program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchExport::ATEN"}, {"coremltools-version", "9.0"}})]
{
    func main<ios17>(tensor<fp16, [1, 2, 442, 801]> spectrogram) {
            tensor<int32, [4]> select_begin_0 = const()[name = tensor<string, []>("select_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_end_0 = const()[name = tensor<string, []>("select_end_0"), val = tensor<int32, [4]>([1, 1, 442, 801])];
            tensor<bool, [4]> select_end_mask_0 = const()[name = tensor<string, []>("select_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_squeeze_mask_0 = const()[name = tensor<string, []>("select_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 442, 801]> select_cast_fp16 = slice_by_index(begin = select_begin_0, end = select_end_0, end_mask = select_end_mask_0, squeeze_mask = select_squeeze_mask_0, x = spectrogram)[name = tensor<string, []>("select_cast_fp16")];
            tensor<int32, [4]> select_1_begin_0 = const()[name = tensor<string, []>("select_1_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_1_end_0 = const()[name = tensor<string, []>("select_1_end_0"), val = tensor<int32, [4]>([1, 2, 442, 801])];
            tensor<bool, [4]> select_1_end_mask_0 = const()[name = tensor<string, []>("select_1_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_1_squeeze_mask_0 = const()[name = tensor<string, []>("select_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 442, 801]> select_1_cast_fp16 = slice_by_index(begin = select_1_begin_0, end = select_1_end_0, end_mask = select_1_end_mask_0, squeeze_mask = select_1_squeeze_mask_0, x = spectrogram)[name = tensor<string, []>("select_1_cast_fp16")];
            tensor<int32, [3]> slice_1_begin_0 = const()[name = tensor<string, []>("slice_1_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_1_end_0 = const()[name = tensor<string, []>("slice_1_end_0"), val = tensor<int32, [3]>([1, 5, 801])];
            tensor<bool, [3]> slice_1_end_mask_0 = const()[name = tensor<string, []>("slice_1_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_1_cast_fp16 = slice_by_index(begin = slice_1_begin_0, end = slice_1_end_0, end_mask = slice_1_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_1_cast_fp16")];
            tensor<int32, [3]> slice_2_begin_0 = const()[name = tensor<string, []>("slice_2_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_2_end_0 = const()[name = tensor<string, []>("slice_2_end_0"), val = tensor<int32, [3]>([1, 5, 801])];
            tensor<bool, [3]> slice_2_end_mask_0 = const()[name = tensor<string, []>("slice_2_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_2_cast_fp16 = slice_by_index(begin = slice_2_begin_0, end = slice_2_end_0, end_mask = slice_2_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_2_cast_fp16")];
            tensor<fp16, []> const_10_promoted_to_fp16 = const()[name = tensor<string, []>("const_10_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_1_cast_fp16 = pow(x = slice_1_cast_fp16, y = const_10_promoted_to_fp16)[name = tensor<string, []>("pow_1_cast_fp16")];
            tensor<fp16, []> const_11_promoted_to_fp16 = const()[name = tensor<string, []>("const_11_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_2_cast_fp16 = pow(x = slice_2_cast_fp16, y = const_11_promoted_to_fp16)[name = tensor<string, []>("pow_2_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_cast_fp16 = add(x = pow_1_cast_fp16, y = pow_2_cast_fp16)[name = tensor<string, []>("add_cast_fp16")];
            tensor<int32, [1]> sum_1_axes_0 = const()[name = tensor<string, []>("sum_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_1_keep_dims_0 = const()[name = tensor<string, []>("sum_1_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_1_cast_fp16 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = add_cast_fp16)[name = tensor<string, []>("sum_1_cast_fp16")];
            tensor<fp16, []> const_14_to_fp16 = const()[name = tensor<string, []>("const_14_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_1_cast_fp16 = add(x = sum_1_cast_fp16, y = const_14_to_fp16)[name = tensor<string, []>("add_1_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_cast_fp16 = sqrt(x = add_1_cast_fp16)[name = tensor<string, []>("sqrt_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_cast_fp16 = real_div(x = slice_1_cast_fp16, y = sqrt_cast_fp16)[name = tensor<string, []>("div_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_1_cast_fp16 = real_div(x = slice_2_cast_fp16, y = sqrt_cast_fp16)[name = tensor<string, []>("div_1_cast_fp16")];
            tensor<fp32, []> log_epsilon_0 = const()[name = tensor<string, []>("log_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_cast_fp16 = log(epsilon = log_epsilon_0, x = sqrt_cast_fp16)[name = tensor<string, []>("log_cast_fp16")];
            tensor<int32, []> const_15 = const()[name = tensor<string, []>("const_15"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_interleave_0 = const()[name = tensor<string, []>("cat_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_cast_fp16 = concat(axis = const_15, interleave = cat_interleave_0, values = (div_cast_fp16, div_1_cast_fp16, log_cast_fp16))[name = tensor<string, []>("cat_cast_fp16")];
            tensor<int32, [1]> unsqueeze_1_axes_0 = const()[name = tensor<string, []>("unsqueeze_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_1_cast_fp16 = expand_dims(axes = unsqueeze_1_axes_0, x = cat_cast_fp16)[name = tensor<string, []>("unsqueeze_1_cast_fp16")];
            tensor<fp16, []> const_20_promoted_to_fp16 = const()[name = tensor<string, []>("const_20_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_3_cast_fp16 = pow(x = unsqueeze_1_cast_fp16, y = const_20_promoted_to_fp16)[name = tensor<string, []>("pow_3_cast_fp16")];
            tensor<int32, [1]> mean_axes_0 = const()[name = tensor<string, []>("mean_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_keep_dims_0 = const()[name = tensor<string, []>("mean_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_cast_fp16 = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = pow_3_cast_fp16)[name = tensor<string, []>("mean_cast_fp16")];
            tensor<fp16, []> const_23_to_fp16 = const()[name = tensor<string, []>("const_23_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_2_cast_fp16 = add(x = mean_cast_fp16, y = const_23_to_fp16)[name = tensor<string, []>("add_2_cast_fp16")];
            tensor<fp32, []> rsqrt_epsilon_0 = const()[name = tensor<string, []>("rsqrt_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_cast_fp16 = rsqrt(epsilon = rsqrt_epsilon_0, x = add_2_cast_fp16)[name = tensor<string, []>("rsqrt_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_cast_fp16 = mul(x = unsqueeze_1_cast_fp16, y = rsqrt_cast_fp16)[name = tensor<string, []>("mul_cast_fp16")];
            tensor<int32, [1]> squeeze_axes_0 = const()[name = tensor<string, []>("squeeze_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_cast_fp16 = squeeze(axes = squeeze_axes_0, x = mul_cast_fp16)[name = tensor<string, []>("squeeze_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_to_fp16 = const()[name = tensor<string, []>("view_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [1, 11, 801]> mul_1_cast_fp16 = mul(x = squeeze_cast_fp16, y = view_to_fp16)[name = tensor<string, []>("mul_1_cast_fp16")];
            tensor<string, []> conv1d_pad_type_0 = const()[name = tensor<string, []>("conv1d_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_strides_0 = const()[name = tensor<string, []>("conv1d_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_pad_0 = const()[name = tensor<string, []>("conv1d_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_dilations_0 = const()[name = tensor<string, []>("conv1d_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_groups_0 = const()[name = tensor<string, []>("conv1d_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_0_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_0_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192)))];
            tensor<fp16, [256]> p_bn_0_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_0_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5888)))];
            tensor<fp16, [1, 256, 801]> conv1d_cast_fp16 = conv(bias = p_bn_0_1_bias_to_fp16, dilations = conv1d_dilations_0, groups = conv1d_groups_0, pad = conv1d_pad_0, pad_type = conv1d_pad_type_0, strides = conv1d_strides_0, weight = p_bn_0_1_weight_to_fp16, x = mul_1_cast_fp16)[name = tensor<string, []>("conv1d_cast_fp16")];
            tensor<int32, [3]> slice_3_begin_0 = const()[name = tensor<string, []>("slice_3_begin_0"), val = tensor<int32, [3]>([0, 5, 0])];
            tensor<int32, [3]> slice_3_end_0 = const()[name = tensor<string, []>("slice_3_end_0"), val = tensor<int32, [3]>([1, 10, 801])];
            tensor<bool, [3]> slice_3_end_mask_0 = const()[name = tensor<string, []>("slice_3_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_3_cast_fp16 = slice_by_index(begin = slice_3_begin_0, end = slice_3_end_0, end_mask = slice_3_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_3_cast_fp16")];
            tensor<int32, [3]> slice_4_begin_0 = const()[name = tensor<string, []>("slice_4_begin_0"), val = tensor<int32, [3]>([0, 5, 0])];
            tensor<int32, [3]> slice_4_end_0 = const()[name = tensor<string, []>("slice_4_end_0"), val = tensor<int32, [3]>([1, 10, 801])];
            tensor<bool, [3]> slice_4_end_mask_0 = const()[name = tensor<string, []>("slice_4_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_4_cast_fp16 = slice_by_index(begin = slice_4_begin_0, end = slice_4_end_0, end_mask = slice_4_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_4_cast_fp16")];
            tensor<fp16, []> const_32_promoted_to_fp16 = const()[name = tensor<string, []>("const_32_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_4_cast_fp16 = pow(x = slice_3_cast_fp16, y = const_32_promoted_to_fp16)[name = tensor<string, []>("pow_4_cast_fp16")];
            tensor<fp16, []> const_33_promoted_to_fp16 = const()[name = tensor<string, []>("const_33_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_5_cast_fp16 = pow(x = slice_4_cast_fp16, y = const_33_promoted_to_fp16)[name = tensor<string, []>("pow_5_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_3_cast_fp16 = add(x = pow_4_cast_fp16, y = pow_5_cast_fp16)[name = tensor<string, []>("add_3_cast_fp16")];
            tensor<int32, [1]> sum_2_axes_0 = const()[name = tensor<string, []>("sum_2_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_2_keep_dims_0 = const()[name = tensor<string, []>("sum_2_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_2_cast_fp16 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = add_3_cast_fp16)[name = tensor<string, []>("sum_2_cast_fp16")];
