[name = string("real_part_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 real_part_groups_0 = const()[name = string("real_part_groups_0"), val = int32(1)];
            tensor<fp16, [256, 1, 400]> model_spec_torchfbank_2_real_kernel_pt_to_fp16 = const()[name = string("model_spec_torchfbank_2_real_kernel_pt_to_fp16"), val = tensor<fp16, [256, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp16, [1, 256, 599]> real_part_cast_fp16 = conv(dilations = real_part_dilations_0, groups = real_part_groups_0, pad = real_part_pad_0, pad_type = real_part_pad_type_0, strides = real_part_strides_0, weight = model_spec_torchfbank_2_real_kernel_pt_to_fp16, x = var_71_cast_fp16)[name = string("real_part_cast_fp16")];
            string imag_part_pad_type_0 = const()[name = string("imag_part_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> imag_part_pad_0 = const()[name = string("imag_part_pad_0"), val = tensor<int32, [2]>([80, 80])];
            tensor<int32, [1]> imag_part_strides_0 = const()[name = string("imag_part_strides_0"), val = tensor<int32, [1]>([160])];
            tensor<int32, [1]> imag_part_dilations_0 = const()[name = string("imag_part_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 imag_part_groups_0 = const()[name = string("imag_part_groups_0"), val = int32(1)];
            tensor<fp16, [256, 1, 400]> model_spec_torchfbank_2_image_kernel_pt_to_fp16 = const()[name = string("model_spec_torchfbank_2_image_kernel_pt_to_fp16"), val = tensor<fp16, [256, 1, 400]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204928)))];
            tensor<fp16, [1, 256, 599]> imag_part_cast_fp16 = conv(dilations = imag_part_dilations_0, groups = imag_part_groups_0, pad = imag_part_pad_0, pad_type = imag_part_pad_type_0, strides = imag_part_strides_0, weight = model_spec_torchfbank_2_image_kernel_pt_to_fp16, x = var_71_cast_fp16)[name = string("imag_part_cast_fp16")];
            tensor<fp16, [1, 256, 599]> var_88_cast_fp16 = mul(x = real_part_cast_fp16, y = real_part_cast_fp16)[name = string("op_88_cast_fp16")];
            tensor<fp16, [1, 256, 599]> var_89_cast_fp16 = mul(x = imag_part_cast_fp16, y = imag_part_cast_fp16)[name = string("op_89_cast_fp16")];
            tensor<fp16, [1, 256, 599]> fft_cast_fp16 = add(x = var_88_cast_fp16, y = var_89_cast_fp16)[name = string("fft_cast_fp16")];
            string fft_cast_fp16_to_fp32_dtype_0 = const()[name = string("fft_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 256, 599]> fft_cast_fp16_to_fp32 = cast(dtype = fft_cast_fp16_to_fp32_dtype_0, x = fft_cast_fp16)[name = string("cast_86")];
            tensor<fp32, [1, 256, 599]> clip_0 = clip(alpha = var_36, beta = var_37, x = fft_cast_fp16_to_fp32)[name = string("clip_0")];
            string mel_spectrograms_pad_type_0 = const()[name = string("mel_spectrograms_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> mel_spectrograms_strides_0 = const()[name = string("mel_spectrograms_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> mel_spectrograms_pad_0 = const()[name = string("mel_spectrograms_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> mel_spectrograms_dilations_0 = const()[name = string("mel_spectrograms_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 mel_spectrograms_groups_0 = const()[name = string("mel_spectrograms_groups_0"), val = int32(1)];
            string clip_0_to_fp16_dtype_0 = const()[name = string("clip_0_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [72, 256, 1]> model_spec_torchfbank_2_melbanks_pt_to_fp16 = const()[name = string("model_spec_torchfbank_2_melbanks_pt_to_fp16"), val = tensor<fp16, [72, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409792)))];
            tensor<fp16, [1, 256, 599]> clip_0_to_fp16 = cast(dtype = clip_0_to_fp16_dtype_0, x = clip_0)[name = string("cast_85")];
            tensor<fp16, [1, 72, 599]> mel_spectrograms_cast_fp16 = conv(dilations = mel_spectrograms_dilations_0, groups = mel_spectrograms_groups_0, pad = mel_spectrograms_pad_0, pad_type = mel_spectrograms_pad_type_0, strides = mel_spectrograms_strides_0, weight = model_spec_torchfbank_2_melbanks_pt_to_fp16, x = clip_0_to_fp16)[name = string("mel_spectrograms_cast_fp16")];
            string mel_spectrograms_cast_fp16_to_fp32_dtype_0 = const()[name = string("mel_spectrograms_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 72, 599]> mel_spectrograms_cast_fp16_to_fp32 = cast(dtype = mel_spectrograms_cast_fp16_to_fp32_dtype_0, x = mel_spectrograms_cast_fp16)[name = string("cast_84")];
            tensor<fp32, [1, 72, 599]> clip_1 = clip(alpha = var_36, beta = var_37, x = mel_spectrograms_cast_fp16_to_fp32)[name = string("clip_1")];
            string clip_1_to_fp16_dtype_0 = const()[name = string("clip_1_to_fp16_dtype_0"), val = string("fp16")];
            fp16 var_99_to_fp16 = const()[name = string("op_99_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 72, 599]> clip_1_to_fp16 = cast(dtype = clip_1_to_fp16_dtype_0, x = clip_1)[name = string("cast_83")];
            tensor<fp16, [1, 72, 599]> x_9_cast_fp16 = add(x = clip_1_to_fp16, y = var_99_to_fp16)[name = string("x_9_cast_fp16")];
            fp32 x_11_epsilon_0 = const()[name = string("x_11_epsilon_0"), val = fp32(0x1p-149)];
            tensor<fp16, [1, 72, 599]> x_11_cast_fp16 = log(epsilon = x_11_epsilon_0, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [1]> var_103_axes_0 = const()[name = string("op_103_axes_0"), val = tensor<int32, [1]>([-1])];
            bool var_103_keep_dims_0 = const()[name = string("op_103_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 72, 1]> var_103_cast_fp16 = reduce_mean(axes = var_103_axes_0, keep_dims = var_103_keep_dims_0, x = x_11_cast_fp16)[name = string("op_103_cast_fp16")];
            tensor<fp16, [1, 72, 599]> x_13_cast_fp16 = sub(x = x_11_cast_fp16, y = var_103_cast_fp16)[name = string("x_13_cast_fp16")];
            tensor<int32, [1]> inp_axes_0 = const()[name = string("inp_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 72, 599]> inp_cast_fp16 = expand_dims(axes = inp_axes_0, x = x_13_cast_fp16)[name = string("inp_cast_fp16")];
            tensor<int32, [4]> input_3_begin_0 = const()[name = string("input_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> input_3_end_0 = const()[name = string("input_3_end_0"), val = tensor<int32, [4]>([1, 1, 72, 596])];
            tensor<bool, [4]> input_3_end_mask_0 = const()[name = string("input_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 1, 72, 596]> input_3_cast_fp16 = slice_by_index(begin = input_3_begin_0, end = input_3_end_0, end_mask = input_3_end_mask_0, x = inp_cast_fp16)[name = string("input_3_cast_fp16")];
            tensor<int32, [2]> var_131 = const()[name = string("op_131"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_132 = const()[name = string("op_132"), val = tensor<int32, [2]>([1, 1])];
            string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("same")];
            tensor<int32, [4]> x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [64, 1, 3, 3]> model_backbone_stem_0_weight_to_fp16 = const()[name = string("model_backbone_stem_0_weight_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446720)))];
            tensor<fp16, [64]> model_backbone_stem_0_bias_to_fp16 = const()[name = string("model_backbone_stem_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447936)))];
            tensor<fp16, [1, 64, 72, 596]> x_17_cast_fp16 = conv(bias = model_backbone_stem_0_bias_to_fp16, dilations = var_132, groups = var_40, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_131, weight = model_backbone_stem_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("x_17_cast_fp16")];
            tensor<int32, [1]> u_1_axes_0 = const()[name = string("u_1_axes_0"), val = tensor<int32, [1]>([1])];
            bool u_1_keep_dims_0 = const()[name = string("u_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 72, 596]> u_1_cast_fp16 = reduce_mean(axes = u_1_axes_0, keep_dims = u_1_keep_dims_0, x = x_17_cast_fp16)[name = string("u_1_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> var_138_cast_fp16 = sub(x = x_17_cast_fp16, y = u_1_cast_fp16)[name = string("op_138_cast_fp16")];
            fp16 var_41_promoted_to_fp16 = const()[name = string("op_41_promoted_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 64, 72, 596]> var_139_cast_fp16 = pow(x = var_138_cast_fp16, y = var_41_promoted_to_fp16)[name = string("op_139_cast_fp16")];
            tensor<int32, [1]> s_1_axes_0 = const()[name = string("s_1_axes_0"), val = tensor<int32, [1]>([1])];
            bool s_1_keep_dims_0 = const()[name = string("s_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 72, 596]> s_1_cast_fp16 = reduce_mean(axes = s_1_axes_0, keep_dims = s_1_keep_dims_0, x = var_139_cast_fp16)[name = string("s_1_cast_fp16")];
            fp16 var_143_to_fp16 = const()[name = string("op_143_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 72, 596]> var_144_cast_fp16 = add(x = s_1_cast_fp16, y = var_143_to_fp16)[name = string("op_144_cast_fp16")];
            tensor<fp16, [1, 1, 72, 596]> var_145_cast_fp16 = sqrt(x = var_144_cast_fp16)[name = string("op_145_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> x_19_cast_fp16 = real_div(x = var_138_cast_fp16, y = var_145_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [64]> x_21_mean_0_to_fp16 = const()[name = string("x_21_mean_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448128)))];
            tensor<fp16, [64]> x_21_variance_0_to_fp16 = const()[name = string("x_21_variance_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448320)))];
            tensor<fp16, [64]> x_21_gamma_0_to_fp16 = const()[name = string("x_21_gamma_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448512)))];
            tensor<fp16, [64]> x_21_beta_0_to_fp16 = const()[name = string("x_21_beta_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448704)))];
            fp16 x_21_epsilon_0_to_fp16 = const()[name = string("x_21_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 72, 596]> x_21_cast_fp16 = batch_norm(beta = x_21_beta_0_to_fp16, epsilon = x_21_epsilon_0_to_fp16, gamma = x_21_gamma_0_to_fp16, mean = x_21_mean_0_to_fp16, variance = x_21_variance_0_to_fp16, x = x_19_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> var_159 = const()[name = string("op_159"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> reshape_0_shape_0 = const()[name = string("reshape_0_shape_0"), val = tensor<int32, [4]>([1, 64, 72, 596])];
            tensor<fp16, [1, 72, 64, 596]> var_160_cast_fp16 = transpose(perm = var_159, x = x_21_cast_fp16)[name = string("transpose_49")];
            tensor<fp16, [1, 64, 72, 596]> reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = var_160_cast_fp16)[name = string("reshape_0_cast_fp16")];
            tensor<int32, [2]> reduce_mean_2_axes_0 = const()[name = string("reduce_mean_2_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_2_keep_dims_0 = const()[name = string("reduce_mean_2_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = reshape_0_cast_fp16)[name = string("reduce_mean_2_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> sub_1_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_2_cast_fp16)[name = string("sub_1_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> square_1_cast_fp16 = square(x = sub_1_cast_fp16)[name = string("square_1_cast_fp16")];
            tensor<int32, [2]> reduce_mean_4_axes_0 = const()[name = string("reduce_mean_4_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_4_keep_dims_0 = const()[name = string("reduce_mean_4_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_4_cast_fp16 = reduce_mean(axes = reduce_mean_4_axes_0, keep_dims = reduce_mean_4_keep_dims_0, x = square_1_cast_fp16)[name = string("reduce_mean_4_cast_fp16")];
            fp16 add_0_y_0_to_fp16 = const()[name = string("add_0_y_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 1]> add_0_cast_fp16 = add(x = reduce_mean_4_cast_fp16, y = add_0_y_0_to_fp16)[name = string("add_0_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> sqrt_1_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = string("sqrt_1_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> real_div_0_cast_fp16 = real_div(x = sub_1_cast_fp16, y = sqrt_1_cast_fp16)[name = string("real_div_0_cast_fp16")];
            tensor<int32, [3]> reshape_1_shape_0 = const()[name = string("reshape_1_shape_0"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 4608, 596]> reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = string("reshape_1_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_2_to_fp16 = const()[name = string("reshape_2_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448896)))];
            tensor<fp16, [1, 4608, 596]> mul_0_cast_fp16 = mul(x = reshape_1_cast_fp16, y = reshape_2_to_fp16)[name = string("mul_0_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_3_to_fp16 = const()[name = string("reshape_3_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458176)))];
            tensor<fp16, [1, 4608, 596]> add_1_cast_fp16 = add(x = mul_0_cast_fp16, y = reshape_3_to_fp16)[name = string("add_1_cast_fp16")];
            tensor<int32, [1]> var_178_axes_0 = const()[name = string("op_178_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 4608, 596]> var_178_cast_fp16 = expand_dims(axes = var_178_axes_0, x = add_1_cast_fp16)[name = string("op_178_cast_fp16")];
            bool xs_1_interleave_0 = const()[name = string("xs_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1, 4608, 596]> xs_1_cast_fp16 = concat(axis = var_40, interleave = xs_1_interleave_0, values = var_178_cast_fp16)[name = string("xs_1_cast_fp16")];
            tensor<int32, [1]> x_23_axes_0 = const()[name = string("x_23_axes_0"), val = tensor<int32, [1]>([1])];
            bool x_23_keep_dims_0 = const()[name = string("x_23_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 4608, 596]> x_23_cast_fp16 = reduce_sum(axes = x_23_axes_0, keep_dims = x_23_keep_dims_0, x = xs_1_cast_fp16)[name = string("x_23_cast_fp16")];
            tensor<int32, [4]> var_186 = const()[name = string("op_186"), val = tensor<int32, [4]>([1, 72, 64, 596])];
            tensor<fp16, [1, 72, 64, 596]> var_187_cast_fp16 = reshape(shape = var_186, x = x_23_cast_fp16)[name = string("op_187_cast_fp16")];
            tensor<int32, [4]> var_188 = const()[name = string("op_188"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(64)];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 1, 1]> model_backbone_stage0_2_weight_to_fp16 = const()[name = string("model_backbone_stage0_2_weight_to_fp16"), val = tensor<fp16, [192, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467456)))];
            tensor<fp16, [192]> model_backbone_stage0_2_bias_to_fp16 = const()[name = string("model_backbone_stage0_2_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467904)))];
            tensor<fp16, [1, 64, 72, 596]> input_9_cast_fp16 = transpose(perm = var_188, x = var_187_cast_fp16)[name = string("transpose_48")];
            tensor<fp16, [1, 192, 72, 596]> input_11_cast_fp16 = conv(bias = model_backbone_stage0_2_bias_to_fp16, dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = model_backbone_stage0_2_weight_to_fp16, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage0_3_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(468352)))];
            tensor<fp16, [1, 192, 72, 596]> input_13_cast_fp16 = conv(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 = model_backbone_stage0_3_conv_block_conv1_weight_to_fp16, x = 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("valid")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            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, [192, 192, 1, 1]> model_backbone_stage0_3_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(471872)))];
            tensor<fp16, [192]> model_backbone_stage0_3_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(545664)))];
            tensor<fp16, [1, 192, 72, 596]> input_15_cast_fp16 = conv(bias = model_backbone_stage0_3_conv_block_conv1pw_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 = model_backbone_stage0_3_conv_block_conv1pw_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_17_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage0_3_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546112)))];
            tensor<fp16, [192]> model_backbone_stage0_3_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546560)))];
            tensor<fp16, [192]> model_backbone_stage0_3_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547008)))];
            tensor<fp16, [192]> model_backbone_stage0_3_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547456)))];
            fp16 var_8_to_fp16 = const()[name = string("op_8_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 192, 72, 596]> input_19_cast_fp16 = batch_norm(beta = model_backbone_stage0_3_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage0_3_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage0_3_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage0_3_conv_block_bn1_running_var_to_fp16, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage0_3_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage0_3_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547904)))];
            tensor<fp16, [1, 192, 72, 596]> input_21_cast_fp16 = conv(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 = model_backbone_stage0_3_conv_block_conv2_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(551424)))];
            tensor<fp16, [192]> const_62_to_fp16 = const()[name = string("const_62_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(625216)))];
            tensor<fp16, [1, 192, 72, 596]> out_1_cast_fp16 = conv(bias = const_62_to_fp16, dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = const_61_to_fp16, x = input_21_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_25_cast_fp16 = add(x = out_1_cast_fp16, y = input_11_cast_fp16)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_27_cast_fp16 = relu(x = input_25_cast_fp16)[name = string("input_27_cast_fp16")];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage0_4_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(625664)))];
            tensor<fp16, [1, 192, 72, 596]> input_29_cast_fp16 = conv(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 = model_backbone_stage0_4_conv_block_conv1_weight_to_fp16, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> model_backbone_stage0_4_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(629184)))];
            tensor<fp16, [192]> model_backbone_stage0_4_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(702976)))];
            tensor<fp16, [1, 192, 72, 596]> input_31_cast_fp16 = conv(bias = model_backbone_stage0_4_conv_block_conv1pw_bias_to_fp16, dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = model_backbone_stage0_4_conv_block_conv1pw_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_33_cast_fp16 = relu(x = input_31_cast_fp16)[name = string("input_33_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage0_4_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(703424)))];
            tensor<fp16, [192]> model_backbone_stage0_4_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(703872)))];
            tensor<fp16, [192]> model_backbone_stage0_4_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(704320)))];
            tensor<fp16, [192]> model_backbone_stage0_4_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(704768)))];
            tensor<fp16, [1, 192, 72, 596]> input_35_cast_fp16 = batch_norm(beta = model_backbone_stage0_4_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage0_4_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage0_4_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage0_4_conv_block_bn1_running_var_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage0_4_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage0_4_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(705216)))];
            tensor<fp16, [1, 192, 72, 596]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = model_backbone_stage0_4_conv_block_conv2_weight_to_fp16, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> const_63_to_fp16 = const()[name = string("const_63_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(708736)))];
            tensor<fp16, [192]> const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782528)))];
            tensor<fp16, [1, 192, 72, 596]> out_3_cast_fp16 = conv(bias = const_64_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 = const_63_to_fp16, x = input_37_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_41_cast_fp16 = add(x = out_3_cast_fp16, y = input_27_cast_fp16)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_43_cast_fp16 = relu(x = input_41_cast_fp16)[name = string("input_43_cast_fp16")];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage0_5_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976)))];
            tensor<fp16, [1, 192, 72, 596]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = model_backbone_stage0_5_conv_block_conv1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> model_backbone_stage0_5_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786496)))];
            tensor<fp16, [192]> model_backbone_stage0_5_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860288)))];
            tensor<fp16, [1, 192, 72, 596]> input_47_cast_fp16 = conv(bias = model_backbone_stage0_5_conv_block_conv1pw_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 = model_backbone_stage0_5_conv_block_conv1pw_weight_to_fp16, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_49_cast_fp16 = relu(x = input_47_cast_fp16)[name = string("input_49_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage0_5_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860736)))];
            tensor<fp16, [192]> model_backbone_stage0_5_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(861184)))];
            tensor<fp16, [192]> model_backbone_stage0_5_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(861632)))];
            tensor<fp16, [192]> model_backbone_stage0_5_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(862080)))];
            tensor<fp16, [1, 192, 72, 596]> input_51_cast_fp16 = batch_norm(beta = model_backbone_stage0_5_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage0_5_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage0_5_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage0_5_conv_block_bn1_running_var_to_fp16, 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])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(192)];
            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])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage0_5_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage0_5_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(862528)))];
            tensor<fp16, [1, 192, 72, 596]> input_53_cast_fp16 = conv(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 = model_backbone_stage0_5_conv_block_conv2_weight_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("valid")];
            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> const_65_to_fp16 = const()[name = string("const_65_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(866048)))];
            tensor<fp16, [192]> const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939840)))];
            tensor<fp16, [1, 192, 72, 596]> out_5_cast_fp16 = conv(bias = const_66_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 = const_65_to_fp16, x = input_53_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_57_cast_fp16 = add(x = out_5_cast_fp16, y = input_43_cast_fp16)[name = string("input_57_cast_fp16")];
            tensor<fp16, [1, 192, 72, 596]> input_59_cast_fp16 = relu(x = input_57_cast_fp16)[name = string("input_59_cast_fp16")];
            tensor<int32, [2]> var_339 = const()[name = string("op_339"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_340 = const()[name = string("op_340"), val = tensor<int32, [2]>([1, 1])];
            string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("same")];
            tensor<int32, [4]> input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [64, 192, 1, 1]> const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = tensor<fp16, [64, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940288)))];
            tensor<fp16, [64]> const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(964928)))];
            tensor<fp16, [1, 64, 72, 596]> x_25_cast_fp16 = conv(bias = const_68_to_fp16, dilations = var_340, groups = var_40, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_339, weight = const_67_to_fp16, x = input_59_cast_fp16)[name = string("x_25_cast_fp16")];
            tensor<int32, [4]> var_353 = const()[name = string("op_353"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_357 = const()[name = string("op_357"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 72, 64, 596]> var_354_cast_fp16 = transpose(perm = var_353, x = x_25_cast_fp16)[name = string("transpose_47")];
            tensor<fp16, [1, 4608, 596]> input_63_cast_fp16 = reshape(shape = var_357, x = var_354_cast_fp16)[name = string("input_63_cast_fp16")];
            string x_27_pad_type_0 = const()[name = string("x_27_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_27_strides_0 = const()[name = string("x_27_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_27_pad_0 = const()[name = string("x_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_27_dilations_0 = const()[name = string("x_27_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_27_groups_0 = const()[name = string("x_27_groups_0"), val = int32(1)];
            tensor<fp16, [72, 4608, 1]> model_backbone_stage0_8_red_dim_conv_0_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_red_dim_conv_0_weight_to_fp16"), val = tensor<fp16, [72, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965120)))];
            tensor<fp16, [72]> model_backbone_stage0_8_red_dim_conv_0_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_red_dim_conv_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628736)))];
            tensor<fp16, [1, 72, 596]> x_27_cast_fp16 = conv(bias = model_backbone_stage0_8_red_dim_conv_0_bias_to_fp16, 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 = model_backbone_stage0_8_red_dim_conv_0_weight_to_fp16, x = input_63_cast_fp16)[name = string("x_27_cast_fp16")];
            tensor<int32, [1]> u_3_axes_0 = const()[name = string("u_3_axes_0"), val = tensor<int32, [1]>([1])];
            bool u_3_keep_dims_0 = const()[name = string("u_3_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 596]> u_3_cast_fp16 = reduce_mean(axes = u_3_axes_0, keep_dims = u_3_keep_dims_0, x = x_27_cast_fp16)[name = string("u_3_cast_fp16")];
            tensor<fp16, [1, 72, 596]> var_375_cast_fp16 = sub(x = x_27_cast_fp16, y = u_3_cast_fp16)[name = string("op_375_cast_fp16")];
            fp16 var_41_promoted_1_to_fp16 = const()[name = string("op_41_promoted_1_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 72, 596]> var_376_cast_fp16 = pow(x = var_375_cast_fp16, y = var_41_promoted_1_to_fp16)[name = string("op_376_cast_fp16")];
            tensor<int32, [1]> s_3_axes_0 = const()[name = string("s_3_axes_0"), val = tensor<int32, [1]>([1])];
            bool s_3_keep_dims_0 = const()[name = string("s_3_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 596]> s_3_cast_fp16 = reduce_mean(axes = s_3_axes_0, keep_dims = s_3_keep_dims_0, x = var_376_cast_fp16)[name = string("s_3_cast_fp16")];
            fp16 var_380_to_fp16 = const()[name = string("op_380_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 596]> var_381_cast_fp16 = add(x = s_3_cast_fp16, y = var_380_to_fp16)[name = string("op_381_cast_fp16")];
            tensor<fp16, [1, 1, 596]> var_382_cast_fp16 = sqrt(x = var_381_cast_fp16)[name = string("op_382_cast_fp16")];
            tensor<fp16, [1, 72, 596]> x_29_cast_fp16 = real_div(x = var_375_cast_fp16, y = var_382_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [72, 1]> w_11_to_fp16 = const()[name = string("w_11_to_fp16"), val = tensor<fp16, [72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628992)))];
            tensor<fp16, [1, 72, 596]> var_386_cast_fp16 = mul(x = w_11_to_fp16, y = x_29_cast_fp16)[name = string("op_386_cast_fp16")];
            tensor<fp16, [72, 1]> b_9_to_fp16 = const()[name = string("b_9_to_fp16"), val = tensor<fp16, [72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1629248)))];
            tensor<fp16, [1, 72, 596]> input_65_cast_fp16 = add(x = var_386_cast_fp16, y = b_9_to_fp16)[name = string("input_65_cast_fp16")];
            tensor<int32, [1]> var_399 = const()[name = string("op_399"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_400 = const()[name = string("op_400"), val = tensor<int32, [1]>([1])];
            string var_401_pad_type_0 = const()[name = string("op_401_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_401_pad_0 = const()[name = string("op_401_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 7]> model_backbone_stage0_8_tcm_0_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1629504)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_0_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1630592)))];
            tensor<fp16, [1, 72, 596]> var_401_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_0_dwconvs_0_bias_to_fp16, dilations = var_400, groups = var_19, pad = var_401_pad_0, pad_type = var_401_pad_type_0, strides = var_399, weight = model_backbone_stage0_8_tcm_0_dwconvs_0_weight_to_fp16, x = input_65_cast_fp16)[name = string("op_401_cast_fp16")];
            bool input_67_interleave_0 = const()[name = string("input_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_67_cast_fp16 = concat(axis = var_40, interleave = input_67_interleave_0, values = var_401_cast_fp16)[name = string("input_67_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_0_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1630848)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_0_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631104)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_0_norm_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631360)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_0_norm_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631616)))];
            tensor<fp16, [1, 72, 596]> input_69_cast_fp16 = batch_norm(beta = model_backbone_stage0_8_tcm_0_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage0_8_tcm_0_norm_weight_to_fp16, mean = model_backbone_stage0_8_tcm_0_norm_running_mean_to_fp16, variance = model_backbone_stage0_8_tcm_0_norm_running_var_to_fp16, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")];
            string input_71_mode_0 = const()[name = string("input_71_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_71_cast_fp16 = gelu(mode = input_71_mode_0, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")];
            string x_31_pad_type_0 = const()[name = string("x_31_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_31_strides_0 = const()[name = string("x_31_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_31_dilations_0 = const()[name = string("x_31_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_31_groups_0 = const()[name = string("x_31_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage0_8_tcm_0_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631872)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_0_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1642304)))];
            tensor<fp16, [1, 72, 596]> x_31_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_0_pwconv1_bias_to_fp16, 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 = model_backbone_stage0_8_tcm_0_pwconv1_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<fp16, [1, 72, 596]> input_73_cast_fp16 = add(x = input_65_cast_fp16, y = x_31_cast_fp16)[name = string("input_73_cast_fp16")];
            tensor<int32, [1]> var_424 = const()[name = string("op_424"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_425 = const()[name = string("op_425"), val = tensor<int32, [1]>([1])];
            string var_426_pad_type_0 = const()[name = string("op_426_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_426_pad_0 = const()[name = string("op_426_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 19]> model_backbone_stage0_8_tcm_1_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 19]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1642560)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_1_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1645376)))];
            tensor<fp16, [1, 72, 596]> var_426_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_1_dwconvs_0_bias_to_fp16, dilations = var_425, groups = var_19, pad = var_426_pad_0, pad_type = var_426_pad_type_0, strides = var_424, weight = model_backbone_stage0_8_tcm_1_dwconvs_0_weight_to_fp16, x = input_73_cast_fp16)[name = string("op_426_cast_fp16")];
            bool input_75_interleave_0 = const()[name = string("input_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_75_cast_fp16 = concat(axis = var_40, interleave = input_75_interleave_0, values = var_426_cast_fp16)[name = string("input_75_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_1_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1645632)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_1_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1645888)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_1_norm_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1646144)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_1_norm_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1646400)))];
            tensor<fp16, [1, 72, 596]> input_77_cast_fp16 = batch_norm(beta = model_backbone_stage0_8_tcm_1_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage0_8_tcm_1_norm_weight_to_fp16, mean = model_backbone_stage0_8_tcm_1_norm_running_mean_to_fp16, variance = model_backbone_stage0_8_tcm_1_norm_running_var_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
            string input_79_mode_0 = const()[name = string("input_79_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_79_cast_fp16 = gelu(mode = input_79_mode_0, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
            string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage0_8_tcm_1_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1646656)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_1_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1657088)))];
            tensor<fp16, [1, 72, 596]> x_33_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_1_pwconv1_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = model_backbone_stage0_8_tcm_1_pwconv1_weight_to_fp16, x = input_79_cast_fp16)[name = string("x_33_cast_fp16")];
            tensor<fp16, [1, 72, 596]> input_81_cast_fp16 = add(x = input_73_cast_fp16, y = x_33_cast_fp16)[name = string("input_81_cast_fp16")];
            tensor<int32, [1]> var_449 = const()[name = string("op_449"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_450 = const()[name = string("op_450"), val = tensor<int32, [1]>([1])];
            string var_451_pad_type_0 = const()[name = string("op_451_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_451_pad_0 = const()[name = string("op_451_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 31]> model_backbone_stage0_8_tcm_2_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 31]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1657344)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_2_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1661888)))];
            tensor<fp16, [1, 72, 596]> var_451_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_2_dwconvs_0_bias_to_fp16, dilations = var_450, groups = var_19, pad = var_451_pad_0, pad_type = var_451_pad_type_0, strides = var_449, weight = model_backbone_stage0_8_tcm_2_dwconvs_0_weight_to_fp16, x = input_81_cast_fp16)[name = string("op_451_cast_fp16")];
            bool input_83_interleave_0 = const()[name = string("input_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_83_cast_fp16 = concat(axis = var_40, interleave = input_83_interleave_0, values = var_451_cast_fp16)[name = string("input_83_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_2_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662144)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_2_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662400)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_2_norm_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662656)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_2_norm_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662912)))];
            tensor<fp16, [1, 72, 596]> input_85_cast_fp16 = batch_norm(beta = model_backbone_stage0_8_tcm_2_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage0_8_tcm_2_norm_weight_to_fp16, mean = model_backbone_stage0_8_tcm_2_norm_running_mean_to_fp16, variance = model_backbone_stage0_8_tcm_2_norm_running_var_to_fp16, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
            string input_87_mode_0 = const()[name = string("input_87_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_87_cast_fp16 = gelu(mode = input_87_mode_0, x = input_85_cast_fp16)[name = string("input_87_cast_fp16")];
            string x_35_pad_type_0 = const()[name = string("x_35_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_35_strides_0 = const()[name = string("x_35_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_35_pad_0 = const()[name = string("x_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_35_dilations_0 = const()[name = string("x_35_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_35_groups_0 = const()[name = string("x_35_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage0_8_tcm_2_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663168)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_2_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673600)))];
            tensor<fp16, [1, 72, 596]> x_35_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_2_pwconv1_bias_to_fp16, dilations = x_35_dilations_0, groups = x_35_groups_0, pad = x_35_pad_0, pad_type = x_35_pad_type_0, strides = x_35_strides_0, weight = model_backbone_stage0_8_tcm_2_pwconv1_weight_to_fp16, x = input_87_cast_fp16)[name = string("x_35_cast_fp16")];
            tensor<fp16, [1, 72, 596]> input_89_cast_fp16 = add(x = input_81_cast_fp16, y = x_35_cast_fp16)[name = string("input_89_cast_fp16")];
            tensor<int32, [1]> var_474 = const()[name = string("op_474"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_475 = const()[name = string("op_475"), val = tensor<int32, [1]>([1])];
            string var_476_pad_type_0 = const()[name = string("op_476_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_476_pad_0 = const()[name = string("op_476_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 59]> model_backbone_stage0_8_tcm_3_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 59]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673856)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_3_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1682432)))];
            tensor<fp16, [1, 72, 596]> var_476_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_3_dwconvs_0_bias_to_fp16, dilations = var_475, groups = var_19, pad = var_476_pad_0, pad_type = var_476_pad_type_0, strides = var_474, weight = model_backbone_stage0_8_tcm_3_dwconvs_0_weight_to_fp16, x = input_89_cast_fp16)[name = string("op_476_cast_fp16")];
            bool input_91_interleave_0 = const()[name = string("input_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_91_cast_fp16 = concat(axis = var_40, interleave = input_91_interleave_0, values = var_476_cast_fp16)[name = string("input_91_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_3_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1682688)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_3_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1682944)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_3_norm_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1683200)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_3_norm_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1683456)))];
            tensor<fp16, [1, 72, 596]> input_93_cast_fp16 = batch_norm(beta = model_backbone_stage0_8_tcm_3_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage0_8_tcm_3_norm_weight_to_fp16, mean = model_backbone_stage0_8_tcm_3_norm_running_mean_to_fp16, variance = model_backbone_stage0_8_tcm_3_norm_running_var_to_fp16, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")];
            string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = input_93_cast_fp16)[name = string("input_95_cast_fp16")];
            string x_37_pad_type_0 = const()[name = string("x_37_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_37_strides_0 = const()[name = string("x_37_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_37_pad_0 = const()[name = string("x_37_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_37_dilations_0 = const()[name = string("x_37_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_37_groups_0 = const()[name = string("x_37_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage0_8_tcm_3_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1683712)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_3_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694144)))];
            tensor<fp16, [1, 72, 596]> x_37_cast_fp16 = conv(bias = model_backbone_stage0_8_tcm_3_pwconv1_bias_to_fp16, dilations = x_37_dilations_0, groups = x_37_groups_0, pad = x_37_pad_0, pad_type = x_37_pad_type_0, strides = x_37_strides_0, weight = model_backbone_stage0_8_tcm_3_pwconv1_weight_to_fp16, x = input_95_cast_fp16)[name = string("x_37_cast_fp16")];
            tensor<fp16, [1, 72, 596]> hidden_states_1_cast_fp16 = add(x = input_89_cast_fp16, y = x_37_cast_fp16)[name = string("hidden_states_1_cast_fp16")];
            tensor<int32, [3]> var_497 = const()[name = string("op_497"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [72, 72]> model_backbone_stage0_8_tcm_4_attention_q_proj_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_q_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1694400)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_attention_q_proj_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_q_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704832)))];
            tensor<fp16, [1, 596, 72]> hidden_states_3_cast_fp16 = transpose(perm = var_497, x = hidden_states_1_cast_fp16)[name = string("transpose_46")];
            tensor<fp16, [1, 596, 72]> linear_0_cast_fp16 = linear(bias = model_backbone_stage0_8_tcm_4_attention_q_proj_bias_to_fp16, weight = model_backbone_stage0_8_tcm_4_attention_q_proj_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_0_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage0_8_tcm_4_attention_k_proj_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_k_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705088)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_attention_k_proj_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_k_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1715520)))];
            tensor<fp16, [1, 596, 72]> linear_1_cast_fp16 = linear(bias = model_backbone_stage0_8_tcm_4_attention_k_proj_bias_to_fp16, weight = model_backbone_stage0_8_tcm_4_attention_k_proj_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_1_cast_fp16")];
            tensor<int32, [4]> var_512 = const()[name = string("op_512"), val = tensor<int32, [4]>([1, -1, 4, 18])];
            tensor<fp16, [1, 596, 4, 18]> var_513_cast_fp16 = reshape(shape = var_512, x = linear_1_cast_fp16)[name = string("op_513_cast_fp16")];
            tensor<int32, [4]> var_514_perm_0 = const()[name = string("op_514_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [72, 72]> model_backbone_stage0_8_tcm_4_attention_v_proj_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_v_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1715776)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_attention_v_proj_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_v_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1726208)))];
            tensor<fp16, [1, 596, 72]> linear_2_cast_fp16 = linear(bias = model_backbone_stage0_8_tcm_4_attention_v_proj_bias_to_fp16, weight = model_backbone_stage0_8_tcm_4_attention_v_proj_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_2_cast_fp16")];
            tensor<int32, [4]> var_519 = const()[name = string("op_519"), val = tensor<int32, [4]>([1, -1, 4, 18])];
            tensor<fp16, [1, 596, 4, 18]> var_520_cast_fp16 = reshape(shape = var_519, x = linear_2_cast_fp16)[name = string("op_520_cast_fp16")];
            tensor<int32, [4]> var_521_perm_0 = const()[name = string("op_521_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_528 = const()[name = string("op_528"), val = tensor<int32, [4]>([1, 596, 4, 18])];
            tensor<fp16, [1, 596, 4, 18]> var_529_cast_fp16 = reshape(shape = var_528, x = linear_0_cast_fp16)[name = string("op_529_cast_fp16")];
            tensor<int32, [4]> var_530_perm_0 = const()[name = string("op_530_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_532 = const()[name = string("op_532"), val = tensor<int32, [3]>([4, -1, 18])];
            tensor<fp16, [1, 4, 596, 18]> var_530_cast_fp16 = transpose(perm = var_530_perm_0, x = var_529_cast_fp16)[name = string("transpose_43")];
            tensor<fp16, [4, 596, 18]> query_states_1_cast_fp16 = reshape(shape = var_532, x = var_530_cast_fp16)[name = string("query_states_1_cast_fp16")];