            tensor<fp16, []> const_36_to_fp16 = const()[name = tensor<string, []>("const_36_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_4_cast_fp16 = add(x = sum_2_cast_fp16, y = const_36_to_fp16)[name = tensor<string, []>("add_4_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_1_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor<string, []>("sqrt_1_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_2_cast_fp16 = real_div(x = slice_3_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor<string, []>("div_2_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_3_cast_fp16 = real_div(x = slice_4_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor<string, []>("div_3_cast_fp16")];
            tensor<fp32, []> log_1_epsilon_0 = const()[name = tensor<string, []>("log_1_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_1_cast_fp16 = log(epsilon = log_1_epsilon_0, x = sqrt_1_cast_fp16)[name = tensor<string, []>("log_1_cast_fp16")];
            tensor<int32, []> const_37 = const()[name = tensor<string, []>("const_37"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_1_interleave_0 = const()[name = tensor<string, []>("cat_1_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_1_cast_fp16 = concat(axis = const_37, interleave = cat_1_interleave_0, values = (div_2_cast_fp16, div_3_cast_fp16, log_1_cast_fp16))[name = tensor<string, []>("cat_1_cast_fp16")];
            tensor<int32, [1]> unsqueeze_3_axes_0 = const()[name = tensor<string, []>("unsqueeze_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_3_cast_fp16 = expand_dims(axes = unsqueeze_3_axes_0, x = cat_1_cast_fp16)[name = tensor<string, []>("unsqueeze_3_cast_fp16")];
            tensor<fp16, []> const_42_promoted_to_fp16 = const()[name = tensor<string, []>("const_42_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_6_cast_fp16 = pow(x = unsqueeze_3_cast_fp16, y = const_42_promoted_to_fp16)[name = tensor<string, []>("pow_6_cast_fp16")];
            tensor<int32, [1]> mean_1_axes_0 = const()[name = tensor<string, []>("mean_1_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_1_keep_dims_0 = const()[name = tensor<string, []>("mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_1_cast_fp16 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = pow_6_cast_fp16)[name = tensor<string, []>("mean_1_cast_fp16")];
            tensor<fp16, []> const_45_to_fp16 = const()[name = tensor<string, []>("const_45_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_5_cast_fp16 = add(x = mean_1_cast_fp16, y = const_45_to_fp16)[name = tensor<string, []>("add_5_cast_fp16")];
            tensor<fp32, []> rsqrt_1_epsilon_0 = const()[name = tensor<string, []>("rsqrt_1_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_1_cast_fp16 = rsqrt(epsilon = rsqrt_1_epsilon_0, x = add_5_cast_fp16)[name = tensor<string, []>("rsqrt_1_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_2_cast_fp16 = mul(x = unsqueeze_3_cast_fp16, y = rsqrt_1_cast_fp16)[name = tensor<string, []>("mul_2_cast_fp16")];
            tensor<int32, [1]> squeeze_1_axes_0 = const()[name = tensor<string, []>("squeeze_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = mul_2_cast_fp16)[name = tensor<string, []>("squeeze_1_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_1_to_fp16 = const()[name = tensor<string, []>("view_1_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6464)))];
            tensor<fp16, [1, 11, 801]> mul_3_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = view_1_to_fp16)[name = tensor<string, []>("mul_3_cast_fp16")];
            tensor<string, []> conv1d_1_pad_type_0 = const()[name = tensor<string, []>("conv1d_1_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_1_strides_0 = const()[name = tensor<string, []>("conv1d_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_1_pad_0 = const()[name = tensor<string, []>("conv1d_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_1_dilations_0 = const()[name = tensor<string, []>("conv1d_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_1_groups_0 = const()[name = tensor<string, []>("conv1d_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_1_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_1_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6592)))];
            tensor<fp16, [256]> p_bn_1_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_1_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12288)))];
            tensor<fp16, [1, 256, 801]> conv1d_1_cast_fp16 = conv(bias = p_bn_1_1_bias_to_fp16, dilations = conv1d_1_dilations_0, groups = conv1d_1_groups_0, pad = conv1d_1_pad_0, pad_type = conv1d_1_pad_type_0, strides = conv1d_1_strides_0, weight = p_bn_1_1_weight_to_fp16, x = mul_3_cast_fp16)[name = tensor<string, []>("conv1d_1_cast_fp16")];
            tensor<int32, [3]> slice_5_begin_0 = const()[name = tensor<string, []>("slice_5_begin_0"), val = tensor<int32, [3]>([0, 10, 0])];
            tensor<int32, [3]> slice_5_end_0 = const()[name = tensor<string, []>("slice_5_end_0"), val = tensor<int32, [3]>([1, 15, 801])];
            tensor<bool, [3]> slice_5_end_mask_0 = const()[name = tensor<string, []>("slice_5_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_5_cast_fp16 = slice_by_index(begin = slice_5_begin_0, end = slice_5_end_0, end_mask = slice_5_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_5_cast_fp16")];
            tensor<int32, [3]> slice_6_begin_0 = const()[name = tensor<string, []>("slice_6_begin_0"), val = tensor<int32, [3]>([0, 10, 0])];
            tensor<int32, [3]> slice_6_end_0 = const()[name = tensor<string, []>("slice_6_end_0"), val = tensor<int32, [3]>([1, 15, 801])];
            tensor<bool, [3]> slice_6_end_mask_0 = const()[name = tensor<string, []>("slice_6_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_6_cast_fp16 = slice_by_index(begin = slice_6_begin_0, end = slice_6_end_0, end_mask = slice_6_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_6_cast_fp16")];
            tensor<fp16, []> const_54_promoted_to_fp16 = const()[name = tensor<string, []>("const_54_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_7_cast_fp16 = pow(x = slice_5_cast_fp16, y = const_54_promoted_to_fp16)[name = tensor<string, []>("pow_7_cast_fp16")];
            tensor<fp16, []> const_55_promoted_to_fp16 = const()[name = tensor<string, []>("const_55_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_8_cast_fp16 = pow(x = slice_6_cast_fp16, y = const_55_promoted_to_fp16)[name = tensor<string, []>("pow_8_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_6_cast_fp16 = add(x = pow_7_cast_fp16, y = pow_8_cast_fp16)[name = tensor<string, []>("add_6_cast_fp16")];
            tensor<int32, [1]> sum_3_axes_0 = const()[name = tensor<string, []>("sum_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_3_keep_dims_0 = const()[name = tensor<string, []>("sum_3_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_3_cast_fp16 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = add_6_cast_fp16)[name = tensor<string, []>("sum_3_cast_fp16")];
            tensor<fp16, []> const_58_to_fp16 = const()[name = tensor<string, []>("const_58_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_7_cast_fp16 = add(x = sum_3_cast_fp16, y = const_58_to_fp16)[name = tensor<string, []>("add_7_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_2_cast_fp16 = sqrt(x = add_7_cast_fp16)[name = tensor<string, []>("sqrt_2_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_4_cast_fp16 = real_div(x = slice_5_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor<string, []>("div_4_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_5_cast_fp16 = real_div(x = slice_6_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor<string, []>("div_5_cast_fp16")];
            tensor<fp32, []> log_2_epsilon_0 = const()[name = tensor<string, []>("log_2_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_2_cast_fp16 = log(epsilon = log_2_epsilon_0, x = sqrt_2_cast_fp16)[name = tensor<string, []>("log_2_cast_fp16")];
            tensor<int32, []> const_59 = const()[name = tensor<string, []>("const_59"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_2_interleave_0 = const()[name = tensor<string, []>("cat_2_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_2_cast_fp16 = concat(axis = const_59, interleave = cat_2_interleave_0, values = (div_4_cast_fp16, div_5_cast_fp16, log_2_cast_fp16))[name = tensor<string, []>("cat_2_cast_fp16")];
            tensor<int32, [1]> unsqueeze_5_axes_0 = const()[name = tensor<string, []>("unsqueeze_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_5_cast_fp16 = expand_dims(axes = unsqueeze_5_axes_0, x = cat_2_cast_fp16)[name = tensor<string, []>("unsqueeze_5_cast_fp16")];
            tensor<fp16, []> const_64_promoted_to_fp16 = const()[name = tensor<string, []>("const_64_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_9_cast_fp16 = pow(x = unsqueeze_5_cast_fp16, y = const_64_promoted_to_fp16)[name = tensor<string, []>("pow_9_cast_fp16")];
            tensor<int32, [1]> mean_2_axes_0 = const()[name = tensor<string, []>("mean_2_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_2_keep_dims_0 = const()[name = tensor<string, []>("mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_2_cast_fp16 = reduce_mean(axes = mean_2_axes_0, keep_dims = mean_2_keep_dims_0, x = pow_9_cast_fp16)[name = tensor<string, []>("mean_2_cast_fp16")];
            tensor<fp16, []> const_67_to_fp16 = const()[name = tensor<string, []>("const_67_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_8_cast_fp16 = add(x = mean_2_cast_fp16, y = const_67_to_fp16)[name = tensor<string, []>("add_8_cast_fp16")];
            tensor<fp32, []> rsqrt_2_epsilon_0 = const()[name = tensor<string, []>("rsqrt_2_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_2_cast_fp16 = rsqrt(epsilon = rsqrt_2_epsilon_0, x = add_8_cast_fp16)[name = tensor<string, []>("rsqrt_2_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_4_cast_fp16 = mul(x = unsqueeze_5_cast_fp16, y = rsqrt_2_cast_fp16)[name = tensor<string, []>("mul_4_cast_fp16")];
            tensor<int32, [1]> squeeze_2_axes_0 = const()[name = tensor<string, []>("squeeze_2_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = mul_4_cast_fp16)[name = tensor<string, []>("squeeze_2_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_2_to_fp16 = const()[name = tensor<string, []>("view_2_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12864)))];
            tensor<fp16, [1, 11, 801]> mul_5_cast_fp16 = mul(x = squeeze_2_cast_fp16, y = view_2_to_fp16)[name = tensor<string, []>("mul_5_cast_fp16")];
            tensor<string, []> conv1d_2_pad_type_0 = const()[name = tensor<string, []>("conv1d_2_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_2_strides_0 = const()[name = tensor<string, []>("conv1d_2_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_2_pad_0 = const()[name = tensor<string, []>("conv1d_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_2_dilations_0 = const()[name = tensor<string, []>("conv1d_2_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_2_groups_0 = const()[name = tensor<string, []>("conv1d_2_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_2_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_2_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12992)))];
            tensor<fp16, [256]> p_bn_2_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_2_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18688)))];
            tensor<fp16, [1, 256, 801]> conv1d_2_cast_fp16 = conv(bias = p_bn_2_1_bias_to_fp16, dilations = conv1d_2_dilations_0, groups = conv1d_2_groups_0, pad = conv1d_2_pad_0, pad_type = conv1d_2_pad_type_0, strides = conv1d_2_strides_0, weight = p_bn_2_1_weight_to_fp16, x = mul_5_cast_fp16)[name = tensor<string, []>("conv1d_2_cast_fp16")];
            tensor<int32, [3]> slice_7_begin_0 = const()[name = tensor<string, []>("slice_7_begin_0"), val = tensor<int32, [3]>([0, 15, 0])];
            tensor<int32, [3]> slice_7_end_0 = const()[name = tensor<string, []>("slice_7_end_0"), val = tensor<int32, [3]>([1, 20, 801])];
            tensor<bool, [3]> slice_7_end_mask_0 = const()[name = tensor<string, []>("slice_7_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_7_cast_fp16 = slice_by_index(begin = slice_7_begin_0, end = slice_7_end_0, end_mask = slice_7_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_7_cast_fp16")];
            tensor<int32, [3]> slice_8_begin_0 = const()[name = tensor<string, []>("slice_8_begin_0"), val = tensor<int32, [3]>([0, 15, 0])];
            tensor<int32, [3]> slice_8_end_0 = const()[name = tensor<string, []>("slice_8_end_0"), val = tensor<int32, [3]>([1, 20, 801])];
            tensor<bool, [3]> slice_8_end_mask_0 = const()[name = tensor<string, []>("slice_8_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_8_cast_fp16 = slice_by_index(begin = slice_8_begin_0, end = slice_8_end_0, end_mask = slice_8_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_8_cast_fp16")];
            tensor<fp16, []> const_76_promoted_to_fp16 = const()[name = tensor<string, []>("const_76_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_10_cast_fp16 = pow(x = slice_7_cast_fp16, y = const_76_promoted_to_fp16)[name = tensor<string, []>("pow_10_cast_fp16")];
            tensor<fp16, []> const_77_promoted_to_fp16 = const()[name = tensor<string, []>("const_77_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_11_cast_fp16 = pow(x = slice_8_cast_fp16, y = const_77_promoted_to_fp16)[name = tensor<string, []>("pow_11_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_9_cast_fp16 = add(x = pow_10_cast_fp16, y = pow_11_cast_fp16)[name = tensor<string, []>("add_9_cast_fp16")];
            tensor<int32, [1]> sum_4_axes_0 = const()[name = tensor<string, []>("sum_4_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_4_keep_dims_0 = const()[name = tensor<string, []>("sum_4_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_4_cast_fp16 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = add_9_cast_fp16)[name = tensor<string, []>("sum_4_cast_fp16")];
            tensor<fp16, []> const_80_to_fp16 = const()[name = tensor<string, []>("const_80_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_10_cast_fp16 = add(x = sum_4_cast_fp16, y = const_80_to_fp16)[name = tensor<string, []>("add_10_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_3_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor<string, []>("sqrt_3_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_6_cast_fp16 = real_div(x = slice_7_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor<string, []>("div_6_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_7_cast_fp16 = real_div(x = slice_8_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor<string, []>("div_7_cast_fp16")];
            tensor<fp32, []> log_3_epsilon_0 = const()[name = tensor<string, []>("log_3_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_3_cast_fp16 = log(epsilon = log_3_epsilon_0, x = sqrt_3_cast_fp16)[name = tensor<string, []>("log_3_cast_fp16")];
            tensor<int32, []> const_81 = const()[name = tensor<string, []>("const_81"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_3_interleave_0 = const()[name = tensor<string, []>("cat_3_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_3_cast_fp16 = concat(axis = const_81, interleave = cat_3_interleave_0, values = (div_6_cast_fp16, div_7_cast_fp16, log_3_cast_fp16))[name = tensor<string, []>("cat_3_cast_fp16")];
            tensor<int32, [1]> unsqueeze_7_axes_0 = const()[name = tensor<string, []>("unsqueeze_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_7_cast_fp16 = expand_dims(axes = unsqueeze_7_axes_0, x = cat_3_cast_fp16)[name = tensor<string, []>("unsqueeze_7_cast_fp16")];
            tensor<fp16, []> const_86_promoted_to_fp16 = const()[name = tensor<string, []>("const_86_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_12_cast_fp16 = pow(x = unsqueeze_7_cast_fp16, y = const_86_promoted_to_fp16)[name = tensor<string, []>("pow_12_cast_fp16")];
            tensor<int32, [1]> mean_3_axes_0 = const()[name = tensor<string, []>("mean_3_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_3_keep_dims_0 = const()[name = tensor<string, []>("mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_3_cast_fp16 = reduce_mean(axes = mean_3_axes_0, keep_dims = mean_3_keep_dims_0, x = pow_12_cast_fp16)[name = tensor<string, []>("mean_3_cast_fp16")];
            tensor<fp16, []> const_89_to_fp16 = const()[name = tensor<string, []>("const_89_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_11_cast_fp16 = add(x = mean_3_cast_fp16, y = const_89_to_fp16)[name = tensor<string, []>("add_11_cast_fp16")];
            tensor<fp32, []> rsqrt_3_epsilon_0 = const()[name = tensor<string, []>("rsqrt_3_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_3_cast_fp16 = rsqrt(epsilon = rsqrt_3_epsilon_0, x = add_11_cast_fp16)[name = tensor<string, []>("rsqrt_3_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_6_cast_fp16 = mul(x = unsqueeze_7_cast_fp16, y = rsqrt_3_cast_fp16)[name = tensor<string, []>("mul_6_cast_fp16")];
            tensor<int32, [1]> squeeze_3_axes_0 = const()[name = tensor<string, []>("squeeze_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = mul_6_cast_fp16)[name = tensor<string, []>("squeeze_3_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_3_to_fp16 = const()[name = tensor<string, []>("view_3_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19264)))];
            tensor<fp16, [1, 11, 801]> mul_7_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = view_3_to_fp16)[name = tensor<string, []>("mul_7_cast_fp16")];
            tensor<string, []> conv1d_3_pad_type_0 = const()[name = tensor<string, []>("conv1d_3_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_3_strides_0 = const()[name = tensor<string, []>("conv1d_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_3_pad_0 = const()[name = tensor<string, []>("conv1d_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_3_dilations_0 = const()[name = tensor<string, []>("conv1d_3_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_3_groups_0 = const()[name = tensor<string, []>("conv1d_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_3_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_3_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19392)))];
            tensor<fp16, [256]> p_bn_3_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_3_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25088)))];
            tensor<fp16, [1, 256, 801]> conv1d_3_cast_fp16 = conv(bias = p_bn_3_1_bias_to_fp16, dilations = conv1d_3_dilations_0, groups = conv1d_3_groups_0, pad = conv1d_3_pad_0, pad_type = conv1d_3_pad_type_0, strides = conv1d_3_strides_0, weight = p_bn_3_1_weight_to_fp16, x = mul_7_cast_fp16)[name = tensor<string, []>("conv1d_3_cast_fp16")];
            tensor<int32, [3]> slice_9_begin_0 = const()[name = tensor<string, []>("slice_9_begin_0"), val = tensor<int32, [3]>([0, 20, 0])];
            tensor<int32, [3]> slice_9_end_0 = const()[name = tensor<string, []>("slice_9_end_0"), val = tensor<int32, [3]>([1, 25, 801])];
            tensor<bool, [3]> slice_9_end_mask_0 = const()[name = tensor<string, []>("slice_9_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_9_cast_fp16 = slice_by_index(begin = slice_9_begin_0, end = slice_9_end_0, end_mask = slice_9_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_9_cast_fp16")];
            tensor<int32, [3]> slice_10_begin_0 = const()[name = tensor<string, []>("slice_10_begin_0"), val = tensor<int32, [3]>([0, 20, 0])];
            tensor<int32, [3]> slice_10_end_0 = const()[name = tensor<string, []>("slice_10_end_0"), val = tensor<int32, [3]>([1, 25, 801])];
            tensor<bool, [3]> slice_10_end_mask_0 = const()[name = tensor<string, []>("slice_10_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_10_cast_fp16 = slice_by_index(begin = slice_10_begin_0, end = slice_10_end_0, end_mask = slice_10_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_10_cast_fp16")];
            tensor<fp16, []> const_98_promoted_to_fp16 = const()[name = tensor<string, []>("const_98_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_13_cast_fp16 = pow(x = slice_9_cast_fp16, y = const_98_promoted_to_fp16)[name = tensor<string, []>("pow_13_cast_fp16")];
            tensor<fp16, []> const_99_promoted_to_fp16 = const()[name = tensor<string, []>("const_99_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_14_cast_fp16 = pow(x = slice_10_cast_fp16, y = const_99_promoted_to_fp16)[name = tensor<string, []>("pow_14_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_12_cast_fp16 = add(x = pow_13_cast_fp16, y = pow_14_cast_fp16)[name = tensor<string, []>("add_12_cast_fp16")];
            tensor<int32, [1]> sum_5_axes_0 = const()[name = tensor<string, []>("sum_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_5_keep_dims_0 = const()[name = tensor<string, []>("sum_5_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_5_cast_fp16 = reduce_sum(axes = sum_5_axes_0, keep_dims = sum_5_keep_dims_0, x = add_12_cast_fp16)[name = tensor<string, []>("sum_5_cast_fp16")];
            tensor<fp16, []> const_102_to_fp16 = const()[name = tensor<string, []>("const_102_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_13_cast_fp16 = add(x = sum_5_cast_fp16, y = const_102_to_fp16)[name = tensor<string, []>("add_13_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_4_cast_fp16 = sqrt(x = add_13_cast_fp16)[name = tensor<string, []>("sqrt_4_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_8_cast_fp16 = real_div(x = slice_9_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor<string, []>("div_8_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_9_cast_fp16 = real_div(x = slice_10_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor<string, []>("div_9_cast_fp16")];