            tensor<int32, [3]> var_534 = const()[name = string("op_534"), val = tensor<int32, [3]>([4, -1, 18])];
            tensor<fp16, [1, 4, 596, 18]> var_514_cast_fp16 = transpose(perm = var_514_perm_0, x = var_513_cast_fp16)[name = string("transpose_45")];
            tensor<fp16, [4, 596, 18]> key_states_3_cast_fp16 = reshape(shape = var_534, x = var_514_cast_fp16)[name = string("key_states_3_cast_fp16")];
            tensor<int32, [3]> var_536 = const()[name = string("op_536"), val = tensor<int32, [3]>([4, -1, 18])];
            tensor<fp16, [1, 4, 596, 18]> var_521_cast_fp16 = transpose(perm = var_521_perm_0, x = var_520_cast_fp16)[name = string("transpose_44")];
            tensor<fp16, [4, 596, 18]> value_states_3_cast_fp16 = reshape(shape = var_536, x = var_521_cast_fp16)[name = string("value_states_3_cast_fp16")];
            bool var_539_transpose_x_1 = const()[name = string("op_539_transpose_x_1"), val = bool(false)];
            bool var_539_transpose_y_1 = const()[name = string("op_539_transpose_y_1"), val = bool(true)];
            tensor<fp16, [4, 596, 596]> var_539_cast_fp16 = matmul(transpose_x = var_539_transpose_x_1, transpose_y = var_539_transpose_y_1, x = query_states_1_cast_fp16, y = key_states_3_cast_fp16)[name = string("op_539_cast_fp16")];
            fp16 var_540_to_fp16 = const()[name = string("op_540_to_fp16"), val = fp16(0x1.e2cp-3)];
            tensor<fp16, [4, 596, 596]> input_97_cast_fp16 = mul(x = var_539_cast_fp16, y = var_540_to_fp16)[name = string("input_97_cast_fp16")];
            tensor<fp16, [4, 596, 596]> input_99_cast_fp16 = softmax(axis = var_43, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")];
            bool attn_output_1_transpose_x_0 = const()[name = string("attn_output_1_transpose_x_0"), val = bool(false)];
            bool attn_output_1_transpose_y_0 = const()[name = string("attn_output_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [4, 596, 18]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = input_99_cast_fp16, y = value_states_3_cast_fp16)[name = string("attn_output_1_cast_fp16")];
            tensor<int32, [4]> var_545 = const()[name = string("op_545"), val = tensor<int32, [4]>([1, 4, 596, 18])];
            tensor<fp16, [1, 4, 596, 18]> attn_output_3_cast_fp16 = reshape(shape = var_545, x = attn_output_1_cast_fp16)[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> attn_output_5_perm_0 = const()[name = string("attn_output_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_548 = const()[name = string("op_548"), val = tensor<int32, [3]>([1, 596, 72])];
            tensor<fp16, [1, 596, 4, 18]> attn_output_5_cast_fp16 = transpose(perm = attn_output_5_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_42")];
            tensor<fp16, [1, 596, 72]> input_101_cast_fp16 = reshape(shape = var_548, x = attn_output_5_cast_fp16)[name = string("input_101_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage0_8_tcm_4_attention_out_proj_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_out_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1726464)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_attention_out_proj_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_attention_out_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896)))];
            tensor<fp16, [1, 596, 72]> linear_3_cast_fp16 = linear(bias = model_backbone_stage0_8_tcm_4_attention_out_proj_bias_to_fp16, weight = model_backbone_stage0_8_tcm_4_attention_out_proj_weight_to_fp16, x = input_101_cast_fp16)[name = string("linear_3_cast_fp16")];
            tensor<fp16, [1, 596, 72]> input_103_cast_fp16 = add(x = hidden_states_3_cast_fp16, y = linear_3_cast_fp16)[name = string("input_103_cast_fp16")];
            tensor<int32, [1]> input_105_axes_0 = const()[name = string("input_105_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_layer_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737152)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_layer_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737408)))];
            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 596, 72]> input_105_cast_fp16 = layer_norm(axes = input_105_axes_0, beta = model_backbone_stage0_8_tcm_4_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage0_8_tcm_4_layer_norm_weight_to_fp16, x = input_103_cast_fp16)[name = string("input_105_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage0_8_tcm_4_feed_forward_intermediate_dense_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_feed_forward_intermediate_dense_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737664)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_feed_forward_intermediate_dense_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_feed_forward_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1748096)))];
            tensor<fp16, [1, 596, 72]> linear_4_cast_fp16 = linear(bias = model_backbone_stage0_8_tcm_4_feed_forward_intermediate_dense_bias_to_fp16, weight = model_backbone_stage0_8_tcm_4_feed_forward_intermediate_dense_weight_to_fp16, x = input_105_cast_fp16)[name = string("linear_4_cast_fp16")];
            string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("TANH_APPROXIMATION")];
            tensor<fp16, [1, 596, 72]> input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_4_cast_fp16)[name = string("input_109_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage0_8_tcm_4_feed_forward_output_dense_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_feed_forward_output_dense_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1748352)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_feed_forward_output_dense_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_feed_forward_output_dense_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1758784)))];
            tensor<fp16, [1, 596, 72]> linear_5_cast_fp16 = linear(bias = model_backbone_stage0_8_tcm_4_feed_forward_output_dense_bias_to_fp16, weight = model_backbone_stage0_8_tcm_4_feed_forward_output_dense_weight_to_fp16, x = input_109_cast_fp16)[name = string("linear_5_cast_fp16")];
            tensor<fp16, [1, 596, 72]> input_115_cast_fp16 = add(x = input_105_cast_fp16, y = linear_5_cast_fp16)[name = string("input_115_cast_fp16")];
            tensor<int32, [1]> hidden_states_7_axes_0 = const()[name = string("hidden_states_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_final_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1759040)))];
            tensor<fp16, [72]> model_backbone_stage0_8_tcm_4_final_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_tcm_4_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1759296)))];
            tensor<fp16, [1, 596, 72]> hidden_states_7_cast_fp16 = layer_norm(axes = hidden_states_7_axes_0, beta = model_backbone_stage0_8_tcm_4_final_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage0_8_tcm_4_final_layer_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<int32, [3]> var_585 = const()[name = string("op_585"), val = tensor<int32, [3]>([0, 2, 1])];
            string x_39_pad_type_0 = const()[name = string("x_39_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_39_strides_0 = const()[name = string("x_39_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_39_pad_0 = const()[name = string("x_39_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_39_dilations_0 = const()[name = string("x_39_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_39_groups_0 = const()[name = string("x_39_groups_0"), val = int32(1)];
            tensor<fp16, [4608, 72, 1]> model_backbone_stage0_8_exp_dim_conv_weight_to_fp16 = const()[name = string("model_backbone_stage0_8_exp_dim_conv_weight_to_fp16"), val = tensor<fp16, [4608, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1759552)))];
            tensor<fp16, [4608]> model_backbone_stage0_8_exp_dim_conv_bias_to_fp16 = const()[name = string("model_backbone_stage0_8_exp_dim_conv_bias_to_fp16"), val = tensor<fp16, [4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2423168)))];
            tensor<fp16, [1, 72, 596]> input_117_cast_fp16 = transpose(perm = var_585, x = hidden_states_7_cast_fp16)[name = string("transpose_41")];
            tensor<fp16, [1, 4608, 596]> x_39_cast_fp16 = conv(bias = model_backbone_stage0_8_exp_dim_conv_bias_to_fp16, dilations = x_39_dilations_0, groups = x_39_groups_0, pad = x_39_pad_0, pad_type = x_39_pad_type_0, strides = x_39_strides_0, weight = model_backbone_stage0_8_exp_dim_conv_weight_to_fp16, x = input_117_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<fp16, [1, 4608, 596]> input_119_cast_fp16 = add(x = input_63_cast_fp16, y = x_39_cast_fp16)[name = string("input_119_cast_fp16")];
            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_119_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
            tensor<int32, [1]> input_121_axes_0 = const()[name = string("input_121_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596]> input_121_cast_fp16 = squeeze(axes = input_121_axes_0, x = expand_dims_0_cast_fp16)[name = string("input_121_cast_fp16")];
            tensor<int32, [4]> reshape_4_shape_0 = const()[name = string("reshape_4_shape_0"), val = tensor<int32, [4]>([1, 64, 72, 596])];
            tensor<fp16, [1, 64, 72, 596]> reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_121_cast_fp16)[name = string("reshape_4_cast_fp16")];
            tensor<int32, [2]> reduce_mean_5_axes_0 = const()[name = string("reduce_mean_5_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_5_keep_dims_0 = const()[name = string("reduce_mean_5_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = reshape_4_cast_fp16)[name = string("reduce_mean_5_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> sub_3_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_5_cast_fp16)[name = string("sub_3_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> square_2_cast_fp16 = square(x = sub_3_cast_fp16)[name = string("square_2_cast_fp16")];
            tensor<int32, [2]> reduce_mean_7_axes_0 = const()[name = string("reduce_mean_7_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_7_keep_dims_0 = const()[name = string("reduce_mean_7_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_7_cast_fp16 = reduce_mean(axes = reduce_mean_7_axes_0, keep_dims = reduce_mean_7_keep_dims_0, x = square_2_cast_fp16)[name = string("reduce_mean_7_cast_fp16")];
            fp16 add_2_y_0_to_fp16 = const()[name = string("add_2_y_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 1]> add_2_cast_fp16 = add(x = reduce_mean_7_cast_fp16, y = add_2_y_0_to_fp16)[name = string("add_2_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> sqrt_2_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = string("sqrt_2_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> real_div_1_cast_fp16 = real_div(x = sub_3_cast_fp16, y = sqrt_2_cast_fp16)[name = string("real_div_1_cast_fp16")];
            tensor<int32, [3]> reshape_5_shape_0 = const()[name = string("reshape_5_shape_0"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 4608, 596]> reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = string("reshape_5_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_6_to_fp16 = const()[name = string("reshape_6_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2432448)))];
            tensor<fp16, [1, 4608, 596]> mul_1_cast_fp16 = mul(x = reshape_5_cast_fp16, y = reshape_6_to_fp16)[name = string("mul_1_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_7_to_fp16 = const()[name = string("reshape_7_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2441728)))];
            tensor<fp16, [1, 4608, 596]> add_3_cast_fp16 = add(x = mul_1_cast_fp16, y = reshape_7_to_fp16)[name = string("add_3_cast_fp16")];
            tensor<int32, [1]> var_612_axes_0 = const()[name = string("op_612_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 4608, 596]> var_612_cast_fp16 = expand_dims(axes = var_612_axes_0, x = add_3_cast_fp16)[name = string("op_612_cast_fp16")];
            bool xs_3_interleave_0 = const()[name = string("xs_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 4608, 596]> xs_3_cast_fp16 = concat(axis = var_40, interleave = xs_3_interleave_0, values = (var_178_cast_fp16, var_612_cast_fp16))[name = string("xs_3_cast_fp16")];
            tensor<fp16, [1, 2, 4608, 1]> w_13_to_fp16 = const()[name = string("w_13_to_fp16"), val = tensor<fp16, [1, 2, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2451008)))];
            tensor<fp16, [1, 2, 4608, 596]> var_615_cast_fp16 = mul(x = w_13_to_fp16, y = xs_3_cast_fp16)[name = string("op_615_cast_fp16")];
            tensor<int32, [1]> x_41_axes_0 = const()[name = string("x_41_axes_0"), val = tensor<int32, [1]>([1])];
            bool x_41_keep_dims_0 = const()[name = string("x_41_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 4608, 596]> x_41_cast_fp16 = reduce_sum(axes = x_41_axes_0, keep_dims = x_41_keep_dims_0, x = var_615_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> var_620 = const()[name = string("op_620"), val = tensor<int32, [4]>([1, 72, 64, 596])];
            tensor<fp16, [1, 72, 64, 596]> var_621_cast_fp16 = reshape(shape = var_620, x = x_41_cast_fp16)[name = string("op_621_cast_fp16")];
            tensor<int32, [4]> var_622 = const()[name = string("op_622"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor<int32, [2]>([2, 1])];
            int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(64)];
            tensor<int32, [4]> input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 2, 1]> model_backbone_stage1_2_weight_to_fp16 = const()[name = string("model_backbone_stage1_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 2, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2469504)))];
            tensor<fp16, [256]> model_backbone_stage1_2_bias_to_fp16 = const()[name = string("model_backbone_stage1_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2470592)))];
            tensor<fp16, [1, 64, 72, 596]> input_125_cast_fp16 = transpose(perm = var_622, x = var_621_cast_fp16)[name = string("transpose_40")];
            tensor<fp16, [1, 256, 36, 596]> input_127_cast_fp16 = conv(bias = model_backbone_stage1_2_bias_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = model_backbone_stage1_2_weight_to_fp16, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")];
            string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_3_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2471168)))];
            tensor<fp16, [1, 256, 36, 596]> input_129_cast_fp16 = conv(dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = model_backbone_stage1_3_conv_block_conv1_weight_to_fp16, x = input_127_cast_fp16)[name = string("input_129_cast_fp16")];
            string input_131_pad_type_0 = const()[name = string("input_131_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_131_strides_0 = const()[name = string("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_131_dilations_0 = const()[name = string("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_131_groups_0 = const()[name = string("input_131_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage1_3_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2475840)))];
            tensor<fp16, [256]> model_backbone_stage1_3_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2606976)))];
            tensor<fp16, [1, 256, 36, 596]> input_131_cast_fp16 = conv(bias = model_backbone_stage1_3_conv_block_conv1pw_bias_to_fp16, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = model_backbone_stage1_3_conv_block_conv1pw_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_133_cast_fp16 = relu(x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage1_3_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2607552)))];
            tensor<fp16, [256]> model_backbone_stage1_3_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2608128)))];
            tensor<fp16, [256]> model_backbone_stage1_3_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2608704)))];
            tensor<fp16, [256]> model_backbone_stage1_3_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2609280)))];
            tensor<fp16, [1, 256, 36, 596]> input_135_cast_fp16 = batch_norm(beta = model_backbone_stage1_3_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_3_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage1_3_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage1_3_conv_block_bn1_running_var_to_fp16, x = input_133_cast_fp16)[name = string("input_135_cast_fp16")];
            string input_137_pad_type_0 = const()[name = string("input_137_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_137_pad_0 = const()[name = string("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_137_groups_0 = const()[name = string("input_137_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_137_strides_0 = const()[name = string("input_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_137_dilations_0 = const()[name = string("input_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_3_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage1_3_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2609856)))];
            tensor<fp16, [1, 256, 36, 596]> input_137_cast_fp16 = conv(dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = model_backbone_stage1_3_conv_block_conv2_weight_to_fp16, x = input_135_cast_fp16)[name = string("input_137_cast_fp16")];
            string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_69_to_fp16 = const()[name = string("const_69_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2614528)))];
            tensor<fp16, [256]> const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2745664)))];
            tensor<fp16, [1, 256, 36, 596]> out_7_cast_fp16 = conv(bias = const_70_to_fp16, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_69_to_fp16, x = input_137_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_141_cast_fp16 = add(x = out_7_cast_fp16, y = input_127_cast_fp16)[name = string("input_141_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_143_cast_fp16 = relu(x = input_141_cast_fp16)[name = string("input_143_cast_fp16")];
            string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_4_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2746240)))];
            tensor<fp16, [1, 256, 36, 596]> input_145_cast_fp16 = conv(dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = model_backbone_stage1_4_conv_block_conv1_weight_to_fp16, x = input_143_cast_fp16)[name = string("input_145_cast_fp16")];
            string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage1_4_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2750912)))];
            tensor<fp16, [256]> model_backbone_stage1_4_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2882048)))];
            tensor<fp16, [1, 256, 36, 596]> input_147_cast_fp16 = conv(bias = model_backbone_stage1_4_conv_block_conv1pw_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = model_backbone_stage1_4_conv_block_conv1pw_weight_to_fp16, x = input_145_cast_fp16)[name = string("input_147_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_149_cast_fp16 = relu(x = input_147_cast_fp16)[name = string("input_149_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage1_4_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2882624)))];
            tensor<fp16, [256]> model_backbone_stage1_4_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2883200)))];
            tensor<fp16, [256]> model_backbone_stage1_4_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2883776)))];
            tensor<fp16, [256]> model_backbone_stage1_4_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2884352)))];
            tensor<fp16, [1, 256, 36, 596]> input_151_cast_fp16 = batch_norm(beta = model_backbone_stage1_4_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_4_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage1_4_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage1_4_conv_block_bn1_running_var_to_fp16, x = input_149_cast_fp16)[name = string("input_151_cast_fp16")];
            string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_4_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage1_4_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2884928)))];
            tensor<fp16, [1, 256, 36, 596]> input_153_cast_fp16 = conv(dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = model_backbone_stage1_4_conv_block_conv2_weight_to_fp16, x = input_151_cast_fp16)[name = string("input_153_cast_fp16")];
            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_71_to_fp16 = const()[name = string("const_71_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2889600)))];
            tensor<fp16, [256]> const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3020736)))];
            tensor<fp16, [1, 256, 36, 596]> out_9_cast_fp16 = conv(bias = const_72_to_fp16, dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = const_71_to_fp16, x = input_153_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_157_cast_fp16 = add(x = out_9_cast_fp16, y = input_143_cast_fp16)[name = string("input_157_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_159_cast_fp16 = relu(x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_5_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3021312)))];
            tensor<fp16, [1, 256, 36, 596]> input_161_cast_fp16 = conv(dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = model_backbone_stage1_5_conv_block_conv1_weight_to_fp16, x = input_159_cast_fp16)[name = string("input_161_cast_fp16")];
            string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage1_5_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3025984)))];
            tensor<fp16, [256]> model_backbone_stage1_5_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3157120)))];
            tensor<fp16, [1, 256, 36, 596]> input_163_cast_fp16 = conv(bias = model_backbone_stage1_5_conv_block_conv1pw_bias_to_fp16, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = model_backbone_stage1_5_conv_block_conv1pw_weight_to_fp16, x = input_161_cast_fp16)[name = string("input_163_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_165_cast_fp16 = relu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage1_5_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3157696)))];
            tensor<fp16, [256]> model_backbone_stage1_5_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3158272)))];
            tensor<fp16, [256]> model_backbone_stage1_5_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3158848)))];
            tensor<fp16, [256]> model_backbone_stage1_5_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3159424)))];
            tensor<fp16, [1, 256, 36, 596]> input_167_cast_fp16 = batch_norm(beta = model_backbone_stage1_5_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_5_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage1_5_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage1_5_conv_block_bn1_running_var_to_fp16, x = input_165_cast_fp16)[name = string("input_167_cast_fp16")];
            string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_5_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage1_5_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3160000)))];
            tensor<fp16, [1, 256, 36, 596]> input_169_cast_fp16 = conv(dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = model_backbone_stage1_5_conv_block_conv2_weight_to_fp16, x = input_167_cast_fp16)[name = string("input_169_cast_fp16")];
            string input_171_pad_type_0 = const()[name = string("input_171_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_171_strides_0 = const()[name = string("input_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_171_pad_0 = const()[name = string("input_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_171_dilations_0 = const()[name = string("input_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_171_groups_0 = const()[name = string("input_171_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_73_to_fp16 = const()[name = string("const_73_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3164672)))];
            tensor<fp16, [256]> const_74_to_fp16 = const()[name = string("const_74_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3295808)))];
            tensor<fp16, [1, 256, 36, 596]> out_11_cast_fp16 = conv(bias = const_74_to_fp16, dilations = input_171_dilations_0, groups = input_171_groups_0, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = input_171_strides_0, weight = const_73_to_fp16, x = input_169_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_173_cast_fp16 = add(x = out_11_cast_fp16, y = input_159_cast_fp16)[name = string("input_173_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_175_cast_fp16 = relu(x = input_173_cast_fp16)[name = string("input_175_cast_fp16")];
            string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_6_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3296384)))];
            tensor<fp16, [1, 256, 36, 596]> input_177_cast_fp16 = conv(dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = model_backbone_stage1_6_conv_block_conv1_weight_to_fp16, x = input_175_cast_fp16)[name = string("input_177_cast_fp16")];
            string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage1_6_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3301056)))];
            tensor<fp16, [256]> model_backbone_stage1_6_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3432192)))];
            tensor<fp16, [1, 256, 36, 596]> input_179_cast_fp16 = conv(bias = model_backbone_stage1_6_conv_block_conv1pw_bias_to_fp16, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = model_backbone_stage1_6_conv_block_conv1pw_weight_to_fp16, x = input_177_cast_fp16)[name = string("input_179_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_181_cast_fp16 = relu(x = input_179_cast_fp16)[name = string("input_181_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage1_6_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3432768)))];
            tensor<fp16, [256]> model_backbone_stage1_6_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3433344)))];
            tensor<fp16, [256]> model_backbone_stage1_6_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3433920)))];
            tensor<fp16, [256]> model_backbone_stage1_6_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3434496)))];
            tensor<fp16, [1, 256, 36, 596]> input_183_cast_fp16 = batch_norm(beta = model_backbone_stage1_6_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_6_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage1_6_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage1_6_conv_block_bn1_running_var_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
            string input_185_pad_type_0 = const()[name = string("input_185_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_185_pad_0 = const()[name = string("input_185_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_185_groups_0 = const()[name = string("input_185_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_185_strides_0 = const()[name = string("input_185_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_185_dilations_0 = const()[name = string("input_185_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage1_6_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage1_6_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3435072)))];
            tensor<fp16, [1, 256, 36, 596]> input_185_cast_fp16 = conv(dilations = input_185_dilations_0, groups = input_185_groups_0, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = input_185_strides_0, weight = model_backbone_stage1_6_conv_block_conv2_weight_to_fp16, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
            string input_187_pad_type_0 = const()[name = string("input_187_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_187_strides_0 = const()[name = string("input_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_187_pad_0 = const()[name = string("input_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_187_dilations_0 = const()[name = string("input_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_187_groups_0 = const()[name = string("input_187_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_75_to_fp16 = const()[name = string("const_75_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3439744)))];
            tensor<fp16, [256]> const_76_to_fp16 = const()[name = string("const_76_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3570880)))];
            tensor<fp16, [1, 256, 36, 596]> out_13_cast_fp16 = conv(bias = const_76_to_fp16, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = const_75_to_fp16, x = input_185_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_189_cast_fp16 = add(x = out_13_cast_fp16, y = input_175_cast_fp16)[name = string("input_189_cast_fp16")];
            tensor<fp16, [1, 256, 36, 596]> input_191_cast_fp16 = relu(x = input_189_cast_fp16)[name = string("input_191_cast_fp16")];
            tensor<int32, [2]> var_819 = const()[name = string("op_819"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_820 = const()[name = string("op_820"), val = tensor<int32, [2]>([1, 1])];
            string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("same")];
            tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [128, 256, 1, 1]> const_77_to_fp16 = const()[name = string("const_77_to_fp16"), val = tensor<fp16, [128, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3571456)))];
            tensor<fp16, [128]> const_78_to_fp16 = const()[name = string("const_78_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3637056)))];
            tensor<fp16, [1, 128, 36, 596]> x_43_cast_fp16 = conv(bias = const_78_to_fp16, dilations = var_820, groups = var_40, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = var_819, weight = const_77_to_fp16, x = input_191_cast_fp16)[name = string("x_43_cast_fp16")];
            tensor<int32, [4]> var_833 = const()[name = string("op_833"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_837 = const()[name = string("op_837"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 36, 128, 596]> var_834_cast_fp16 = transpose(perm = var_833, x = x_43_cast_fp16)[name = string("transpose_39")];
            tensor<fp16, [1, 4608, 596]> input_195_cast_fp16 = reshape(shape = var_837, x = var_834_cast_fp16)[name = string("input_195_cast_fp16")];
            string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)];
            tensor<fp16, [72, 4608, 1]> model_backbone_stage1_9_red_dim_conv_0_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_red_dim_conv_0_weight_to_fp16"), val = tensor<fp16, [72, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3637376)))];
            tensor<fp16, [72]> model_backbone_stage1_9_red_dim_conv_0_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_red_dim_conv_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4300992)))];
            tensor<fp16, [1, 72, 596]> x_45_cast_fp16 = conv(bias = model_backbone_stage1_9_red_dim_conv_0_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = model_backbone_stage1_9_red_dim_conv_0_weight_to_fp16, x = input_195_cast_fp16)[name = string("x_45_cast_fp16")];
            tensor<int32, [1]> u_5_axes_0 = const()[name = string("u_5_axes_0"), val = tensor<int32, [1]>([1])];
            bool u_5_keep_dims_0 = const()[name = string("u_5_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 596]> u_5_cast_fp16 = reduce_mean(axes = u_5_axes_0, keep_dims = u_5_keep_dims_0, x = x_45_cast_fp16)[name = string("u_5_cast_fp16")];
            tensor<fp16, [1, 72, 596]> var_855_cast_fp16 = sub(x = x_45_cast_fp16, y = u_5_cast_fp16)[name = string("op_855_cast_fp16")];
            fp16 var_41_promoted_2_to_fp16 = const()[name = string("op_41_promoted_2_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 72, 596]> var_856_cast_fp16 = pow(x = var_855_cast_fp16, y = var_41_promoted_2_to_fp16)[name = string("op_856_cast_fp16")];
            tensor<int32, [1]> s_5_axes_0 = const()[name = string("s_5_axes_0"), val = tensor<int32, [1]>([1])];
            bool s_5_keep_dims_0 = const()[name = string("s_5_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 596]> s_5_cast_fp16 = reduce_mean(axes = s_5_axes_0, keep_dims = s_5_keep_dims_0, x = var_856_cast_fp16)[name = string("s_5_cast_fp16")];
            fp16 var_860_to_fp16 = const()[name = string("op_860_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 596]> var_861_cast_fp16 = add(x = s_5_cast_fp16, y = var_860_to_fp16)[name = string("op_861_cast_fp16")];
            tensor<fp16, [1, 1, 596]> var_862_cast_fp16 = sqrt(x = var_861_cast_fp16)[name = string("op_862_cast_fp16")];
            tensor<fp16, [1, 72, 596]> x_47_cast_fp16 = real_div(x = var_855_cast_fp16, y = var_862_cast_fp16)[name = string("x_47_cast_fp16")];
            tensor<fp16, [72, 1]> w_17_to_fp16 = const()[name = string("w_17_to_fp16"), val = tensor<fp16, [72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4301248)))];
            tensor<fp16, [1, 72, 596]> var_866_cast_fp16 = mul(x = w_17_to_fp16, y = x_47_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<fp16, [72, 1]> b_13_to_fp16 = const()[name = string("b_13_to_fp16"), val = tensor<fp16, [72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4301504)))];
            tensor<fp16, [1, 72, 596]> input_197_cast_fp16 = add(x = var_866_cast_fp16, y = b_13_to_fp16)[name = string("input_197_cast_fp16")];
            tensor<int32, [1]> var_879 = const()[name = string("op_879"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_880 = const()[name = string("op_880"), val = tensor<int32, [1]>([1])];
            string var_881_pad_type_0 = const()[name = string("op_881_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_881_pad_0 = const()[name = string("op_881_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 7]> model_backbone_stage1_9_tcm_0_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4301760)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_0_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4302848)))];
            tensor<fp16, [1, 72, 596]> var_881_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_0_dwconvs_0_bias_to_fp16, dilations = var_880, groups = var_19, pad = var_881_pad_0, pad_type = var_881_pad_type_0, strides = var_879, weight = model_backbone_stage1_9_tcm_0_dwconvs_0_weight_to_fp16, x = input_197_cast_fp16)[name = string("op_881_cast_fp16")];
            bool input_199_interleave_0 = const()[name = string("input_199_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_199_cast_fp16 = concat(axis = var_40, interleave = input_199_interleave_0, values = var_881_cast_fp16)[name = string("input_199_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_0_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4303104)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_0_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4303360)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_0_norm_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4303616)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_0_norm_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4303872)))];
            tensor<fp16, [1, 72, 596]> input_201_cast_fp16 = batch_norm(beta = model_backbone_stage1_9_tcm_0_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_9_tcm_0_norm_weight_to_fp16, mean = model_backbone_stage1_9_tcm_0_norm_running_mean_to_fp16, variance = model_backbone_stage1_9_tcm_0_norm_running_var_to_fp16, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")];
            string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = input_201_cast_fp16)[name = string("input_203_cast_fp16")];
            string x_49_pad_type_0 = const()[name = string("x_49_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_49_strides_0 = const()[name = string("x_49_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_49_pad_0 = const()[name = string("x_49_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_49_dilations_0 = const()[name = string("x_49_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_49_groups_0 = const()[name = string("x_49_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage1_9_tcm_0_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304128)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_0_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4314560)))];
            tensor<fp16, [1, 72, 596]> x_49_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_0_pwconv1_bias_to_fp16, dilations = x_49_dilations_0, groups = x_49_groups_0, pad = x_49_pad_0, pad_type = x_49_pad_type_0, strides = x_49_strides_0, weight = model_backbone_stage1_9_tcm_0_pwconv1_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<fp16, [1, 72, 596]> input_205_cast_fp16 = add(x = input_197_cast_fp16, y = x_49_cast_fp16)[name = string("input_205_cast_fp16")];
            tensor<int32, [1]> var_904 = const()[name = string("op_904"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_905 = const()[name = string("op_905"), val = tensor<int32, [1]>([1])];
            string var_906_pad_type_0 = const()[name = string("op_906_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_906_pad_0 = const()[name = string("op_906_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 19]> model_backbone_stage1_9_tcm_1_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 19]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4314816)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_1_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4317632)))];
            tensor<fp16, [1, 72, 596]> var_906_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_1_dwconvs_0_bias_to_fp16, dilations = var_905, groups = var_19, pad = var_906_pad_0, pad_type = var_906_pad_type_0, strides = var_904, weight = model_backbone_stage1_9_tcm_1_dwconvs_0_weight_to_fp16, x = input_205_cast_fp16)[name = string("op_906_cast_fp16")];
            bool input_207_interleave_0 = const()[name = string("input_207_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_207_cast_fp16 = concat(axis = var_40, interleave = input_207_interleave_0, values = var_906_cast_fp16)[name = string("input_207_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_1_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4317888)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_1_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4318144)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_1_norm_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4318400)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_1_norm_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4318656)))];
            tensor<fp16, [1, 72, 596]> input_209_cast_fp16 = batch_norm(beta = model_backbone_stage1_9_tcm_1_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_9_tcm_1_norm_weight_to_fp16, mean = model_backbone_stage1_9_tcm_1_norm_running_mean_to_fp16, variance = model_backbone_stage1_9_tcm_1_norm_running_var_to_fp16, x = input_207_cast_fp16)[name = string("input_209_cast_fp16")];
            string input_211_mode_0 = const()[name = string("input_211_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_211_cast_fp16 = gelu(mode = input_211_mode_0, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
            string x_51_pad_type_0 = const()[name = string("x_51_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_51_strides_0 = const()[name = string("x_51_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_51_dilations_0 = const()[name = string("x_51_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_51_groups_0 = const()[name = string("x_51_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage1_9_tcm_1_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4318912)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_1_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4329344)))];
            tensor<fp16, [1, 72, 596]> x_51_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_1_pwconv1_bias_to_fp16, dilations = x_51_dilations_0, groups = x_51_groups_0, pad = x_51_pad_0, pad_type = x_51_pad_type_0, strides = x_51_strides_0, weight = model_backbone_stage1_9_tcm_1_pwconv1_weight_to_fp16, x = input_211_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<fp16, [1, 72, 596]> input_213_cast_fp16 = add(x = input_205_cast_fp16, y = x_51_cast_fp16)[name = string("input_213_cast_fp16")];
            tensor<int32, [1]> var_929 = const()[name = string("op_929"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_930 = const()[name = string("op_930"), val = tensor<int32, [1]>([1])];
            string var_931_pad_type_0 = const()[name = string("op_931_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_931_pad_0 = const()[name = string("op_931_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 31]> model_backbone_stage1_9_tcm_2_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 31]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4329600)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_2_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4334144)))];
            tensor<fp16, [1, 72, 596]> var_931_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_2_dwconvs_0_bias_to_fp16, dilations = var_930, groups = var_19, pad = var_931_pad_0, pad_type = var_931_pad_type_0, strides = var_929, weight = model_backbone_stage1_9_tcm_2_dwconvs_0_weight_to_fp16, x = input_213_cast_fp16)[name = string("op_931_cast_fp16")];
            bool input_215_interleave_0 = const()[name = string("input_215_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_215_cast_fp16 = concat(axis = var_40, interleave = input_215_interleave_0, values = var_931_cast_fp16)[name = string("input_215_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_2_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4334400)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_2_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4334656)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_2_norm_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4334912)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_2_norm_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4335168)))];
            tensor<fp16, [1, 72, 596]> input_217_cast_fp16 = batch_norm(beta = model_backbone_stage1_9_tcm_2_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_9_tcm_2_norm_weight_to_fp16, mean = model_backbone_stage1_9_tcm_2_norm_running_mean_to_fp16, variance = model_backbone_stage1_9_tcm_2_norm_running_var_to_fp16, x = input_215_cast_fp16)[name = string("input_217_cast_fp16")];
            string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = input_217_cast_fp16)[name = string("input_219_cast_fp16")];
            string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage1_9_tcm_2_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4335424)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_2_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345856)))];
            tensor<fp16, [1, 72, 596]> x_53_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_2_pwconv1_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = model_backbone_stage1_9_tcm_2_pwconv1_weight_to_fp16, x = input_219_cast_fp16)[name = string("x_53_cast_fp16")];
            tensor<fp16, [1, 72, 596]> input_221_cast_fp16 = add(x = input_213_cast_fp16, y = x_53_cast_fp16)[name = string("input_221_cast_fp16")];
            tensor<int32, [1]> var_954 = const()[name = string("op_954"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_955 = const()[name = string("op_955"), val = tensor<int32, [1]>([1])];
            string var_956_pad_type_0 = const()[name = string("op_956_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_956_pad_0 = const()[name = string("op_956_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [72, 1, 59]> model_backbone_stage1_9_tcm_3_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [72, 1, 59]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4346112)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_3_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4354688)))];
            tensor<fp16, [1, 72, 596]> var_956_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_3_dwconvs_0_bias_to_fp16, dilations = var_955, groups = var_19, pad = var_956_pad_0, pad_type = var_956_pad_type_0, strides = var_954, weight = model_backbone_stage1_9_tcm_3_dwconvs_0_weight_to_fp16, x = input_221_cast_fp16)[name = string("op_956_cast_fp16")];
            bool input_223_interleave_0 = const()[name = string("input_223_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 72, 596]> input_223_cast_fp16 = concat(axis = var_40, interleave = input_223_interleave_0, values = var_956_cast_fp16)[name = string("input_223_cast_fp16")];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_3_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_norm_running_mean_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4354944)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_3_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_norm_running_var_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4355200)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_3_norm_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4355456)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_3_norm_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4355712)))];
            tensor<fp16, [1, 72, 596]> input_225_cast_fp16 = batch_norm(beta = model_backbone_stage1_9_tcm_3_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage1_9_tcm_3_norm_weight_to_fp16, mean = model_backbone_stage1_9_tcm_3_norm_running_mean_to_fp16, variance = model_backbone_stage1_9_tcm_3_norm_running_var_to_fp16, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")];