            tensor<fp32, []> log_4_epsilon_0 = const()[name = tensor<string, []>("log_4_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_4_cast_fp16 = log(epsilon = log_4_epsilon_0, x = sqrt_4_cast_fp16)[name = tensor<string, []>("log_4_cast_fp16")];
            tensor<int32, []> const_103 = const()[name = tensor<string, []>("const_103"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_4_interleave_0 = const()[name = tensor<string, []>("cat_4_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_4_cast_fp16 = concat(axis = const_103, interleave = cat_4_interleave_0, values = (div_8_cast_fp16, div_9_cast_fp16, log_4_cast_fp16))[name = tensor<string, []>("cat_4_cast_fp16")];
            tensor<int32, [1]> unsqueeze_9_axes_0 = const()[name = tensor<string, []>("unsqueeze_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_9_cast_fp16 = expand_dims(axes = unsqueeze_9_axes_0, x = cat_4_cast_fp16)[name = tensor<string, []>("unsqueeze_9_cast_fp16")];
            tensor<fp16, []> const_108_promoted_to_fp16 = const()[name = tensor<string, []>("const_108_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_15_cast_fp16 = pow(x = unsqueeze_9_cast_fp16, y = const_108_promoted_to_fp16)[name = tensor<string, []>("pow_15_cast_fp16")];
            tensor<int32, [1]> mean_4_axes_0 = const()[name = tensor<string, []>("mean_4_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_4_keep_dims_0 = const()[name = tensor<string, []>("mean_4_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_4_cast_fp16 = reduce_mean(axes = mean_4_axes_0, keep_dims = mean_4_keep_dims_0, x = pow_15_cast_fp16)[name = tensor<string, []>("mean_4_cast_fp16")];
            tensor<fp16, []> const_111_to_fp16 = const()[name = tensor<string, []>("const_111_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_14_cast_fp16 = add(x = mean_4_cast_fp16, y = const_111_to_fp16)[name = tensor<string, []>("add_14_cast_fp16")];
            tensor<fp32, []> rsqrt_4_epsilon_0 = const()[name = tensor<string, []>("rsqrt_4_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_4_cast_fp16 = rsqrt(epsilon = rsqrt_4_epsilon_0, x = add_14_cast_fp16)[name = tensor<string, []>("rsqrt_4_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_8_cast_fp16 = mul(x = unsqueeze_9_cast_fp16, y = rsqrt_4_cast_fp16)[name = tensor<string, []>("mul_8_cast_fp16")];
            tensor<int32, [1]> squeeze_4_axes_0 = const()[name = tensor<string, []>("squeeze_4_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = mul_8_cast_fp16)[name = tensor<string, []>("squeeze_4_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_4_to_fp16 = const()[name = tensor<string, []>("view_4_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25664)))];
            tensor<fp16, [1, 11, 801]> mul_9_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = view_4_to_fp16)[name = tensor<string, []>("mul_9_cast_fp16")];
            tensor<string, []> conv1d_4_pad_type_0 = const()[name = tensor<string, []>("conv1d_4_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_4_strides_0 = const()[name = tensor<string, []>("conv1d_4_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_4_pad_0 = const()[name = tensor<string, []>("conv1d_4_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_4_dilations_0 = const()[name = tensor<string, []>("conv1d_4_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_4_groups_0 = const()[name = tensor<string, []>("conv1d_4_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_4_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_4_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25792)))];
            tensor<fp16, [256]> p_bn_4_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_4_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31488)))];
            tensor<fp16, [1, 256, 801]> conv1d_4_cast_fp16 = conv(bias = p_bn_4_1_bias_to_fp16, dilations = conv1d_4_dilations_0, groups = conv1d_4_groups_0, pad = conv1d_4_pad_0, pad_type = conv1d_4_pad_type_0, strides = conv1d_4_strides_0, weight = p_bn_4_1_weight_to_fp16, x = mul_9_cast_fp16)[name = tensor<string, []>("conv1d_4_cast_fp16")];
            tensor<int32, [3]> slice_11_begin_0 = const()[name = tensor<string, []>("slice_11_begin_0"), val = tensor<int32, [3]>([0, 25, 0])];
            tensor<int32, [3]> slice_11_end_0 = const()[name = tensor<string, []>("slice_11_end_0"), val = tensor<int32, [3]>([1, 30, 801])];
            tensor<bool, [3]> slice_11_end_mask_0 = const()[name = tensor<string, []>("slice_11_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_11_cast_fp16 = slice_by_index(begin = slice_11_begin_0, end = slice_11_end_0, end_mask = slice_11_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_11_cast_fp16")];
            tensor<int32, [3]> slice_12_begin_0 = const()[name = tensor<string, []>("slice_12_begin_0"), val = tensor<int32, [3]>([0, 25, 0])];
            tensor<int32, [3]> slice_12_end_0 = const()[name = tensor<string, []>("slice_12_end_0"), val = tensor<int32, [3]>([1, 30, 801])];
            tensor<bool, [3]> slice_12_end_mask_0 = const()[name = tensor<string, []>("slice_12_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_12_cast_fp16 = slice_by_index(begin = slice_12_begin_0, end = slice_12_end_0, end_mask = slice_12_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_12_cast_fp16")];
            tensor<fp16, []> const_120_promoted_to_fp16 = const()[name = tensor<string, []>("const_120_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_16_cast_fp16 = pow(x = slice_11_cast_fp16, y = const_120_promoted_to_fp16)[name = tensor<string, []>("pow_16_cast_fp16")];
            tensor<fp16, []> const_121_promoted_to_fp16 = const()[name = tensor<string, []>("const_121_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_17_cast_fp16 = pow(x = slice_12_cast_fp16, y = const_121_promoted_to_fp16)[name = tensor<string, []>("pow_17_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_15_cast_fp16 = add(x = pow_16_cast_fp16, y = pow_17_cast_fp16)[name = tensor<string, []>("add_15_cast_fp16")];
            tensor<int32, [1]> sum_6_axes_0 = const()[name = tensor<string, []>("sum_6_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_6_keep_dims_0 = const()[name = tensor<string, []>("sum_6_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_6_cast_fp16 = reduce_sum(axes = sum_6_axes_0, keep_dims = sum_6_keep_dims_0, x = add_15_cast_fp16)[name = tensor<string, []>("sum_6_cast_fp16")];
            tensor<fp16, []> const_124_to_fp16 = const()[name = tensor<string, []>("const_124_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_16_cast_fp16 = add(x = sum_6_cast_fp16, y = const_124_to_fp16)[name = tensor<string, []>("add_16_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_5_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor<string, []>("sqrt_5_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_10_cast_fp16 = real_div(x = slice_11_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor<string, []>("div_10_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_11_cast_fp16 = real_div(x = slice_12_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor<string, []>("div_11_cast_fp16")];
            tensor<fp32, []> log_5_epsilon_0 = const()[name = tensor<string, []>("log_5_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_5_cast_fp16 = log(epsilon = log_5_epsilon_0, x = sqrt_5_cast_fp16)[name = tensor<string, []>("log_5_cast_fp16")];
            tensor<int32, []> const_125 = const()[name = tensor<string, []>("const_125"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_5_interleave_0 = const()[name = tensor<string, []>("cat_5_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_5_cast_fp16 = concat(axis = const_125, interleave = cat_5_interleave_0, values = (div_10_cast_fp16, div_11_cast_fp16, log_5_cast_fp16))[name = tensor<string, []>("cat_5_cast_fp16")];
            tensor<int32, [1]> unsqueeze_11_axes_0 = const()[name = tensor<string, []>("unsqueeze_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_11_cast_fp16 = expand_dims(axes = unsqueeze_11_axes_0, x = cat_5_cast_fp16)[name = tensor<string, []>("unsqueeze_11_cast_fp16")];
            tensor<fp16, []> const_130_promoted_to_fp16 = const()[name = tensor<string, []>("const_130_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_18_cast_fp16 = pow(x = unsqueeze_11_cast_fp16, y = const_130_promoted_to_fp16)[name = tensor<string, []>("pow_18_cast_fp16")];
            tensor<int32, [1]> mean_5_axes_0 = const()[name = tensor<string, []>("mean_5_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_5_keep_dims_0 = const()[name = tensor<string, []>("mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_5_cast_fp16 = reduce_mean(axes = mean_5_axes_0, keep_dims = mean_5_keep_dims_0, x = pow_18_cast_fp16)[name = tensor<string, []>("mean_5_cast_fp16")];
            tensor<fp16, []> const_133_to_fp16 = const()[name = tensor<string, []>("const_133_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_17_cast_fp16 = add(x = mean_5_cast_fp16, y = const_133_to_fp16)[name = tensor<string, []>("add_17_cast_fp16")];
            tensor<fp32, []> rsqrt_5_epsilon_0 = const()[name = tensor<string, []>("rsqrt_5_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_5_cast_fp16 = rsqrt(epsilon = rsqrt_5_epsilon_0, x = add_17_cast_fp16)[name = tensor<string, []>("rsqrt_5_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_10_cast_fp16 = mul(x = unsqueeze_11_cast_fp16, y = rsqrt_5_cast_fp16)[name = tensor<string, []>("mul_10_cast_fp16")];
            tensor<int32, [1]> squeeze_5_axes_0 = const()[name = tensor<string, []>("squeeze_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = mul_10_cast_fp16)[name = tensor<string, []>("squeeze_5_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_5_to_fp16 = const()[name = tensor<string, []>("view_5_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32064)))];
            tensor<fp16, [1, 11, 801]> mul_11_cast_fp16 = mul(x = squeeze_5_cast_fp16, y = view_5_to_fp16)[name = tensor<string, []>("mul_11_cast_fp16")];
            tensor<string, []> conv1d_5_pad_type_0 = const()[name = tensor<string, []>("conv1d_5_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_5_strides_0 = const()[name = tensor<string, []>("conv1d_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_5_pad_0 = const()[name = tensor<string, []>("conv1d_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_5_dilations_0 = const()[name = tensor<string, []>("conv1d_5_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_5_groups_0 = const()[name = tensor<string, []>("conv1d_5_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_5_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_5_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32192)))];
            tensor<fp16, [256]> p_bn_5_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_5_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37888)))];
            tensor<fp16, [1, 256, 801]> conv1d_5_cast_fp16 = conv(bias = p_bn_5_1_bias_to_fp16, dilations = conv1d_5_dilations_0, groups = conv1d_5_groups_0, pad = conv1d_5_pad_0, pad_type = conv1d_5_pad_type_0, strides = conv1d_5_strides_0, weight = p_bn_5_1_weight_to_fp16, x = mul_11_cast_fp16)[name = tensor<string, []>("conv1d_5_cast_fp16")];
            tensor<int32, [3]> slice_13_begin_0 = const()[name = tensor<string, []>("slice_13_begin_0"), val = tensor<int32, [3]>([0, 30, 0])];
            tensor<int32, [3]> slice_13_end_0 = const()[name = tensor<string, []>("slice_13_end_0"), val = tensor<int32, [3]>([1, 35, 801])];
            tensor<bool, [3]> slice_13_end_mask_0 = const()[name = tensor<string, []>("slice_13_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_13_cast_fp16 = slice_by_index(begin = slice_13_begin_0, end = slice_13_end_0, end_mask = slice_13_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_13_cast_fp16")];
            tensor<int32, [3]> slice_14_begin_0 = const()[name = tensor<string, []>("slice_14_begin_0"), val = tensor<int32, [3]>([0, 30, 0])];
            tensor<int32, [3]> slice_14_end_0 = const()[name = tensor<string, []>("slice_14_end_0"), val = tensor<int32, [3]>([1, 35, 801])];
            tensor<bool, [3]> slice_14_end_mask_0 = const()[name = tensor<string, []>("slice_14_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_14_cast_fp16 = slice_by_index(begin = slice_14_begin_0, end = slice_14_end_0, end_mask = slice_14_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_14_cast_fp16")];
            tensor<fp16, []> const_142_promoted_to_fp16 = const()[name = tensor<string, []>("const_142_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_19_cast_fp16 = pow(x = slice_13_cast_fp16, y = const_142_promoted_to_fp16)[name = tensor<string, []>("pow_19_cast_fp16")];
            tensor<fp16, []> const_143_promoted_to_fp16 = const()[name = tensor<string, []>("const_143_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_20_cast_fp16 = pow(x = slice_14_cast_fp16, y = const_143_promoted_to_fp16)[name = tensor<string, []>("pow_20_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_18_cast_fp16 = add(x = pow_19_cast_fp16, y = pow_20_cast_fp16)[name = tensor<string, []>("add_18_cast_fp16")];
            tensor<int32, [1]> sum_7_axes_0 = const()[name = tensor<string, []>("sum_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_7_keep_dims_0 = const()[name = tensor<string, []>("sum_7_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_7_cast_fp16 = reduce_sum(axes = sum_7_axes_0, keep_dims = sum_7_keep_dims_0, x = add_18_cast_fp16)[name = tensor<string, []>("sum_7_cast_fp16")];
            tensor<fp16, []> const_146_to_fp16 = const()[name = tensor<string, []>("const_146_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_19_cast_fp16 = add(x = sum_7_cast_fp16, y = const_146_to_fp16)[name = tensor<string, []>("add_19_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_6_cast_fp16 = sqrt(x = add_19_cast_fp16)[name = tensor<string, []>("sqrt_6_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_12_cast_fp16 = real_div(x = slice_13_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor<string, []>("div_12_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_13_cast_fp16 = real_div(x = slice_14_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor<string, []>("div_13_cast_fp16")];
            tensor<fp32, []> log_6_epsilon_0 = const()[name = tensor<string, []>("log_6_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_6_cast_fp16 = log(epsilon = log_6_epsilon_0, x = sqrt_6_cast_fp16)[name = tensor<string, []>("log_6_cast_fp16")];
            tensor<int32, []> const_147 = const()[name = tensor<string, []>("const_147"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_6_interleave_0 = const()[name = tensor<string, []>("cat_6_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_6_cast_fp16 = concat(axis = const_147, interleave = cat_6_interleave_0, values = (div_12_cast_fp16, div_13_cast_fp16, log_6_cast_fp16))[name = tensor<string, []>("cat_6_cast_fp16")];
            tensor<int32, [1]> unsqueeze_13_axes_0 = const()[name = tensor<string, []>("unsqueeze_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_13_cast_fp16 = expand_dims(axes = unsqueeze_13_axes_0, x = cat_6_cast_fp16)[name = tensor<string, []>("unsqueeze_13_cast_fp16")];
            tensor<fp16, []> const_152_promoted_to_fp16 = const()[name = tensor<string, []>("const_152_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_21_cast_fp16 = pow(x = unsqueeze_13_cast_fp16, y = const_152_promoted_to_fp16)[name = tensor<string, []>("pow_21_cast_fp16")];
            tensor<int32, [1]> mean_6_axes_0 = const()[name = tensor<string, []>("mean_6_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_6_keep_dims_0 = const()[name = tensor<string, []>("mean_6_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_6_cast_fp16 = reduce_mean(axes = mean_6_axes_0, keep_dims = mean_6_keep_dims_0, x = pow_21_cast_fp16)[name = tensor<string, []>("mean_6_cast_fp16")];
            tensor<fp16, []> const_155_to_fp16 = const()[name = tensor<string, []>("const_155_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_20_cast_fp16 = add(x = mean_6_cast_fp16, y = const_155_to_fp16)[name = tensor<string, []>("add_20_cast_fp16")];
            tensor<fp32, []> rsqrt_6_epsilon_0 = const()[name = tensor<string, []>("rsqrt_6_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_6_cast_fp16 = rsqrt(epsilon = rsqrt_6_epsilon_0, x = add_20_cast_fp16)[name = tensor<string, []>("rsqrt_6_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_12_cast_fp16 = mul(x = unsqueeze_13_cast_fp16, y = rsqrt_6_cast_fp16)[name = tensor<string, []>("mul_12_cast_fp16")];
            tensor<int32, [1]> squeeze_6_axes_0 = const()[name = tensor<string, []>("squeeze_6_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = mul_12_cast_fp16)[name = tensor<string, []>("squeeze_6_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_6_to_fp16 = const()[name = tensor<string, []>("view_6_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38464)))];
            tensor<fp16, [1, 11, 801]> mul_13_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = view_6_to_fp16)[name = tensor<string, []>("mul_13_cast_fp16")];
            tensor<string, []> conv1d_6_pad_type_0 = const()[name = tensor<string, []>("conv1d_6_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_6_strides_0 = const()[name = tensor<string, []>("conv1d_6_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_6_pad_0 = const()[name = tensor<string, []>("conv1d_6_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_6_dilations_0 = const()[name = tensor<string, []>("conv1d_6_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_6_groups_0 = const()[name = tensor<string, []>("conv1d_6_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_6_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_6_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38592)))];
            tensor<fp16, [256]> p_bn_6_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_6_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44288)))];
            tensor<fp16, [1, 256, 801]> conv1d_6_cast_fp16 = conv(bias = p_bn_6_1_bias_to_fp16, dilations = conv1d_6_dilations_0, groups = conv1d_6_groups_0, pad = conv1d_6_pad_0, pad_type = conv1d_6_pad_type_0, strides = conv1d_6_strides_0, weight = p_bn_6_1_weight_to_fp16, x = mul_13_cast_fp16)[name = tensor<string, []>("conv1d_6_cast_fp16")];
            tensor<int32, [3]> slice_15_begin_0 = const()[name = tensor<string, []>("slice_15_begin_0"), val = tensor<int32, [3]>([0, 35, 0])];
            tensor<int32, [3]> slice_15_end_0 = const()[name = tensor<string, []>("slice_15_end_0"), val = tensor<int32, [3]>([1, 40, 801])];
            tensor<bool, [3]> slice_15_end_mask_0 = const()[name = tensor<string, []>("slice_15_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_15_cast_fp16 = slice_by_index(begin = slice_15_begin_0, end = slice_15_end_0, end_mask = slice_15_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_15_cast_fp16")];
            tensor<int32, [3]> slice_16_begin_0 = const()[name = tensor<string, []>("slice_16_begin_0"), val = tensor<int32, [3]>([0, 35, 0])];
            tensor<int32, [3]> slice_16_end_0 = const()[name = tensor<string, []>("slice_16_end_0"), val = tensor<int32, [3]>([1, 40, 801])];
            tensor<bool, [3]> slice_16_end_mask_0 = const()[name = tensor<string, []>("slice_16_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_16_cast_fp16 = slice_by_index(begin = slice_16_begin_0, end = slice_16_end_0, end_mask = slice_16_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_16_cast_fp16")];
            tensor<fp16, []> const_164_promoted_to_fp16 = const()[name = tensor<string, []>("const_164_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_22_cast_fp16 = pow(x = slice_15_cast_fp16, y = const_164_promoted_to_fp16)[name = tensor<string, []>("pow_22_cast_fp16")];
            tensor<fp16, []> const_165_promoted_to_fp16 = const()[name = tensor<string, []>("const_165_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_23_cast_fp16 = pow(x = slice_16_cast_fp16, y = const_165_promoted_to_fp16)[name = tensor<string, []>("pow_23_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_21_cast_fp16 = add(x = pow_22_cast_fp16, y = pow_23_cast_fp16)[name = tensor<string, []>("add_21_cast_fp16")];
            tensor<int32, [1]> sum_8_axes_0 = const()[name = tensor<string, []>("sum_8_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_8_keep_dims_0 = const()[name = tensor<string, []>("sum_8_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_8_cast_fp16 = reduce_sum(axes = sum_8_axes_0, keep_dims = sum_8_keep_dims_0, x = add_21_cast_fp16)[name = tensor<string, []>("sum_8_cast_fp16")];
            tensor<fp16, []> const_168_to_fp16 = const()[name = tensor<string, []>("const_168_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_22_cast_fp16 = add(x = sum_8_cast_fp16, y = const_168_to_fp16)[name = tensor<string, []>("add_22_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_7_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor<string, []>("sqrt_7_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_14_cast_fp16 = real_div(x = slice_15_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor<string, []>("div_14_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_15_cast_fp16 = real_div(x = slice_16_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor<string, []>("div_15_cast_fp16")];
            tensor<fp32, []> log_7_epsilon_0 = const()[name = tensor<string, []>("log_7_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_7_cast_fp16 = log(epsilon = log_7_epsilon_0, x = sqrt_7_cast_fp16)[name = tensor<string, []>("log_7_cast_fp16")];