            string input_227_mode_0 = const()[name = string("input_227_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 72, 596]> input_227_cast_fp16 = gelu(mode = input_227_mode_0, x = input_225_cast_fp16)[name = string("input_227_cast_fp16")];
            string x_55_pad_type_0 = const()[name = string("x_55_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_55_strides_0 = const()[name = string("x_55_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_55_pad_0 = const()[name = string("x_55_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_55_dilations_0 = const()[name = string("x_55_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_55_groups_0 = const()[name = string("x_55_groups_0"), val = int32(1)];
            tensor<fp16, [72, 72, 1]> model_backbone_stage1_9_tcm_3_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [72, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4355968)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_3_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4366400)))];
            tensor<fp16, [1, 72, 596]> x_55_cast_fp16 = conv(bias = model_backbone_stage1_9_tcm_3_pwconv1_bias_to_fp16, dilations = x_55_dilations_0, groups = x_55_groups_0, pad = x_55_pad_0, pad_type = x_55_pad_type_0, strides = x_55_strides_0, weight = model_backbone_stage1_9_tcm_3_pwconv1_weight_to_fp16, x = input_227_cast_fp16)[name = string("x_55_cast_fp16")];
            tensor<fp16, [1, 72, 596]> hidden_states_9_cast_fp16 = add(x = input_221_cast_fp16, y = x_55_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            tensor<int32, [3]> var_977 = const()[name = string("op_977"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [72, 72]> model_backbone_stage1_9_tcm_4_attention_q_proj_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_q_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4366656)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_attention_q_proj_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_q_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4377088)))];
            tensor<fp16, [1, 596, 72]> hidden_states_11_cast_fp16 = transpose(perm = var_977, x = hidden_states_9_cast_fp16)[name = string("transpose_38")];
            tensor<fp16, [1, 596, 72]> linear_6_cast_fp16 = linear(bias = model_backbone_stage1_9_tcm_4_attention_q_proj_bias_to_fp16, weight = model_backbone_stage1_9_tcm_4_attention_q_proj_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_6_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage1_9_tcm_4_attention_k_proj_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_k_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4377344)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_attention_k_proj_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_k_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4387776)))];
            tensor<fp16, [1, 596, 72]> linear_7_cast_fp16 = linear(bias = model_backbone_stage1_9_tcm_4_attention_k_proj_bias_to_fp16, weight = model_backbone_stage1_9_tcm_4_attention_k_proj_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_7_cast_fp16")];
            tensor<int32, [4]> var_992 = const()[name = string("op_992"), val = tensor<int32, [4]>([1, -1, 4, 18])];
            tensor<fp16, [1, 596, 4, 18]> var_993_cast_fp16 = reshape(shape = var_992, x = linear_7_cast_fp16)[name = string("op_993_cast_fp16")];
            tensor<int32, [4]> var_994_perm_0 = const()[name = string("op_994_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [72, 72]> model_backbone_stage1_9_tcm_4_attention_v_proj_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_v_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4388032)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_attention_v_proj_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_v_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4398464)))];
            tensor<fp16, [1, 596, 72]> linear_8_cast_fp16 = linear(bias = model_backbone_stage1_9_tcm_4_attention_v_proj_bias_to_fp16, weight = model_backbone_stage1_9_tcm_4_attention_v_proj_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_8_cast_fp16")];
            tensor<int32, [4]> var_999 = const()[name = string("op_999"), val = tensor<int32, [4]>([1, -1, 4, 18])];
            tensor<fp16, [1, 596, 4, 18]> var_1000_cast_fp16 = reshape(shape = var_999, x = linear_8_cast_fp16)[name = string("op_1000_cast_fp16")];
            tensor<int32, [4]> var_1001_perm_0 = const()[name = string("op_1001_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1008 = const()[name = string("op_1008"), val = tensor<int32, [4]>([1, 596, 4, 18])];
            tensor<fp16, [1, 596, 4, 18]> var_1009_cast_fp16 = reshape(shape = var_1008, x = linear_6_cast_fp16)[name = string("op_1009_cast_fp16")];
            tensor<int32, [4]> var_1010_perm_0 = const()[name = string("op_1010_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1012 = const()[name = string("op_1012"), val = tensor<int32, [3]>([4, -1, 18])];
            tensor<fp16, [1, 4, 596, 18]> var_1010_cast_fp16 = transpose(perm = var_1010_perm_0, x = var_1009_cast_fp16)[name = string("transpose_35")];
            tensor<fp16, [4, 596, 18]> query_states_3_cast_fp16 = reshape(shape = var_1012, x = var_1010_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<int32, [3]> var_1014 = const()[name = string("op_1014"), val = tensor<int32, [3]>([4, -1, 18])];
            tensor<fp16, [1, 4, 596, 18]> var_994_cast_fp16 = transpose(perm = var_994_perm_0, x = var_993_cast_fp16)[name = string("transpose_37")];
            tensor<fp16, [4, 596, 18]> key_states_7_cast_fp16 = reshape(shape = var_1014, x = var_994_cast_fp16)[name = string("key_states_7_cast_fp16")];
            tensor<int32, [3]> var_1016 = const()[name = string("op_1016"), val = tensor<int32, [3]>([4, -1, 18])];
            tensor<fp16, [1, 4, 596, 18]> var_1001_cast_fp16 = transpose(perm = var_1001_perm_0, x = var_1000_cast_fp16)[name = string("transpose_36")];
            tensor<fp16, [4, 596, 18]> value_states_7_cast_fp16 = reshape(shape = var_1016, x = var_1001_cast_fp16)[name = string("value_states_7_cast_fp16")];
            bool var_1019_transpose_x_1 = const()[name = string("op_1019_transpose_x_1"), val = bool(false)];
            bool var_1019_transpose_y_1 = const()[name = string("op_1019_transpose_y_1"), val = bool(true)];
            tensor<fp16, [4, 596, 596]> var_1019_cast_fp16 = matmul(transpose_x = var_1019_transpose_x_1, transpose_y = var_1019_transpose_y_1, x = query_states_3_cast_fp16, y = key_states_7_cast_fp16)[name = string("op_1019_cast_fp16")];
            fp16 var_1020_to_fp16 = const()[name = string("op_1020_to_fp16"), val = fp16(0x1.e2cp-3)];
            tensor<fp16, [4, 596, 596]> input_229_cast_fp16 = mul(x = var_1019_cast_fp16, y = var_1020_to_fp16)[name = string("input_229_cast_fp16")];
            tensor<fp16, [4, 596, 596]> input_231_cast_fp16 = softmax(axis = var_43, x = input_229_cast_fp16)[name = string("input_231_cast_fp16")];
            bool attn_output_7_transpose_x_0 = const()[name = string("attn_output_7_transpose_x_0"), val = bool(false)];
            bool attn_output_7_transpose_y_0 = const()[name = string("attn_output_7_transpose_y_0"), val = bool(false)];
            tensor<fp16, [4, 596, 18]> attn_output_7_cast_fp16 = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = input_231_cast_fp16, y = value_states_7_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<int32, [4]> var_1025 = const()[name = string("op_1025"), val = tensor<int32, [4]>([1, 4, 596, 18])];
            tensor<fp16, [1, 4, 596, 18]> attn_output_9_cast_fp16 = reshape(shape = var_1025, x = attn_output_7_cast_fp16)[name = string("attn_output_9_cast_fp16")];
            tensor<int32, [4]> attn_output_11_perm_0 = const()[name = string("attn_output_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1028 = const()[name = string("op_1028"), val = tensor<int32, [3]>([1, 596, 72])];
            tensor<fp16, [1, 596, 4, 18]> attn_output_11_cast_fp16 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast_fp16)[name = string("transpose_34")];
            tensor<fp16, [1, 596, 72]> input_233_cast_fp16 = reshape(shape = var_1028, x = attn_output_11_cast_fp16)[name = string("input_233_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage1_9_tcm_4_attention_out_proj_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_out_proj_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4398720)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_attention_out_proj_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_attention_out_proj_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4409152)))];
            tensor<fp16, [1, 596, 72]> linear_9_cast_fp16 = linear(bias = model_backbone_stage1_9_tcm_4_attention_out_proj_bias_to_fp16, weight = model_backbone_stage1_9_tcm_4_attention_out_proj_weight_to_fp16, x = input_233_cast_fp16)[name = string("linear_9_cast_fp16")];
            tensor<fp16, [1, 596, 72]> input_235_cast_fp16 = add(x = hidden_states_11_cast_fp16, y = linear_9_cast_fp16)[name = string("input_235_cast_fp16")];
            tensor<int32, [1]> input_237_axes_0 = const()[name = string("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_layer_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4409408)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_layer_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4409664)))];
            tensor<fp16, [1, 596, 72]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = model_backbone_stage1_9_tcm_4_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage1_9_tcm_4_layer_norm_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage1_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4409920)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4420352)))];
            tensor<fp16, [1, 596, 72]> linear_10_cast_fp16 = linear(bias = model_backbone_stage1_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16, weight = model_backbone_stage1_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16, x = input_237_cast_fp16)[name = string("linear_10_cast_fp16")];
            string input_241_mode_0 = const()[name = string("input_241_mode_0"), val = string("TANH_APPROXIMATION")];
            tensor<fp16, [1, 596, 72]> input_241_cast_fp16 = gelu(mode = input_241_mode_0, x = linear_10_cast_fp16)[name = string("input_241_cast_fp16")];
            tensor<fp16, [72, 72]> model_backbone_stage1_9_tcm_4_feed_forward_output_dense_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_feed_forward_output_dense_weight_to_fp16"), val = tensor<fp16, [72, 72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4420608)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_feed_forward_output_dense_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_feed_forward_output_dense_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4431040)))];
            tensor<fp16, [1, 596, 72]> linear_11_cast_fp16 = linear(bias = model_backbone_stage1_9_tcm_4_feed_forward_output_dense_bias_to_fp16, weight = model_backbone_stage1_9_tcm_4_feed_forward_output_dense_weight_to_fp16, x = input_241_cast_fp16)[name = string("linear_11_cast_fp16")];
            tensor<fp16, [1, 596, 72]> input_247_cast_fp16 = add(x = input_237_cast_fp16, y = linear_11_cast_fp16)[name = string("input_247_cast_fp16")];
            tensor<int32, [1]> hidden_states_15_axes_0 = const()[name = string("hidden_states_15_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_final_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4431296)))];
            tensor<fp16, [72]> model_backbone_stage1_9_tcm_4_final_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_tcm_4_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4431552)))];
            tensor<fp16, [1, 596, 72]> hidden_states_15_cast_fp16 = layer_norm(axes = hidden_states_15_axes_0, beta = model_backbone_stage1_9_tcm_4_final_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage1_9_tcm_4_final_layer_norm_weight_to_fp16, x = input_247_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            tensor<int32, [3]> var_1065 = const()[name = string("op_1065"), val = tensor<int32, [3]>([0, 2, 1])];
            string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)];
            tensor<fp16, [4608, 72, 1]> model_backbone_stage1_9_exp_dim_conv_weight_to_fp16 = const()[name = string("model_backbone_stage1_9_exp_dim_conv_weight_to_fp16"), val = tensor<fp16, [4608, 72, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4431808)))];
            tensor<fp16, [4608]> model_backbone_stage1_9_exp_dim_conv_bias_to_fp16 = const()[name = string("model_backbone_stage1_9_exp_dim_conv_bias_to_fp16"), val = tensor<fp16, [4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5095424)))];
            tensor<fp16, [1, 72, 596]> input_249_cast_fp16 = transpose(perm = var_1065, x = hidden_states_15_cast_fp16)[name = string("transpose_33")];
            tensor<fp16, [1, 4608, 596]> x_57_cast_fp16 = conv(bias = model_backbone_stage1_9_exp_dim_conv_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = model_backbone_stage1_9_exp_dim_conv_weight_to_fp16, x = input_249_cast_fp16)[name = string("x_57_cast_fp16")];
            tensor<fp16, [1, 4608, 596]> input_251_cast_fp16 = add(x = input_195_cast_fp16, y = x_57_cast_fp16)[name = string("input_251_cast_fp16")];
            tensor<int32, [1]> expand_dims_1_axes_0 = const()[name = string("expand_dims_1_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596, 1]> expand_dims_1_cast_fp16 = expand_dims(axes = expand_dims_1_axes_0, x = input_251_cast_fp16)[name = string("expand_dims_1_cast_fp16")];
            tensor<int32, [1]> input_253_axes_0 = const()[name = string("input_253_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596]> input_253_cast_fp16 = squeeze(axes = input_253_axes_0, x = expand_dims_1_cast_fp16)[name = string("input_253_cast_fp16")];
            tensor<int32, [4]> reshape_8_shape_0 = const()[name = string("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 64, 72, 596])];
            tensor<fp16, [1, 64, 72, 596]> reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = input_253_cast_fp16)[name = string("reshape_8_cast_fp16")];
            tensor<int32, [2]> reduce_mean_8_axes_0 = const()[name = string("reduce_mean_8_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_8_keep_dims_0 = const()[name = string("reduce_mean_8_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = reshape_8_cast_fp16)[name = string("reduce_mean_8_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> sub_5_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_8_cast_fp16)[name = string("sub_5_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> square_3_cast_fp16 = square(x = sub_5_cast_fp16)[name = string("square_3_cast_fp16")];
            tensor<int32, [2]> reduce_mean_10_axes_0 = const()[name = string("reduce_mean_10_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_10_keep_dims_0 = const()[name = string("reduce_mean_10_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_10_cast_fp16 = reduce_mean(axes = reduce_mean_10_axes_0, keep_dims = reduce_mean_10_keep_dims_0, x = square_3_cast_fp16)[name = string("reduce_mean_10_cast_fp16")];
            fp16 add_4_y_0_to_fp16 = const()[name = string("add_4_y_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 1]> add_4_cast_fp16 = add(x = reduce_mean_10_cast_fp16, y = add_4_y_0_to_fp16)[name = string("add_4_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> sqrt_3_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = string("sqrt_3_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> real_div_2_cast_fp16 = real_div(x = sub_5_cast_fp16, y = sqrt_3_cast_fp16)[name = string("real_div_2_cast_fp16")];
            tensor<int32, [3]> reshape_9_shape_0 = const()[name = string("reshape_9_shape_0"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 4608, 596]> reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = string("reshape_9_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_10_to_fp16 = const()[name = string("reshape_10_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5104704)))];
            tensor<fp16, [1, 4608, 596]> mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = string("mul_2_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_11_to_fp16 = const()[name = string("reshape_11_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5113984)))];
            tensor<fp16, [1, 4608, 596]> add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = string("add_5_cast_fp16")];
            tensor<int32, [1]> var_1094_axes_0 = const()[name = string("op_1094_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 4608, 596]> var_1094_cast_fp16 = expand_dims(axes = var_1094_axes_0, x = add_5_cast_fp16)[name = string("op_1094_cast_fp16")];
            bool xs_5_interleave_0 = const()[name = string("xs_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 3, 4608, 596]> xs_5_cast_fp16 = concat(axis = var_40, interleave = xs_5_interleave_0, values = (var_178_cast_fp16, var_612_cast_fp16, var_1094_cast_fp16))[name = string("xs_5_cast_fp16")];
            tensor<fp16, [1, 3, 4608, 1]> w_19_to_fp16 = const()[name = string("w_19_to_fp16"), val = tensor<fp16, [1, 3, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5123264)))];
            tensor<fp16, [1, 3, 4608, 596]> var_1097_cast_fp16 = mul(x = w_19_to_fp16, y = xs_5_cast_fp16)[name = string("op_1097_cast_fp16")];
            tensor<int32, [1]> x_59_axes_0 = const()[name = string("x_59_axes_0"), val = tensor<int32, [1]>([1])];
            bool x_59_keep_dims_0 = const()[name = string("x_59_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 4608, 596]> x_59_cast_fp16 = reduce_sum(axes = x_59_axes_0, keep_dims = x_59_keep_dims_0, x = var_1097_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [4]> var_1102 = const()[name = string("op_1102"), val = tensor<int32, [4]>([1, 36, 128, 596])];
            tensor<fp16, [1, 36, 128, 596]> var_1103_cast_fp16 = reshape(shape = var_1102, x = x_59_cast_fp16)[name = string("op_1103_cast_fp16")];
            tensor<int32, [4]> var_1104 = const()[name = string("op_1104"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor<int32, [2]>([1, 2])];
            int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(128)];
            tensor<int32, [4]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 1, 2]> model_backbone_stage2_2_weight_to_fp16 = const()[name = string("model_backbone_stage2_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 1, 2]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150976)))];
            tensor<fp16, [256]> model_backbone_stage2_2_bias_to_fp16 = const()[name = string("model_backbone_stage2_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5152064)))];
            tensor<fp16, [1, 128, 36, 596]> input_257_cast_fp16 = transpose(perm = var_1104, x = var_1103_cast_fp16)[name = string("transpose_32")];
            tensor<fp16, [1, 256, 36, 298]> input_259_cast_fp16 = conv(bias = model_backbone_stage2_2_bias_to_fp16, dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = model_backbone_stage2_2_weight_to_fp16, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")];
            string input_261_pad_type_0 = const()[name = string("input_261_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_261_pad_0 = const()[name = string("input_261_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_261_groups_0 = const()[name = string("input_261_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_261_strides_0 = const()[name = string("input_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_261_dilations_0 = const()[name = string("input_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_3_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5152640)))];
            tensor<fp16, [1, 256, 36, 298]> input_261_cast_fp16 = conv(dilations = input_261_dilations_0, groups = input_261_groups_0, pad = input_261_pad_0, pad_type = input_261_pad_type_0, strides = input_261_strides_0, weight = model_backbone_stage2_3_conv_block_conv1_weight_to_fp16, x = input_259_cast_fp16)[name = string("input_261_cast_fp16")];
            string input_263_pad_type_0 = const()[name = string("input_263_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_263_strides_0 = const()[name = string("input_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_263_pad_0 = const()[name = string("input_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_263_dilations_0 = const()[name = string("input_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_263_groups_0 = const()[name = string("input_263_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage2_3_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5157312)))];
            tensor<fp16, [256]> model_backbone_stage2_3_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5288448)))];
            tensor<fp16, [1, 256, 36, 298]> input_263_cast_fp16 = conv(bias = model_backbone_stage2_3_conv_block_conv1pw_bias_to_fp16, dilations = input_263_dilations_0, groups = input_263_groups_0, pad = input_263_pad_0, pad_type = input_263_pad_type_0, strides = input_263_strides_0, weight = model_backbone_stage2_3_conv_block_conv1pw_weight_to_fp16, x = input_261_cast_fp16)[name = string("input_263_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_265_cast_fp16 = relu(x = input_263_cast_fp16)[name = string("input_265_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage2_3_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5289024)))];
            tensor<fp16, [256]> model_backbone_stage2_3_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5289600)))];
            tensor<fp16, [256]> model_backbone_stage2_3_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5290176)))];
            tensor<fp16, [256]> model_backbone_stage2_3_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5290752)))];
            tensor<fp16, [1, 256, 36, 298]> input_267_cast_fp16 = batch_norm(beta = model_backbone_stage2_3_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_3_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage2_3_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage2_3_conv_block_bn1_running_var_to_fp16, x = input_265_cast_fp16)[name = string("input_267_cast_fp16")];
            string input_269_pad_type_0 = const()[name = string("input_269_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_269_pad_0 = const()[name = string("input_269_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_269_groups_0 = const()[name = string("input_269_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_269_strides_0 = const()[name = string("input_269_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_269_dilations_0 = const()[name = string("input_269_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_3_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage2_3_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5291328)))];
            tensor<fp16, [1, 256, 36, 298]> input_269_cast_fp16 = conv(dilations = input_269_dilations_0, groups = input_269_groups_0, pad = input_269_pad_0, pad_type = input_269_pad_type_0, strides = input_269_strides_0, weight = model_backbone_stage2_3_conv_block_conv2_weight_to_fp16, x = input_267_cast_fp16)[name = string("input_269_cast_fp16")];
            string input_271_pad_type_0 = const()[name = string("input_271_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_271_strides_0 = const()[name = string("input_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_271_pad_0 = const()[name = string("input_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_271_dilations_0 = const()[name = string("input_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_271_groups_0 = const()[name = string("input_271_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5296000)))];
            tensor<fp16, [256]> const_80_to_fp16 = const()[name = string("const_80_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5427136)))];
            tensor<fp16, [1, 256, 36, 298]> out_15_cast_fp16 = conv(bias = const_80_to_fp16, dilations = input_271_dilations_0, groups = input_271_groups_0, pad = input_271_pad_0, pad_type = input_271_pad_type_0, strides = input_271_strides_0, weight = const_79_to_fp16, x = input_269_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_273_cast_fp16 = add(x = out_15_cast_fp16, y = input_259_cast_fp16)[name = string("input_273_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_275_cast_fp16 = relu(x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
            string input_277_pad_type_0 = const()[name = string("input_277_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_277_pad_0 = const()[name = string("input_277_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_277_groups_0 = const()[name = string("input_277_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_277_strides_0 = const()[name = string("input_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_277_dilations_0 = const()[name = string("input_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_4_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5427712)))];
            tensor<fp16, [1, 256, 36, 298]> input_277_cast_fp16 = conv(dilations = input_277_dilations_0, groups = input_277_groups_0, pad = input_277_pad_0, pad_type = input_277_pad_type_0, strides = input_277_strides_0, weight = model_backbone_stage2_4_conv_block_conv1_weight_to_fp16, x = input_275_cast_fp16)[name = string("input_277_cast_fp16")];
            string input_279_pad_type_0 = const()[name = string("input_279_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_279_strides_0 = const()[name = string("input_279_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_279_pad_0 = const()[name = string("input_279_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_279_dilations_0 = const()[name = string("input_279_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_279_groups_0 = const()[name = string("input_279_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage2_4_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5432384)))];
            tensor<fp16, [256]> model_backbone_stage2_4_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5563520)))];
            tensor<fp16, [1, 256, 36, 298]> input_279_cast_fp16 = conv(bias = model_backbone_stage2_4_conv_block_conv1pw_bias_to_fp16, dilations = input_279_dilations_0, groups = input_279_groups_0, pad = input_279_pad_0, pad_type = input_279_pad_type_0, strides = input_279_strides_0, weight = model_backbone_stage2_4_conv_block_conv1pw_weight_to_fp16, x = input_277_cast_fp16)[name = string("input_279_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_281_cast_fp16 = relu(x = input_279_cast_fp16)[name = string("input_281_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage2_4_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564096)))];
            tensor<fp16, [256]> model_backbone_stage2_4_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5564672)))];
            tensor<fp16, [256]> model_backbone_stage2_4_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5565248)))];
            tensor<fp16, [256]> model_backbone_stage2_4_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5565824)))];
            tensor<fp16, [1, 256, 36, 298]> input_283_cast_fp16 = batch_norm(beta = model_backbone_stage2_4_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_4_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage2_4_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage2_4_conv_block_bn1_running_var_to_fp16, x = input_281_cast_fp16)[name = string("input_283_cast_fp16")];
            string input_285_pad_type_0 = const()[name = string("input_285_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_285_pad_0 = const()[name = string("input_285_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_285_groups_0 = const()[name = string("input_285_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_285_strides_0 = const()[name = string("input_285_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_285_dilations_0 = const()[name = string("input_285_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_4_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage2_4_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5566400)))];
            tensor<fp16, [1, 256, 36, 298]> input_285_cast_fp16 = conv(dilations = input_285_dilations_0, groups = input_285_groups_0, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = input_285_strides_0, weight = model_backbone_stage2_4_conv_block_conv2_weight_to_fp16, x = input_283_cast_fp16)[name = string("input_285_cast_fp16")];
            string input_287_pad_type_0 = const()[name = string("input_287_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_287_strides_0 = const()[name = string("input_287_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_287_pad_0 = const()[name = string("input_287_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_287_dilations_0 = const()[name = string("input_287_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_287_groups_0 = const()[name = string("input_287_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_81_to_fp16 = const()[name = string("const_81_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5571072)))];
            tensor<fp16, [256]> const_82_to_fp16 = const()[name = string("const_82_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5702208)))];
            tensor<fp16, [1, 256, 36, 298]> out_17_cast_fp16 = conv(bias = const_82_to_fp16, dilations = input_287_dilations_0, groups = input_287_groups_0, pad = input_287_pad_0, pad_type = input_287_pad_type_0, strides = input_287_strides_0, weight = const_81_to_fp16, x = input_285_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_289_cast_fp16 = add(x = out_17_cast_fp16, y = input_275_cast_fp16)[name = string("input_289_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_291_cast_fp16 = relu(x = input_289_cast_fp16)[name = string("input_291_cast_fp16")];
            string input_293_pad_type_0 = const()[name = string("input_293_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_293_pad_0 = const()[name = string("input_293_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_293_groups_0 = const()[name = string("input_293_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_293_strides_0 = const()[name = string("input_293_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_293_dilations_0 = const()[name = string("input_293_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_5_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5702784)))];
            tensor<fp16, [1, 256, 36, 298]> input_293_cast_fp16 = conv(dilations = input_293_dilations_0, groups = input_293_groups_0, pad = input_293_pad_0, pad_type = input_293_pad_type_0, strides = input_293_strides_0, weight = model_backbone_stage2_5_conv_block_conv1_weight_to_fp16, x = input_291_cast_fp16)[name = string("input_293_cast_fp16")];
            string input_295_pad_type_0 = const()[name = string("input_295_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_295_strides_0 = const()[name = string("input_295_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_295_dilations_0 = const()[name = string("input_295_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_295_groups_0 = const()[name = string("input_295_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage2_5_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5707456)))];
            tensor<fp16, [256]> model_backbone_stage2_5_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5838592)))];
            tensor<fp16, [1, 256, 36, 298]> input_295_cast_fp16 = conv(bias = model_backbone_stage2_5_conv_block_conv1pw_bias_to_fp16, dilations = input_295_dilations_0, groups = input_295_groups_0, pad = input_295_pad_0, pad_type = input_295_pad_type_0, strides = input_295_strides_0, weight = model_backbone_stage2_5_conv_block_conv1pw_weight_to_fp16, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_297_cast_fp16 = relu(x = input_295_cast_fp16)[name = string("input_297_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage2_5_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5839168)))];
            tensor<fp16, [256]> model_backbone_stage2_5_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5839744)))];
            tensor<fp16, [256]> model_backbone_stage2_5_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5840320)))];
            tensor<fp16, [256]> model_backbone_stage2_5_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5840896)))];
            tensor<fp16, [1, 256, 36, 298]> input_299_cast_fp16 = batch_norm(beta = model_backbone_stage2_5_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_5_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage2_5_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage2_5_conv_block_bn1_running_var_to_fp16, x = input_297_cast_fp16)[name = string("input_299_cast_fp16")];
            string input_301_pad_type_0 = const()[name = string("input_301_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_301_pad_0 = const()[name = string("input_301_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_301_groups_0 = const()[name = string("input_301_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_301_strides_0 = const()[name = string("input_301_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_301_dilations_0 = const()[name = string("input_301_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_5_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage2_5_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5841472)))];
            tensor<fp16, [1, 256, 36, 298]> input_301_cast_fp16 = conv(dilations = input_301_dilations_0, groups = input_301_groups_0, pad = input_301_pad_0, pad_type = input_301_pad_type_0, strides = input_301_strides_0, weight = model_backbone_stage2_5_conv_block_conv2_weight_to_fp16, x = input_299_cast_fp16)[name = string("input_301_cast_fp16")];
            string input_303_pad_type_0 = const()[name = string("input_303_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_303_strides_0 = const()[name = string("input_303_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_303_pad_0 = const()[name = string("input_303_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_303_dilations_0 = const()[name = string("input_303_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_303_groups_0 = const()[name = string("input_303_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_83_to_fp16 = const()[name = string("const_83_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846144)))];
            tensor<fp16, [256]> const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5977280)))];
            tensor<fp16, [1, 256, 36, 298]> out_19_cast_fp16 = conv(bias = const_84_to_fp16, dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = const_83_to_fp16, x = input_301_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_305_cast_fp16 = add(x = out_19_cast_fp16, y = input_291_cast_fp16)[name = string("input_305_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_307_cast_fp16 = relu(x = input_305_cast_fp16)[name = string("input_307_cast_fp16")];
            string input_309_pad_type_0 = const()[name = string("input_309_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_309_pad_0 = const()[name = string("input_309_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_309_groups_0 = const()[name = string("input_309_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_309_strides_0 = const()[name = string("input_309_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_309_dilations_0 = const()[name = string("input_309_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_6_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5977856)))];
            tensor<fp16, [1, 256, 36, 298]> input_309_cast_fp16 = conv(dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = model_backbone_stage2_6_conv_block_conv1_weight_to_fp16, x = input_307_cast_fp16)[name = string("input_309_cast_fp16")];
            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage2_6_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5982528)))];
            tensor<fp16, [256]> model_backbone_stage2_6_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6113664)))];
            tensor<fp16, [1, 256, 36, 298]> input_311_cast_fp16 = conv(bias = model_backbone_stage2_6_conv_block_conv1pw_bias_to_fp16, dilations = input_311_dilations_0, groups = input_311_groups_0, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = input_311_strides_0, weight = model_backbone_stage2_6_conv_block_conv1pw_weight_to_fp16, x = input_309_cast_fp16)[name = string("input_311_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_313_cast_fp16 = relu(x = input_311_cast_fp16)[name = string("input_313_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage2_6_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6114240)))];
            tensor<fp16, [256]> model_backbone_stage2_6_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6114816)))];
            tensor<fp16, [256]> model_backbone_stage2_6_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6115392)))];
            tensor<fp16, [256]> model_backbone_stage2_6_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6115968)))];
            tensor<fp16, [1, 256, 36, 298]> input_315_cast_fp16 = batch_norm(beta = model_backbone_stage2_6_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_6_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage2_6_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage2_6_conv_block_bn1_running_var_to_fp16, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
            string input_317_pad_type_0 = const()[name = string("input_317_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_317_pad_0 = const()[name = string("input_317_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_317_groups_0 = const()[name = string("input_317_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_317_strides_0 = const()[name = string("input_317_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_317_dilations_0 = const()[name = string("input_317_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_6_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage2_6_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6116544)))];
            tensor<fp16, [1, 256, 36, 298]> input_317_cast_fp16 = conv(dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = model_backbone_stage2_6_conv_block_conv2_weight_to_fp16, x = input_315_cast_fp16)[name = string("input_317_cast_fp16")];
            string input_319_pad_type_0 = const()[name = string("input_319_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_319_strides_0 = const()[name = string("input_319_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_319_dilations_0 = const()[name = string("input_319_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_319_groups_0 = const()[name = string("input_319_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_85_to_fp16 = const()[name = string("const_85_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6121216)))];
            tensor<fp16, [256]> const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6252352)))];
            tensor<fp16, [1, 256, 36, 298]> out_21_cast_fp16 = conv(bias = const_86_to_fp16, dilations = input_319_dilations_0, groups = input_319_groups_0, pad = input_319_pad_0, pad_type = input_319_pad_type_0, strides = input_319_strides_0, weight = const_85_to_fp16, x = input_317_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_321_cast_fp16 = add(x = out_21_cast_fp16, y = input_307_cast_fp16)[name = string("input_321_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_323_cast_fp16 = relu(x = input_321_cast_fp16)[name = string("input_323_cast_fp16")];
            string input_325_pad_type_0 = const()[name = string("input_325_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_325_pad_0 = const()[name = string("input_325_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_325_groups_0 = const()[name = string("input_325_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_325_strides_0 = const()[name = string("input_325_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_325_dilations_0 = const()[name = string("input_325_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_7_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6252928)))];
            tensor<fp16, [1, 256, 36, 298]> input_325_cast_fp16 = conv(dilations = input_325_dilations_0, groups = input_325_groups_0, pad = input_325_pad_0, pad_type = input_325_pad_type_0, strides = input_325_strides_0, weight = model_backbone_stage2_7_conv_block_conv1_weight_to_fp16, x = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
            string input_327_pad_type_0 = const()[name = string("input_327_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_327_strides_0 = const()[name = string("input_327_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_327_pad_0 = const()[name = string("input_327_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_327_dilations_0 = const()[name = string("input_327_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_327_groups_0 = const()[name = string("input_327_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage2_7_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6257600)))];
            tensor<fp16, [256]> model_backbone_stage2_7_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6388736)))];
            tensor<fp16, [1, 256, 36, 298]> input_327_cast_fp16 = conv(bias = model_backbone_stage2_7_conv_block_conv1pw_bias_to_fp16, dilations = input_327_dilations_0, groups = input_327_groups_0, pad = input_327_pad_0, pad_type = input_327_pad_type_0, strides = input_327_strides_0, weight = model_backbone_stage2_7_conv_block_conv1pw_weight_to_fp16, x = input_325_cast_fp16)[name = string("input_327_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_329_cast_fp16 = relu(x = input_327_cast_fp16)[name = string("input_329_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage2_7_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6389312)))];
            tensor<fp16, [256]> model_backbone_stage2_7_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6389888)))];
            tensor<fp16, [256]> model_backbone_stage2_7_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6390464)))];
            tensor<fp16, [256]> model_backbone_stage2_7_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6391040)))];
            tensor<fp16, [1, 256, 36, 298]> input_331_cast_fp16 = batch_norm(beta = model_backbone_stage2_7_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_7_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage2_7_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage2_7_conv_block_bn1_running_var_to_fp16, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")];
            string input_333_pad_type_0 = const()[name = string("input_333_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_333_pad_0 = const()[name = string("input_333_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_333_groups_0 = const()[name = string("input_333_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_333_strides_0 = const()[name = string("input_333_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_333_dilations_0 = const()[name = string("input_333_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage2_7_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage2_7_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6391616)))];
            tensor<fp16, [1, 256, 36, 298]> input_333_cast_fp16 = conv(dilations = input_333_dilations_0, groups = input_333_groups_0, pad = input_333_pad_0, pad_type = input_333_pad_type_0, strides = input_333_strides_0, weight = model_backbone_stage2_7_conv_block_conv2_weight_to_fp16, x = input_331_cast_fp16)[name = string("input_333_cast_fp16")];
            string input_335_pad_type_0 = const()[name = string("input_335_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_335_strides_0 = const()[name = string("input_335_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_335_pad_0 = const()[name = string("input_335_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_335_dilations_0 = const()[name = string("input_335_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_335_groups_0 = const()[name = string("input_335_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_87_to_fp16 = const()[name = string("const_87_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6396288)))];
            tensor<fp16, [256]> const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6527424)))];
            tensor<fp16, [1, 256, 36, 298]> out_23_cast_fp16 = conv(bias = const_88_to_fp16, dilations = input_335_dilations_0, groups = input_335_groups_0, pad = input_335_pad_0, pad_type = input_335_pad_type_0, strides = input_335_strides_0, weight = const_87_to_fp16, x = input_333_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_337_cast_fp16 = add(x = out_23_cast_fp16, y = input_323_cast_fp16)[name = string("input_337_cast_fp16")];
            tensor<fp16, [1, 256, 36, 298]> input_339_cast_fp16 = relu(x = input_337_cast_fp16)[name = string("input_339_cast_fp16")];
            tensor<int32, [2]> var_1347 = const()[name = string("op_1347"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1348 = const()[name = string("op_1348"), val = tensor<int32, [2]>([1, 1])];
            string input_341_pad_type_0 = const()[name = string("input_341_pad_type_0"), val = string("same")];
            tensor<int32, [4]> input_341_pad_0 = const()[name = string("input_341_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [128, 256, 1, 1]> const_89_to_fp16 = const()[name = string("const_89_to_fp16"), val = tensor<fp16, [128, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6528000)))];
            tensor<fp16, [128]> const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6593600)))];