            tensor<int32, []> const_169 = const()[name = tensor<string, []>("const_169"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_7_interleave_0 = const()[name = tensor<string, []>("cat_7_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_7_cast_fp16 = concat(axis = const_169, interleave = cat_7_interleave_0, values = (div_14_cast_fp16, div_15_cast_fp16, log_7_cast_fp16))[name = tensor<string, []>("cat_7_cast_fp16")];
            tensor<int32, [1]> unsqueeze_15_axes_0 = const()[name = tensor<string, []>("unsqueeze_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_15_cast_fp16 = expand_dims(axes = unsqueeze_15_axes_0, x = cat_7_cast_fp16)[name = tensor<string, []>("unsqueeze_15_cast_fp16")];
            tensor<fp16, []> const_174_promoted_to_fp16 = const()[name = tensor<string, []>("const_174_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_24_cast_fp16 = pow(x = unsqueeze_15_cast_fp16, y = const_174_promoted_to_fp16)[name = tensor<string, []>("pow_24_cast_fp16")];
            tensor<int32, [1]> mean_7_axes_0 = const()[name = tensor<string, []>("mean_7_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_7_keep_dims_0 = const()[name = tensor<string, []>("mean_7_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_7_cast_fp16 = reduce_mean(axes = mean_7_axes_0, keep_dims = mean_7_keep_dims_0, x = pow_24_cast_fp16)[name = tensor<string, []>("mean_7_cast_fp16")];
            tensor<fp16, []> const_177_to_fp16 = const()[name = tensor<string, []>("const_177_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_23_cast_fp16 = add(x = mean_7_cast_fp16, y = const_177_to_fp16)[name = tensor<string, []>("add_23_cast_fp16")];
            tensor<fp32, []> rsqrt_7_epsilon_0 = const()[name = tensor<string, []>("rsqrt_7_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_7_cast_fp16 = rsqrt(epsilon = rsqrt_7_epsilon_0, x = add_23_cast_fp16)[name = tensor<string, []>("rsqrt_7_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_14_cast_fp16 = mul(x = unsqueeze_15_cast_fp16, y = rsqrt_7_cast_fp16)[name = tensor<string, []>("mul_14_cast_fp16")];
            tensor<int32, [1]> squeeze_7_axes_0 = const()[name = tensor<string, []>("squeeze_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = mul_14_cast_fp16)[name = tensor<string, []>("squeeze_7_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_7_to_fp16 = const()[name = tensor<string, []>("view_7_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44864)))];
            tensor<fp16, [1, 11, 801]> mul_15_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = view_7_to_fp16)[name = tensor<string, []>("mul_15_cast_fp16")];
            tensor<string, []> conv1d_7_pad_type_0 = const()[name = tensor<string, []>("conv1d_7_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_7_strides_0 = const()[name = tensor<string, []>("conv1d_7_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_7_pad_0 = const()[name = tensor<string, []>("conv1d_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_7_dilations_0 = const()[name = tensor<string, []>("conv1d_7_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_7_groups_0 = const()[name = tensor<string, []>("conv1d_7_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_7_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_7_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44992)))];
            tensor<fp16, [256]> p_bn_7_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_7_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50688)))];
            tensor<fp16, [1, 256, 801]> conv1d_7_cast_fp16 = conv(bias = p_bn_7_1_bias_to_fp16, dilations = conv1d_7_dilations_0, groups = conv1d_7_groups_0, pad = conv1d_7_pad_0, pad_type = conv1d_7_pad_type_0, strides = conv1d_7_strides_0, weight = p_bn_7_1_weight_to_fp16, x = mul_15_cast_fp16)[name = tensor<string, []>("conv1d_7_cast_fp16")];
            tensor<int32, [3]> slice_17_begin_0 = const()[name = tensor<string, []>("slice_17_begin_0"), val = tensor<int32, [3]>([0, 40, 0])];
            tensor<int32, [3]> slice_17_end_0 = const()[name = tensor<string, []>("slice_17_end_0"), val = tensor<int32, [3]>([1, 45, 801])];
            tensor<bool, [3]> slice_17_end_mask_0 = const()[name = tensor<string, []>("slice_17_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_17_cast_fp16 = slice_by_index(begin = slice_17_begin_0, end = slice_17_end_0, end_mask = slice_17_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_17_cast_fp16")];
            tensor<int32, [3]> slice_18_begin_0 = const()[name = tensor<string, []>("slice_18_begin_0"), val = tensor<int32, [3]>([0, 40, 0])];
            tensor<int32, [3]> slice_18_end_0 = const()[name = tensor<string, []>("slice_18_end_0"), val = tensor<int32, [3]>([1, 45, 801])];
            tensor<bool, [3]> slice_18_end_mask_0 = const()[name = tensor<string, []>("slice_18_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_18_cast_fp16 = slice_by_index(begin = slice_18_begin_0, end = slice_18_end_0, end_mask = slice_18_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_18_cast_fp16")];
            tensor<fp16, []> const_186_promoted_to_fp16 = const()[name = tensor<string, []>("const_186_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_25_cast_fp16 = pow(x = slice_17_cast_fp16, y = const_186_promoted_to_fp16)[name = tensor<string, []>("pow_25_cast_fp16")];
            tensor<fp16, []> const_187_promoted_to_fp16 = const()[name = tensor<string, []>("const_187_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_26_cast_fp16 = pow(x = slice_18_cast_fp16, y = const_187_promoted_to_fp16)[name = tensor<string, []>("pow_26_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_24_cast_fp16 = add(x = pow_25_cast_fp16, y = pow_26_cast_fp16)[name = tensor<string, []>("add_24_cast_fp16")];
            tensor<int32, [1]> sum_9_axes_0 = const()[name = tensor<string, []>("sum_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_9_keep_dims_0 = const()[name = tensor<string, []>("sum_9_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_9_cast_fp16 = reduce_sum(axes = sum_9_axes_0, keep_dims = sum_9_keep_dims_0, x = add_24_cast_fp16)[name = tensor<string, []>("sum_9_cast_fp16")];
            tensor<fp16, []> const_190_to_fp16 = const()[name = tensor<string, []>("const_190_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_25_cast_fp16 = add(x = sum_9_cast_fp16, y = const_190_to_fp16)[name = tensor<string, []>("add_25_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_8_cast_fp16 = sqrt(x = add_25_cast_fp16)[name = tensor<string, []>("sqrt_8_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_16_cast_fp16 = real_div(x = slice_17_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor<string, []>("div_16_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_17_cast_fp16 = real_div(x = slice_18_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor<string, []>("div_17_cast_fp16")];
            tensor<fp32, []> log_8_epsilon_0 = const()[name = tensor<string, []>("log_8_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_8_cast_fp16 = log(epsilon = log_8_epsilon_0, x = sqrt_8_cast_fp16)[name = tensor<string, []>("log_8_cast_fp16")];
            tensor<int32, []> const_191 = const()[name = tensor<string, []>("const_191"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_8_interleave_0 = const()[name = tensor<string, []>("cat_8_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_8_cast_fp16 = concat(axis = const_191, interleave = cat_8_interleave_0, values = (div_16_cast_fp16, div_17_cast_fp16, log_8_cast_fp16))[name = tensor<string, []>("cat_8_cast_fp16")];
            tensor<int32, [1]> unsqueeze_17_axes_0 = const()[name = tensor<string, []>("unsqueeze_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_17_cast_fp16 = expand_dims(axes = unsqueeze_17_axes_0, x = cat_8_cast_fp16)[name = tensor<string, []>("unsqueeze_17_cast_fp16")];
            tensor<fp16, []> const_196_promoted_to_fp16 = const()[name = tensor<string, []>("const_196_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_27_cast_fp16 = pow(x = unsqueeze_17_cast_fp16, y = const_196_promoted_to_fp16)[name = tensor<string, []>("pow_27_cast_fp16")];
            tensor<int32, [1]> mean_8_axes_0 = const()[name = tensor<string, []>("mean_8_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_8_keep_dims_0 = const()[name = tensor<string, []>("mean_8_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_8_cast_fp16 = reduce_mean(axes = mean_8_axes_0, keep_dims = mean_8_keep_dims_0, x = pow_27_cast_fp16)[name = tensor<string, []>("mean_8_cast_fp16")];
            tensor<fp16, []> const_199_to_fp16 = const()[name = tensor<string, []>("const_199_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_26_cast_fp16 = add(x = mean_8_cast_fp16, y = const_199_to_fp16)[name = tensor<string, []>("add_26_cast_fp16")];
            tensor<fp32, []> rsqrt_8_epsilon_0 = const()[name = tensor<string, []>("rsqrt_8_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_8_cast_fp16 = rsqrt(epsilon = rsqrt_8_epsilon_0, x = add_26_cast_fp16)[name = tensor<string, []>("rsqrt_8_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_16_cast_fp16 = mul(x = unsqueeze_17_cast_fp16, y = rsqrt_8_cast_fp16)[name = tensor<string, []>("mul_16_cast_fp16")];
            tensor<int32, [1]> squeeze_8_axes_0 = const()[name = tensor<string, []>("squeeze_8_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = mul_16_cast_fp16)[name = tensor<string, []>("squeeze_8_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_8_to_fp16 = const()[name = tensor<string, []>("view_8_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51264)))];
            tensor<fp16, [1, 11, 801]> mul_17_cast_fp16 = mul(x = squeeze_8_cast_fp16, y = view_8_to_fp16)[name = tensor<string, []>("mul_17_cast_fp16")];
            tensor<string, []> conv1d_8_pad_type_0 = const()[name = tensor<string, []>("conv1d_8_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_8_strides_0 = const()[name = tensor<string, []>("conv1d_8_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_8_pad_0 = const()[name = tensor<string, []>("conv1d_8_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_8_dilations_0 = const()[name = tensor<string, []>("conv1d_8_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_8_groups_0 = const()[name = tensor<string, []>("conv1d_8_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_8_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_8_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51392)))];
            tensor<fp16, [256]> p_bn_8_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_8_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57088)))];
            tensor<fp16, [1, 256, 801]> conv1d_8_cast_fp16 = conv(bias = p_bn_8_1_bias_to_fp16, dilations = conv1d_8_dilations_0, groups = conv1d_8_groups_0, pad = conv1d_8_pad_0, pad_type = conv1d_8_pad_type_0, strides = conv1d_8_strides_0, weight = p_bn_8_1_weight_to_fp16, x = mul_17_cast_fp16)[name = tensor<string, []>("conv1d_8_cast_fp16")];