            tensor<fp16, [1, 128, 36, 298]> x_61_cast_fp16 = conv(bias = const_90_to_fp16, dilations = var_1348, groups = var_40, pad = input_341_pad_0, pad_type = input_341_pad_type_0, strides = var_1347, weight = const_89_to_fp16, x = input_339_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> var_1361 = const()[name = string("op_1361"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1365 = const()[name = string("op_1365"), val = tensor<int32, [3]>([1, 4608, 298])];
            tensor<fp16, [1, 36, 128, 298]> var_1362_cast_fp16 = transpose(perm = var_1361, x = x_61_cast_fp16)[name = string("transpose_31")];
            tensor<fp16, [1, 4608, 298]> input_343_cast_fp16 = reshape(shape = var_1365, x = var_1362_cast_fp16)[name = string("input_343_cast_fp16")];
            string x_63_pad_type_0 = const()[name = string("x_63_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_63_strides_0 = const()[name = string("x_63_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_63_pad_0 = const()[name = string("x_63_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_63_dilations_0 = const()[name = string("x_63_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_63_groups_0 = const()[name = string("x_63_groups_0"), val = int32(1)];
            tensor<fp16, [96, 4608, 1]> model_backbone_stage2_10_red_dim_conv_0_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_red_dim_conv_0_weight_to_fp16"), val = tensor<fp16, [96, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6593920)))];
            tensor<fp16, [96]> model_backbone_stage2_10_red_dim_conv_0_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_red_dim_conv_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7478720)))];
            tensor<fp16, [1, 96, 298]> x_63_cast_fp16 = conv(bias = model_backbone_stage2_10_red_dim_conv_0_bias_to_fp16, dilations = x_63_dilations_0, groups = x_63_groups_0, pad = x_63_pad_0, pad_type = x_63_pad_type_0, strides = x_63_strides_0, weight = model_backbone_stage2_10_red_dim_conv_0_weight_to_fp16, x = input_343_cast_fp16)[name = string("x_63_cast_fp16")];
            tensor<int32, [1]> u_7_axes_0 = const()[name = string("u_7_axes_0"), val = tensor<int32, [1]>([1])];
            bool u_7_keep_dims_0 = const()[name = string("u_7_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 298]> u_7_cast_fp16 = reduce_mean(axes = u_7_axes_0, keep_dims = u_7_keep_dims_0, x = x_63_cast_fp16)[name = string("u_7_cast_fp16")];
            tensor<fp16, [1, 96, 298]> var_1383_cast_fp16 = sub(x = x_63_cast_fp16, y = u_7_cast_fp16)[name = string("op_1383_cast_fp16")];
            fp16 var_41_promoted_3_to_fp16 = const()[name = string("op_41_promoted_3_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 96, 298]> var_1384_cast_fp16 = pow(x = var_1383_cast_fp16, y = var_41_promoted_3_to_fp16)[name = string("op_1384_cast_fp16")];
            tensor<int32, [1]> s_7_axes_0 = const()[name = string("s_7_axes_0"), val = tensor<int32, [1]>([1])];
            bool s_7_keep_dims_0 = const()[name = string("s_7_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 298]> s_7_cast_fp16 = reduce_mean(axes = s_7_axes_0, keep_dims = s_7_keep_dims_0, x = var_1384_cast_fp16)[name = string("s_7_cast_fp16")];
            fp16 var_1388_to_fp16 = const()[name = string("op_1388_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 298]> var_1389_cast_fp16 = add(x = s_7_cast_fp16, y = var_1388_to_fp16)[name = string("op_1389_cast_fp16")];
            tensor<fp16, [1, 1, 298]> var_1390_cast_fp16 = sqrt(x = var_1389_cast_fp16)[name = string("op_1390_cast_fp16")];
            tensor<fp16, [1, 96, 298]> x_65_cast_fp16 = real_div(x = var_1383_cast_fp16, y = var_1390_cast_fp16)[name = string("x_65_cast_fp16")];
            tensor<fp16, [96, 1]> w_23_to_fp16 = const()[name = string("w_23_to_fp16"), val = tensor<fp16, [96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7478976)))];
            tensor<fp16, [1, 96, 298]> var_1394_cast_fp16 = mul(x = w_23_to_fp16, y = x_65_cast_fp16)[name = string("op_1394_cast_fp16")];
            tensor<fp16, [96, 1]> b_17_to_fp16 = const()[name = string("b_17_to_fp16"), val = tensor<fp16, [96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7479232)))];
            tensor<fp16, [1, 96, 298]> input_345_cast_fp16 = add(x = var_1394_cast_fp16, y = b_17_to_fp16)[name = string("input_345_cast_fp16")];
            tensor<int32, [1]> var_1407 = const()[name = string("op_1407"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1408 = const()[name = string("op_1408"), val = tensor<int32, [1]>([1])];
            string var_1409_pad_type_0 = const()[name = string("op_1409_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_1409_pad_0 = const()[name = string("op_1409_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 7]> model_backbone_stage2_10_tcm_0_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7479488)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_0_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480896)))];
            tensor<fp16, [1, 96, 298]> var_1409_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_0_dwconvs_0_bias_to_fp16, dilations = var_1408, groups = var_23, pad = var_1409_pad_0, pad_type = var_1409_pad_type_0, strides = var_1407, weight = model_backbone_stage2_10_tcm_0_dwconvs_0_weight_to_fp16, x = input_345_cast_fp16)[name = string("op_1409_cast_fp16")];
            bool input_347_interleave_0 = const()[name = string("input_347_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_347_cast_fp16 = concat(axis = var_40, interleave = input_347_interleave_0, values = var_1409_cast_fp16)[name = string("input_347_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_0_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7481152)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_0_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7481408)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_0_norm_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7481664)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_0_norm_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7481920)))];
            tensor<fp16, [1, 96, 298]> input_349_cast_fp16 = batch_norm(beta = model_backbone_stage2_10_tcm_0_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_10_tcm_0_norm_weight_to_fp16, mean = model_backbone_stage2_10_tcm_0_norm_running_mean_to_fp16, variance = model_backbone_stage2_10_tcm_0_norm_running_var_to_fp16, x = input_347_cast_fp16)[name = string("input_349_cast_fp16")];
            string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = input_349_cast_fp16)[name = string("input_351_cast_fp16")];
            string x_67_pad_type_0 = const()[name = string("x_67_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_67_strides_0 = const()[name = string("x_67_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_67_pad_0 = const()[name = string("x_67_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_67_dilations_0 = const()[name = string("x_67_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_67_groups_0 = const()[name = string("x_67_groups_0"), val = int32(1)];
            tensor<fp16, [96, 96, 1]> model_backbone_stage2_10_tcm_0_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7482176)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_0_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7500672)))];
            tensor<fp16, [1, 96, 298]> x_67_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_0_pwconv1_bias_to_fp16, dilations = x_67_dilations_0, groups = x_67_groups_0, pad = x_67_pad_0, pad_type = x_67_pad_type_0, strides = x_67_strides_0, weight = model_backbone_stage2_10_tcm_0_pwconv1_weight_to_fp16, x = input_351_cast_fp16)[name = string("x_67_cast_fp16")];
            tensor<fp16, [1, 96, 298]> input_353_cast_fp16 = add(x = input_345_cast_fp16, y = x_67_cast_fp16)[name = string("input_353_cast_fp16")];
            tensor<int32, [1]> var_1432 = const()[name = string("op_1432"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1433 = const()[name = string("op_1433"), val = tensor<int32, [1]>([1])];
            string var_1434_pad_type_0 = const()[name = string("op_1434_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_1434_pad_0 = const()[name = string("op_1434_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 19]> model_backbone_stage2_10_tcm_1_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 19]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7500928)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_1_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))];
            tensor<fp16, [1, 96, 298]> var_1434_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_1_dwconvs_0_bias_to_fp16, dilations = var_1433, groups = var_23, pad = var_1434_pad_0, pad_type = var_1434_pad_type_0, strides = var_1432, weight = model_backbone_stage2_10_tcm_1_dwconvs_0_weight_to_fp16, x = input_353_cast_fp16)[name = string("op_1434_cast_fp16")];
            bool input_355_interleave_0 = const()[name = string("input_355_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_355_cast_fp16 = concat(axis = var_40, interleave = input_355_interleave_0, values = var_1434_cast_fp16)[name = string("input_355_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_1_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504896)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_1_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505152)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_1_norm_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505408)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_1_norm_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505664)))];
            tensor<fp16, [1, 96, 298]> input_357_cast_fp16 = batch_norm(beta = model_backbone_stage2_10_tcm_1_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_10_tcm_1_norm_weight_to_fp16, mean = model_backbone_stage2_10_tcm_1_norm_running_mean_to_fp16, variance = model_backbone_stage2_10_tcm_1_norm_running_var_to_fp16, x = input_355_cast_fp16)[name = string("input_357_cast_fp16")];
            string input_359_mode_0 = const()[name = string("input_359_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_359_cast_fp16 = gelu(mode = input_359_mode_0, x = input_357_cast_fp16)[name = string("input_359_cast_fp16")];
            string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)];
            tensor<fp16, [96, 96, 1]> model_backbone_stage2_10_tcm_1_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505920)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_1_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7524416)))];
            tensor<fp16, [1, 96, 298]> x_69_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_1_pwconv1_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = model_backbone_stage2_10_tcm_1_pwconv1_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<fp16, [1, 96, 298]> input_361_cast_fp16 = add(x = input_353_cast_fp16, y = x_69_cast_fp16)[name = string("input_361_cast_fp16")];
            tensor<int32, [1]> var_1457 = const()[name = string("op_1457"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1458 = const()[name = string("op_1458"), val = tensor<int32, [1]>([1])];
            string var_1459_pad_type_0 = const()[name = string("op_1459_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_1459_pad_0 = const()[name = string("op_1459_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 31]> model_backbone_stage2_10_tcm_2_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 31]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7524672)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_2_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7530688)))];
            tensor<fp16, [1, 96, 298]> var_1459_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_2_dwconvs_0_bias_to_fp16, dilations = var_1458, groups = var_23, pad = var_1459_pad_0, pad_type = var_1459_pad_type_0, strides = var_1457, weight = model_backbone_stage2_10_tcm_2_dwconvs_0_weight_to_fp16, x = input_361_cast_fp16)[name = string("op_1459_cast_fp16")];
            bool input_363_interleave_0 = const()[name = string("input_363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_363_cast_fp16 = concat(axis = var_40, interleave = input_363_interleave_0, values = var_1459_cast_fp16)[name = string("input_363_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_2_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7530944)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_2_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7531200)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_2_norm_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7531456)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_2_norm_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7531712)))];
            tensor<fp16, [1, 96, 298]> input_365_cast_fp16 = batch_norm(beta = model_backbone_stage2_10_tcm_2_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_10_tcm_2_norm_weight_to_fp16, mean = model_backbone_stage2_10_tcm_2_norm_running_mean_to_fp16, variance = model_backbone_stage2_10_tcm_2_norm_running_var_to_fp16, x = input_363_cast_fp16)[name = string("input_365_cast_fp16")];
            string input_367_mode_0 = const()[name = string("input_367_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_367_cast_fp16 = gelu(mode = input_367_mode_0, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")];
            string x_71_pad_type_0 = const()[name = string("x_71_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_71_strides_0 = const()[name = string("x_71_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_71_pad_0 = const()[name = string("x_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_71_dilations_0 = const()[name = string("x_71_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_71_groups_0 = const()[name = string("x_71_groups_0"), val = int32(1)];
            tensor<fp16, [96, 96, 1]> model_backbone_stage2_10_tcm_2_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7531968)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_2_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7550464)))];
            tensor<fp16, [1, 96, 298]> x_71_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_2_pwconv1_bias_to_fp16, dilations = x_71_dilations_0, groups = x_71_groups_0, pad = x_71_pad_0, pad_type = x_71_pad_type_0, strides = x_71_strides_0, weight = model_backbone_stage2_10_tcm_2_pwconv1_weight_to_fp16, x = input_367_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<fp16, [1, 96, 298]> input_369_cast_fp16 = add(x = input_361_cast_fp16, y = x_71_cast_fp16)[name = string("input_369_cast_fp16")];
            tensor<int32, [1]> var_1482 = const()[name = string("op_1482"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1483 = const()[name = string("op_1483"), val = tensor<int32, [1]>([1])];
            string var_1484_pad_type_0 = const()[name = string("op_1484_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_1484_pad_0 = const()[name = string("op_1484_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 59]> model_backbone_stage2_10_tcm_3_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 59]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7550720)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_3_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7562112)))];
            tensor<fp16, [1, 96, 298]> var_1484_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_3_dwconvs_0_bias_to_fp16, dilations = var_1483, groups = var_23, pad = var_1484_pad_0, pad_type = var_1484_pad_type_0, strides = var_1482, weight = model_backbone_stage2_10_tcm_3_dwconvs_0_weight_to_fp16, x = input_369_cast_fp16)[name = string("op_1484_cast_fp16")];
            bool input_371_interleave_0 = const()[name = string("input_371_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_371_cast_fp16 = concat(axis = var_40, interleave = input_371_interleave_0, values = var_1484_cast_fp16)[name = string("input_371_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_3_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7562368)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_3_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7562624)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_3_norm_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7562880)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_3_norm_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7563136)))];
            tensor<fp16, [1, 96, 298]> input_373_cast_fp16 = batch_norm(beta = model_backbone_stage2_10_tcm_3_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage2_10_tcm_3_norm_weight_to_fp16, mean = model_backbone_stage2_10_tcm_3_norm_running_mean_to_fp16, variance = model_backbone_stage2_10_tcm_3_norm_running_var_to_fp16, x = input_371_cast_fp16)[name = string("input_373_cast_fp16")];
            string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = input_373_cast_fp16)[name = string("input_375_cast_fp16")];
            string x_73_pad_type_0 = const()[name = string("x_73_pad_type_0"), val = string("valid")];
            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])];
            int32 x_73_groups_0 = const()[name = string("x_73_groups_0"), val = int32(1)];
            tensor<fp16, [96, 96, 1]> model_backbone_stage2_10_tcm_3_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7563392)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_3_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7581888)))];
            tensor<fp16, [1, 96, 298]> x_73_cast_fp16 = conv(bias = model_backbone_stage2_10_tcm_3_pwconv1_bias_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 = model_backbone_stage2_10_tcm_3_pwconv1_weight_to_fp16, x = input_375_cast_fp16)[name = string("x_73_cast_fp16")];
            tensor<fp16, [1, 96, 298]> hidden_states_17_cast_fp16 = add(x = input_369_cast_fp16, y = x_73_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<int32, [3]> var_1505 = const()[name = string("op_1505"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [96, 96]> model_backbone_stage2_10_tcm_4_attention_q_proj_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_q_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7582144)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_attention_q_proj_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_q_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7600640)))];
            tensor<fp16, [1, 298, 96]> hidden_states_19_cast_fp16 = transpose(perm = var_1505, x = hidden_states_17_cast_fp16)[name = string("transpose_30")];
            tensor<fp16, [1, 298, 96]> linear_12_cast_fp16 = linear(bias = model_backbone_stage2_10_tcm_4_attention_q_proj_bias_to_fp16, weight = model_backbone_stage2_10_tcm_4_attention_q_proj_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_12_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage2_10_tcm_4_attention_k_proj_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_k_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7600896)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_attention_k_proj_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_k_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7619392)))];
            tensor<fp16, [1, 298, 96]> linear_13_cast_fp16 = linear(bias = model_backbone_stage2_10_tcm_4_attention_k_proj_bias_to_fp16, weight = model_backbone_stage2_10_tcm_4_attention_k_proj_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_13_cast_fp16")];
            tensor<int32, [4]> var_1520 = const()[name = string("op_1520"), val = tensor<int32, [4]>([1, -1, 4, 24])];
            tensor<fp16, [1, 298, 4, 24]> var_1521_cast_fp16 = reshape(shape = var_1520, x = linear_13_cast_fp16)[name = string("op_1521_cast_fp16")];
            tensor<int32, [4]> var_1522_perm_0 = const()[name = string("op_1522_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [96, 96]> model_backbone_stage2_10_tcm_4_attention_v_proj_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_v_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7619648)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_attention_v_proj_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_v_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7638144)))];
            tensor<fp16, [1, 298, 96]> linear_14_cast_fp16 = linear(bias = model_backbone_stage2_10_tcm_4_attention_v_proj_bias_to_fp16, weight = model_backbone_stage2_10_tcm_4_attention_v_proj_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_14_cast_fp16")];
            tensor<int32, [4]> var_1527 = const()[name = string("op_1527"), val = tensor<int32, [4]>([1, -1, 4, 24])];
            tensor<fp16, [1, 298, 4, 24]> var_1528_cast_fp16 = reshape(shape = var_1527, x = linear_14_cast_fp16)[name = string("op_1528_cast_fp16")];
            tensor<int32, [4]> var_1529_perm_0 = const()[name = string("op_1529_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1536 = const()[name = string("op_1536"), val = tensor<int32, [4]>([1, 298, 4, 24])];
            tensor<fp16, [1, 298, 4, 24]> var_1537_cast_fp16 = reshape(shape = var_1536, x = linear_12_cast_fp16)[name = string("op_1537_cast_fp16")];
            tensor<int32, [4]> var_1538_perm_0 = const()[name = string("op_1538_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1540 = const()[name = string("op_1540"), val = tensor<int32, [3]>([4, -1, 24])];
            tensor<fp16, [1, 4, 298, 24]> var_1538_cast_fp16 = transpose(perm = var_1538_perm_0, x = var_1537_cast_fp16)[name = string("transpose_27")];
            tensor<fp16, [4, 298, 24]> query_states_5_cast_fp16 = reshape(shape = var_1540, x = var_1538_cast_fp16)[name = string("query_states_5_cast_fp16")];
            tensor<int32, [3]> var_1542 = const()[name = string("op_1542"), val = tensor<int32, [3]>([4, -1, 24])];
            tensor<fp16, [1, 4, 298, 24]> var_1522_cast_fp16 = transpose(perm = var_1522_perm_0, x = var_1521_cast_fp16)[name = string("transpose_29")];
            tensor<fp16, [4, 298, 24]> key_states_11_cast_fp16 = reshape(shape = var_1542, x = var_1522_cast_fp16)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [3]> var_1544 = const()[name = string("op_1544"), val = tensor<int32, [3]>([4, -1, 24])];
            tensor<fp16, [1, 4, 298, 24]> var_1529_cast_fp16 = transpose(perm = var_1529_perm_0, x = var_1528_cast_fp16)[name = string("transpose_28")];
            tensor<fp16, [4, 298, 24]> value_states_11_cast_fp16 = reshape(shape = var_1544, x = var_1529_cast_fp16)[name = string("value_states_11_cast_fp16")];
            bool var_1547_transpose_x_1 = const()[name = string("op_1547_transpose_x_1"), val = bool(false)];
            bool var_1547_transpose_y_1 = const()[name = string("op_1547_transpose_y_1"), val = bool(true)];
            tensor<fp16, [4, 298, 298]> var_1547_cast_fp16 = matmul(transpose_x = var_1547_transpose_x_1, transpose_y = var_1547_transpose_y_1, x = query_states_5_cast_fp16, y = key_states_11_cast_fp16)[name = string("op_1547_cast_fp16")];
            fp16 var_1548_to_fp16 = const()[name = string("op_1548_to_fp16"), val = fp16(0x1.a2p-3)];
            tensor<fp16, [4, 298, 298]> input_377_cast_fp16 = mul(x = var_1547_cast_fp16, y = var_1548_to_fp16)[name = string("input_377_cast_fp16")];
            tensor<fp16, [4, 298, 298]> input_379_cast_fp16 = softmax(axis = var_43, x = input_377_cast_fp16)[name = string("input_379_cast_fp16")];
            bool attn_output_13_transpose_x_0 = const()[name = string("attn_output_13_transpose_x_0"), val = bool(false)];
            bool attn_output_13_transpose_y_0 = const()[name = string("attn_output_13_transpose_y_0"), val = bool(false)];
            tensor<fp16, [4, 298, 24]> attn_output_13_cast_fp16 = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = input_379_cast_fp16, y = value_states_11_cast_fp16)[name = string("attn_output_13_cast_fp16")];
            tensor<int32, [4]> var_1553 = const()[name = string("op_1553"), val = tensor<int32, [4]>([1, 4, 298, 24])];
            tensor<fp16, [1, 4, 298, 24]> attn_output_15_cast_fp16 = reshape(shape = var_1553, x = attn_output_13_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<int32, [4]> attn_output_17_perm_0 = const()[name = string("attn_output_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1556 = const()[name = string("op_1556"), val = tensor<int32, [3]>([1, 298, 96])];
            tensor<fp16, [1, 298, 4, 24]> attn_output_17_cast_fp16 = transpose(perm = attn_output_17_perm_0, x = attn_output_15_cast_fp16)[name = string("transpose_26")];
            tensor<fp16, [1, 298, 96]> input_381_cast_fp16 = reshape(shape = var_1556, x = attn_output_17_cast_fp16)[name = string("input_381_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage2_10_tcm_4_attention_out_proj_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_out_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7638400)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_attention_out_proj_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_attention_out_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7656896)))];
            tensor<fp16, [1, 298, 96]> linear_15_cast_fp16 = linear(bias = model_backbone_stage2_10_tcm_4_attention_out_proj_bias_to_fp16, weight = model_backbone_stage2_10_tcm_4_attention_out_proj_weight_to_fp16, x = input_381_cast_fp16)[name = string("linear_15_cast_fp16")];
            tensor<fp16, [1, 298, 96]> input_383_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = linear_15_cast_fp16)[name = string("input_383_cast_fp16")];
            tensor<int32, [1]> input_385_axes_0 = const()[name = string("input_385_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_layer_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7657152)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_layer_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7657408)))];
            tensor<fp16, [1, 298, 96]> input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = model_backbone_stage2_10_tcm_4_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage2_10_tcm_4_layer_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("input_385_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage2_10_tcm_4_feed_forward_intermediate_dense_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_feed_forward_intermediate_dense_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7657664)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_feed_forward_intermediate_dense_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_feed_forward_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7676160)))];
            tensor<fp16, [1, 298, 96]> linear_16_cast_fp16 = linear(bias = model_backbone_stage2_10_tcm_4_feed_forward_intermediate_dense_bias_to_fp16, weight = model_backbone_stage2_10_tcm_4_feed_forward_intermediate_dense_weight_to_fp16, x = input_385_cast_fp16)[name = string("linear_16_cast_fp16")];
            string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("TANH_APPROXIMATION")];
            tensor<fp16, [1, 298, 96]> input_389_cast_fp16 = gelu(mode = input_389_mode_0, x = linear_16_cast_fp16)[name = string("input_389_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage2_10_tcm_4_feed_forward_output_dense_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_feed_forward_output_dense_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7676416)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_feed_forward_output_dense_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_feed_forward_output_dense_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7694912)))];
            tensor<fp16, [1, 298, 96]> linear_17_cast_fp16 = linear(bias = model_backbone_stage2_10_tcm_4_feed_forward_output_dense_bias_to_fp16, weight = model_backbone_stage2_10_tcm_4_feed_forward_output_dense_weight_to_fp16, x = input_389_cast_fp16)[name = string("linear_17_cast_fp16")];
            tensor<fp16, [1, 298, 96]> input_395_cast_fp16 = add(x = input_385_cast_fp16, y = linear_17_cast_fp16)[name = string("input_395_cast_fp16")];
            tensor<int32, [1]> hidden_states_23_axes_0 = const()[name = string("hidden_states_23_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_final_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7695168)))];
            tensor<fp16, [96]> model_backbone_stage2_10_tcm_4_final_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_tcm_4_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7695424)))];
            tensor<fp16, [1, 298, 96]> hidden_states_23_cast_fp16 = layer_norm(axes = hidden_states_23_axes_0, beta = model_backbone_stage2_10_tcm_4_final_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage2_10_tcm_4_final_layer_norm_weight_to_fp16, x = input_395_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<int32, [3]> var_1593 = const()[name = string("op_1593"), val = tensor<int32, [3]>([0, 2, 1])];
            string x_75_pad_type_0 = const()[name = string("x_75_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_75_strides_0 = const()[name = string("x_75_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_75_dilations_0 = const()[name = string("x_75_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_75_groups_0 = const()[name = string("x_75_groups_0"), val = int32(1)];
            tensor<fp16, [4608, 96, 1]> model_backbone_stage2_10_exp_dim_conv_weight_to_fp16 = const()[name = string("model_backbone_stage2_10_exp_dim_conv_weight_to_fp16"), val = tensor<fp16, [4608, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7695680)))];
            tensor<fp16, [4608]> model_backbone_stage2_10_exp_dim_conv_bias_to_fp16 = const()[name = string("model_backbone_stage2_10_exp_dim_conv_bias_to_fp16"), val = tensor<fp16, [4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8580480)))];
            tensor<fp16, [1, 96, 298]> input_397_cast_fp16 = transpose(perm = var_1593, x = hidden_states_23_cast_fp16)[name = string("transpose_25")];
            tensor<fp16, [1, 4608, 298]> x_75_cast_fp16 = conv(bias = model_backbone_stage2_10_exp_dim_conv_bias_to_fp16, dilations = x_75_dilations_0, groups = x_75_groups_0, pad = x_75_pad_0, pad_type = x_75_pad_type_0, strides = x_75_strides_0, weight = model_backbone_stage2_10_exp_dim_conv_weight_to_fp16, x = input_397_cast_fp16)[name = string("x_75_cast_fp16")];
            tensor<fp16, [1, 4608, 298]> input_399_cast_fp16 = add(x = input_343_cast_fp16, y = x_75_cast_fp16)[name = string("input_399_cast_fp16")];
            tensor<int32, [1]> expand_dims_2_axes_0 = const()[name = string("expand_dims_2_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 298, 1]> expand_dims_2_cast_fp16 = expand_dims(axes = expand_dims_2_axes_0, x = input_399_cast_fp16)[name = string("expand_dims_2_cast_fp16")];
            int32 upsample_nearest_neighbor_2_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_2_scale_factor_height_0"), val = int32(2)];
            int32 upsample_nearest_neighbor_2_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_2_scale_factor_width_0"), val = int32(1)];
            tensor<fp16, [1, 4608, 596, 1]> upsample_nearest_neighbor_2_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_2_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_2_scale_factor_width_0, x = expand_dims_2_cast_fp16)[name = string("upsample_nearest_neighbor_2_cast_fp16")];
            tensor<int32, [1]> input_401_axes_0 = const()[name = string("input_401_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596]> input_401_cast_fp16 = squeeze(axes = input_401_axes_0, x = upsample_nearest_neighbor_2_cast_fp16)[name = string("input_401_cast_fp16")];
            tensor<int32, [4]> reshape_12_shape_0 = const()[name = string("reshape_12_shape_0"), val = tensor<int32, [4]>([1, 64, 72, 596])];
            tensor<fp16, [1, 64, 72, 596]> reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_401_cast_fp16)[name = string("reshape_12_cast_fp16")];
            tensor<int32, [2]> reduce_mean_11_axes_0 = const()[name = string("reduce_mean_11_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_11_keep_dims_0 = const()[name = string("reduce_mean_11_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = reshape_12_cast_fp16)[name = string("reduce_mean_11_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> sub_7_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_11_cast_fp16)[name = string("sub_7_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> square_4_cast_fp16 = square(x = sub_7_cast_fp16)[name = string("square_4_cast_fp16")];
            tensor<int32, [2]> reduce_mean_13_axes_0 = const()[name = string("reduce_mean_13_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_13_keep_dims_0 = const()[name = string("reduce_mean_13_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_13_cast_fp16 = reduce_mean(axes = reduce_mean_13_axes_0, keep_dims = reduce_mean_13_keep_dims_0, x = square_4_cast_fp16)[name = string("reduce_mean_13_cast_fp16")];
            fp16 add_6_y_0_to_fp16 = const()[name = string("add_6_y_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 1]> add_6_cast_fp16 = add(x = reduce_mean_13_cast_fp16, y = add_6_y_0_to_fp16)[name = string("add_6_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> sqrt_4_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = string("sqrt_4_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> real_div_3_cast_fp16 = real_div(x = sub_7_cast_fp16, y = sqrt_4_cast_fp16)[name = string("real_div_3_cast_fp16")];
            tensor<int32, [3]> reshape_13_shape_0 = const()[name = string("reshape_13_shape_0"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 4608, 596]> reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = string("reshape_13_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_14_to_fp16 = const()[name = string("reshape_14_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8589760)))];
            tensor<fp16, [1, 4608, 596]> mul_3_cast_fp16 = mul(x = reshape_13_cast_fp16, y = reshape_14_to_fp16)[name = string("mul_3_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_15_to_fp16 = const()[name = string("reshape_15_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8599040)))];
            tensor<fp16, [1, 4608, 596]> add_7_cast_fp16 = add(x = mul_3_cast_fp16, y = reshape_15_to_fp16)[name = string("add_7_cast_fp16")];
            tensor<int32, [1]> var_1622_axes_0 = const()[name = string("op_1622_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 4608, 596]> var_1622_cast_fp16 = expand_dims(axes = var_1622_axes_0, x = add_7_cast_fp16)[name = string("op_1622_cast_fp16")];
            bool xs_7_interleave_0 = const()[name = string("xs_7_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4, 4608, 596]> xs_7_cast_fp16 = concat(axis = var_40, interleave = xs_7_interleave_0, values = (var_178_cast_fp16, var_612_cast_fp16, var_1094_cast_fp16, var_1622_cast_fp16))[name = string("xs_7_cast_fp16")];
            tensor<fp16, [1, 4, 4608, 1]> w_25_to_fp16 = const()[name = string("w_25_to_fp16"), val = tensor<fp16, [1, 4, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8608320)))];
            tensor<fp16, [1, 4, 4608, 596]> var_1625_cast_fp16 = mul(x = w_25_to_fp16, y = xs_7_cast_fp16)[name = string("op_1625_cast_fp16")];
            tensor<int32, [1]> x_77_axes_0 = const()[name = string("x_77_axes_0"), val = tensor<int32, [1]>([1])];
            bool x_77_keep_dims_0 = const()[name = string("x_77_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 4608, 596]> x_77_cast_fp16 = reduce_sum(axes = x_77_axes_0, keep_dims = x_77_keep_dims_0, x = var_1625_cast_fp16)[name = string("x_77_cast_fp16")];
            tensor<int32, [4]> var_1630 = const()[name = string("op_1630"), val = tensor<int32, [4]>([1, 36, 128, 596])];
            tensor<fp16, [1, 36, 128, 596]> var_1631_cast_fp16 = reshape(shape = var_1630, x = x_77_cast_fp16)[name = string("op_1631_cast_fp16")];
            tensor<int32, [4]> var_1632 = const()[name = string("op_1632"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            string input_407_pad_type_0 = const()[name = string("input_407_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_407_strides_0 = const()[name = string("input_407_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_407_groups_0 = const()[name = string("input_407_groups_0"), val = int32(128)];
            tensor<int32, [4]> input_407_pad_0 = const()[name = string("input_407_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_407_dilations_0 = const()[name = string("input_407_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 2, 2]> model_backbone_stage3_2_weight_to_fp16 = const()[name = string("model_backbone_stage3_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 2, 2]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8645248)))];
            tensor<fp16, [256]> model_backbone_stage3_2_bias_to_fp16 = const()[name = string("model_backbone_stage3_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8647360)))];
            tensor<fp16, [1, 128, 36, 596]> input_405_cast_fp16 = transpose(perm = var_1632, x = var_1631_cast_fp16)[name = string("transpose_24")];
            tensor<fp16, [1, 256, 18, 298]> input_407_cast_fp16 = conv(bias = model_backbone_stage3_2_bias_to_fp16, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = model_backbone_stage3_2_weight_to_fp16, x = input_405_cast_fp16)[name = string("input_407_cast_fp16")];
            string input_409_pad_type_0 = const()[name = string("input_409_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_409_pad_0 = const()[name = string("input_409_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_409_groups_0 = const()[name = string("input_409_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_409_strides_0 = const()[name = string("input_409_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_409_dilations_0 = const()[name = string("input_409_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_3_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8647936)))];
            tensor<fp16, [1, 256, 18, 298]> input_409_cast_fp16 = conv(dilations = input_409_dilations_0, groups = input_409_groups_0, pad = input_409_pad_0, pad_type = input_409_pad_type_0, strides = input_409_strides_0, weight = model_backbone_stage3_3_conv_block_conv1_weight_to_fp16, x = input_407_cast_fp16)[name = string("input_409_cast_fp16")];
            string input_411_pad_type_0 = const()[name = string("input_411_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_411_strides_0 = const()[name = string("input_411_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_411_pad_0 = const()[name = string("input_411_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_411_dilations_0 = const()[name = string("input_411_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_411_groups_0 = const()[name = string("input_411_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage3_3_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8652608)))];
            tensor<fp16, [256]> model_backbone_stage3_3_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8783744)))];
            tensor<fp16, [1, 256, 18, 298]> input_411_cast_fp16 = conv(bias = model_backbone_stage3_3_conv_block_conv1pw_bias_to_fp16, dilations = input_411_dilations_0, groups = input_411_groups_0, pad = input_411_pad_0, pad_type = input_411_pad_type_0, strides = input_411_strides_0, weight = model_backbone_stage3_3_conv_block_conv1pw_weight_to_fp16, x = input_409_cast_fp16)[name = string("input_411_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_413_cast_fp16 = relu(x = input_411_cast_fp16)[name = string("input_413_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage3_3_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8784320)))];
            tensor<fp16, [256]> model_backbone_stage3_3_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8784896)))];
            tensor<fp16, [256]> model_backbone_stage3_3_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8785472)))];
            tensor<fp16, [256]> model_backbone_stage3_3_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8786048)))];
            tensor<fp16, [1, 256, 18, 298]> input_415_cast_fp16 = batch_norm(beta = model_backbone_stage3_3_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_3_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage3_3_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage3_3_conv_block_bn1_running_var_to_fp16, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")];
            string input_417_pad_type_0 = const()[name = string("input_417_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_417_pad_0 = const()[name = string("input_417_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_417_groups_0 = const()[name = string("input_417_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_417_strides_0 = const()[name = string("input_417_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_417_dilations_0 = const()[name = string("input_417_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_3_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage3_3_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8786624)))];
            tensor<fp16, [1, 256, 18, 298]> input_417_cast_fp16 = conv(dilations = input_417_dilations_0, groups = input_417_groups_0, pad = input_417_pad_0, pad_type = input_417_pad_type_0, strides = input_417_strides_0, weight = model_backbone_stage3_3_conv_block_conv2_weight_to_fp16, x = input_415_cast_fp16)[name = string("input_417_cast_fp16")];
            string input_419_pad_type_0 = const()[name = string("input_419_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_419_strides_0 = const()[name = string("input_419_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_419_pad_0 = const()[name = string("input_419_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_419_dilations_0 = const()[name = string("input_419_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_419_groups_0 = const()[name = string("input_419_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_91_to_fp16 = const()[name = string("const_91_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791296)))];
            tensor<fp16, [256]> const_92_to_fp16 = const()[name = string("const_92_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8922432)))];
            tensor<fp16, [1, 256, 18, 298]> out_25_cast_fp16 = conv(bias = const_92_to_fp16, dilations = input_419_dilations_0, groups = input_419_groups_0, pad = input_419_pad_0, pad_type = input_419_pad_type_0, strides = input_419_strides_0, weight = const_91_to_fp16, x = input_417_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_421_cast_fp16 = add(x = out_25_cast_fp16, y = input_407_cast_fp16)[name = string("input_421_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_423_cast_fp16 = relu(x = input_421_cast_fp16)[name = string("input_423_cast_fp16")];
            string input_425_pad_type_0 = const()[name = string("input_425_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_425_groups_0 = const()[name = string("input_425_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_425_strides_0 = const()[name = string("input_425_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_425_dilations_0 = const()[name = string("input_425_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_4_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8923008)))];
            tensor<fp16, [1, 256, 18, 298]> input_425_cast_fp16 = conv(dilations = input_425_dilations_0, groups = input_425_groups_0, pad = input_425_pad_0, pad_type = input_425_pad_type_0, strides = input_425_strides_0, weight = model_backbone_stage3_4_conv_block_conv1_weight_to_fp16, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")];
            string input_427_pad_type_0 = const()[name = string("input_427_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_427_strides_0 = const()[name = string("input_427_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_427_pad_0 = const()[name = string("input_427_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_427_dilations_0 = const()[name = string("input_427_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_427_groups_0 = const()[name = string("input_427_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage3_4_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8927680)))];
            tensor<fp16, [256]> model_backbone_stage3_4_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9058816)))];
            tensor<fp16, [1, 256, 18, 298]> input_427_cast_fp16 = conv(bias = model_backbone_stage3_4_conv_block_conv1pw_bias_to_fp16, dilations = input_427_dilations_0, groups = input_427_groups_0, pad = input_427_pad_0, pad_type = input_427_pad_type_0, strides = input_427_strides_0, weight = model_backbone_stage3_4_conv_block_conv1pw_weight_to_fp16, x = input_425_cast_fp16)[name = string("input_427_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_429_cast_fp16 = relu(x = input_427_cast_fp16)[name = string("input_429_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage3_4_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9059392)))];
            tensor<fp16, [256]> model_backbone_stage3_4_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9059968)))];
            tensor<fp16, [256]> model_backbone_stage3_4_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9060544)))];
            tensor<fp16, [256]> model_backbone_stage3_4_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9061120)))];
            tensor<fp16, [1, 256, 18, 298]> input_431_cast_fp16 = batch_norm(beta = model_backbone_stage3_4_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_4_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage3_4_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage3_4_conv_block_bn1_running_var_to_fp16, x = input_429_cast_fp16)[name = string("input_431_cast_fp16")];