            tensor<int32, [3]> slice_19_begin_0 = const()[name = tensor<string, []>("slice_19_begin_0"), val = tensor<int32, [3]>([0, 45, 0])];
            tensor<int32, [3]> slice_19_end_0 = const()[name = tensor<string, []>("slice_19_end_0"), val = tensor<int32, [3]>([1, 50, 801])];
            tensor<bool, [3]> slice_19_end_mask_0 = const()[name = tensor<string, []>("slice_19_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_19_cast_fp16 = slice_by_index(begin = slice_19_begin_0, end = slice_19_end_0, end_mask = slice_19_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_19_cast_fp16")];
            tensor<int32, [3]> slice_20_begin_0 = const()[name = tensor<string, []>("slice_20_begin_0"), val = tensor<int32, [3]>([0, 45, 0])];
            tensor<int32, [3]> slice_20_end_0 = const()[name = tensor<string, []>("slice_20_end_0"), val = tensor<int32, [3]>([1, 50, 801])];
            tensor<bool, [3]> slice_20_end_mask_0 = const()[name = tensor<string, []>("slice_20_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_20_cast_fp16 = slice_by_index(begin = slice_20_begin_0, end = slice_20_end_0, end_mask = slice_20_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_20_cast_fp16")];
            tensor<fp16, []> const_208_promoted_to_fp16 = const()[name = tensor<string, []>("const_208_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_28_cast_fp16 = pow(x = slice_19_cast_fp16, y = const_208_promoted_to_fp16)[name = tensor<string, []>("pow_28_cast_fp16")];
            tensor<fp16, []> const_209_promoted_to_fp16 = const()[name = tensor<string, []>("const_209_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_29_cast_fp16 = pow(x = slice_20_cast_fp16, y = const_209_promoted_to_fp16)[name = tensor<string, []>("pow_29_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_27_cast_fp16 = add(x = pow_28_cast_fp16, y = pow_29_cast_fp16)[name = tensor<string, []>("add_27_cast_fp16")];
            tensor<int32, [1]> sum_10_axes_0 = const()[name = tensor<string, []>("sum_10_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_10_keep_dims_0 = const()[name = tensor<string, []>("sum_10_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_10_cast_fp16 = reduce_sum(axes = sum_10_axes_0, keep_dims = sum_10_keep_dims_0, x = add_27_cast_fp16)[name = tensor<string, []>("sum_10_cast_fp16")];
            tensor<fp16, []> const_212_to_fp16 = const()[name = tensor<string, []>("const_212_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_28_cast_fp16 = add(x = sum_10_cast_fp16, y = const_212_to_fp16)[name = tensor<string, []>("add_28_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_9_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor<string, []>("sqrt_9_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_18_cast_fp16 = real_div(x = slice_19_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor<string, []>("div_18_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_19_cast_fp16 = real_div(x = slice_20_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor<string, []>("div_19_cast_fp16")];
            tensor<fp32, []> log_9_epsilon_0 = const()[name = tensor<string, []>("log_9_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_9_cast_fp16 = log(epsilon = log_9_epsilon_0, x = sqrt_9_cast_fp16)[name = tensor<string, []>("log_9_cast_fp16")];
            tensor<int32, []> const_213 = const()[name = tensor<string, []>("const_213"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_9_interleave_0 = const()[name = tensor<string, []>("cat_9_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_9_cast_fp16 = concat(axis = const_213, interleave = cat_9_interleave_0, values = (div_18_cast_fp16, div_19_cast_fp16, log_9_cast_fp16))[name = tensor<string, []>("cat_9_cast_fp16")];
            tensor<int32, [1]> unsqueeze_19_axes_0 = const()[name = tensor<string, []>("unsqueeze_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_19_cast_fp16 = expand_dims(axes = unsqueeze_19_axes_0, x = cat_9_cast_fp16)[name = tensor<string, []>("unsqueeze_19_cast_fp16")];
            tensor<fp16, []> const_218_promoted_to_fp16 = const()[name = tensor<string, []>("const_218_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_30_cast_fp16 = pow(x = unsqueeze_19_cast_fp16, y = const_218_promoted_to_fp16)[name = tensor<string, []>("pow_30_cast_fp16")];
            tensor<int32, [1]> mean_9_axes_0 = const()[name = tensor<string, []>("mean_9_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_9_keep_dims_0 = const()[name = tensor<string, []>("mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_9_cast_fp16 = reduce_mean(axes = mean_9_axes_0, keep_dims = mean_9_keep_dims_0, x = pow_30_cast_fp16)[name = tensor<string, []>("mean_9_cast_fp16")];
            tensor<fp16, []> const_221_to_fp16 = const()[name = tensor<string, []>("const_221_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_29_cast_fp16 = add(x = mean_9_cast_fp16, y = const_221_to_fp16)[name = tensor<string, []>("add_29_cast_fp16")];
            tensor<fp32, []> rsqrt_9_epsilon_0 = const()[name = tensor<string, []>("rsqrt_9_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_9_cast_fp16 = rsqrt(epsilon = rsqrt_9_epsilon_0, x = add_29_cast_fp16)[name = tensor<string, []>("rsqrt_9_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_18_cast_fp16 = mul(x = unsqueeze_19_cast_fp16, y = rsqrt_9_cast_fp16)[name = tensor<string, []>("mul_18_cast_fp16")];
            tensor<int32, [1]> squeeze_9_axes_0 = const()[name = tensor<string, []>("squeeze_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = mul_18_cast_fp16)[name = tensor<string, []>("squeeze_9_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_9_to_fp16 = const()[name = tensor<string, []>("view_9_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57664)))];
            tensor<fp16, [1, 11, 801]> mul_19_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = view_9_to_fp16)[name = tensor<string, []>("mul_19_cast_fp16")];
            tensor<string, []> conv1d_9_pad_type_0 = const()[name = tensor<string, []>("conv1d_9_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_9_strides_0 = const()[name = tensor<string, []>("conv1d_9_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_9_pad_0 = const()[name = tensor<string, []>("conv1d_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_9_dilations_0 = const()[name = tensor<string, []>("conv1d_9_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_9_groups_0 = const()[name = tensor<string, []>("conv1d_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_9_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_9_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57792)))];
            tensor<fp16, [256]> p_bn_9_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_9_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63488)))];
            tensor<fp16, [1, 256, 801]> conv1d_9_cast_fp16 = conv(bias = p_bn_9_1_bias_to_fp16, dilations = conv1d_9_dilations_0, groups = conv1d_9_groups_0, pad = conv1d_9_pad_0, pad_type = conv1d_9_pad_type_0, strides = conv1d_9_strides_0, weight = p_bn_9_1_weight_to_fp16, x = mul_19_cast_fp16)[name = tensor<string, []>("conv1d_9_cast_fp16")];
            tensor<int32, [3]> slice_21_begin_0 = const()[name = tensor<string, []>("slice_21_begin_0"), val = tensor<int32, [3]>([0, 50, 0])];
            tensor<int32, [3]> slice_21_end_0 = const()[name = tensor<string, []>("slice_21_end_0"), val = tensor<int32, [3]>([1, 55, 801])];
            tensor<bool, [3]> slice_21_end_mask_0 = const()[name = tensor<string, []>("slice_21_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_21_cast_fp16 = slice_by_index(begin = slice_21_begin_0, end = slice_21_end_0, end_mask = slice_21_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_21_cast_fp16")];
            tensor<int32, [3]> slice_22_begin_0 = const()[name = tensor<string, []>("slice_22_begin_0"), val = tensor<int32, [3]>([0, 50, 0])];
            tensor<int32, [3]> slice_22_end_0 = const()[name = tensor<string, []>("slice_22_end_0"), val = tensor<int32, [3]>([1, 55, 801])];
            tensor<bool, [3]> slice_22_end_mask_0 = const()[name = tensor<string, []>("slice_22_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_22_cast_fp16 = slice_by_index(begin = slice_22_begin_0, end = slice_22_end_0, end_mask = slice_22_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_22_cast_fp16")];
            tensor<fp16, []> const_230_promoted_to_fp16 = const()[name = tensor<string, []>("const_230_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_31_cast_fp16 = pow(x = slice_21_cast_fp16, y = const_230_promoted_to_fp16)[name = tensor<string, []>("pow_31_cast_fp16")];
            tensor<fp16, []> const_231_promoted_to_fp16 = const()[name = tensor<string, []>("const_231_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_32_cast_fp16 = pow(x = slice_22_cast_fp16, y = const_231_promoted_to_fp16)[name = tensor<string, []>("pow_32_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_30_cast_fp16 = add(x = pow_31_cast_fp16, y = pow_32_cast_fp16)[name = tensor<string, []>("add_30_cast_fp16")];
            tensor<int32, [1]> sum_11_axes_0 = const()[name = tensor<string, []>("sum_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_11_keep_dims_0 = const()[name = tensor<string, []>("sum_11_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_11_cast_fp16 = reduce_sum(axes = sum_11_axes_0, keep_dims = sum_11_keep_dims_0, x = add_30_cast_fp16)[name = tensor<string, []>("sum_11_cast_fp16")];
            tensor<fp16, []> const_234_to_fp16 = const()[name = tensor<string, []>("const_234_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_31_cast_fp16 = add(x = sum_11_cast_fp16, y = const_234_to_fp16)[name = tensor<string, []>("add_31_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_10_cast_fp16 = sqrt(x = add_31_cast_fp16)[name = tensor<string, []>("sqrt_10_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_20_cast_fp16 = real_div(x = slice_21_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor<string, []>("div_20_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_21_cast_fp16 = real_div(x = slice_22_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor<string, []>("div_21_cast_fp16")];
            tensor<fp32, []> log_10_epsilon_0 = const()[name = tensor<string, []>("log_10_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_10_cast_fp16 = log(epsilon = log_10_epsilon_0, x = sqrt_10_cast_fp16)[name = tensor<string, []>("log_10_cast_fp16")];
            tensor<int32, []> const_235 = const()[name = tensor<string, []>("const_235"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_10_interleave_0 = const()[name = tensor<string, []>("cat_10_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_10_cast_fp16 = concat(axis = const_235, interleave = cat_10_interleave_0, values = (div_20_cast_fp16, div_21_cast_fp16, log_10_cast_fp16))[name = tensor<string, []>("cat_10_cast_fp16")];