            string input_433_pad_type_0 = const()[name = string("input_433_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_433_pad_0 = const()[name = string("input_433_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_433_groups_0 = const()[name = string("input_433_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_433_strides_0 = const()[name = string("input_433_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_433_dilations_0 = const()[name = string("input_433_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_4_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage3_4_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9061696)))];
            tensor<fp16, [1, 256, 18, 298]> input_433_cast_fp16 = conv(dilations = input_433_dilations_0, groups = input_433_groups_0, pad = input_433_pad_0, pad_type = input_433_pad_type_0, strides = input_433_strides_0, weight = model_backbone_stage3_4_conv_block_conv2_weight_to_fp16, x = input_431_cast_fp16)[name = string("input_433_cast_fp16")];
            string input_435_pad_type_0 = const()[name = string("input_435_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_435_strides_0 = const()[name = string("input_435_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_435_pad_0 = const()[name = string("input_435_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_435_dilations_0 = const()[name = string("input_435_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_435_groups_0 = const()[name = string("input_435_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_93_to_fp16 = const()[name = string("const_93_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9066368)))];
            tensor<fp16, [256]> const_94_to_fp16 = const()[name = string("const_94_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9197504)))];
            tensor<fp16, [1, 256, 18, 298]> out_27_cast_fp16 = conv(bias = const_94_to_fp16, dilations = input_435_dilations_0, groups = input_435_groups_0, pad = input_435_pad_0, pad_type = input_435_pad_type_0, strides = input_435_strides_0, weight = const_93_to_fp16, x = input_433_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_437_cast_fp16 = add(x = out_27_cast_fp16, y = input_423_cast_fp16)[name = string("input_437_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_439_cast_fp16 = relu(x = input_437_cast_fp16)[name = string("input_439_cast_fp16")];
            string input_441_pad_type_0 = const()[name = string("input_441_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_441_pad_0 = const()[name = string("input_441_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_441_groups_0 = const()[name = string("input_441_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_441_strides_0 = const()[name = string("input_441_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_441_dilations_0 = const()[name = string("input_441_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_5_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9198080)))];
            tensor<fp16, [1, 256, 18, 298]> input_441_cast_fp16 = conv(dilations = input_441_dilations_0, groups = input_441_groups_0, pad = input_441_pad_0, pad_type = input_441_pad_type_0, strides = input_441_strides_0, weight = model_backbone_stage3_5_conv_block_conv1_weight_to_fp16, x = input_439_cast_fp16)[name = string("input_441_cast_fp16")];
            string input_443_pad_type_0 = const()[name = string("input_443_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_443_strides_0 = const()[name = string("input_443_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_443_pad_0 = const()[name = string("input_443_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_443_dilations_0 = const()[name = string("input_443_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_443_groups_0 = const()[name = string("input_443_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage3_5_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9202752)))];
            tensor<fp16, [256]> model_backbone_stage3_5_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9333888)))];
            tensor<fp16, [1, 256, 18, 298]> input_443_cast_fp16 = conv(bias = model_backbone_stage3_5_conv_block_conv1pw_bias_to_fp16, dilations = input_443_dilations_0, groups = input_443_groups_0, pad = input_443_pad_0, pad_type = input_443_pad_type_0, strides = input_443_strides_0, weight = model_backbone_stage3_5_conv_block_conv1pw_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_445_cast_fp16 = relu(x = input_443_cast_fp16)[name = string("input_445_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage3_5_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9334464)))];
            tensor<fp16, [256]> model_backbone_stage3_5_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9335040)))];
            tensor<fp16, [256]> model_backbone_stage3_5_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9335616)))];
            tensor<fp16, [256]> model_backbone_stage3_5_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9336192)))];
            tensor<fp16, [1, 256, 18, 298]> input_447_cast_fp16 = batch_norm(beta = model_backbone_stage3_5_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_5_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage3_5_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage3_5_conv_block_bn1_running_var_to_fp16, x = input_445_cast_fp16)[name = string("input_447_cast_fp16")];
            string input_449_pad_type_0 = const()[name = string("input_449_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_449_pad_0 = const()[name = string("input_449_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_449_groups_0 = const()[name = string("input_449_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_449_strides_0 = const()[name = string("input_449_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_449_dilations_0 = const()[name = string("input_449_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_5_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage3_5_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9336768)))];
            tensor<fp16, [1, 256, 18, 298]> input_449_cast_fp16 = conv(dilations = input_449_dilations_0, groups = input_449_groups_0, pad = input_449_pad_0, pad_type = input_449_pad_type_0, strides = input_449_strides_0, weight = model_backbone_stage3_5_conv_block_conv2_weight_to_fp16, x = input_447_cast_fp16)[name = string("input_449_cast_fp16")];
            string input_451_pad_type_0 = const()[name = string("input_451_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_451_strides_0 = const()[name = string("input_451_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_451_pad_0 = const()[name = string("input_451_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_451_dilations_0 = const()[name = string("input_451_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_451_groups_0 = const()[name = string("input_451_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_95_to_fp16 = const()[name = string("const_95_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9341440)))];
            tensor<fp16, [256]> const_96_to_fp16 = const()[name = string("const_96_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9472576)))];
            tensor<fp16, [1, 256, 18, 298]> out_29_cast_fp16 = conv(bias = const_96_to_fp16, dilations = input_451_dilations_0, groups = input_451_groups_0, pad = input_451_pad_0, pad_type = input_451_pad_type_0, strides = input_451_strides_0, weight = const_95_to_fp16, x = input_449_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_453_cast_fp16 = add(x = out_29_cast_fp16, y = input_439_cast_fp16)[name = string("input_453_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_455_cast_fp16 = relu(x = input_453_cast_fp16)[name = string("input_455_cast_fp16")];
            string input_457_pad_type_0 = const()[name = string("input_457_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_457_pad_0 = const()[name = string("input_457_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_457_groups_0 = const()[name = string("input_457_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_457_strides_0 = const()[name = string("input_457_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_457_dilations_0 = const()[name = string("input_457_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_6_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9473152)))];
            tensor<fp16, [1, 256, 18, 298]> input_457_cast_fp16 = conv(dilations = input_457_dilations_0, groups = input_457_groups_0, pad = input_457_pad_0, pad_type = input_457_pad_type_0, strides = input_457_strides_0, weight = model_backbone_stage3_6_conv_block_conv1_weight_to_fp16, x = input_455_cast_fp16)[name = string("input_457_cast_fp16")];
            string input_459_pad_type_0 = const()[name = string("input_459_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_459_strides_0 = const()[name = string("input_459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_459_pad_0 = const()[name = string("input_459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_459_dilations_0 = const()[name = string("input_459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_459_groups_0 = const()[name = string("input_459_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage3_6_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9477824)))];
            tensor<fp16, [256]> model_backbone_stage3_6_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9608960)))];
            tensor<fp16, [1, 256, 18, 298]> input_459_cast_fp16 = conv(bias = model_backbone_stage3_6_conv_block_conv1pw_bias_to_fp16, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = model_backbone_stage3_6_conv_block_conv1pw_weight_to_fp16, x = input_457_cast_fp16)[name = string("input_459_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_461_cast_fp16 = relu(x = input_459_cast_fp16)[name = string("input_461_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage3_6_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9609536)))];
            tensor<fp16, [256]> model_backbone_stage3_6_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9610112)))];
            tensor<fp16, [256]> model_backbone_stage3_6_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9610688)))];
            tensor<fp16, [256]> model_backbone_stage3_6_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9611264)))];
            tensor<fp16, [1, 256, 18, 298]> input_463_cast_fp16 = batch_norm(beta = model_backbone_stage3_6_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_6_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage3_6_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage3_6_conv_block_bn1_running_var_to_fp16, x = input_461_cast_fp16)[name = string("input_463_cast_fp16")];
            string input_465_pad_type_0 = const()[name = string("input_465_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_465_pad_0 = const()[name = string("input_465_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_465_groups_0 = const()[name = string("input_465_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_465_strides_0 = const()[name = string("input_465_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_465_dilations_0 = const()[name = string("input_465_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_6_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage3_6_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9611840)))];
            tensor<fp16, [1, 256, 18, 298]> input_465_cast_fp16 = conv(dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = model_backbone_stage3_6_conv_block_conv2_weight_to_fp16, x = input_463_cast_fp16)[name = string("input_465_cast_fp16")];
            string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_97_to_fp16 = const()[name = string("const_97_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9616512)))];
            tensor<fp16, [256]> const_98_to_fp16 = const()[name = string("const_98_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9747648)))];
            tensor<fp16, [1, 256, 18, 298]> out_31_cast_fp16 = conv(bias = const_98_to_fp16, dilations = input_467_dilations_0, groups = input_467_groups_0, pad = input_467_pad_0, pad_type = input_467_pad_type_0, strides = input_467_strides_0, weight = const_97_to_fp16, x = input_465_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_469_cast_fp16 = add(x = out_31_cast_fp16, y = input_455_cast_fp16)[name = string("input_469_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_471_cast_fp16 = relu(x = input_469_cast_fp16)[name = string("input_471_cast_fp16")];
            string input_473_pad_type_0 = const()[name = string("input_473_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_473_pad_0 = const()[name = string("input_473_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_473_groups_0 = const()[name = string("input_473_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_473_strides_0 = const()[name = string("input_473_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_473_dilations_0 = const()[name = string("input_473_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_7_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9748224)))];
            tensor<fp16, [1, 256, 18, 298]> input_473_cast_fp16 = conv(dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = model_backbone_stage3_7_conv_block_conv1_weight_to_fp16, x = input_471_cast_fp16)[name = string("input_473_cast_fp16")];
            string input_475_pad_type_0 = const()[name = string("input_475_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_475_strides_0 = const()[name = string("input_475_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_475_pad_0 = const()[name = string("input_475_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_475_dilations_0 = const()[name = string("input_475_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_475_groups_0 = const()[name = string("input_475_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage3_7_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9752896)))];
            tensor<fp16, [256]> model_backbone_stage3_7_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9884032)))];
            tensor<fp16, [1, 256, 18, 298]> input_475_cast_fp16 = conv(bias = model_backbone_stage3_7_conv_block_conv1pw_bias_to_fp16, dilations = input_475_dilations_0, groups = input_475_groups_0, pad = input_475_pad_0, pad_type = input_475_pad_type_0, strides = input_475_strides_0, weight = model_backbone_stage3_7_conv_block_conv1pw_weight_to_fp16, x = input_473_cast_fp16)[name = string("input_475_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_477_cast_fp16 = relu(x = input_475_cast_fp16)[name = string("input_477_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage3_7_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9884608)))];
            tensor<fp16, [256]> model_backbone_stage3_7_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9885184)))];
            tensor<fp16, [256]> model_backbone_stage3_7_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9885760)))];
            tensor<fp16, [256]> model_backbone_stage3_7_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9886336)))];
            tensor<fp16, [1, 256, 18, 298]> input_479_cast_fp16 = batch_norm(beta = model_backbone_stage3_7_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_7_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage3_7_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage3_7_conv_block_bn1_running_var_to_fp16, x = input_477_cast_fp16)[name = string("input_479_cast_fp16")];
            string input_481_pad_type_0 = const()[name = string("input_481_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_481_pad_0 = const()[name = string("input_481_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_481_groups_0 = const()[name = string("input_481_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_481_strides_0 = const()[name = string("input_481_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_481_dilations_0 = const()[name = string("input_481_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage3_7_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage3_7_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9886912)))];
            tensor<fp16, [1, 256, 18, 298]> input_481_cast_fp16 = conv(dilations = input_481_dilations_0, groups = input_481_groups_0, pad = input_481_pad_0, pad_type = input_481_pad_type_0, strides = input_481_strides_0, weight = model_backbone_stage3_7_conv_block_conv2_weight_to_fp16, x = input_479_cast_fp16)[name = string("input_481_cast_fp16")];
            string input_483_pad_type_0 = const()[name = string("input_483_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_483_strides_0 = const()[name = string("input_483_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_483_pad_0 = const()[name = string("input_483_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_483_dilations_0 = const()[name = string("input_483_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_483_groups_0 = const()[name = string("input_483_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_99_to_fp16 = const()[name = string("const_99_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9891584)))];
            tensor<fp16, [256]> const_100_to_fp16 = const()[name = string("const_100_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10022720)))];
            tensor<fp16, [1, 256, 18, 298]> out_33_cast_fp16 = conv(bias = const_100_to_fp16, dilations = input_483_dilations_0, groups = input_483_groups_0, pad = input_483_pad_0, pad_type = input_483_pad_type_0, strides = input_483_strides_0, weight = const_99_to_fp16, x = input_481_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> input_485_cast_fp16 = add(x = out_33_cast_fp16, y = input_471_cast_fp16)[name = string("input_485_cast_fp16")];
            tensor<fp16, [1, 256, 18, 298]> x_79_cast_fp16 = relu(x = input_485_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<int32, [4]> var_1877 = const()[name = string("op_1877"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1881 = const()[name = string("op_1881"), val = tensor<int32, [3]>([1, 4608, 298])];
            tensor<fp16, [1, 18, 256, 298]> var_1878_cast_fp16 = transpose(perm = var_1877, x = x_79_cast_fp16)[name = string("transpose_23")];
            tensor<fp16, [1, 4608, 298]> input_487_cast_fp16 = reshape(shape = var_1881, x = var_1878_cast_fp16)[name = string("input_487_cast_fp16")];
            string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)];
            tensor<fp16, [96, 4608, 1]> model_backbone_stage3_9_red_dim_conv_0_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_red_dim_conv_0_weight_to_fp16"), val = tensor<fp16, [96, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10023296)))];
            tensor<fp16, [96]> model_backbone_stage3_9_red_dim_conv_0_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_red_dim_conv_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10908096)))];
            tensor<fp16, [1, 96, 298]> x_81_cast_fp16 = conv(bias = model_backbone_stage3_9_red_dim_conv_0_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = model_backbone_stage3_9_red_dim_conv_0_weight_to_fp16, x = input_487_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [1]> u_9_axes_0 = const()[name = string("u_9_axes_0"), val = tensor<int32, [1]>([1])];
            bool u_9_keep_dims_0 = const()[name = string("u_9_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 298]> u_9_cast_fp16 = reduce_mean(axes = u_9_axes_0, keep_dims = u_9_keep_dims_0, x = x_81_cast_fp16)[name = string("u_9_cast_fp16")];
            tensor<fp16, [1, 96, 298]> var_1899_cast_fp16 = sub(x = x_81_cast_fp16, y = u_9_cast_fp16)[name = string("op_1899_cast_fp16")];
            fp16 var_41_promoted_4_to_fp16 = const()[name = string("op_41_promoted_4_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 96, 298]> var_1900_cast_fp16 = pow(x = var_1899_cast_fp16, y = var_41_promoted_4_to_fp16)[name = string("op_1900_cast_fp16")];
            tensor<int32, [1]> s_9_axes_0 = const()[name = string("s_9_axes_0"), val = tensor<int32, [1]>([1])];
            bool s_9_keep_dims_0 = const()[name = string("s_9_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 298]> s_9_cast_fp16 = reduce_mean(axes = s_9_axes_0, keep_dims = s_9_keep_dims_0, x = var_1900_cast_fp16)[name = string("s_9_cast_fp16")];
            fp16 var_1904_to_fp16 = const()[name = string("op_1904_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 298]> var_1905_cast_fp16 = add(x = s_9_cast_fp16, y = var_1904_to_fp16)[name = string("op_1905_cast_fp16")];
            tensor<fp16, [1, 1, 298]> var_1906_cast_fp16 = sqrt(x = var_1905_cast_fp16)[name = string("op_1906_cast_fp16")];
            tensor<fp16, [1, 96, 298]> x_83_cast_fp16 = real_div(x = var_1899_cast_fp16, y = var_1906_cast_fp16)[name = string("x_83_cast_fp16")];
            tensor<fp16, [96, 1]> w_29_to_fp16 = const()[name = string("w_29_to_fp16"), val = tensor<fp16, [96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10908352)))];
            tensor<fp16, [1, 96, 298]> var_1910_cast_fp16 = mul(x = w_29_to_fp16, y = x_83_cast_fp16)[name = string("op_1910_cast_fp16")];
            tensor<fp16, [96, 1]> b_21_to_fp16 = const()[name = string("b_21_to_fp16"), val = tensor<fp16, [96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10908608)))];
            tensor<fp16, [1, 96, 298]> input_489_cast_fp16 = add(x = var_1910_cast_fp16, y = b_21_to_fp16)[name = string("input_489_cast_fp16")];
            tensor<int32, [1]> var_1923 = const()[name = string("op_1923"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1924 = const()[name = string("op_1924"), val = tensor<int32, [1]>([1])];
            string var_1925_pad_type_0 = const()[name = string("op_1925_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_1925_pad_0 = const()[name = string("op_1925_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 7]> model_backbone_stage3_9_tcm_0_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10908864)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_0_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10910272)))];
            tensor<fp16, [1, 96, 298]> var_1925_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_0_dwconvs_0_bias_to_fp16, dilations = var_1924, groups = var_23, pad = var_1925_pad_0, pad_type = var_1925_pad_type_0, strides = var_1923, weight = model_backbone_stage3_9_tcm_0_dwconvs_0_weight_to_fp16, x = input_489_cast_fp16)[name = string("op_1925_cast_fp16")];
            bool input_491_interleave_0 = const()[name = string("input_491_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_491_cast_fp16 = concat(axis = var_40, interleave = input_491_interleave_0, values = var_1925_cast_fp16)[name = string("input_491_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_0_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10910528)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_0_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10910784)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_0_norm_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10911040)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_0_norm_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10911296)))];
            tensor<fp16, [1, 96, 298]> input_493_cast_fp16 = batch_norm(beta = model_backbone_stage3_9_tcm_0_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_9_tcm_0_norm_weight_to_fp16, mean = model_backbone_stage3_9_tcm_0_norm_running_mean_to_fp16, variance = model_backbone_stage3_9_tcm_0_norm_running_var_to_fp16, x = input_491_cast_fp16)[name = string("input_493_cast_fp16")];
            string input_495_mode_0 = const()[name = string("input_495_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_495_cast_fp16 = gelu(mode = input_495_mode_0, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")];
            string x_85_pad_type_0 = const()[name = string("x_85_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_85_strides_0 = const()[name = string("x_85_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_85_pad_0 = const()[name = string("x_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_85_dilations_0 = const()[name = string("x_85_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_85_groups_0 = const()[name = string("x_85_groups_0"), val = int32(1)];
            tensor<fp16, [96, 96, 1]> model_backbone_stage3_9_tcm_0_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10911552)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_0_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10930048)))];
            tensor<fp16, [1, 96, 298]> x_85_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_0_pwconv1_bias_to_fp16, dilations = x_85_dilations_0, groups = x_85_groups_0, pad = x_85_pad_0, pad_type = x_85_pad_type_0, strides = x_85_strides_0, weight = model_backbone_stage3_9_tcm_0_pwconv1_weight_to_fp16, x = input_495_cast_fp16)[name = string("x_85_cast_fp16")];
            tensor<fp16, [1, 96, 298]> input_497_cast_fp16 = add(x = input_489_cast_fp16, y = x_85_cast_fp16)[name = string("input_497_cast_fp16")];
            tensor<int32, [1]> var_1948 = const()[name = string("op_1948"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1949 = const()[name = string("op_1949"), val = tensor<int32, [1]>([1])];
            string var_1950_pad_type_0 = const()[name = string("op_1950_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_1950_pad_0 = const()[name = string("op_1950_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 19]> model_backbone_stage3_9_tcm_1_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 19]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10930304)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_1_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10934016)))];
            tensor<fp16, [1, 96, 298]> var_1950_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_1_dwconvs_0_bias_to_fp16, dilations = var_1949, groups = var_23, pad = var_1950_pad_0, pad_type = var_1950_pad_type_0, strides = var_1948, weight = model_backbone_stage3_9_tcm_1_dwconvs_0_weight_to_fp16, x = input_497_cast_fp16)[name = string("op_1950_cast_fp16")];
            bool input_499_interleave_0 = const()[name = string("input_499_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_499_cast_fp16 = concat(axis = var_40, interleave = input_499_interleave_0, values = var_1950_cast_fp16)[name = string("input_499_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_1_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10934272)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_1_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10934528)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_1_norm_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10934784)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_1_norm_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10935040)))];
            tensor<fp16, [1, 96, 298]> input_501_cast_fp16 = batch_norm(beta = model_backbone_stage3_9_tcm_1_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_9_tcm_1_norm_weight_to_fp16, mean = model_backbone_stage3_9_tcm_1_norm_running_mean_to_fp16, variance = model_backbone_stage3_9_tcm_1_norm_running_var_to_fp16, x = input_499_cast_fp16)[name = string("input_501_cast_fp16")];
            string input_503_mode_0 = const()[name = string("input_503_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_503_cast_fp16 = gelu(mode = input_503_mode_0, x = input_501_cast_fp16)[name = string("input_503_cast_fp16")];
            string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)];
            tensor<fp16, [96, 96, 1]> model_backbone_stage3_9_tcm_1_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10935296)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_1_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10953792)))];
            tensor<fp16, [1, 96, 298]> x_87_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_1_pwconv1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = model_backbone_stage3_9_tcm_1_pwconv1_weight_to_fp16, x = input_503_cast_fp16)[name = string("x_87_cast_fp16")];
            tensor<fp16, [1, 96, 298]> input_505_cast_fp16 = add(x = input_497_cast_fp16, y = x_87_cast_fp16)[name = string("input_505_cast_fp16")];
            tensor<int32, [1]> var_1973 = const()[name = string("op_1973"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1974 = const()[name = string("op_1974"), val = tensor<int32, [1]>([1])];
            string var_1975_pad_type_0 = const()[name = string("op_1975_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_1975_pad_0 = const()[name = string("op_1975_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 31]> model_backbone_stage3_9_tcm_2_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 31]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10954048)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_2_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10960064)))];
            tensor<fp16, [1, 96, 298]> var_1975_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_2_dwconvs_0_bias_to_fp16, dilations = var_1974, groups = var_23, pad = var_1975_pad_0, pad_type = var_1975_pad_type_0, strides = var_1973, weight = model_backbone_stage3_9_tcm_2_dwconvs_0_weight_to_fp16, x = input_505_cast_fp16)[name = string("op_1975_cast_fp16")];
            bool input_507_interleave_0 = const()[name = string("input_507_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_507_cast_fp16 = concat(axis = var_40, interleave = input_507_interleave_0, values = var_1975_cast_fp16)[name = string("input_507_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_2_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10960320)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_2_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10960576)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_2_norm_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10960832)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_2_norm_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10961088)))];
            tensor<fp16, [1, 96, 298]> input_509_cast_fp16 = batch_norm(beta = model_backbone_stage3_9_tcm_2_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_9_tcm_2_norm_weight_to_fp16, mean = model_backbone_stage3_9_tcm_2_norm_running_mean_to_fp16, variance = model_backbone_stage3_9_tcm_2_norm_running_var_to_fp16, x = input_507_cast_fp16)[name = string("input_509_cast_fp16")];
            string input_511_mode_0 = const()[name = string("input_511_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_511_cast_fp16 = gelu(mode = input_511_mode_0, x = input_509_cast_fp16)[name = string("input_511_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, [96, 96, 1]> model_backbone_stage3_9_tcm_2_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10961344)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_2_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10979840)))];
            tensor<fp16, [1, 96, 298]> x_89_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_2_pwconv1_bias_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 = model_backbone_stage3_9_tcm_2_pwconv1_weight_to_fp16, x = input_511_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<fp16, [1, 96, 298]> input_513_cast_fp16 = add(x = input_505_cast_fp16, y = x_89_cast_fp16)[name = string("input_513_cast_fp16")];
            tensor<int32, [1]> var_1998 = const()[name = string("op_1998"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_1999 = const()[name = string("op_1999"), val = tensor<int32, [1]>([1])];
            string var_2000_pad_type_0 = const()[name = string("op_2000_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2000_pad_0 = const()[name = string("op_2000_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [96, 1, 59]> model_backbone_stage3_9_tcm_3_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [96, 1, 59]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10980096)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_3_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10991488)))];
            tensor<fp16, [1, 96, 298]> var_2000_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_3_dwconvs_0_bias_to_fp16, dilations = var_1999, groups = var_23, pad = var_2000_pad_0, pad_type = var_2000_pad_type_0, strides = var_1998, weight = model_backbone_stage3_9_tcm_3_dwconvs_0_weight_to_fp16, x = input_513_cast_fp16)[name = string("op_2000_cast_fp16")];
            bool input_515_interleave_0 = const()[name = string("input_515_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 96, 298]> input_515_cast_fp16 = concat(axis = var_40, interleave = input_515_interleave_0, values = var_2000_cast_fp16)[name = string("input_515_cast_fp16")];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_3_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_norm_running_mean_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10991744)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_3_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_norm_running_var_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10992000)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_3_norm_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10992256)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_3_norm_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10992512)))];
            tensor<fp16, [1, 96, 298]> input_517_cast_fp16 = batch_norm(beta = model_backbone_stage3_9_tcm_3_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage3_9_tcm_3_norm_weight_to_fp16, mean = model_backbone_stage3_9_tcm_3_norm_running_mean_to_fp16, variance = model_backbone_stage3_9_tcm_3_norm_running_var_to_fp16, x = input_515_cast_fp16)[name = string("input_517_cast_fp16")];
            string input_519_mode_0 = const()[name = string("input_519_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 96, 298]> input_519_cast_fp16 = gelu(mode = input_519_mode_0, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")];
            string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(1)];
            tensor<fp16, [96, 96, 1]> model_backbone_stage3_9_tcm_3_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10992768)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_3_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11011264)))];
            tensor<fp16, [1, 96, 298]> x_91_cast_fp16 = conv(bias = model_backbone_stage3_9_tcm_3_pwconv1_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = model_backbone_stage3_9_tcm_3_pwconv1_weight_to_fp16, x = input_519_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<fp16, [1, 96, 298]> hidden_states_25_cast_fp16 = add(x = input_513_cast_fp16, y = x_91_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            tensor<int32, [3]> var_2021 = const()[name = string("op_2021"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [96, 96]> model_backbone_stage3_9_tcm_4_attention_q_proj_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_q_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11011520)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_attention_q_proj_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_q_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11030016)))];
            tensor<fp16, [1, 298, 96]> hidden_states_27_cast_fp16 = transpose(perm = var_2021, x = hidden_states_25_cast_fp16)[name = string("transpose_22")];
            tensor<fp16, [1, 298, 96]> linear_18_cast_fp16 = linear(bias = model_backbone_stage3_9_tcm_4_attention_q_proj_bias_to_fp16, weight = model_backbone_stage3_9_tcm_4_attention_q_proj_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = string("linear_18_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage3_9_tcm_4_attention_k_proj_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_k_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11030272)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_attention_k_proj_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_k_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048768)))];
            tensor<fp16, [1, 298, 96]> linear_19_cast_fp16 = linear(bias = model_backbone_stage3_9_tcm_4_attention_k_proj_bias_to_fp16, weight = model_backbone_stage3_9_tcm_4_attention_k_proj_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = string("linear_19_cast_fp16")];
            tensor<int32, [4]> var_2036 = const()[name = string("op_2036"), val = tensor<int32, [4]>([1, -1, 4, 24])];
            tensor<fp16, [1, 298, 4, 24]> var_2037_cast_fp16 = reshape(shape = var_2036, x = linear_19_cast_fp16)[name = string("op_2037_cast_fp16")];
            tensor<int32, [4]> var_2038_perm_0 = const()[name = string("op_2038_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [96, 96]> model_backbone_stage3_9_tcm_4_attention_v_proj_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_v_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11049024)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_attention_v_proj_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_v_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11067520)))];
            tensor<fp16, [1, 298, 96]> linear_20_cast_fp16 = linear(bias = model_backbone_stage3_9_tcm_4_attention_v_proj_bias_to_fp16, weight = model_backbone_stage3_9_tcm_4_attention_v_proj_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = string("linear_20_cast_fp16")];
            tensor<int32, [4]> var_2043 = const()[name = string("op_2043"), val = tensor<int32, [4]>([1, -1, 4, 24])];
            tensor<fp16, [1, 298, 4, 24]> var_2044_cast_fp16 = reshape(shape = var_2043, x = linear_20_cast_fp16)[name = string("op_2044_cast_fp16")];
            tensor<int32, [4]> var_2045_perm_0 = const()[name = string("op_2045_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2052 = const()[name = string("op_2052"), val = tensor<int32, [4]>([1, 298, 4, 24])];
            tensor<fp16, [1, 298, 4, 24]> var_2053_cast_fp16 = reshape(shape = var_2052, x = linear_18_cast_fp16)[name = string("op_2053_cast_fp16")];
            tensor<int32, [4]> var_2054_perm_0 = const()[name = string("op_2054_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2056 = const()[name = string("op_2056"), val = tensor<int32, [3]>([4, -1, 24])];
            tensor<fp16, [1, 4, 298, 24]> var_2054_cast_fp16 = transpose(perm = var_2054_perm_0, x = var_2053_cast_fp16)[name = string("transpose_19")];
            tensor<fp16, [4, 298, 24]> query_states_7_cast_fp16 = reshape(shape = var_2056, x = var_2054_cast_fp16)[name = string("query_states_7_cast_fp16")];
            tensor<int32, [3]> var_2058 = const()[name = string("op_2058"), val = tensor<int32, [3]>([4, -1, 24])];
            tensor<fp16, [1, 4, 298, 24]> var_2038_cast_fp16 = transpose(perm = var_2038_perm_0, x = var_2037_cast_fp16)[name = string("transpose_21")];
            tensor<fp16, [4, 298, 24]> key_states_15_cast_fp16 = reshape(shape = var_2058, x = var_2038_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [3]> var_2060 = const()[name = string("op_2060"), val = tensor<int32, [3]>([4, -1, 24])];
            tensor<fp16, [1, 4, 298, 24]> var_2045_cast_fp16 = transpose(perm = var_2045_perm_0, x = var_2044_cast_fp16)[name = string("transpose_20")];
            tensor<fp16, [4, 298, 24]> value_states_15_cast_fp16 = reshape(shape = var_2060, x = var_2045_cast_fp16)[name = string("value_states_15_cast_fp16")];
            bool var_2063_transpose_x_1 = const()[name = string("op_2063_transpose_x_1"), val = bool(false)];
            bool var_2063_transpose_y_1 = const()[name = string("op_2063_transpose_y_1"), val = bool(true)];
            tensor<fp16, [4, 298, 298]> var_2063_cast_fp16 = matmul(transpose_x = var_2063_transpose_x_1, transpose_y = var_2063_transpose_y_1, x = query_states_7_cast_fp16, y = key_states_15_cast_fp16)[name = string("op_2063_cast_fp16")];
            fp16 var_2064_to_fp16 = const()[name = string("op_2064_to_fp16"), val = fp16(0x1.a2p-3)];
            tensor<fp16, [4, 298, 298]> input_521_cast_fp16 = mul(x = var_2063_cast_fp16, y = var_2064_to_fp16)[name = string("input_521_cast_fp16")];
            tensor<fp16, [4, 298, 298]> input_523_cast_fp16 = softmax(axis = var_43, x = input_521_cast_fp16)[name = string("input_523_cast_fp16")];
            bool attn_output_19_transpose_x_0 = const()[name = string("attn_output_19_transpose_x_0"), val = bool(false)];
            bool attn_output_19_transpose_y_0 = const()[name = string("attn_output_19_transpose_y_0"), val = bool(false)];
            tensor<fp16, [4, 298, 24]> attn_output_19_cast_fp16 = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = input_523_cast_fp16, y = value_states_15_cast_fp16)[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_2069 = const()[name = string("op_2069"), val = tensor<int32, [4]>([1, 4, 298, 24])];
            tensor<fp16, [1, 4, 298, 24]> attn_output_21_cast_fp16 = reshape(shape = var_2069, x = attn_output_19_cast_fp16)[name = string("attn_output_21_cast_fp16")];
            tensor<int32, [4]> attn_output_23_perm_0 = const()[name = string("attn_output_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2072 = const()[name = string("op_2072"), val = tensor<int32, [3]>([1, 298, 96])];
            tensor<fp16, [1, 298, 4, 24]> attn_output_23_cast_fp16 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast_fp16)[name = string("transpose_18")];
            tensor<fp16, [1, 298, 96]> input_525_cast_fp16 = reshape(shape = var_2072, x = attn_output_23_cast_fp16)[name = string("input_525_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage3_9_tcm_4_attention_out_proj_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_out_proj_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11067776)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_attention_out_proj_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_attention_out_proj_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11086272)))];
            tensor<fp16, [1, 298, 96]> linear_21_cast_fp16 = linear(bias = model_backbone_stage3_9_tcm_4_attention_out_proj_bias_to_fp16, weight = model_backbone_stage3_9_tcm_4_attention_out_proj_weight_to_fp16, x = input_525_cast_fp16)[name = string("linear_21_cast_fp16")];
            tensor<fp16, [1, 298, 96]> input_527_cast_fp16 = add(x = hidden_states_27_cast_fp16, y = linear_21_cast_fp16)[name = string("input_527_cast_fp16")];
            tensor<int32, [1]> input_529_axes_0 = const()[name = string("input_529_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_layer_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11086528)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_layer_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11086784)))];
            tensor<fp16, [1, 298, 96]> input_529_cast_fp16 = layer_norm(axes = input_529_axes_0, beta = model_backbone_stage3_9_tcm_4_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage3_9_tcm_4_layer_norm_weight_to_fp16, x = input_527_cast_fp16)[name = string("input_529_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage3_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11087040)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11105536)))];
            tensor<fp16, [1, 298, 96]> linear_22_cast_fp16 = linear(bias = model_backbone_stage3_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16, weight = model_backbone_stage3_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16, x = input_529_cast_fp16)[name = string("linear_22_cast_fp16")];
            string input_533_mode_0 = const()[name = string("input_533_mode_0"), val = string("TANH_APPROXIMATION")];
            tensor<fp16, [1, 298, 96]> input_533_cast_fp16 = gelu(mode = input_533_mode_0, x = linear_22_cast_fp16)[name = string("input_533_cast_fp16")];
            tensor<fp16, [96, 96]> model_backbone_stage3_9_tcm_4_feed_forward_output_dense_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_feed_forward_output_dense_weight_to_fp16"), val = tensor<fp16, [96, 96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11105792)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_feed_forward_output_dense_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_feed_forward_output_dense_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11124288)))];
            tensor<fp16, [1, 298, 96]> linear_23_cast_fp16 = linear(bias = model_backbone_stage3_9_tcm_4_feed_forward_output_dense_bias_to_fp16, weight = model_backbone_stage3_9_tcm_4_feed_forward_output_dense_weight_to_fp16, x = input_533_cast_fp16)[name = string("linear_23_cast_fp16")];
            tensor<fp16, [1, 298, 96]> input_539_cast_fp16 = add(x = input_529_cast_fp16, y = linear_23_cast_fp16)[name = string("input_539_cast_fp16")];
            tensor<int32, [1]> hidden_states_31_axes_0 = const()[name = string("hidden_states_31_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_final_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11124544)))];
            tensor<fp16, [96]> model_backbone_stage3_9_tcm_4_final_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_tcm_4_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11124800)))];
            tensor<fp16, [1, 298, 96]> hidden_states_31_cast_fp16 = layer_norm(axes = hidden_states_31_axes_0, beta = model_backbone_stage3_9_tcm_4_final_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage3_9_tcm_4_final_layer_norm_weight_to_fp16, x = input_539_cast_fp16)[name = string("hidden_states_31_cast_fp16")];
            tensor<int32, [3]> var_2109 = const()[name = string("op_2109"), val = tensor<int32, [3]>([0, 2, 1])];
            string x_93_pad_type_0 = const()[name = string("x_93_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_93_strides_0 = const()[name = string("x_93_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_93_pad_0 = const()[name = string("x_93_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_93_dilations_0 = const()[name = string("x_93_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_93_groups_0 = const()[name = string("x_93_groups_0"), val = int32(1)];
            tensor<fp16, [4608, 96, 1]> model_backbone_stage3_9_exp_dim_conv_weight_to_fp16 = const()[name = string("model_backbone_stage3_9_exp_dim_conv_weight_to_fp16"), val = tensor<fp16, [4608, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11125056)))];
            tensor<fp16, [4608]> model_backbone_stage3_9_exp_dim_conv_bias_to_fp16 = const()[name = string("model_backbone_stage3_9_exp_dim_conv_bias_to_fp16"), val = tensor<fp16, [4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12009856)))];
            tensor<fp16, [1, 96, 298]> input_541_cast_fp16 = transpose(perm = var_2109, x = hidden_states_31_cast_fp16)[name = string("transpose_17")];
            tensor<fp16, [1, 4608, 298]> x_93_cast_fp16 = conv(bias = model_backbone_stage3_9_exp_dim_conv_bias_to_fp16, dilations = x_93_dilations_0, groups = x_93_groups_0, pad = x_93_pad_0, pad_type = x_93_pad_type_0, strides = x_93_strides_0, weight = model_backbone_stage3_9_exp_dim_conv_weight_to_fp16, x = input_541_cast_fp16)[name = string("x_93_cast_fp16")];
            tensor<fp16, [1, 4608, 298]> input_543_cast_fp16 = add(x = input_487_cast_fp16, y = x_93_cast_fp16)[name = string("input_543_cast_fp16")];
            tensor<int32, [1]> expand_dims_3_axes_0 = const()[name = string("expand_dims_3_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 298, 1]> expand_dims_3_cast_fp16 = expand_dims(axes = expand_dims_3_axes_0, x = input_543_cast_fp16)[name = string("expand_dims_3_cast_fp16")];
            int32 upsample_nearest_neighbor_3_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_3_scale_factor_height_0"), val = int32(2)];
            int32 upsample_nearest_neighbor_3_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_3_scale_factor_width_0"), val = int32(1)];
            tensor<fp16, [1, 4608, 596, 1]> upsample_nearest_neighbor_3_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_3_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_3_scale_factor_width_0, x = expand_dims_3_cast_fp16)[name = string("upsample_nearest_neighbor_3_cast_fp16")];
            tensor<int32, [1]> input_545_axes_0 = const()[name = string("input_545_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596]> input_545_cast_fp16 = squeeze(axes = input_545_axes_0, x = upsample_nearest_neighbor_3_cast_fp16)[name = string("input_545_cast_fp16")];
            tensor<int32, [4]> reshape_16_shape_0 = const()[name = string("reshape_16_shape_0"), val = tensor<int32, [4]>([1, 64, 72, 596])];
            tensor<fp16, [1, 64, 72, 596]> reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_545_cast_fp16)[name = string("reshape_16_cast_fp16")];
            tensor<int32, [2]> reduce_mean_14_axes_0 = const()[name = string("reduce_mean_14_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_14_keep_dims_0 = const()[name = string("reduce_mean_14_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = reshape_16_cast_fp16)[name = string("reduce_mean_14_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> sub_9_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_14_cast_fp16)[name = string("sub_9_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> square_5_cast_fp16 = square(x = sub_9_cast_fp16)[name = string("square_5_cast_fp16")];
            tensor<int32, [2]> reduce_mean_16_axes_0 = const()[name = string("reduce_mean_16_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_16_keep_dims_0 = const()[name = string("reduce_mean_16_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_16_cast_fp16 = reduce_mean(axes = reduce_mean_16_axes_0, keep_dims = reduce_mean_16_keep_dims_0, x = square_5_cast_fp16)[name = string("reduce_mean_16_cast_fp16")];
            fp16 add_8_y_0_to_fp16 = const()[name = string("add_8_y_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 1]> add_8_cast_fp16 = add(x = reduce_mean_16_cast_fp16, y = add_8_y_0_to_fp16)[name = string("add_8_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> sqrt_5_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = string("sqrt_5_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> real_div_4_cast_fp16 = real_div(x = sub_9_cast_fp16, y = sqrt_5_cast_fp16)[name = string("real_div_4_cast_fp16")];
            tensor<int32, [3]> reshape_17_shape_0 = const()[name = string("reshape_17_shape_0"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 4608, 596]> reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = string("reshape_17_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_18_to_fp16 = const()[name = string("reshape_18_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12019136)))];
            tensor<fp16, [1, 4608, 596]> mul_4_cast_fp16 = mul(x = reshape_17_cast_fp16, y = reshape_18_to_fp16)[name = string("mul_4_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_19_to_fp16 = const()[name = string("reshape_19_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12028416)))];
            tensor<fp16, [1, 4608, 596]> add_9_cast_fp16 = add(x = mul_4_cast_fp16, y = reshape_19_to_fp16)[name = string("add_9_cast_fp16")];
            tensor<int32, [1]> var_2139_axes_0 = const()[name = string("op_2139_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 4608, 596]> var_2139_cast_fp16 = expand_dims(axes = var_2139_axes_0, x = add_9_cast_fp16)[name = string("op_2139_cast_fp16")];
            bool xs_9_interleave_0 = const()[name = string("xs_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 5, 4608, 596]> xs_9_cast_fp16 = concat(axis = var_40, interleave = xs_9_interleave_0, values = (var_178_cast_fp16, var_612_cast_fp16, var_1094_cast_fp16, var_1622_cast_fp16, var_2139_cast_fp16))[name = string("xs_9_cast_fp16")];
            tensor<fp16, [1, 5, 4608, 1]> w_31_to_fp16 = const()[name = string("w_31_to_fp16"), val = tensor<fp16, [1, 5, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12037696)))];
            tensor<fp16, [1, 5, 4608, 596]> var_2142_cast_fp16 = mul(x = w_31_to_fp16, y = xs_9_cast_fp16)[name = string("op_2142_cast_fp16")];
            tensor<int32, [1]> x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor<int32, [1]>([1])];
            bool x_95_keep_dims_0 = const()[name = string("x_95_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 4608, 596]> x_95_cast_fp16 = reduce_sum(axes = x_95_axes_0, keep_dims = x_95_keep_dims_0, x = var_2142_cast_fp16)[name = string("x_95_cast_fp16")];
            tensor<int32, [4]> var_2147 = const()[name = string("op_2147"), val = tensor<int32, [4]>([1, 18, 256, 596])];
            tensor<fp16, [1, 18, 256, 596]> var_2148_cast_fp16 = reshape(shape = var_2147, x = x_95_cast_fp16)[name = string("op_2148_cast_fp16")];
            tensor<int32, [4]> var_2149 = const()[name = string("op_2149"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            string input_551_pad_type_0 = const()[name = string("input_551_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_551_strides_0 = const()[name = string("input_551_strides_0"), val = tensor<int32, [2]>([1, 4])];
            int32 input_551_groups_0 = const()[name = string("input_551_groups_0"), val = int32(64)];
            tensor<int32, [4]> input_551_pad_0 = const()[name = string("input_551_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_551_dilations_0 = const()[name = string("input_551_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 4, 1, 4]> model_backbone_stage4_2_weight_to_fp16 = const()[name = string("model_backbone_stage4_2_weight_to_fp16"), val = tensor<fp16, [192, 4, 1, 4]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12083840)))];
            tensor<fp16, [192]> model_backbone_stage4_2_bias_to_fp16 = const()[name = string("model_backbone_stage4_2_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12090048)))];
            tensor<fp16, [1, 256, 18, 596]> input_549_cast_fp16 = transpose(perm = var_2149, x = var_2148_cast_fp16)[name = string("transpose_16")];
            tensor<fp16, [1, 192, 18, 149]> input_551_cast_fp16 = conv(bias = model_backbone_stage4_2_bias_to_fp16, dilations = input_551_dilations_0, groups = input_551_groups_0, pad = input_551_pad_0, pad_type = input_551_pad_type_0, strides = input_551_strides_0, weight = model_backbone_stage4_2_weight_to_fp16, x = input_549_cast_fp16)[name = string("input_551_cast_fp16")];
            string input_553_pad_type_0 = const()[name = string("input_553_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_553_pad_0 = const()[name = string("input_553_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_553_groups_0 = const()[name = string("input_553_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_553_strides_0 = const()[name = string("input_553_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_553_dilations_0 = const()[name = string("input_553_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_3_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12090496)))];
            tensor<fp16, [1, 192, 18, 149]> input_553_cast_fp16 = conv(dilations = input_553_dilations_0, groups = input_553_groups_0, pad = input_553_pad_0, pad_type = input_553_pad_type_0, strides = input_553_strides_0, weight = model_backbone_stage4_3_conv_block_conv1_weight_to_fp16, x = input_551_cast_fp16)[name = string("input_553_cast_fp16")];
            string input_555_pad_type_0 = const()[name = string("input_555_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_555_strides_0 = const()[name = string("input_555_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_555_pad_0 = const()[name = string("input_555_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_555_dilations_0 = const()[name = string("input_555_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_555_groups_0 = const()[name = string("input_555_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> model_backbone_stage4_3_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12094016)))];
            tensor<fp16, [192]> model_backbone_stage4_3_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12167808)))];
            tensor<fp16, [1, 192, 18, 149]> input_555_cast_fp16 = conv(bias = model_backbone_stage4_3_conv_block_conv1pw_bias_to_fp16, dilations = input_555_dilations_0, groups = input_555_groups_0, pad = input_555_pad_0, pad_type = input_555_pad_type_0, strides = input_555_strides_0, weight = model_backbone_stage4_3_conv_block_conv1pw_weight_to_fp16, x = input_553_cast_fp16)[name = string("input_555_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_557_cast_fp16 = relu(x = input_555_cast_fp16)[name = string("input_557_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage4_3_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12168256)))];
            tensor<fp16, [192]> model_backbone_stage4_3_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12168704)))];
            tensor<fp16, [192]> model_backbone_stage4_3_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12169152)))];
            tensor<fp16, [192]> model_backbone_stage4_3_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12169600)))];
            tensor<fp16, [1, 192, 18, 149]> input_559_cast_fp16 = batch_norm(beta = model_backbone_stage4_3_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_3_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage4_3_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage4_3_conv_block_bn1_running_var_to_fp16, x = input_557_cast_fp16)[name = string("input_559_cast_fp16")];
            string input_561_pad_type_0 = const()[name = string("input_561_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_561_pad_0 = const()[name = string("input_561_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_561_groups_0 = const()[name = string("input_561_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_561_strides_0 = const()[name = string("input_561_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_561_dilations_0 = const()[name = string("input_561_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_3_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage4_3_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12170048)))];
            tensor<fp16, [1, 192, 18, 149]> input_561_cast_fp16 = conv(dilations = input_561_dilations_0, groups = input_561_groups_0, pad = input_561_pad_0, pad_type = input_561_pad_type_0, strides = input_561_strides_0, weight = model_backbone_stage4_3_conv_block_conv2_weight_to_fp16, x = input_559_cast_fp16)[name = string("input_561_cast_fp16")];
            string input_563_pad_type_0 = const()[name = string("input_563_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_563_strides_0 = const()[name = string("input_563_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_563_pad_0 = const()[name = string("input_563_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_563_dilations_0 = const()[name = string("input_563_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_563_groups_0 = const()[name = string("input_563_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> const_101_to_fp16 = const()[name = string("const_101_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12173568)))];
            tensor<fp16, [192]> const_102_to_fp16 = const()[name = string("const_102_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12247360)))];
            tensor<fp16, [1, 192, 18, 149]> out_35_cast_fp16 = conv(bias = const_102_to_fp16, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = const_101_to_fp16, x = input_561_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_565_cast_fp16 = add(x = out_35_cast_fp16, y = input_551_cast_fp16)[name = string("input_565_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_567_cast_fp16 = relu(x = input_565_cast_fp16)[name = string("input_567_cast_fp16")];
            string input_569_pad_type_0 = const()[name = string("input_569_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_569_pad_0 = const()[name = string("input_569_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_569_groups_0 = const()[name = string("input_569_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_569_strides_0 = const()[name = string("input_569_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_569_dilations_0 = const()[name = string("input_569_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_4_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12247808)))];
            tensor<fp16, [1, 192, 18, 149]> input_569_cast_fp16 = conv(dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = model_backbone_stage4_4_conv_block_conv1_weight_to_fp16, x = input_567_cast_fp16)[name = string("input_569_cast_fp16")];
            string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> model_backbone_stage4_4_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12251328)))];
            tensor<fp16, [192]> model_backbone_stage4_4_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12325120)))];
            tensor<fp16, [1, 192, 18, 149]> input_571_cast_fp16 = conv(bias = model_backbone_stage4_4_conv_block_conv1pw_bias_to_fp16, dilations = input_571_dilations_0, groups = input_571_groups_0, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = input_571_strides_0, weight = model_backbone_stage4_4_conv_block_conv1pw_weight_to_fp16, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_573_cast_fp16 = relu(x = input_571_cast_fp16)[name = string("input_573_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage4_4_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12325568)))];
            tensor<fp16, [192]> model_backbone_stage4_4_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12326016)))];
            tensor<fp16, [192]> model_backbone_stage4_4_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12326464)))];
            tensor<fp16, [192]> model_backbone_stage4_4_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12326912)))];
            tensor<fp16, [1, 192, 18, 149]> input_575_cast_fp16 = batch_norm(beta = model_backbone_stage4_4_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_4_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage4_4_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage4_4_conv_block_bn1_running_var_to_fp16, x = input_573_cast_fp16)[name = string("input_575_cast_fp16")];
            string input_577_pad_type_0 = const()[name = string("input_577_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_577_pad_0 = const()[name = string("input_577_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_577_groups_0 = const()[name = string("input_577_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_577_strides_0 = const()[name = string("input_577_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_577_dilations_0 = const()[name = string("input_577_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_4_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage4_4_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12327360)))];
            tensor<fp16, [1, 192, 18, 149]> input_577_cast_fp16 = conv(dilations = input_577_dilations_0, groups = input_577_groups_0, pad = input_577_pad_0, pad_type = input_577_pad_type_0, strides = input_577_strides_0, weight = model_backbone_stage4_4_conv_block_conv2_weight_to_fp16, x = input_575_cast_fp16)[name = string("input_577_cast_fp16")];
            string input_579_pad_type_0 = const()[name = string("input_579_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_579_strides_0 = const()[name = string("input_579_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_579_pad_0 = const()[name = string("input_579_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_579_dilations_0 = const()[name = string("input_579_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_579_groups_0 = const()[name = string("input_579_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> const_103_to_fp16 = const()[name = string("const_103_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12330880)))];
            tensor<fp16, [192]> const_104_to_fp16 = const()[name = string("const_104_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12404672)))];
            tensor<fp16, [1, 192, 18, 149]> out_37_cast_fp16 = conv(bias = const_104_to_fp16, dilations = input_579_dilations_0, groups = input_579_groups_0, pad = input_579_pad_0, pad_type = input_579_pad_type_0, strides = input_579_strides_0, weight = const_103_to_fp16, x = input_577_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_581_cast_fp16 = add(x = out_37_cast_fp16, y = input_567_cast_fp16)[name = string("input_581_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_583_cast_fp16 = relu(x = input_581_cast_fp16)[name = string("input_583_cast_fp16")];
            string input_585_pad_type_0 = const()[name = string("input_585_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_585_pad_0 = const()[name = string("input_585_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_585_groups_0 = const()[name = string("input_585_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_585_strides_0 = const()[name = string("input_585_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_585_dilations_0 = const()[name = string("input_585_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_5_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12405120)))];
            tensor<fp16, [1, 192, 18, 149]> input_585_cast_fp16 = conv(dilations = input_585_dilations_0, groups = input_585_groups_0, pad = input_585_pad_0, pad_type = input_585_pad_type_0, strides = input_585_strides_0, weight = model_backbone_stage4_5_conv_block_conv1_weight_to_fp16, x = input_583_cast_fp16)[name = string("input_585_cast_fp16")];
            string input_587_pad_type_0 = const()[name = string("input_587_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_587_strides_0 = const()[name = string("input_587_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_587_pad_0 = const()[name = string("input_587_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_587_dilations_0 = const()[name = string("input_587_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_587_groups_0 = const()[name = string("input_587_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> model_backbone_stage4_5_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12408640)))];
            tensor<fp16, [192]> model_backbone_stage4_5_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12482432)))];
            tensor<fp16, [1, 192, 18, 149]> input_587_cast_fp16 = conv(bias = model_backbone_stage4_5_conv_block_conv1pw_bias_to_fp16, dilations = input_587_dilations_0, groups = input_587_groups_0, pad = input_587_pad_0, pad_type = input_587_pad_type_0, strides = input_587_strides_0, weight = model_backbone_stage4_5_conv_block_conv1pw_weight_to_fp16, x = input_585_cast_fp16)[name = string("input_587_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_589_cast_fp16 = relu(x = input_587_cast_fp16)[name = string("input_589_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage4_5_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12482880)))];
            tensor<fp16, [192]> model_backbone_stage4_5_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12483328)))];
            tensor<fp16, [192]> model_backbone_stage4_5_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12483776)))];
            tensor<fp16, [192]> model_backbone_stage4_5_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12484224)))];
            tensor<fp16, [1, 192, 18, 149]> input_591_cast_fp16 = batch_norm(beta = model_backbone_stage4_5_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_5_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage4_5_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage4_5_conv_block_bn1_running_var_to_fp16, x = input_589_cast_fp16)[name = string("input_591_cast_fp16")];
            string input_593_pad_type_0 = const()[name = string("input_593_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_593_pad_0 = const()[name = string("input_593_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_593_groups_0 = const()[name = string("input_593_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_593_strides_0 = const()[name = string("input_593_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_593_dilations_0 = const()[name = string("input_593_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_5_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage4_5_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12484672)))];
            tensor<fp16, [1, 192, 18, 149]> input_593_cast_fp16 = conv(dilations = input_593_dilations_0, groups = input_593_groups_0, pad = input_593_pad_0, pad_type = input_593_pad_type_0, strides = input_593_strides_0, weight = model_backbone_stage4_5_conv_block_conv2_weight_to_fp16, x = input_591_cast_fp16)[name = string("input_593_cast_fp16")];
            string input_595_pad_type_0 = const()[name = string("input_595_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_595_strides_0 = const()[name = string("input_595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_595_pad_0 = const()[name = string("input_595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_595_dilations_0 = const()[name = string("input_595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_595_groups_0 = const()[name = string("input_595_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> const_105_to_fp16 = const()[name = string("const_105_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12488192)))];
            tensor<fp16, [192]> const_106_to_fp16 = const()[name = string("const_106_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12561984)))];
            tensor<fp16, [1, 192, 18, 149]> out_39_cast_fp16 = conv(bias = const_106_to_fp16, dilations = input_595_dilations_0, groups = input_595_groups_0, pad = input_595_pad_0, pad_type = input_595_pad_type_0, strides = input_595_strides_0, weight = const_105_to_fp16, x = input_593_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_597_cast_fp16 = add(x = out_39_cast_fp16, y = input_583_cast_fp16)[name = string("input_597_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_599_cast_fp16 = relu(x = input_597_cast_fp16)[name = string("input_599_cast_fp16")];
            string input_601_pad_type_0 = const()[name = string("input_601_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_601_pad_0 = const()[name = string("input_601_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_601_groups_0 = const()[name = string("input_601_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_601_strides_0 = const()[name = string("input_601_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_601_dilations_0 = const()[name = string("input_601_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_6_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12562432)))];
            tensor<fp16, [1, 192, 18, 149]> input_601_cast_fp16 = conv(dilations = input_601_dilations_0, groups = input_601_groups_0, pad = input_601_pad_0, pad_type = input_601_pad_type_0, strides = input_601_strides_0, weight = model_backbone_stage4_6_conv_block_conv1_weight_to_fp16, x = input_599_cast_fp16)[name = string("input_601_cast_fp16")];
            string input_603_pad_type_0 = const()[name = string("input_603_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_603_strides_0 = const()[name = string("input_603_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_603_pad_0 = const()[name = string("input_603_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_603_dilations_0 = const()[name = string("input_603_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_603_groups_0 = const()[name = string("input_603_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> model_backbone_stage4_6_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12565952)))];
            tensor<fp16, [192]> model_backbone_stage4_6_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12639744)))];
            tensor<fp16, [1, 192, 18, 149]> input_603_cast_fp16 = conv(bias = model_backbone_stage4_6_conv_block_conv1pw_bias_to_fp16, dilations = input_603_dilations_0, groups = input_603_groups_0, pad = input_603_pad_0, pad_type = input_603_pad_type_0, strides = input_603_strides_0, weight = model_backbone_stage4_6_conv_block_conv1pw_weight_to_fp16, x = input_601_cast_fp16)[name = string("input_603_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_605_cast_fp16 = relu(x = input_603_cast_fp16)[name = string("input_605_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage4_6_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12640192)))];
            tensor<fp16, [192]> model_backbone_stage4_6_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12640640)))];
            tensor<fp16, [192]> model_backbone_stage4_6_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12641088)))];
            tensor<fp16, [192]> model_backbone_stage4_6_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12641536)))];
            tensor<fp16, [1, 192, 18, 149]> input_607_cast_fp16 = batch_norm(beta = model_backbone_stage4_6_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_6_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage4_6_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage4_6_conv_block_bn1_running_var_to_fp16, x = input_605_cast_fp16)[name = string("input_607_cast_fp16")];
            string input_609_pad_type_0 = const()[name = string("input_609_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_609_pad_0 = const()[name = string("input_609_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_609_groups_0 = const()[name = string("input_609_groups_0"), val = int32(192)];
            tensor<int32, [2]> input_609_strides_0 = const()[name = string("input_609_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_609_dilations_0 = const()[name = string("input_609_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [192, 1, 3, 3]> model_backbone_stage4_6_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage4_6_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [192, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12641984)))];
            tensor<fp16, [1, 192, 18, 149]> input_609_cast_fp16 = conv(dilations = input_609_dilations_0, groups = input_609_groups_0, pad = input_609_pad_0, pad_type = input_609_pad_type_0, strides = input_609_strides_0, weight = model_backbone_stage4_6_conv_block_conv2_weight_to_fp16, x = input_607_cast_fp16)[name = string("input_609_cast_fp16")];
            string input_611_pad_type_0 = const()[name = string("input_611_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_611_strides_0 = const()[name = string("input_611_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_611_pad_0 = const()[name = string("input_611_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_611_dilations_0 = const()[name = string("input_611_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_611_groups_0 = const()[name = string("input_611_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1, 1]> const_107_to_fp16 = const()[name = string("const_107_to_fp16"), val = tensor<fp16, [192, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12645504)))];
            tensor<fp16, [192]> const_108_to_fp16 = const()[name = string("const_108_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12719296)))];
            tensor<fp16, [1, 192, 18, 149]> out_41_cast_fp16 = conv(bias = const_108_to_fp16, dilations = input_611_dilations_0, groups = input_611_groups_0, pad = input_611_pad_0, pad_type = input_611_pad_type_0, strides = input_611_strides_0, weight = const_107_to_fp16, x = input_609_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_613_cast_fp16 = add(x = out_41_cast_fp16, y = input_599_cast_fp16)[name = string("input_613_cast_fp16")];
            tensor<fp16, [1, 192, 18, 149]> input_615_cast_fp16 = relu(x = input_613_cast_fp16)[name = string("input_615_cast_fp16")];
            tensor<int32, [2]> var_2346 = const()[name = string("op_2346"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2347 = const()[name = string("op_2347"), val = tensor<int32, [2]>([1, 1])];
            string input_617_pad_type_0 = const()[name = string("input_617_pad_type_0"), val = string("same")];
            tensor<int32, [4]> input_617_pad_0 = const()[name = string("input_617_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [256, 192, 1, 1]> const_109_to_fp16 = const()[name = string("const_109_to_fp16"), val = tensor<fp16, [256, 192, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12719744)))];
            tensor<fp16, [256]> const_110_to_fp16 = const()[name = string("const_110_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818112)))];
            tensor<fp16, [1, 256, 18, 149]> x_97_cast_fp16 = conv(bias = const_110_to_fp16, dilations = var_2347, groups = var_40, pad = input_617_pad_0, pad_type = input_617_pad_type_0, strides = var_2346, weight = const_109_to_fp16, x = input_615_cast_fp16)[name = string("x_97_cast_fp16")];
            tensor<int32, [4]> var_2360 = const()[name = string("op_2360"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2364 = const()[name = string("op_2364"), val = tensor<int32, [3]>([1, 4608, 149])];
            tensor<fp16, [1, 18, 256, 149]> var_2361_cast_fp16 = transpose(perm = var_2360, x = x_97_cast_fp16)[name = string("transpose_15")];
            tensor<fp16, [1, 4608, 149]> input_619_cast_fp16 = reshape(shape = var_2364, x = var_2361_cast_fp16)[name = string("input_619_cast_fp16")];
            string x_99_pad_type_0 = const()[name = string("x_99_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_99_strides_0 = const()[name = string("x_99_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_99_pad_0 = const()[name = string("x_99_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_99_dilations_0 = const()[name = string("x_99_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_99_groups_0 = const()[name = string("x_99_groups_0"), val = int32(1)];
            tensor<fp16, [144, 4608, 1]> model_backbone_stage4_9_red_dim_conv_0_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_red_dim_conv_0_weight_to_fp16"), val = tensor<fp16, [144, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818688)))];
            tensor<fp16, [144]> model_backbone_stage4_9_red_dim_conv_0_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_red_dim_conv_0_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14145856)))];
            tensor<fp16, [1, 144, 149]> x_99_cast_fp16 = conv(bias = model_backbone_stage4_9_red_dim_conv_0_bias_to_fp16, dilations = x_99_dilations_0, groups = x_99_groups_0, pad = x_99_pad_0, pad_type = x_99_pad_type_0, strides = x_99_strides_0, weight = model_backbone_stage4_9_red_dim_conv_0_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [1]> u_11_axes_0 = const()[name = string("u_11_axes_0"), val = tensor<int32, [1]>([1])];
            bool u_11_keep_dims_0 = const()[name = string("u_11_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 149]> u_11_cast_fp16 = reduce_mean(axes = u_11_axes_0, keep_dims = u_11_keep_dims_0, x = x_99_cast_fp16)[name = string("u_11_cast_fp16")];
            tensor<fp16, [1, 144, 149]> var_2382_cast_fp16 = sub(x = x_99_cast_fp16, y = u_11_cast_fp16)[name = string("op_2382_cast_fp16")];
            fp16 var_41_promoted_5_to_fp16 = const()[name = string("op_41_promoted_5_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 144, 149]> var_2383_cast_fp16 = pow(x = var_2382_cast_fp16, y = var_41_promoted_5_to_fp16)[name = string("op_2383_cast_fp16")];
            tensor<int32, [1]> s_11_axes_0 = const()[name = string("s_11_axes_0"), val = tensor<int32, [1]>([1])];
            bool s_11_keep_dims_0 = const()[name = string("s_11_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 149]> s_11_cast_fp16 = reduce_mean(axes = s_11_axes_0, keep_dims = s_11_keep_dims_0, x = var_2383_cast_fp16)[name = string("s_11_cast_fp16")];
            fp16 var_2387_to_fp16 = const()[name = string("op_2387_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 149]> var_2388_cast_fp16 = add(x = s_11_cast_fp16, y = var_2387_to_fp16)[name = string("op_2388_cast_fp16")];
            tensor<fp16, [1, 1, 149]> var_2389_cast_fp16 = sqrt(x = var_2388_cast_fp16)[name = string("op_2389_cast_fp16")];
            tensor<fp16, [1, 144, 149]> x_101_cast_fp16 = real_div(x = var_2382_cast_fp16, y = var_2389_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<fp16, [144, 1]> w_35_to_fp16 = const()[name = string("w_35_to_fp16"), val = tensor<fp16, [144, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14146240)))];
            tensor<fp16, [1, 144, 149]> var_2393_cast_fp16 = mul(x = w_35_to_fp16, y = x_101_cast_fp16)[name = string("op_2393_cast_fp16")];
            tensor<fp16, [144, 1]> b_25_to_fp16 = const()[name = string("b_25_to_fp16"), val = tensor<fp16, [144, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14146624)))];
            tensor<fp16, [1, 144, 149]> input_621_cast_fp16 = add(x = var_2393_cast_fp16, y = b_25_to_fp16)[name = string("input_621_cast_fp16")];
            tensor<int32, [1]> var_2406 = const()[name = string("op_2406"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2407 = const()[name = string("op_2407"), val = tensor<int32, [1]>([1])];
            string var_2408_pad_type_0 = const()[name = string("op_2408_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2408_pad_0 = const()[name = string("op_2408_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [144, 1, 7]> model_backbone_stage4_9_tcm_0_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [144, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14147008)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_0_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14149120)))];
            tensor<fp16, [1, 144, 149]> var_2408_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_0_dwconvs_0_bias_to_fp16, dilations = var_2407, groups = var_27, pad = var_2408_pad_0, pad_type = var_2408_pad_type_0, strides = var_2406, weight = model_backbone_stage4_9_tcm_0_dwconvs_0_weight_to_fp16, x = input_621_cast_fp16)[name = string("op_2408_cast_fp16")];
            bool input_623_interleave_0 = const()[name = string("input_623_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 144, 149]> input_623_cast_fp16 = concat(axis = var_40, interleave = input_623_interleave_0, values = var_2408_cast_fp16)[name = string("input_623_cast_fp16")];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_0_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_norm_running_mean_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14149504)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_0_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_norm_running_var_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14149888)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_0_norm_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_norm_weight_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14150272)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_0_norm_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_norm_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14150656)))];
            tensor<fp16, [1, 144, 149]> input_625_cast_fp16 = batch_norm(beta = model_backbone_stage4_9_tcm_0_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_9_tcm_0_norm_weight_to_fp16, mean = model_backbone_stage4_9_tcm_0_norm_running_mean_to_fp16, variance = model_backbone_stage4_9_tcm_0_norm_running_var_to_fp16, x = input_623_cast_fp16)[name = string("input_625_cast_fp16")];
            string input_627_mode_0 = const()[name = string("input_627_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 144, 149]> input_627_cast_fp16 = gelu(mode = input_627_mode_0, x = input_625_cast_fp16)[name = string("input_627_cast_fp16")];
            string x_103_pad_type_0 = const()[name = string("x_103_pad_type_0"), val = string("valid")];
            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])];
            int32 x_103_groups_0 = const()[name = string("x_103_groups_0"), val = int32(1)];
            tensor<fp16, [144, 144, 1]> model_backbone_stage4_9_tcm_0_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [144, 144, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14151040)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_0_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14192576)))];
            tensor<fp16, [1, 144, 149]> x_103_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_0_pwconv1_bias_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 = model_backbone_stage4_9_tcm_0_pwconv1_weight_to_fp16, x = input_627_cast_fp16)[name = string("x_103_cast_fp16")];
            tensor<fp16, [1, 144, 149]> input_629_cast_fp16 = add(x = input_621_cast_fp16, y = x_103_cast_fp16)[name = string("input_629_cast_fp16")];
            tensor<int32, [1]> var_2431 = const()[name = string("op_2431"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2432 = const()[name = string("op_2432"), val = tensor<int32, [1]>([1])];
            string var_2433_pad_type_0 = const()[name = string("op_2433_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2433_pad_0 = const()[name = string("op_2433_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [144, 1, 19]> model_backbone_stage4_9_tcm_1_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [144, 1, 19]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14192960)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_1_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14198528)))];
            tensor<fp16, [1, 144, 149]> var_2433_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_1_dwconvs_0_bias_to_fp16, dilations = var_2432, groups = var_27, pad = var_2433_pad_0, pad_type = var_2433_pad_type_0, strides = var_2431, weight = model_backbone_stage4_9_tcm_1_dwconvs_0_weight_to_fp16, x = input_629_cast_fp16)[name = string("op_2433_cast_fp16")];
            bool input_631_interleave_0 = const()[name = string("input_631_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 144, 149]> input_631_cast_fp16 = concat(axis = var_40, interleave = input_631_interleave_0, values = var_2433_cast_fp16)[name = string("input_631_cast_fp16")];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_1_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_norm_running_mean_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14198912)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_1_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_norm_running_var_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14199296)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_1_norm_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_norm_weight_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14199680)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_1_norm_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_norm_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14200064)))];
            tensor<fp16, [1, 144, 149]> input_633_cast_fp16 = batch_norm(beta = model_backbone_stage4_9_tcm_1_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_9_tcm_1_norm_weight_to_fp16, mean = model_backbone_stage4_9_tcm_1_norm_running_mean_to_fp16, variance = model_backbone_stage4_9_tcm_1_norm_running_var_to_fp16, x = input_631_cast_fp16)[name = string("input_633_cast_fp16")];
            string input_635_mode_0 = const()[name = string("input_635_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 144, 149]> input_635_cast_fp16 = gelu(mode = input_635_mode_0, x = input_633_cast_fp16)[name = string("input_635_cast_fp16")];
            string x_105_pad_type_0 = const()[name = string("x_105_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_105_strides_0 = const()[name = string("x_105_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_105_pad_0 = const()[name = string("x_105_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_105_dilations_0 = const()[name = string("x_105_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_105_groups_0 = const()[name = string("x_105_groups_0"), val = int32(1)];
            tensor<fp16, [144, 144, 1]> model_backbone_stage4_9_tcm_1_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [144, 144, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14200448)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_1_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14241984)))];
            tensor<fp16, [1, 144, 149]> x_105_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_1_pwconv1_bias_to_fp16, dilations = x_105_dilations_0, groups = x_105_groups_0, pad = x_105_pad_0, pad_type = x_105_pad_type_0, strides = x_105_strides_0, weight = model_backbone_stage4_9_tcm_1_pwconv1_weight_to_fp16, x = input_635_cast_fp16)[name = string("x_105_cast_fp16")];
            tensor<fp16, [1, 144, 149]> input_637_cast_fp16 = add(x = input_629_cast_fp16, y = x_105_cast_fp16)[name = string("input_637_cast_fp16")];
            tensor<int32, [1]> var_2456 = const()[name = string("op_2456"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2457 = const()[name = string("op_2457"), val = tensor<int32, [1]>([1])];
            string var_2458_pad_type_0 = const()[name = string("op_2458_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2458_pad_0 = const()[name = string("op_2458_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [144, 1, 31]> model_backbone_stage4_9_tcm_2_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [144, 1, 31]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14242368)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_2_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14251392)))];
            tensor<fp16, [1, 144, 149]> var_2458_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_2_dwconvs_0_bias_to_fp16, dilations = var_2457, groups = var_27, pad = var_2458_pad_0, pad_type = var_2458_pad_type_0, strides = var_2456, weight = model_backbone_stage4_9_tcm_2_dwconvs_0_weight_to_fp16, x = input_637_cast_fp16)[name = string("op_2458_cast_fp16")];
            bool input_639_interleave_0 = const()[name = string("input_639_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 144, 149]> input_639_cast_fp16 = concat(axis = var_40, interleave = input_639_interleave_0, values = var_2458_cast_fp16)[name = string("input_639_cast_fp16")];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_2_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_norm_running_mean_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14251776)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_2_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_norm_running_var_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14252160)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_2_norm_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_norm_weight_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14252544)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_2_norm_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_norm_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14252928)))];
            tensor<fp16, [1, 144, 149]> input_641_cast_fp16 = batch_norm(beta = model_backbone_stage4_9_tcm_2_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_9_tcm_2_norm_weight_to_fp16, mean = model_backbone_stage4_9_tcm_2_norm_running_mean_to_fp16, variance = model_backbone_stage4_9_tcm_2_norm_running_var_to_fp16, x = input_639_cast_fp16)[name = string("input_641_cast_fp16")];
            string input_643_mode_0 = const()[name = string("input_643_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 144, 149]> input_643_cast_fp16 = gelu(mode = input_643_mode_0, x = input_641_cast_fp16)[name = string("input_643_cast_fp16")];
            string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)];
            tensor<fp16, [144, 144, 1]> model_backbone_stage4_9_tcm_2_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [144, 144, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14253312)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_2_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14294848)))];
            tensor<fp16, [1, 144, 149]> x_107_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_2_pwconv1_bias_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = model_backbone_stage4_9_tcm_2_pwconv1_weight_to_fp16, x = input_643_cast_fp16)[name = string("x_107_cast_fp16")];
            tensor<fp16, [1, 144, 149]> input_645_cast_fp16 = add(x = input_637_cast_fp16, y = x_107_cast_fp16)[name = string("input_645_cast_fp16")];
            tensor<int32, [1]> var_2481 = const()[name = string("op_2481"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2482 = const()[name = string("op_2482"), val = tensor<int32, [1]>([1])];
            string var_2483_pad_type_0 = const()[name = string("op_2483_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2483_pad_0 = const()[name = string("op_2483_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [144, 1, 59]> model_backbone_stage4_9_tcm_3_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [144, 1, 59]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14295232)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_3_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14312320)))];
            tensor<fp16, [1, 144, 149]> var_2483_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_3_dwconvs_0_bias_to_fp16, dilations = var_2482, groups = var_27, pad = var_2483_pad_0, pad_type = var_2483_pad_type_0, strides = var_2481, weight = model_backbone_stage4_9_tcm_3_dwconvs_0_weight_to_fp16, x = input_645_cast_fp16)[name = string("op_2483_cast_fp16")];
            bool input_647_interleave_0 = const()[name = string("input_647_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 144, 149]> input_647_cast_fp16 = concat(axis = var_40, interleave = input_647_interleave_0, values = var_2483_cast_fp16)[name = string("input_647_cast_fp16")];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_3_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_norm_running_mean_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14312704)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_3_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_norm_running_var_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14313088)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_3_norm_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_norm_weight_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14313472)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_3_norm_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_norm_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14313856)))];
            tensor<fp16, [1, 144, 149]> input_649_cast_fp16 = batch_norm(beta = model_backbone_stage4_9_tcm_3_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage4_9_tcm_3_norm_weight_to_fp16, mean = model_backbone_stage4_9_tcm_3_norm_running_mean_to_fp16, variance = model_backbone_stage4_9_tcm_3_norm_running_var_to_fp16, x = input_647_cast_fp16)[name = string("input_649_cast_fp16")];
            string input_651_mode_0 = const()[name = string("input_651_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 144, 149]> input_651_cast_fp16 = gelu(mode = input_651_mode_0, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")];
            string x_109_pad_type_0 = const()[name = string("x_109_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_109_strides_0 = const()[name = string("x_109_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_109_pad_0 = const()[name = string("x_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_109_dilations_0 = const()[name = string("x_109_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_109_groups_0 = const()[name = string("x_109_groups_0"), val = int32(1)];
            tensor<fp16, [144, 144, 1]> model_backbone_stage4_9_tcm_3_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [144, 144, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14314240)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_3_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14355776)))];
            tensor<fp16, [1, 144, 149]> x_109_cast_fp16 = conv(bias = model_backbone_stage4_9_tcm_3_pwconv1_bias_to_fp16, dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = model_backbone_stage4_9_tcm_3_pwconv1_weight_to_fp16, x = input_651_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<fp16, [1, 144, 149]> hidden_states_33_cast_fp16 = add(x = input_645_cast_fp16, y = x_109_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<int32, [3]> var_2504 = const()[name = string("op_2504"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [144, 144]> model_backbone_stage4_9_tcm_4_attention_q_proj_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_q_proj_weight_to_fp16"), val = tensor<fp16, [144, 144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14356160)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_attention_q_proj_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_q_proj_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14397696)))];
            tensor<fp16, [1, 149, 144]> hidden_states_35_cast_fp16 = transpose(perm = var_2504, x = hidden_states_33_cast_fp16)[name = string("transpose_14")];
            tensor<fp16, [1, 149, 144]> linear_24_cast_fp16 = linear(bias = model_backbone_stage4_9_tcm_4_attention_q_proj_bias_to_fp16, weight = model_backbone_stage4_9_tcm_4_attention_q_proj_weight_to_fp16, x = hidden_states_35_cast_fp16)[name = string("linear_24_cast_fp16")];
            tensor<fp16, [144, 144]> model_backbone_stage4_9_tcm_4_attention_k_proj_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_k_proj_weight_to_fp16"), val = tensor<fp16, [144, 144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14398080)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_attention_k_proj_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_k_proj_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14439616)))];
            tensor<fp16, [1, 149, 144]> linear_25_cast_fp16 = linear(bias = model_backbone_stage4_9_tcm_4_attention_k_proj_bias_to_fp16, weight = model_backbone_stage4_9_tcm_4_attention_k_proj_weight_to_fp16, x = hidden_states_35_cast_fp16)[name = string("linear_25_cast_fp16")];
            tensor<int32, [4]> var_2519 = const()[name = string("op_2519"), val = tensor<int32, [4]>([1, -1, 4, 36])];
            tensor<fp16, [1, 149, 4, 36]> var_2520_cast_fp16 = reshape(shape = var_2519, x = linear_25_cast_fp16)[name = string("op_2520_cast_fp16")];
            tensor<int32, [4]> var_2521_perm_0 = const()[name = string("op_2521_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [144, 144]> model_backbone_stage4_9_tcm_4_attention_v_proj_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_v_proj_weight_to_fp16"), val = tensor<fp16, [144, 144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14440000)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_attention_v_proj_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_v_proj_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14481536)))];
            tensor<fp16, [1, 149, 144]> linear_26_cast_fp16 = linear(bias = model_backbone_stage4_9_tcm_4_attention_v_proj_bias_to_fp16, weight = model_backbone_stage4_9_tcm_4_attention_v_proj_weight_to_fp16, x = hidden_states_35_cast_fp16)[name = string("linear_26_cast_fp16")];
            tensor<int32, [4]> var_2526 = const()[name = string("op_2526"), val = tensor<int32, [4]>([1, -1, 4, 36])];
            tensor<fp16, [1, 149, 4, 36]> var_2527_cast_fp16 = reshape(shape = var_2526, x = linear_26_cast_fp16)[name = string("op_2527_cast_fp16")];
            tensor<int32, [4]> var_2528_perm_0 = const()[name = string("op_2528_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2535 = const()[name = string("op_2535"), val = tensor<int32, [4]>([1, 149, 4, 36])];
            tensor<fp16, [1, 149, 4, 36]> var_2536_cast_fp16 = reshape(shape = var_2535, x = linear_24_cast_fp16)[name = string("op_2536_cast_fp16")];
            tensor<int32, [4]> var_2537_perm_0 = const()[name = string("op_2537_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2539 = const()[name = string("op_2539"), val = tensor<int32, [3]>([4, -1, 36])];
            tensor<fp16, [1, 4, 149, 36]> var_2537_cast_fp16 = transpose(perm = var_2537_perm_0, x = var_2536_cast_fp16)[name = string("transpose_11")];
            tensor<fp16, [4, 149, 36]> query_states_9_cast_fp16 = reshape(shape = var_2539, x = var_2537_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<int32, [3]> var_2541 = const()[name = string("op_2541"), val = tensor<int32, [3]>([4, -1, 36])];
            tensor<fp16, [1, 4, 149, 36]> var_2521_cast_fp16 = transpose(perm = var_2521_perm_0, x = var_2520_cast_fp16)[name = string("transpose_13")];
            tensor<fp16, [4, 149, 36]> key_states_19_cast_fp16 = reshape(shape = var_2541, x = var_2521_cast_fp16)[name = string("key_states_19_cast_fp16")];
            tensor<int32, [3]> var_2543 = const()[name = string("op_2543"), val = tensor<int32, [3]>([4, -1, 36])];
            tensor<fp16, [1, 4, 149, 36]> var_2528_cast_fp16 = transpose(perm = var_2528_perm_0, x = var_2527_cast_fp16)[name = string("transpose_12")];
            tensor<fp16, [4, 149, 36]> value_states_19_cast_fp16 = reshape(shape = var_2543, x = var_2528_cast_fp16)[name = string("value_states_19_cast_fp16")];
            bool var_2546_transpose_x_1 = const()[name = string("op_2546_transpose_x_1"), val = bool(false)];
            bool var_2546_transpose_y_1 = const()[name = string("op_2546_transpose_y_1"), val = bool(true)];
            tensor<fp16, [4, 149, 149]> var_2546_cast_fp16 = matmul(transpose_x = var_2546_transpose_x_1, transpose_y = var_2546_transpose_y_1, x = query_states_9_cast_fp16, y = key_states_19_cast_fp16)[name = string("op_2546_cast_fp16")];
            fp16 var_2547_to_fp16 = const()[name = string("op_2547_to_fp16"), val = fp16(0x1.554p-3)];
            tensor<fp16, [4, 149, 149]> input_653_cast_fp16 = mul(x = var_2546_cast_fp16, y = var_2547_to_fp16)[name = string("input_653_cast_fp16")];
            tensor<fp16, [4, 149, 149]> input_655_cast_fp16 = softmax(axis = var_43, x = input_653_cast_fp16)[name = string("input_655_cast_fp16")];
            bool attn_output_25_transpose_x_0 = const()[name = string("attn_output_25_transpose_x_0"), val = bool(false)];
            bool attn_output_25_transpose_y_0 = const()[name = string("attn_output_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [4, 149, 36]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = input_655_cast_fp16, y = value_states_19_cast_fp16)[name = string("attn_output_25_cast_fp16")];
            tensor<int32, [4]> var_2552 = const()[name = string("op_2552"), val = tensor<int32, [4]>([1, 4, 149, 36])];
            tensor<fp16, [1, 4, 149, 36]> attn_output_27_cast_fp16 = reshape(shape = var_2552, x = attn_output_25_cast_fp16)[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> attn_output_29_perm_0 = const()[name = string("attn_output_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2555 = const()[name = string("op_2555"), val = tensor<int32, [3]>([1, 149, 144])];
            tensor<fp16, [1, 149, 4, 36]> attn_output_29_cast_fp16 = transpose(perm = attn_output_29_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_10")];
            tensor<fp16, [1, 149, 144]> input_657_cast_fp16 = reshape(shape = var_2555, x = attn_output_29_cast_fp16)[name = string("input_657_cast_fp16")];
            tensor<fp16, [144, 144]> model_backbone_stage4_9_tcm_4_attention_out_proj_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_out_proj_weight_to_fp16"), val = tensor<fp16, [144, 144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14481920)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_attention_out_proj_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_attention_out_proj_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14523456)))];
            tensor<fp16, [1, 149, 144]> linear_27_cast_fp16 = linear(bias = model_backbone_stage4_9_tcm_4_attention_out_proj_bias_to_fp16, weight = model_backbone_stage4_9_tcm_4_attention_out_proj_weight_to_fp16, x = input_657_cast_fp16)[name = string("linear_27_cast_fp16")];
            tensor<fp16, [1, 149, 144]> input_659_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = linear_27_cast_fp16)[name = string("input_659_cast_fp16")];
            tensor<int32, [1]> input_661_axes_0 = const()[name = string("input_661_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_layer_norm_weight_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14523840)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_layer_norm_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14524224)))];
            tensor<fp16, [1, 149, 144]> input_661_cast_fp16 = layer_norm(axes = input_661_axes_0, beta = model_backbone_stage4_9_tcm_4_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage4_9_tcm_4_layer_norm_weight_to_fp16, x = input_659_cast_fp16)[name = string("input_661_cast_fp16")];
            tensor<fp16, [144, 144]> model_backbone_stage4_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16"), val = tensor<fp16, [144, 144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14524608)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14566144)))];
            tensor<fp16, [1, 149, 144]> linear_28_cast_fp16 = linear(bias = model_backbone_stage4_9_tcm_4_feed_forward_intermediate_dense_bias_to_fp16, weight = model_backbone_stage4_9_tcm_4_feed_forward_intermediate_dense_weight_to_fp16, x = input_661_cast_fp16)[name = string("linear_28_cast_fp16")];
            string input_665_mode_0 = const()[name = string("input_665_mode_0"), val = string("TANH_APPROXIMATION")];
            tensor<fp16, [1, 149, 144]> input_665_cast_fp16 = gelu(mode = input_665_mode_0, x = linear_28_cast_fp16)[name = string("input_665_cast_fp16")];
            tensor<fp16, [144, 144]> model_backbone_stage4_9_tcm_4_feed_forward_output_dense_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_feed_forward_output_dense_weight_to_fp16"), val = tensor<fp16, [144, 144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14566528)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_feed_forward_output_dense_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_feed_forward_output_dense_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14608064)))];
            tensor<fp16, [1, 149, 144]> linear_29_cast_fp16 = linear(bias = model_backbone_stage4_9_tcm_4_feed_forward_output_dense_bias_to_fp16, weight = model_backbone_stage4_9_tcm_4_feed_forward_output_dense_weight_to_fp16, x = input_665_cast_fp16)[name = string("linear_29_cast_fp16")];
            tensor<fp16, [1, 149, 144]> input_671_cast_fp16 = add(x = input_661_cast_fp16, y = linear_29_cast_fp16)[name = string("input_671_cast_fp16")];
            tensor<int32, [1]> hidden_states_39_axes_0 = const()[name = string("hidden_states_39_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_final_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14608448)))];
            tensor<fp16, [144]> model_backbone_stage4_9_tcm_4_final_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_tcm_4_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [144]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14608832)))];
            tensor<fp16, [1, 149, 144]> hidden_states_39_cast_fp16 = layer_norm(axes = hidden_states_39_axes_0, beta = model_backbone_stage4_9_tcm_4_final_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage4_9_tcm_4_final_layer_norm_weight_to_fp16, x = input_671_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            tensor<int32, [3]> var_2592 = const()[name = string("op_2592"), val = tensor<int32, [3]>([0, 2, 1])];
            string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(1)];
            tensor<fp16, [4608, 144, 1]> model_backbone_stage4_9_exp_dim_conv_weight_to_fp16 = const()[name = string("model_backbone_stage4_9_exp_dim_conv_weight_to_fp16"), val = tensor<fp16, [4608, 144, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14609216)))];
            tensor<fp16, [4608]> model_backbone_stage4_9_exp_dim_conv_bias_to_fp16 = const()[name = string("model_backbone_stage4_9_exp_dim_conv_bias_to_fp16"), val = tensor<fp16, [4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15936384)))];
            tensor<fp16, [1, 144, 149]> input_673_cast_fp16 = transpose(perm = var_2592, x = hidden_states_39_cast_fp16)[name = string("transpose_9")];
            tensor<fp16, [1, 4608, 149]> x_111_cast_fp16 = conv(bias = model_backbone_stage4_9_exp_dim_conv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = model_backbone_stage4_9_exp_dim_conv_weight_to_fp16, x = input_673_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<fp16, [1, 4608, 149]> input_675_cast_fp16 = add(x = input_619_cast_fp16, y = x_111_cast_fp16)[name = string("input_675_cast_fp16")];
            tensor<int32, [1]> expand_dims_4_axes_0 = const()[name = string("expand_dims_4_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 149, 1]> expand_dims_4_cast_fp16 = expand_dims(axes = expand_dims_4_axes_0, x = input_675_cast_fp16)[name = string("expand_dims_4_cast_fp16")];
            int32 upsample_nearest_neighbor_4_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_4_scale_factor_height_0"), val = int32(4)];
            int32 upsample_nearest_neighbor_4_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_4_scale_factor_width_0"), val = int32(1)];
            tensor<fp16, [1, 4608, 596, 1]> upsample_nearest_neighbor_4_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_4_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_4_scale_factor_width_0, x = expand_dims_4_cast_fp16)[name = string("upsample_nearest_neighbor_4_cast_fp16")];
            tensor<int32, [1]> input_677_axes_0 = const()[name = string("input_677_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596]> input_677_cast_fp16 = squeeze(axes = input_677_axes_0, x = upsample_nearest_neighbor_4_cast_fp16)[name = string("input_677_cast_fp16")];
            tensor<int32, [4]> reshape_20_shape_0 = const()[name = string("reshape_20_shape_0"), val = tensor<int32, [4]>([1, 64, 72, 596])];
            tensor<fp16, [1, 64, 72, 596]> reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = input_677_cast_fp16)[name = string("reshape_20_cast_fp16")];
            tensor<int32, [2]> reduce_mean_17_axes_0 = const()[name = string("reduce_mean_17_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_17_keep_dims_0 = const()[name = string("reduce_mean_17_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = reshape_20_cast_fp16)[name = string("reduce_mean_17_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> sub_11_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_17_cast_fp16)[name = string("sub_11_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> square_6_cast_fp16 = square(x = sub_11_cast_fp16)[name = string("square_6_cast_fp16")];
            tensor<int32, [2]> reduce_mean_19_axes_0 = const()[name = string("reduce_mean_19_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_19_keep_dims_0 = const()[name = string("reduce_mean_19_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_19_cast_fp16 = reduce_mean(axes = reduce_mean_19_axes_0, keep_dims = reduce_mean_19_keep_dims_0, x = square_6_cast_fp16)[name = string("reduce_mean_19_cast_fp16")];
            fp16 add_10_y_0_to_fp16 = const()[name = string("add_10_y_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 1]> add_10_cast_fp16 = add(x = reduce_mean_19_cast_fp16, y = add_10_y_0_to_fp16)[name = string("add_10_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> sqrt_6_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = string("sqrt_6_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> real_div_5_cast_fp16 = real_div(x = sub_11_cast_fp16, y = sqrt_6_cast_fp16)[name = string("real_div_5_cast_fp16")];
            tensor<int32, [3]> reshape_21_shape_0 = const()[name = string("reshape_21_shape_0"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 4608, 596]> reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = string("reshape_21_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_22_to_fp16 = const()[name = string("reshape_22_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15945664)))];
            tensor<fp16, [1, 4608, 596]> mul_5_cast_fp16 = mul(x = reshape_21_cast_fp16, y = reshape_22_to_fp16)[name = string("mul_5_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_23_to_fp16 = const()[name = string("reshape_23_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15954944)))];
            tensor<fp16, [1, 4608, 596]> add_11_cast_fp16 = add(x = mul_5_cast_fp16, y = reshape_23_to_fp16)[name = string("add_11_cast_fp16")];
            tensor<int32, [1]> var_2622_axes_0 = const()[name = string("op_2622_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 4608, 596]> var_2622_cast_fp16 = expand_dims(axes = var_2622_axes_0, x = add_11_cast_fp16)[name = string("op_2622_cast_fp16")];
            bool xs_11_interleave_0 = const()[name = string("xs_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 6, 4608, 596]> xs_11_cast_fp16 = concat(axis = var_40, interleave = xs_11_interleave_0, values = (var_178_cast_fp16, var_612_cast_fp16, var_1094_cast_fp16, var_1622_cast_fp16, var_2139_cast_fp16, var_2622_cast_fp16))[name = string("xs_11_cast_fp16")];
            tensor<fp16, [1, 6, 4608, 1]> w_37_to_fp16 = const()[name = string("w_37_to_fp16"), val = tensor<fp16, [1, 6, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15964224)))];
            tensor<fp16, [1, 6, 4608, 596]> var_2625_cast_fp16 = mul(x = w_37_to_fp16, y = xs_11_cast_fp16)[name = string("op_2625_cast_fp16")];
            tensor<int32, [1]> x_113_axes_0 = const()[name = string("x_113_axes_0"), val = tensor<int32, [1]>([1])];
            bool x_113_keep_dims_0 = const()[name = string("x_113_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 4608, 596]> x_113_cast_fp16 = reduce_sum(axes = x_113_axes_0, keep_dims = x_113_keep_dims_0, x = var_2625_cast_fp16)[name = string("x_113_cast_fp16")];
            tensor<int32, [4]> var_2630 = const()[name = string("op_2630"), val = tensor<int32, [4]>([1, 18, 256, 596])];
            tensor<fp16, [1, 18, 256, 596]> var_2631_cast_fp16 = reshape(shape = var_2630, x = x_113_cast_fp16)[name = string("op_2631_cast_fp16")];
            tensor<int32, [4]> var_2632 = const()[name = string("op_2632"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            string input_683_pad_type_0 = const()[name = string("input_683_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_683_strides_0 = const()[name = string("input_683_strides_0"), val = tensor<int32, [2]>([2, 4])];
            int32 input_683_groups_0 = const()[name = string("input_683_groups_0"), val = int32(256)];
            tensor<int32, [4]> input_683_pad_0 = const()[name = string("input_683_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_683_dilations_0 = const()[name = string("input_683_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 2, 4]> model_backbone_stage5_2_weight_to_fp16 = const()[name = string("model_backbone_stage5_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 2, 4]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16019584)))];
            tensor<fp16, [256]> model_backbone_stage5_2_bias_to_fp16 = const()[name = string("model_backbone_stage5_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16023744)))];
            tensor<fp16, [1, 256, 18, 596]> input_681_cast_fp16 = transpose(perm = var_2632, x = var_2631_cast_fp16)[name = string("transpose_8")];
            tensor<fp16, [1, 256, 9, 149]> input_683_cast_fp16 = conv(bias = model_backbone_stage5_2_bias_to_fp16, dilations = input_683_dilations_0, groups = input_683_groups_0, pad = input_683_pad_0, pad_type = input_683_pad_type_0, strides = input_683_strides_0, weight = model_backbone_stage5_2_weight_to_fp16, x = input_681_cast_fp16)[name = string("input_683_cast_fp16")];
            string input_685_pad_type_0 = const()[name = string("input_685_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_685_pad_0 = const()[name = string("input_685_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_685_groups_0 = const()[name = string("input_685_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_685_strides_0 = const()[name = string("input_685_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_685_dilations_0 = const()[name = string("input_685_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage5_3_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16024320)))];
            tensor<fp16, [1, 256, 9, 149]> input_685_cast_fp16 = conv(dilations = input_685_dilations_0, groups = input_685_groups_0, pad = input_685_pad_0, pad_type = input_685_pad_type_0, strides = input_685_strides_0, weight = model_backbone_stage5_3_conv_block_conv1_weight_to_fp16, x = input_683_cast_fp16)[name = string("input_685_cast_fp16")];
            string input_687_pad_type_0 = const()[name = string("input_687_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_687_strides_0 = const()[name = string("input_687_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_687_pad_0 = const()[name = string("input_687_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_687_dilations_0 = const()[name = string("input_687_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_687_groups_0 = const()[name = string("input_687_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage5_3_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16028992)))];
            tensor<fp16, [256]> model_backbone_stage5_3_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16160128)))];
            tensor<fp16, [1, 256, 9, 149]> input_687_cast_fp16 = conv(bias = model_backbone_stage5_3_conv_block_conv1pw_bias_to_fp16, dilations = input_687_dilations_0, groups = input_687_groups_0, pad = input_687_pad_0, pad_type = input_687_pad_type_0, strides = input_687_strides_0, weight = model_backbone_stage5_3_conv_block_conv1pw_weight_to_fp16, x = input_685_cast_fp16)[name = string("input_687_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_689_cast_fp16 = relu(x = input_687_cast_fp16)[name = string("input_689_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage5_3_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16160704)))];
            tensor<fp16, [256]> model_backbone_stage5_3_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16161280)))];
            tensor<fp16, [256]> model_backbone_stage5_3_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16161856)))];
            tensor<fp16, [256]> model_backbone_stage5_3_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16162432)))];
            tensor<fp16, [1, 256, 9, 149]> input_691_cast_fp16 = batch_norm(beta = model_backbone_stage5_3_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage5_3_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage5_3_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage5_3_conv_block_bn1_running_var_to_fp16, x = input_689_cast_fp16)[name = string("input_691_cast_fp16")];
            string input_693_pad_type_0 = const()[name = string("input_693_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_693_pad_0 = const()[name = string("input_693_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_693_groups_0 = const()[name = string("input_693_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_693_strides_0 = const()[name = string("input_693_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_693_dilations_0 = const()[name = string("input_693_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage5_3_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage5_3_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16163008)))];
            tensor<fp16, [1, 256, 9, 149]> input_693_cast_fp16 = conv(dilations = input_693_dilations_0, groups = input_693_groups_0, pad = input_693_pad_0, pad_type = input_693_pad_type_0, strides = input_693_strides_0, weight = model_backbone_stage5_3_conv_block_conv2_weight_to_fp16, x = input_691_cast_fp16)[name = string("input_693_cast_fp16")];
            string input_695_pad_type_0 = const()[name = string("input_695_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_695_strides_0 = const()[name = string("input_695_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_695_pad_0 = const()[name = string("input_695_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_695_dilations_0 = const()[name = string("input_695_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_695_groups_0 = const()[name = string("input_695_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_111_to_fp16 = const()[name = string("const_111_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16167680)))];
            tensor<fp16, [256]> const_112_to_fp16 = const()[name = string("const_112_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16298816)))];
            tensor<fp16, [1, 256, 9, 149]> out_43_cast_fp16 = conv(bias = const_112_to_fp16, dilations = input_695_dilations_0, groups = input_695_groups_0, pad = input_695_pad_0, pad_type = input_695_pad_type_0, strides = input_695_strides_0, weight = const_111_to_fp16, x = input_693_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_697_cast_fp16 = add(x = out_43_cast_fp16, y = input_683_cast_fp16)[name = string("input_697_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_699_cast_fp16 = relu(x = input_697_cast_fp16)[name = string("input_699_cast_fp16")];
            string input_701_pad_type_0 = const()[name = string("input_701_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_701_pad_0 = const()[name = string("input_701_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_701_groups_0 = const()[name = string("input_701_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_701_strides_0 = const()[name = string("input_701_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_701_dilations_0 = const()[name = string("input_701_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage5_4_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16299392)))];
            tensor<fp16, [1, 256, 9, 149]> input_701_cast_fp16 = conv(dilations = input_701_dilations_0, groups = input_701_groups_0, pad = input_701_pad_0, pad_type = input_701_pad_type_0, strides = input_701_strides_0, weight = model_backbone_stage5_4_conv_block_conv1_weight_to_fp16, x = input_699_cast_fp16)[name = string("input_701_cast_fp16")];
            string input_703_pad_type_0 = const()[name = string("input_703_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_703_strides_0 = const()[name = string("input_703_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_703_pad_0 = const()[name = string("input_703_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_703_dilations_0 = const()[name = string("input_703_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_703_groups_0 = const()[name = string("input_703_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage5_4_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16304064)))];
            tensor<fp16, [256]> model_backbone_stage5_4_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16435200)))];
            tensor<fp16, [1, 256, 9, 149]> input_703_cast_fp16 = conv(bias = model_backbone_stage5_4_conv_block_conv1pw_bias_to_fp16, dilations = input_703_dilations_0, groups = input_703_groups_0, pad = input_703_pad_0, pad_type = input_703_pad_type_0, strides = input_703_strides_0, weight = model_backbone_stage5_4_conv_block_conv1pw_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_705_cast_fp16 = relu(x = input_703_cast_fp16)[name = string("input_705_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage5_4_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16435776)))];
            tensor<fp16, [256]> model_backbone_stage5_4_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16436352)))];
            tensor<fp16, [256]> model_backbone_stage5_4_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16436928)))];
            tensor<fp16, [256]> model_backbone_stage5_4_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16437504)))];
            tensor<fp16, [1, 256, 9, 149]> input_707_cast_fp16 = batch_norm(beta = model_backbone_stage5_4_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage5_4_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage5_4_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage5_4_conv_block_bn1_running_var_to_fp16, x = input_705_cast_fp16)[name = string("input_707_cast_fp16")];
            string input_709_pad_type_0 = const()[name = string("input_709_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_709_pad_0 = const()[name = string("input_709_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_709_groups_0 = const()[name = string("input_709_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_709_strides_0 = const()[name = string("input_709_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_709_dilations_0 = const()[name = string("input_709_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage5_4_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage5_4_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16438080)))];
            tensor<fp16, [1, 256, 9, 149]> input_709_cast_fp16 = conv(dilations = input_709_dilations_0, groups = input_709_groups_0, pad = input_709_pad_0, pad_type = input_709_pad_type_0, strides = input_709_strides_0, weight = model_backbone_stage5_4_conv_block_conv2_weight_to_fp16, x = input_707_cast_fp16)[name = string("input_709_cast_fp16")];
            string input_711_pad_type_0 = const()[name = string("input_711_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_711_strides_0 = const()[name = string("input_711_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_711_pad_0 = const()[name = string("input_711_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_711_dilations_0 = const()[name = string("input_711_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_711_groups_0 = const()[name = string("input_711_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_113_to_fp16 = const()[name = string("const_113_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16442752)))];
            tensor<fp16, [256]> const_114_to_fp16 = const()[name = string("const_114_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16573888)))];
            tensor<fp16, [1, 256, 9, 149]> out_45_cast_fp16 = conv(bias = const_114_to_fp16, dilations = input_711_dilations_0, groups = input_711_groups_0, pad = input_711_pad_0, pad_type = input_711_pad_type_0, strides = input_711_strides_0, weight = const_113_to_fp16, x = input_709_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_713_cast_fp16 = add(x = out_45_cast_fp16, y = input_699_cast_fp16)[name = string("input_713_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_715_cast_fp16 = relu(x = input_713_cast_fp16)[name = string("input_715_cast_fp16")];
            string input_717_pad_type_0 = const()[name = string("input_717_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_717_pad_0 = const()[name = string("input_717_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_717_groups_0 = const()[name = string("input_717_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_717_strides_0 = const()[name = string("input_717_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_717_dilations_0 = const()[name = string("input_717_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage5_5_conv_block_conv1_weight_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_conv1_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16574464)))];
            tensor<fp16, [1, 256, 9, 149]> input_717_cast_fp16 = conv(dilations = input_717_dilations_0, groups = input_717_groups_0, pad = input_717_pad_0, pad_type = input_717_pad_type_0, strides = input_717_strides_0, weight = model_backbone_stage5_5_conv_block_conv1_weight_to_fp16, x = input_715_cast_fp16)[name = string("input_717_cast_fp16")];
            string input_719_pad_type_0 = const()[name = string("input_719_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_719_strides_0 = const()[name = string("input_719_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_719_pad_0 = const()[name = string("input_719_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_719_dilations_0 = const()[name = string("input_719_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_719_groups_0 = const()[name = string("input_719_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> model_backbone_stage5_5_conv_block_conv1pw_weight_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_conv1pw_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16579136)))];
            tensor<fp16, [256]> model_backbone_stage5_5_conv_block_conv1pw_bias_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_conv1pw_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16710272)))];
            tensor<fp16, [1, 256, 9, 149]> input_719_cast_fp16 = conv(bias = model_backbone_stage5_5_conv_block_conv1pw_bias_to_fp16, dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = model_backbone_stage5_5_conv_block_conv1pw_weight_to_fp16, x = input_717_cast_fp16)[name = string("input_719_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_721_cast_fp16 = relu(x = input_719_cast_fp16)[name = string("input_721_cast_fp16")];
            tensor<fp16, [256]> model_backbone_stage5_5_conv_block_bn1_running_mean_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_bn1_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16710848)))];
            tensor<fp16, [256]> model_backbone_stage5_5_conv_block_bn1_running_var_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_bn1_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16711424)))];
            tensor<fp16, [256]> model_backbone_stage5_5_conv_block_bn1_weight_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_bn1_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16712000)))];
            tensor<fp16, [256]> model_backbone_stage5_5_conv_block_bn1_bias_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_bn1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16712576)))];
            tensor<fp16, [1, 256, 9, 149]> input_723_cast_fp16 = batch_norm(beta = model_backbone_stage5_5_conv_block_bn1_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage5_5_conv_block_bn1_weight_to_fp16, mean = model_backbone_stage5_5_conv_block_bn1_running_mean_to_fp16, variance = model_backbone_stage5_5_conv_block_bn1_running_var_to_fp16, x = input_721_cast_fp16)[name = string("input_723_cast_fp16")];
            string input_725_pad_type_0 = const()[name = string("input_725_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_725_pad_0 = const()[name = string("input_725_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            int32 input_725_groups_0 = const()[name = string("input_725_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_725_strides_0 = const()[name = string("input_725_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_725_dilations_0 = const()[name = string("input_725_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> model_backbone_stage5_5_conv_block_conv2_weight_to_fp16 = const()[name = string("model_backbone_stage5_5_conv_block_conv2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16713152)))];
            tensor<fp16, [1, 256, 9, 149]> input_725_cast_fp16 = conv(dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = model_backbone_stage5_5_conv_block_conv2_weight_to_fp16, x = input_723_cast_fp16)[name = string("input_725_cast_fp16")];
            string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> const_115_to_fp16 = const()[name = string("const_115_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16717824)))];
            tensor<fp16, [256]> const_116_to_fp16 = const()[name = string("const_116_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16848960)))];
            tensor<fp16, [1, 256, 9, 149]> out_47_cast_fp16 = conv(bias = const_116_to_fp16, dilations = input_727_dilations_0, groups = input_727_groups_0, pad = input_727_pad_0, pad_type = input_727_pad_type_0, strides = input_727_strides_0, weight = const_115_to_fp16, x = input_725_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_729_cast_fp16 = add(x = out_47_cast_fp16, y = input_715_cast_fp16)[name = string("input_729_cast_fp16")];
            tensor<fp16, [1, 256, 9, 149]> input_731_cast_fp16 = relu(x = input_729_cast_fp16)[name = string("input_731_cast_fp16")];
            tensor<int32, [2]> var_2783 = const()[name = string("op_2783"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2784 = const()[name = string("op_2784"), val = tensor<int32, [2]>([1, 1])];
            string input_733_pad_type_0 = const()[name = string("input_733_pad_type_0"), val = string("same")];
            tensor<int32, [4]> input_733_pad_0 = const()[name = string("input_733_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [512, 256, 1, 1]> const_117_to_fp16 = const()[name = string("const_117_to_fp16"), val = tensor<fp16, [512, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16849536)))];
            tensor<fp16, [512]> const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17111744)))];
            tensor<fp16, [1, 512, 9, 149]> x_115_cast_fp16 = conv(bias = const_118_to_fp16, dilations = var_2784, groups = var_40, pad = input_733_pad_0, pad_type = input_733_pad_type_0, strides = var_2783, weight = const_117_to_fp16, x = input_731_cast_fp16)[name = string("x_115_cast_fp16")];
            tensor<int32, [4]> var_2797 = const()[name = string("op_2797"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2801 = const()[name = string("op_2801"), val = tensor<int32, [3]>([1, 4608, 149])];
            tensor<fp16, [1, 9, 512, 149]> var_2798_cast_fp16 = transpose(perm = var_2797, x = x_115_cast_fp16)[name = string("transpose_7")];
            tensor<fp16, [1, 4608, 149]> input_735_cast_fp16 = reshape(shape = var_2801, x = var_2798_cast_fp16)[name = string("input_735_cast_fp16")];
            string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)];
            tensor<fp16, [192, 4608, 1]> model_backbone_stage5_8_red_dim_conv_0_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_red_dim_conv_0_weight_to_fp16"), val = tensor<fp16, [192, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17112832)))];
            tensor<fp16, [192]> model_backbone_stage5_8_red_dim_conv_0_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_red_dim_conv_0_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18882368)))];
            tensor<fp16, [1, 192, 149]> x_117_cast_fp16 = conv(bias = model_backbone_stage5_8_red_dim_conv_0_bias_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = model_backbone_stage5_8_red_dim_conv_0_weight_to_fp16, x = input_735_cast_fp16)[name = string("x_117_cast_fp16")];
            tensor<int32, [1]> u_axes_0 = const()[name = string("u_axes_0"), val = tensor<int32, [1]>([1])];
            bool u_keep_dims_0 = const()[name = string("u_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 149]> u_cast_fp16 = reduce_mean(axes = u_axes_0, keep_dims = u_keep_dims_0, x = x_117_cast_fp16)[name = string("u_cast_fp16")];
            tensor<fp16, [1, 192, 149]> var_2819_cast_fp16 = sub(x = x_117_cast_fp16, y = u_cast_fp16)[name = string("op_2819_cast_fp16")];
            fp16 var_41_promoted_6_to_fp16 = const()[name = string("op_41_promoted_6_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 192, 149]> var_2820_cast_fp16 = pow(x = var_2819_cast_fp16, y = var_41_promoted_6_to_fp16)[name = string("op_2820_cast_fp16")];
            tensor<int32, [1]> s_axes_0 = const()[name = string("s_axes_0"), val = tensor<int32, [1]>([1])];
            bool s_keep_dims_0 = const()[name = string("s_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 149]> s_cast_fp16 = reduce_mean(axes = s_axes_0, keep_dims = s_keep_dims_0, x = var_2820_cast_fp16)[name = string("s_cast_fp16")];
            fp16 var_2824_to_fp16 = const()[name = string("op_2824_to_fp16"), val = fp16(0x1.1p-20)];
            tensor<fp16, [1, 1, 149]> var_2825_cast_fp16 = add(x = s_cast_fp16, y = var_2824_to_fp16)[name = string("op_2825_cast_fp16")];
            tensor<fp16, [1, 1, 149]> var_2826_cast_fp16 = sqrt(x = var_2825_cast_fp16)[name = string("op_2826_cast_fp16")];
            tensor<fp16, [1, 192, 149]> x_119_cast_fp16 = real_div(x = var_2819_cast_fp16, y = var_2826_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<fp16, [192, 1]> w_41_to_fp16 = const()[name = string("w_41_to_fp16"), val = tensor<fp16, [192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18882816)))];
            tensor<fp16, [1, 192, 149]> var_2830_cast_fp16 = mul(x = w_41_to_fp16, y = x_119_cast_fp16)[name = string("op_2830_cast_fp16")];
            tensor<fp16, [192, 1]> b_to_fp16 = const()[name = string("b_to_fp16"), val = tensor<fp16, [192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18883264)))];
            tensor<fp16, [1, 192, 149]> input_737_cast_fp16 = add(x = var_2830_cast_fp16, y = b_to_fp16)[name = string("input_737_cast_fp16")];
            tensor<int32, [1]> var_2843 = const()[name = string("op_2843"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2844 = const()[name = string("op_2844"), val = tensor<int32, [1]>([1])];
            string var_2845_pad_type_0 = const()[name = string("op_2845_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2845_pad_0 = const()[name = string("op_2845_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [192, 1, 7]> model_backbone_stage5_8_tcm_0_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [192, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18883712)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_0_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18886464)))];
            tensor<fp16, [1, 192, 149]> var_2845_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_0_dwconvs_0_bias_to_fp16, dilations = var_2844, groups = var_18, pad = var_2845_pad_0, pad_type = var_2845_pad_type_0, strides = var_2843, weight = model_backbone_stage5_8_tcm_0_dwconvs_0_weight_to_fp16, x = input_737_cast_fp16)[name = string("op_2845_cast_fp16")];
            bool input_739_interleave_0 = const()[name = string("input_739_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 192, 149]> input_739_cast_fp16 = concat(axis = var_40, interleave = input_739_interleave_0, values = var_2845_cast_fp16)[name = string("input_739_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_0_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_norm_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18886912)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_0_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_norm_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18887360)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_0_norm_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_norm_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18887808)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_0_norm_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_norm_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18888256)))];
            tensor<fp16, [1, 192, 149]> input_741_cast_fp16 = batch_norm(beta = model_backbone_stage5_8_tcm_0_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage5_8_tcm_0_norm_weight_to_fp16, mean = model_backbone_stage5_8_tcm_0_norm_running_mean_to_fp16, variance = model_backbone_stage5_8_tcm_0_norm_running_var_to_fp16, x = input_739_cast_fp16)[name = string("input_741_cast_fp16")];
            string input_743_mode_0 = const()[name = string("input_743_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 192, 149]> input_743_cast_fp16 = gelu(mode = input_743_mode_0, x = input_741_cast_fp16)[name = string("input_743_cast_fp16")];
            string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1]> model_backbone_stage5_8_tcm_0_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [192, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18888704)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_0_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18962496)))];
            tensor<fp16, [1, 192, 149]> x_121_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_0_pwconv1_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = model_backbone_stage5_8_tcm_0_pwconv1_weight_to_fp16, x = input_743_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<fp16, [1, 192, 149]> input_745_cast_fp16 = add(x = input_737_cast_fp16, y = x_121_cast_fp16)[name = string("input_745_cast_fp16")];
            tensor<int32, [1]> var_2868 = const()[name = string("op_2868"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2869 = const()[name = string("op_2869"), val = tensor<int32, [1]>([1])];
            string var_2870_pad_type_0 = const()[name = string("op_2870_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2870_pad_0 = const()[name = string("op_2870_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [192, 1, 19]> model_backbone_stage5_8_tcm_1_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [192, 1, 19]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18962944)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_1_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18970304)))];
            tensor<fp16, [1, 192, 149]> var_2870_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_1_dwconvs_0_bias_to_fp16, dilations = var_2869, groups = var_18, pad = var_2870_pad_0, pad_type = var_2870_pad_type_0, strides = var_2868, weight = model_backbone_stage5_8_tcm_1_dwconvs_0_weight_to_fp16, x = input_745_cast_fp16)[name = string("op_2870_cast_fp16")];
            bool input_747_interleave_0 = const()[name = string("input_747_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 192, 149]> input_747_cast_fp16 = concat(axis = var_40, interleave = input_747_interleave_0, values = var_2870_cast_fp16)[name = string("input_747_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_1_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_norm_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18970752)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_1_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_norm_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18971200)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_1_norm_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_norm_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18971648)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_1_norm_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_norm_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18972096)))];
            tensor<fp16, [1, 192, 149]> input_749_cast_fp16 = batch_norm(beta = model_backbone_stage5_8_tcm_1_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage5_8_tcm_1_norm_weight_to_fp16, mean = model_backbone_stage5_8_tcm_1_norm_running_mean_to_fp16, variance = model_backbone_stage5_8_tcm_1_norm_running_var_to_fp16, x = input_747_cast_fp16)[name = string("input_749_cast_fp16")];
            string input_751_mode_0 = const()[name = string("input_751_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 192, 149]> input_751_cast_fp16 = gelu(mode = input_751_mode_0, x = input_749_cast_fp16)[name = string("input_751_cast_fp16")];
            string x_123_pad_type_0 = const()[name = string("x_123_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_123_strides_0 = const()[name = string("x_123_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_123_pad_0 = const()[name = string("x_123_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_123_dilations_0 = const()[name = string("x_123_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_123_groups_0 = const()[name = string("x_123_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1]> model_backbone_stage5_8_tcm_1_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [192, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18972544)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_1_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19046336)))];
            tensor<fp16, [1, 192, 149]> x_123_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_1_pwconv1_bias_to_fp16, dilations = x_123_dilations_0, groups = x_123_groups_0, pad = x_123_pad_0, pad_type = x_123_pad_type_0, strides = x_123_strides_0, weight = model_backbone_stage5_8_tcm_1_pwconv1_weight_to_fp16, x = input_751_cast_fp16)[name = string("x_123_cast_fp16")];
            tensor<fp16, [1, 192, 149]> input_753_cast_fp16 = add(x = input_745_cast_fp16, y = x_123_cast_fp16)[name = string("input_753_cast_fp16")];
            tensor<int32, [1]> var_2893 = const()[name = string("op_2893"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2894 = const()[name = string("op_2894"), val = tensor<int32, [1]>([1])];
            string var_2895_pad_type_0 = const()[name = string("op_2895_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2895_pad_0 = const()[name = string("op_2895_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [192, 1, 31]> model_backbone_stage5_8_tcm_2_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [192, 1, 31]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19046784)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_2_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19058752)))];
            tensor<fp16, [1, 192, 149]> var_2895_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_2_dwconvs_0_bias_to_fp16, dilations = var_2894, groups = var_18, pad = var_2895_pad_0, pad_type = var_2895_pad_type_0, strides = var_2893, weight = model_backbone_stage5_8_tcm_2_dwconvs_0_weight_to_fp16, x = input_753_cast_fp16)[name = string("op_2895_cast_fp16")];
            bool input_755_interleave_0 = const()[name = string("input_755_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 192, 149]> input_755_cast_fp16 = concat(axis = var_40, interleave = input_755_interleave_0, values = var_2895_cast_fp16)[name = string("input_755_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_2_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_norm_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19059200)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_2_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_norm_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19059648)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_2_norm_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_norm_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19060096)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_2_norm_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_norm_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19060544)))];
            tensor<fp16, [1, 192, 149]> input_757_cast_fp16 = batch_norm(beta = model_backbone_stage5_8_tcm_2_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage5_8_tcm_2_norm_weight_to_fp16, mean = model_backbone_stage5_8_tcm_2_norm_running_mean_to_fp16, variance = model_backbone_stage5_8_tcm_2_norm_running_var_to_fp16, x = input_755_cast_fp16)[name = string("input_757_cast_fp16")];
            string input_759_mode_0 = const()[name = string("input_759_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 192, 149]> input_759_cast_fp16 = gelu(mode = input_759_mode_0, x = input_757_cast_fp16)[name = string("input_759_cast_fp16")];
            string x_125_pad_type_0 = const()[name = string("x_125_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_125_strides_0 = const()[name = string("x_125_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_125_pad_0 = const()[name = string("x_125_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_125_dilations_0 = const()[name = string("x_125_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_125_groups_0 = const()[name = string("x_125_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1]> model_backbone_stage5_8_tcm_2_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [192, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19060992)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_2_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19134784)))];
            tensor<fp16, [1, 192, 149]> x_125_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_2_pwconv1_bias_to_fp16, dilations = x_125_dilations_0, groups = x_125_groups_0, pad = x_125_pad_0, pad_type = x_125_pad_type_0, strides = x_125_strides_0, weight = model_backbone_stage5_8_tcm_2_pwconv1_weight_to_fp16, x = input_759_cast_fp16)[name = string("x_125_cast_fp16")];
            tensor<fp16, [1, 192, 149]> input_761_cast_fp16 = add(x = input_753_cast_fp16, y = x_125_cast_fp16)[name = string("input_761_cast_fp16")];
            tensor<int32, [1]> var_2918 = const()[name = string("op_2918"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_2919 = const()[name = string("op_2919"), val = tensor<int32, [1]>([1])];
            string var_2920_pad_type_0 = const()[name = string("op_2920_pad_type_0"), val = string("same")];
            tensor<int32, [2]> var_2920_pad_0 = const()[name = string("op_2920_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [192, 1, 59]> model_backbone_stage5_8_tcm_3_dwconvs_0_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_dwconvs_0_weight_to_fp16"), val = tensor<fp16, [192, 1, 59]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19135232)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_3_dwconvs_0_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_dwconvs_0_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19157952)))];
            tensor<fp16, [1, 192, 149]> var_2920_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_3_dwconvs_0_bias_to_fp16, dilations = var_2919, groups = var_18, pad = var_2920_pad_0, pad_type = var_2920_pad_type_0, strides = var_2918, weight = model_backbone_stage5_8_tcm_3_dwconvs_0_weight_to_fp16, x = input_761_cast_fp16)[name = string("op_2920_cast_fp16")];
            bool input_763_interleave_0 = const()[name = string("input_763_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 192, 149]> input_763_cast_fp16 = concat(axis = var_40, interleave = input_763_interleave_0, values = var_2920_cast_fp16)[name = string("input_763_cast_fp16")];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_3_norm_running_mean_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_norm_running_mean_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19158400)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_3_norm_running_var_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_norm_running_var_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19158848)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_3_norm_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_norm_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19159296)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_3_norm_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_norm_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19159744)))];
            tensor<fp16, [1, 192, 149]> input_765_cast_fp16 = batch_norm(beta = model_backbone_stage5_8_tcm_3_norm_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_backbone_stage5_8_tcm_3_norm_weight_to_fp16, mean = model_backbone_stage5_8_tcm_3_norm_running_mean_to_fp16, variance = model_backbone_stage5_8_tcm_3_norm_running_var_to_fp16, x = input_763_cast_fp16)[name = string("input_765_cast_fp16")];
            string input_767_mode_0 = const()[name = string("input_767_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 192, 149]> input_767_cast_fp16 = gelu(mode = input_767_mode_0, x = input_765_cast_fp16)[name = string("input_767_cast_fp16")];
            string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)];
            tensor<fp16, [192, 192, 1]> model_backbone_stage5_8_tcm_3_pwconv1_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [192, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19160192)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_3_pwconv1_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19233984)))];
            tensor<fp16, [1, 192, 149]> x_127_cast_fp16 = conv(bias = model_backbone_stage5_8_tcm_3_pwconv1_bias_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = model_backbone_stage5_8_tcm_3_pwconv1_weight_to_fp16, x = input_767_cast_fp16)[name = string("x_127_cast_fp16")];
            tensor<fp16, [1, 192, 149]> hidden_states_41_cast_fp16 = add(x = input_761_cast_fp16, y = x_127_cast_fp16)[name = string("hidden_states_41_cast_fp16")];
            tensor<int32, [3]> var_2941 = const()[name = string("op_2941"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [192, 192]> model_backbone_stage5_8_tcm_4_attention_q_proj_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_q_proj_weight_to_fp16"), val = tensor<fp16, [192, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19234432)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_attention_q_proj_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_q_proj_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19308224)))];
            tensor<fp16, [1, 149, 192]> hidden_states_43_cast_fp16 = transpose(perm = var_2941, x = hidden_states_41_cast_fp16)[name = string("transpose_6")];
            tensor<fp16, [1, 149, 192]> linear_30_cast_fp16 = linear(bias = model_backbone_stage5_8_tcm_4_attention_q_proj_bias_to_fp16, weight = model_backbone_stage5_8_tcm_4_attention_q_proj_weight_to_fp16, x = hidden_states_43_cast_fp16)[name = string("linear_30_cast_fp16")];
            tensor<fp16, [192, 192]> model_backbone_stage5_8_tcm_4_attention_k_proj_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_k_proj_weight_to_fp16"), val = tensor<fp16, [192, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19308672)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_attention_k_proj_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_k_proj_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19382464)))];
            tensor<fp16, [1, 149, 192]> linear_31_cast_fp16 = linear(bias = model_backbone_stage5_8_tcm_4_attention_k_proj_bias_to_fp16, weight = model_backbone_stage5_8_tcm_4_attention_k_proj_weight_to_fp16, x = hidden_states_43_cast_fp16)[name = string("linear_31_cast_fp16")];
            tensor<int32, [4]> var_2956 = const()[name = string("op_2956"), val = tensor<int32, [4]>([1, -1, 4, 48])];
            tensor<fp16, [1, 149, 4, 48]> var_2957_cast_fp16 = reshape(shape = var_2956, x = linear_31_cast_fp16)[name = string("op_2957_cast_fp16")];
            tensor<int32, [4]> var_2958_perm_0 = const()[name = string("op_2958_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [192, 192]> model_backbone_stage5_8_tcm_4_attention_v_proj_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_v_proj_weight_to_fp16"), val = tensor<fp16, [192, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19382912)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_attention_v_proj_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_v_proj_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19456704)))];
            tensor<fp16, [1, 149, 192]> linear_32_cast_fp16 = linear(bias = model_backbone_stage5_8_tcm_4_attention_v_proj_bias_to_fp16, weight = model_backbone_stage5_8_tcm_4_attention_v_proj_weight_to_fp16, x = hidden_states_43_cast_fp16)[name = string("linear_32_cast_fp16")];
            tensor<int32, [4]> var_2963 = const()[name = string("op_2963"), val = tensor<int32, [4]>([1, -1, 4, 48])];
            tensor<fp16, [1, 149, 4, 48]> var_2964_cast_fp16 = reshape(shape = var_2963, x = linear_32_cast_fp16)[name = string("op_2964_cast_fp16")];
            tensor<int32, [4]> var_2965_perm_0 = const()[name = string("op_2965_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2972 = const()[name = string("op_2972"), val = tensor<int32, [4]>([1, 149, 4, 48])];
            tensor<fp16, [1, 149, 4, 48]> var_2973_cast_fp16 = reshape(shape = var_2972, x = linear_30_cast_fp16)[name = string("op_2973_cast_fp16")];
            tensor<int32, [4]> var_2974_perm_0 = const()[name = string("op_2974_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2976 = const()[name = string("op_2976"), val = tensor<int32, [3]>([4, -1, 48])];
            tensor<fp16, [1, 4, 149, 48]> var_2974_cast_fp16 = transpose(perm = var_2974_perm_0, x = var_2973_cast_fp16)[name = string("transpose_3")];
            tensor<fp16, [4, 149, 48]> query_states_cast_fp16 = reshape(shape = var_2976, x = var_2974_cast_fp16)[name = string("query_states_cast_fp16")];
            tensor<int32, [3]> var_2978 = const()[name = string("op_2978"), val = tensor<int32, [3]>([4, -1, 48])];
            tensor<fp16, [1, 4, 149, 48]> var_2958_cast_fp16 = transpose(perm = var_2958_perm_0, x = var_2957_cast_fp16)[name = string("transpose_5")];
            tensor<fp16, [4, 149, 48]> key_states_cast_fp16 = reshape(shape = var_2978, x = var_2958_cast_fp16)[name = string("key_states_cast_fp16")];
            tensor<int32, [3]> var_2980 = const()[name = string("op_2980"), val = tensor<int32, [3]>([4, -1, 48])];
            tensor<fp16, [1, 4, 149, 48]> var_2965_cast_fp16 = transpose(perm = var_2965_perm_0, x = var_2964_cast_fp16)[name = string("transpose_4")];
            tensor<fp16, [4, 149, 48]> value_states_cast_fp16 = reshape(shape = var_2980, x = var_2965_cast_fp16)[name = string("value_states_cast_fp16")];
            bool var_2983_transpose_x_1 = const()[name = string("op_2983_transpose_x_1"), val = bool(false)];
            bool var_2983_transpose_y_1 = const()[name = string("op_2983_transpose_y_1"), val = bool(true)];
            tensor<fp16, [4, 149, 149]> var_2983_cast_fp16 = matmul(transpose_x = var_2983_transpose_x_1, transpose_y = var_2983_transpose_y_1, x = query_states_cast_fp16, y = key_states_cast_fp16)[name = string("op_2983_cast_fp16")];
            fp16 var_2984_to_fp16 = const()[name = string("op_2984_to_fp16"), val = fp16(0x1.278p-3)];
            tensor<fp16, [4, 149, 149]> input_769_cast_fp16 = mul(x = var_2983_cast_fp16, y = var_2984_to_fp16)[name = string("input_769_cast_fp16")];
            tensor<fp16, [4, 149, 149]> input_771_cast_fp16 = softmax(axis = var_43, x = input_769_cast_fp16)[name = string("input_771_cast_fp16")];
            bool attn_output_31_transpose_x_0 = const()[name = string("attn_output_31_transpose_x_0"), val = bool(false)];
            bool attn_output_31_transpose_y_0 = const()[name = string("attn_output_31_transpose_y_0"), val = bool(false)];
            tensor<fp16, [4, 149, 48]> attn_output_31_cast_fp16 = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = input_771_cast_fp16, y = value_states_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [4]> var_2989 = const()[name = string("op_2989"), val = tensor<int32, [4]>([1, 4, 149, 48])];
            tensor<fp16, [1, 4, 149, 48]> attn_output_33_cast_fp16 = reshape(shape = var_2989, x = attn_output_31_cast_fp16)[name = string("attn_output_33_cast_fp16")];
            tensor<int32, [4]> attn_output_perm_0 = const()[name = string("attn_output_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2992 = const()[name = string("op_2992"), val = tensor<int32, [3]>([1, 149, 192])];
            tensor<fp16, [1, 149, 4, 48]> attn_output_cast_fp16 = transpose(perm = attn_output_perm_0, x = attn_output_33_cast_fp16)[name = string("transpose_2")];
            tensor<fp16, [1, 149, 192]> input_773_cast_fp16 = reshape(shape = var_2992, x = attn_output_cast_fp16)[name = string("input_773_cast_fp16")];
            tensor<fp16, [192, 192]> model_backbone_stage5_8_tcm_4_attention_out_proj_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_out_proj_weight_to_fp16"), val = tensor<fp16, [192, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19457152)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_attention_out_proj_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_attention_out_proj_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19530944)))];
            tensor<fp16, [1, 149, 192]> linear_33_cast_fp16 = linear(bias = model_backbone_stage5_8_tcm_4_attention_out_proj_bias_to_fp16, weight = model_backbone_stage5_8_tcm_4_attention_out_proj_weight_to_fp16, x = input_773_cast_fp16)[name = string("linear_33_cast_fp16")];
            tensor<fp16, [1, 149, 192]> input_775_cast_fp16 = add(x = hidden_states_43_cast_fp16, y = linear_33_cast_fp16)[name = string("input_775_cast_fp16")];
            tensor<int32, [1]> input_777_axes_0 = const()[name = string("input_777_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_layer_norm_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19531392)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_layer_norm_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19531840)))];
            tensor<fp16, [1, 149, 192]> input_777_cast_fp16 = layer_norm(axes = input_777_axes_0, beta = model_backbone_stage5_8_tcm_4_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage5_8_tcm_4_layer_norm_weight_to_fp16, x = input_775_cast_fp16)[name = string("input_777_cast_fp16")];
            tensor<fp16, [192, 192]> model_backbone_stage5_8_tcm_4_feed_forward_intermediate_dense_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_feed_forward_intermediate_dense_weight_to_fp16"), val = tensor<fp16, [192, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19532288)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_feed_forward_intermediate_dense_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_feed_forward_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19606080)))];
            tensor<fp16, [1, 149, 192]> linear_34_cast_fp16 = linear(bias = model_backbone_stage5_8_tcm_4_feed_forward_intermediate_dense_bias_to_fp16, weight = model_backbone_stage5_8_tcm_4_feed_forward_intermediate_dense_weight_to_fp16, x = input_777_cast_fp16)[name = string("linear_34_cast_fp16")];
            string input_781_mode_0 = const()[name = string("input_781_mode_0"), val = string("TANH_APPROXIMATION")];
            tensor<fp16, [1, 149, 192]> input_781_cast_fp16 = gelu(mode = input_781_mode_0, x = linear_34_cast_fp16)[name = string("input_781_cast_fp16")];
            tensor<fp16, [192, 192]> model_backbone_stage5_8_tcm_4_feed_forward_output_dense_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_feed_forward_output_dense_weight_to_fp16"), val = tensor<fp16, [192, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19606528)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_feed_forward_output_dense_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_feed_forward_output_dense_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19680320)))];
            tensor<fp16, [1, 149, 192]> linear_35_cast_fp16 = linear(bias = model_backbone_stage5_8_tcm_4_feed_forward_output_dense_bias_to_fp16, weight = model_backbone_stage5_8_tcm_4_feed_forward_output_dense_weight_to_fp16, x = input_781_cast_fp16)[name = string("linear_35_cast_fp16")];
            tensor<fp16, [1, 149, 192]> input_787_cast_fp16 = add(x = input_777_cast_fp16, y = linear_35_cast_fp16)[name = string("input_787_cast_fp16")];
            tensor<int32, [1]> hidden_states_axes_0 = const()[name = string("hidden_states_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_final_layer_norm_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19680768)))];
            tensor<fp16, [192]> model_backbone_stage5_8_tcm_4_final_layer_norm_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_tcm_4_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19681216)))];
            tensor<fp16, [1, 149, 192]> hidden_states_cast_fp16 = layer_norm(axes = hidden_states_axes_0, beta = model_backbone_stage5_8_tcm_4_final_layer_norm_bias_to_fp16, epsilon = var_16_to_fp16, gamma = model_backbone_stage5_8_tcm_4_final_layer_norm_weight_to_fp16, x = input_787_cast_fp16)[name = string("hidden_states_cast_fp16")];
            tensor<int32, [3]> var_3029 = const()[name = string("op_3029"), val = tensor<int32, [3]>([0, 2, 1])];
            string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)];
            tensor<fp16, [4608, 192, 1]> model_backbone_stage5_8_exp_dim_conv_weight_to_fp16 = const()[name = string("model_backbone_stage5_8_exp_dim_conv_weight_to_fp16"), val = tensor<fp16, [4608, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19681664)))];
            tensor<fp16, [4608]> model_backbone_stage5_8_exp_dim_conv_bias_to_fp16 = const()[name = string("model_backbone_stage5_8_exp_dim_conv_bias_to_fp16"), val = tensor<fp16, [4608]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21451200)))];
            tensor<fp16, [1, 192, 149]> input_789_cast_fp16 = transpose(perm = var_3029, x = hidden_states_cast_fp16)[name = string("transpose_1")];
            tensor<fp16, [1, 4608, 149]> x_129_cast_fp16 = conv(bias = model_backbone_stage5_8_exp_dim_conv_bias_to_fp16, dilations = x_129_dilations_0, groups = x_129_groups_0, pad = x_129_pad_0, pad_type = x_129_pad_type_0, strides = x_129_strides_0, weight = model_backbone_stage5_8_exp_dim_conv_weight_to_fp16, x = input_789_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<fp16, [1, 4608, 149]> input_791_cast_fp16 = add(x = input_735_cast_fp16, y = x_129_cast_fp16)[name = string("input_791_cast_fp16")];
            tensor<int32, [1]> expand_dims_5_axes_0 = const()[name = string("expand_dims_5_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 149, 1]> expand_dims_5_cast_fp16 = expand_dims(axes = expand_dims_5_axes_0, x = input_791_cast_fp16)[name = string("expand_dims_5_cast_fp16")];
            int32 upsample_nearest_neighbor_5_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_5_scale_factor_height_0"), val = int32(4)];
            int32 upsample_nearest_neighbor_5_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_5_scale_factor_width_0"), val = int32(1)];
            tensor<fp16, [1, 4608, 596, 1]> upsample_nearest_neighbor_5_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_5_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_5_scale_factor_width_0, x = expand_dims_5_cast_fp16)[name = string("upsample_nearest_neighbor_5_cast_fp16")];
            tensor<int32, [1]> input_793_axes_0 = const()[name = string("input_793_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp16, [1, 4608, 596]> input_793_cast_fp16 = squeeze(axes = input_793_axes_0, x = upsample_nearest_neighbor_5_cast_fp16)[name = string("input_793_cast_fp16")];
            tensor<int32, [4]> reshape_24_shape_0 = const()[name = string("reshape_24_shape_0"), val = tensor<int32, [4]>([1, 64, 72, 596])];
            tensor<fp16, [1, 64, 72, 596]> reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_793_cast_fp16)[name = string("reshape_24_cast_fp16")];
            tensor<int32, [2]> reduce_mean_20_axes_0 = const()[name = string("reduce_mean_20_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_20_keep_dims_0 = const()[name = string("reduce_mean_20_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = reshape_24_cast_fp16)[name = string("reduce_mean_20_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> sub_13_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_20_cast_fp16)[name = string("sub_13_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> square_7_cast_fp16 = square(x = sub_13_cast_fp16)[name = string("square_7_cast_fp16")];
            tensor<int32, [2]> reduce_mean_22_axes_0 = const()[name = string("reduce_mean_22_axes_0"), val = tensor<int32, [2]>([2, 3])];
            bool reduce_mean_22_keep_dims_0 = const()[name = string("reduce_mean_22_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1, 1]> reduce_mean_22_cast_fp16 = reduce_mean(axes = reduce_mean_22_axes_0, keep_dims = reduce_mean_22_keep_dims_0, x = square_7_cast_fp16)[name = string("reduce_mean_22_cast_fp16")];
            fp16 add_12_y_0_to_fp16 = const()[name = string("add_12_y_0_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 1]> add_12_cast_fp16 = add(x = reduce_mean_22_cast_fp16, y = add_12_y_0_to_fp16)[name = string("add_12_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> sqrt_7_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = string("sqrt_7_cast_fp16")];
            tensor<fp16, [1, 64, 72, 596]> real_div_6_cast_fp16 = real_div(x = sub_13_cast_fp16, y = sqrt_7_cast_fp16)[name = string("real_div_6_cast_fp16")];
            tensor<int32, [3]> reshape_25_shape_0 = const()[name = string("reshape_25_shape_0"), val = tensor<int32, [3]>([1, 4608, 596])];
            tensor<fp16, [1, 4608, 596]> reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = string("reshape_25_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_26_to_fp16 = const()[name = string("reshape_26_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21460480)))];
            tensor<fp16, [1, 4608, 596]> mul_6_cast_fp16 = mul(x = reshape_25_cast_fp16, y = reshape_26_to_fp16)[name = string("mul_6_cast_fp16")];
            tensor<fp16, [1, 4608, 1]> reshape_27_to_fp16 = const()[name = string("reshape_27_to_fp16"), val = tensor<fp16, [1, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21469760)))];
            tensor<fp16, [1, 4608, 596]> add_13_cast_fp16 = add(x = mul_6_cast_fp16, y = reshape_27_to_fp16)[name = string("add_13_cast_fp16")];
            tensor<int32, [1]> var_3052_axes_0 = const()[name = string("op_3052_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 4608, 596]> var_3052_cast_fp16 = expand_dims(axes = var_3052_axes_0, x = add_13_cast_fp16)[name = string("op_3052_cast_fp16")];
            bool xs_interleave_0 = const()[name = string("xs_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 7, 4608, 596]> xs_cast_fp16 = concat(axis = var_40, interleave = xs_interleave_0, values = (var_178_cast_fp16, var_612_cast_fp16, var_1094_cast_fp16, var_1622_cast_fp16, var_2139_cast_fp16, var_2622_cast_fp16, var_3052_cast_fp16))[name = string("xs_cast_fp16")];
            tensor<fp16, [1, 7, 4608, 1]> w_43_to_fp16 = const()[name = string("w_43_to_fp16"), val = tensor<fp16, [1, 7, 4608, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21479040)))];
            tensor<fp16, [1, 7, 4608, 596]> var_3055_cast_fp16 = mul(x = w_43_to_fp16, y = xs_cast_fp16)[name = string("op_3055_cast_fp16")];
            tensor<int32, [1]> x_131_axes_0 = const()[name = string("x_131_axes_0"), val = tensor<int32, [1]>([1])];
            bool x_131_keep_dims_0 = const()[name = string("x_131_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 4608, 596]> x_131_cast_fp16 = reduce_sum(axes = x_131_axes_0, keep_dims = x_131_keep_dims_0, x = var_3055_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> var_3060 = const()[name = string("op_3060"), val = tensor<int32, [4]>([1, 9, 512, 596])];
            tensor<fp16, [1, 9, 512, 596]> var_3061_cast_fp16 = reshape(shape = var_3060, x = x_131_cast_fp16)[name = string("op_3061_cast_fp16")];
            tensor<int32, [4]> var_3062 = const()[name = string("op_3062"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            string out_pad_type_0 = const()[name = string("out_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> out_strides_0 = const()[name = string("out_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> out_pad_0 = const()[name = string("out_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> out_dilations_0 = const()[name = string("out_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 out_groups_0 = const()[name = string("out_groups_0"), val = int32(1)];
            tensor<fp16, [224, 512, 1, 1]> model_backbone_head_weight_to_fp16 = const()[name = string("model_backbone_head_weight_to_fp16"), val = tensor<fp16, [224, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21543616)))];
            tensor<fp16, [224]> model_backbone_head_bias_to_fp16 = const()[name = string("model_backbone_head_bias_to_fp16"), val = tensor<fp16, [224]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21773056)))];
            tensor<fp16, [1, 512, 9, 596]> input_797_cast_fp16 = transpose(perm = var_3062, x = var_3061_cast_fp16)[name = string("transpose_0")];
            tensor<fp16, [1, 224, 9, 596]> out_cast_fp16 = conv(bias = model_backbone_head_bias_to_fp16, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = model_backbone_head_weight_to_fp16, x = input_797_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [3]> var_3079 = const()[name = string("op_3079"), val = tensor<int32, [3]>([1, 2016, 596])];
            tensor<fp16, [1, 2016, 596]> x_cast_fp16 = reshape(shape = var_3079, x = out_cast_fp16)[name = string("x_cast_fp16")];
            tensor<int32, [1]> var_3084_axes_0 = const()[name = string("op_3084_axes_0"), val = tensor<int32, [1]>([-1])];
            bool var_3084_keep_dims_0 = const()[name = string("op_3084_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 2016, 1]> var_3084_cast_fp16 = reduce_mean(axes = var_3084_axes_0, keep_dims = var_3084_keep_dims_0, x = x_cast_fp16)[name = string("op_3084_cast_fp16")];
            tensor<int32, [3]> context_mean_reps_0 = const()[name = string("context_mean_reps_0"), val = tensor<int32, [3]>([1, 1, 596])];
            tensor<fp16, [1, 2016, 596]> context_mean_cast_fp16 = tile(reps = context_mean_reps_0, x = var_3084_cast_fp16)[name = string("context_mean_cast_fp16")];
            tensor<fp16, [1, 2016, 596]> sub_15_cast_fp16 = sub(x = x_cast_fp16, y = var_3084_cast_fp16)[name = string("sub_15_cast_fp16")];
            tensor<fp16, [1, 2016, 596]> square_8_cast_fp16 = square(x = sub_15_cast_fp16)[name = string("square_8_cast_fp16")];
            tensor<int32, [1]> reduce_mean_24_axes_0 = const()[name = string("reduce_mean_24_axes_0"), val = tensor<int32, [1]>([-1])];
            bool reduce_mean_24_keep_dims_0 = const()[name = string("reduce_mean_24_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 2016, 1]> reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = square_8_cast_fp16)[name = string("reduce_mean_24_cast_fp16")];
            fp16 real_div_7_to_fp16 = const()[name = string("real_div_7_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 2016, 1]> mul_7_cast_fp16 = mul(x = reduce_mean_24_cast_fp16, y = real_div_7_to_fp16)[name = string("mul_7_cast_fp16")];
            fp16 var_3088_to_fp16 = const()[name = string("op_3088_to_fp16"), val = fp16(0x1p-23)];
            tensor<fp16, [1, 2016, 1]> var_3089_cast_fp16 = add(x = mul_7_cast_fp16, y = var_3088_to_fp16)[name = string("op_3089_cast_fp16")];
            tensor<fp16, [1, 2016, 1]> var_3090_cast_fp16 = sqrt(x = var_3089_cast_fp16)[name = string("op_3090_cast_fp16")];
            tensor<int32, [3]> context_std_reps_0 = const()[name = string("context_std_reps_0"), val = tensor<int32, [3]>([1, 1, 596])];
            tensor<fp16, [1, 2016, 596]> context_std_cast_fp16 = tile(reps = context_std_reps_0, x = var_3090_cast_fp16)[name = string("context_std_cast_fp16")];
            bool input_799_interleave_0 = const()[name = string("input_799_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 6048, 596]> input_799_cast_fp16 = concat(axis = var_40, interleave = input_799_interleave_0, values = (x_cast_fp16, context_mean_cast_fp16, context_std_cast_fp16))[name = string("input_799_cast_fp16")];
            string var_3100_pad_type_0 = const()[name = string("op_3100_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_3100_strides_0 = const()[name = string("op_3100_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_3100_pad_0 = const()[name = string("op_3100_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_3100_dilations_0 = const()[name = string("op_3100_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_3100_groups_0 = const()[name = string("op_3100_groups_0"), val = int32(1)];
            tensor<fp16, [128, 6048, 1]> model_pool_linear1_weight_to_fp16 = const()[name = string("model_pool_linear1_weight_to_fp16"), val = tensor<fp16, [128, 6048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21773568)))];
            tensor<fp16, [128]> model_pool_linear1_bias_to_fp16 = const()[name = string("model_pool_linear1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23321920)))];
            tensor<fp16, [1, 128, 596]> var_3100_cast_fp16 = conv(bias = model_pool_linear1_bias_to_fp16, dilations = var_3100_dilations_0, groups = var_3100_groups_0, pad = var_3100_pad_0, pad_type = var_3100_pad_type_0, strides = var_3100_strides_0, weight = model_pool_linear1_weight_to_fp16, x = input_799_cast_fp16)[name = string("op_3100_cast_fp16")];
            tensor<fp16, [1, 128, 596]> input_801_cast_fp16 = tanh(x = var_3100_cast_fp16)[name = string("input_801_cast_fp16")];
            string var_3108_pad_type_0 = const()[name = string("op_3108_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_3108_strides_0 = const()[name = string("op_3108_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_3108_pad_0 = const()[name = string("op_3108_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_3108_dilations_0 = const()[name = string("op_3108_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_3108_groups_0 = const()[name = string("op_3108_groups_0"), val = int32(1)];
            tensor<fp16, [2016, 128, 1]> model_pool_linear2_weight_to_fp16 = const()[name = string("model_pool_linear2_weight_to_fp16"), val = tensor<fp16, [2016, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23322240)))];
            tensor<fp16, [2016]> model_pool_linear2_bias_to_fp16 = const()[name = string("model_pool_linear2_bias_to_fp16"), val = tensor<fp16, [2016]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23838400)))];
            tensor<fp16, [1, 2016, 596]> var_3108_cast_fp16 = conv(bias = model_pool_linear2_bias_to_fp16, dilations = var_3108_dilations_0, groups = var_3108_groups_0, pad = var_3108_pad_0, pad_type = var_3108_pad_type_0, strides = var_3108_strides_0, weight = model_pool_linear2_weight_to_fp16, x = input_801_cast_fp16)[name = string("op_3108_cast_fp16")];
            tensor<fp16, [1, 2016, 596]> alpha_cast_fp16 = softmax(axis = var_41, x = var_3108_cast_fp16)[name = string("alpha_cast_fp16")];
            tensor<fp16, [1, 2016, 596]> var_3110_cast_fp16 = mul(x = alpha_cast_fp16, y = x_cast_fp16)[name = string("op_3110_cast_fp16")];
            tensor<int32, [1]> mean_axes_0 = const()[name = string("mean_axes_0"), val = tensor<int32, [1]>([2])];
            bool mean_keep_dims_0 = const()[name = string("mean_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 2016]> mean_cast_fp16 = reduce_sum(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = var_3110_cast_fp16)[name = string("mean_cast_fp16")];
            fp16 var_41_promoted_7_to_fp16 = const()[name = string("op_41_promoted_7_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 2016, 596]> var_3113_cast_fp16 = pow(x = x_cast_fp16, y = var_41_promoted_7_to_fp16)[name = string("op_3113_cast_fp16")];
            tensor<fp16, [1, 2016, 596]> var_3114_cast_fp16 = mul(x = alpha_cast_fp16, y = var_3113_cast_fp16)[name = string("op_3114_cast_fp16")];
            tensor<int32, [1]> var_3116_axes_0 = const()[name = string("op_3116_axes_0"), val = tensor<int32, [1]>([2])];
            bool var_3116_keep_dims_0 = const()[name = string("op_3116_keep_dims_0"), val = bool(false)];
            tensor<fp16, [1, 2016]> var_3116_cast_fp16 = reduce_sum(axes = var_3116_axes_0, keep_dims = var_3116_keep_dims_0, x = var_3114_cast_fp16)[name = string("op_3116_cast_fp16")];
            fp16 var_41_promoted_8_to_fp16 = const()[name = string("op_41_promoted_8_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1, 2016]> var_3117_cast_fp16 = pow(x = mean_cast_fp16, y = var_41_promoted_8_to_fp16)[name = string("op_3117_cast_fp16")];
            tensor<fp16, [1, 2016]> var_cast_fp16 = sub(x = var_3116_cast_fp16, y = var_3117_cast_fp16)[name = string("var_cast_fp16")];
            fp16 var_3_to_fp16 = const()[name = string("op_3_to_fp16"), val = fp16(0x1p-23)];
            fp16 const_60_to_fp16 = const()[name = string("const_60_to_fp16"), val = fp16(inf)];
            tensor<fp16, [1, 2016]> clip_2_cast_fp16 = clip(alpha = var_3_to_fp16, beta = const_60_to_fp16, x = var_cast_fp16)[name = string("clip_2_cast_fp16")];
            tensor<fp16, [1, 2016]> std_cast_fp16 = sqrt(x = clip_2_cast_fp16)[name = string("std_cast_fp16")];
            bool input_803_interleave_0 = const()[name = string("input_803_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4032]> input_803_cast_fp16 = concat(axis = var_40, interleave = input_803_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = string("input_803_cast_fp16")];
            tensor<int32, [1]> input_805_rank2_expansion_axes_0 = const()[name = string("input_805_rank2_expansion_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 4032, 1]> input_805_rank2_expansion_cast_fp16 = expand_dims(axes = input_805_rank2_expansion_axes_0, x = input_803_cast_fp16)[name = string("input_805_rank2_expansion_cast_fp16")];
            tensor<fp16, [4032]> model_bn_running_mean_to_fp16 = const()[name = string("model_bn_running_mean_to_fp16"), val = tensor<fp16, [4032]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23842496)))];
            tensor<fp16, [4032]> model_bn_running_var_to_fp16 = const()[name = string("model_bn_running_var_to_fp16"), val = tensor<fp16, [4032]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23850624)))];
            tensor<fp16, [4032]> model_bn_weight_to_fp16 = const()[name = string("model_bn_weight_to_fp16"), val = tensor<fp16, [4032]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23858752)))];
            tensor<fp16, [4032]> model_bn_bias_to_fp16 = const()[name = string("model_bn_bias_to_fp16"), val = tensor<fp16, [4032]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23866880)))];
            tensor<fp16, [1, 4032, 1]> input_805_batch_norm_1d_cast_fp16 = batch_norm(beta = model_bn_bias_to_fp16, epsilon = var_8_to_fp16, gamma = model_bn_weight_to_fp16, mean = model_bn_running_mean_to_fp16, variance = model_bn_running_var_to_fp16, x = input_805_rank2_expansion_cast_fp16)[name = string("input_805_batch_norm_1d_cast_fp16")];
            tensor<int32, [1]> input_805_axes_0 = const()[name = string("input_805_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 4032]> input_805_cast_fp16 = squeeze(axes = input_805_axes_0, x = input_805_batch_norm_1d_cast_fp16)[name = string("input_805_cast_fp16")];
            tensor<fp16, [192, 4032]> model_linear_weight_to_fp16 = const()[name = string("model_linear_weight_to_fp16"), val = tensor<fp16, [192, 4032]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23875008)))];
            tensor<fp16, [192]> model_linear_bias_to_fp16 = const()[name = string("model_linear_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25423360)))];
            tensor<fp16, [1, 192]> linear_36_cast_fp16 = linear(bias = model_linear_bias_to_fp16, weight = model_linear_weight_to_fp16, x = input_805_cast_fp16)[name = string("linear_36_cast_fp16")];
            tensor<int32, [1]> var_3133 = const()[name = string("op_3133"), val = tensor<int32, [1]>([-1])];
            bool var_3134 = const()[name = string("op_3134"), val = bool(true)];
            tensor<fp16, [1, 1]> var_3136_cast_fp16 = reduce_l2_norm(axes = var_3133, keep_dims = var_3134, x = linear_36_cast_fp16)[name = string("op_3136_cast_fp16")];
            fp16 var_3137_to_fp16 = const()[name = string("op_3137_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1]> var_3138_cast_fp16 = maximum(x = var_3136_cast_fp16, y = var_3137_to_fp16)[name = string("op_3138_cast_fp16")];
            tensor<int32, [2]> denom_reps_0 = const()[name = string("denom_reps_0"), val = tensor<int32, [2]>([1, 192])];
            tensor<fp16, [1, 192]> denom_cast_fp16 = tile(reps = denom_reps_0, x = var_3138_cast_fp16)[name = string("denom_cast_fp16")];
            tensor<fp16, [1, 192]> var_3140_cast_fp16 = real_div(x = linear_36_cast_fp16, y = denom_cast_fp16)[name = string("op_3140_cast_fp16")];
            string var_3140_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_3140_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 192]> embedding = cast(dtype = var_3140_cast_fp16_to_fp32_dtype_0, x = var_3140_cast_fp16)[name = string("cast_82")];
        } -> (embedding);
}