            tensor<int32, [1]> unsqueeze_21_axes_0 = const()[name = tensor<string, []>("unsqueeze_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_21_cast_fp16 = expand_dims(axes = unsqueeze_21_axes_0, x = cat_10_cast_fp16)[name = tensor<string, []>("unsqueeze_21_cast_fp16")];
            tensor<fp16, []> const_240_promoted_to_fp16 = const()[name = tensor<string, []>("const_240_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_33_cast_fp16 = pow(x = unsqueeze_21_cast_fp16, y = const_240_promoted_to_fp16)[name = tensor<string, []>("pow_33_cast_fp16")];
            tensor<int32, [1]> mean_10_axes_0 = const()[name = tensor<string, []>("mean_10_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_10_keep_dims_0 = const()[name = tensor<string, []>("mean_10_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_10_cast_fp16 = reduce_mean(axes = mean_10_axes_0, keep_dims = mean_10_keep_dims_0, x = pow_33_cast_fp16)[name = tensor<string, []>("mean_10_cast_fp16")];
            tensor<fp16, []> const_243_to_fp16 = const()[name = tensor<string, []>("const_243_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_32_cast_fp16 = add(x = mean_10_cast_fp16, y = const_243_to_fp16)[name = tensor<string, []>("add_32_cast_fp16")];
            tensor<fp32, []> rsqrt_10_epsilon_0 = const()[name = tensor<string, []>("rsqrt_10_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_10_cast_fp16 = rsqrt(epsilon = rsqrt_10_epsilon_0, x = add_32_cast_fp16)[name = tensor<string, []>("rsqrt_10_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_20_cast_fp16 = mul(x = unsqueeze_21_cast_fp16, y = rsqrt_10_cast_fp16)[name = tensor<string, []>("mul_20_cast_fp16")];
            tensor<int32, [1]> squeeze_10_axes_0 = const()[name = tensor<string, []>("squeeze_10_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = mul_20_cast_fp16)[name = tensor<string, []>("squeeze_10_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_10_to_fp16 = const()[name = tensor<string, []>("view_10_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64064)))];
            tensor<fp16, [1, 11, 801]> mul_21_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = view_10_to_fp16)[name = tensor<string, []>("mul_21_cast_fp16")];
            tensor<string, []> conv1d_10_pad_type_0 = const()[name = tensor<string, []>("conv1d_10_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_10_strides_0 = const()[name = tensor<string, []>("conv1d_10_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_10_pad_0 = const()[name = tensor<string, []>("conv1d_10_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_10_dilations_0 = const()[name = tensor<string, []>("conv1d_10_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_10_groups_0 = const()[name = tensor<string, []>("conv1d_10_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_10_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_10_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64192)))];
            tensor<fp16, [256]> p_bn_10_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_10_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69888)))];
            tensor<fp16, [1, 256, 801]> conv1d_10_cast_fp16 = conv(bias = p_bn_10_1_bias_to_fp16, dilations = conv1d_10_dilations_0, groups = conv1d_10_groups_0, pad = conv1d_10_pad_0, pad_type = conv1d_10_pad_type_0, strides = conv1d_10_strides_0, weight = p_bn_10_1_weight_to_fp16, x = mul_21_cast_fp16)[name = tensor<string, []>("conv1d_10_cast_fp16")];
            tensor<int32, [3]> slice_23_begin_0 = const()[name = tensor<string, []>("slice_23_begin_0"), val = tensor<int32, [3]>([0, 55, 0])];
            tensor<int32, [3]> slice_23_end_0 = const()[name = tensor<string, []>("slice_23_end_0"), val = tensor<int32, [3]>([1, 60, 801])];
            tensor<bool, [3]> slice_23_end_mask_0 = const()[name = tensor<string, []>("slice_23_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_23_cast_fp16 = slice_by_index(begin = slice_23_begin_0, end = slice_23_end_0, end_mask = slice_23_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_23_cast_fp16")];
            tensor<int32, [3]> slice_24_begin_0 = const()[name = tensor<string, []>("slice_24_begin_0"), val = tensor<int32, [3]>([0, 55, 0])];
            tensor<int32, [3]> slice_24_end_0 = const()[name = tensor<string, []>("slice_24_end_0"), val = tensor<int32, [3]>([1, 60, 801])];
            tensor<bool, [3]> slice_24_end_mask_0 = const()[name = tensor<string, []>("slice_24_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_24_cast_fp16 = slice_by_index(begin = slice_24_begin_0, end = slice_24_end_0, end_mask = slice_24_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_24_cast_fp16")];
            tensor<fp16, []> const_252_promoted_to_fp16 = const()[name = tensor<string, []>("const_252_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_34_cast_fp16 = pow(x = slice_23_cast_fp16, y = const_252_promoted_to_fp16)[name = tensor<string, []>("pow_34_cast_fp16")];
            tensor<fp16, []> const_253_promoted_to_fp16 = const()[name = tensor<string, []>("const_253_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_35_cast_fp16 = pow(x = slice_24_cast_fp16, y = const_253_promoted_to_fp16)[name = tensor<string, []>("pow_35_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_33_cast_fp16 = add(x = pow_34_cast_fp16, y = pow_35_cast_fp16)[name = tensor<string, []>("add_33_cast_fp16")];
            tensor<int32, [1]> sum_12_axes_0 = const()[name = tensor<string, []>("sum_12_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_12_keep_dims_0 = const()[name = tensor<string, []>("sum_12_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_12_cast_fp16 = reduce_sum(axes = sum_12_axes_0, keep_dims = sum_12_keep_dims_0, x = add_33_cast_fp16)[name = tensor<string, []>("sum_12_cast_fp16")];
            tensor<fp16, []> const_256_to_fp16 = const()[name = tensor<string, []>("const_256_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_34_cast_fp16 = add(x = sum_12_cast_fp16, y = const_256_to_fp16)[name = tensor<string, []>("add_34_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_11_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor<string, []>("sqrt_11_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_22_cast_fp16 = real_div(x = slice_23_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor<string, []>("div_22_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_23_cast_fp16 = real_div(x = slice_24_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor<string, []>("div_23_cast_fp16")];
            tensor<fp32, []> log_11_epsilon_0 = const()[name = tensor<string, []>("log_11_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_11_cast_fp16 = log(epsilon = log_11_epsilon_0, x = sqrt_11_cast_fp16)[name = tensor<string, []>("log_11_cast_fp16")];
            tensor<int32, []> const_257 = const()[name = tensor<string, []>("const_257"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_11_interleave_0 = const()[name = tensor<string, []>("cat_11_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_11_cast_fp16 = concat(axis = const_257, interleave = cat_11_interleave_0, values = (div_22_cast_fp16, div_23_cast_fp16, log_11_cast_fp16))[name = tensor<string, []>("cat_11_cast_fp16")];
            tensor<int32, [1]> unsqueeze_23_axes_0 = const()[name = tensor<string, []>("unsqueeze_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_23_cast_fp16 = expand_dims(axes = unsqueeze_23_axes_0, x = cat_11_cast_fp16)[name = tensor<string, []>("unsqueeze_23_cast_fp16")];
            tensor<fp16, []> const_262_promoted_to_fp16 = const()[name = tensor<string, []>("const_262_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_36_cast_fp16 = pow(x = unsqueeze_23_cast_fp16, y = const_262_promoted_to_fp16)[name = tensor<string, []>("pow_36_cast_fp16")];
            tensor<int32, [1]> mean_11_axes_0 = const()[name = tensor<string, []>("mean_11_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_11_keep_dims_0 = const()[name = tensor<string, []>("mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_11_cast_fp16 = reduce_mean(axes = mean_11_axes_0, keep_dims = mean_11_keep_dims_0, x = pow_36_cast_fp16)[name = tensor<string, []>("mean_11_cast_fp16")];
            tensor<fp16, []> const_265_to_fp16 = const()[name = tensor<string, []>("const_265_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_35_cast_fp16 = add(x = mean_11_cast_fp16, y = const_265_to_fp16)[name = tensor<string, []>("add_35_cast_fp16")];
            tensor<fp32, []> rsqrt_11_epsilon_0 = const()[name = tensor<string, []>("rsqrt_11_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_11_cast_fp16 = rsqrt(epsilon = rsqrt_11_epsilon_0, x = add_35_cast_fp16)[name = tensor<string, []>("rsqrt_11_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_22_cast_fp16 = mul(x = unsqueeze_23_cast_fp16, y = rsqrt_11_cast_fp16)[name = tensor<string, []>("mul_22_cast_fp16")];
            tensor<int32, [1]> squeeze_11_axes_0 = const()[name = tensor<string, []>("squeeze_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = mul_22_cast_fp16)[name = tensor<string, []>("squeeze_11_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_11_to_fp16 = const()[name = tensor<string, []>("view_11_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(70464)))];
            tensor<fp16, [1, 11, 801]> mul_23_cast_fp16 = mul(x = squeeze_11_cast_fp16, y = view_11_to_fp16)[name = tensor<string, []>("mul_23_cast_fp16")];
            tensor<string, []> conv1d_11_pad_type_0 = const()[name = tensor<string, []>("conv1d_11_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_11_strides_0 = const()[name = tensor<string, []>("conv1d_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_11_pad_0 = const()[name = tensor<string, []>("conv1d_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_11_dilations_0 = const()[name = tensor<string, []>("conv1d_11_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_11_groups_0 = const()[name = tensor<string, []>("conv1d_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_11_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_11_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(70592)))];
            tensor<fp16, [256]> p_bn_11_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_11_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76288)))];
            tensor<fp16, [1, 256, 801]> conv1d_11_cast_fp16 = conv(bias = p_bn_11_1_bias_to_fp16, dilations = conv1d_11_dilations_0, groups = conv1d_11_groups_0, pad = conv1d_11_pad_0, pad_type = conv1d_11_pad_type_0, strides = conv1d_11_strides_0, weight = p_bn_11_1_weight_to_fp16, x = mul_23_cast_fp16)[name = tensor<string, []>("conv1d_11_cast_fp16")];
            tensor<int32, [3]> slice_25_begin_0 = const()[name = tensor<string, []>("slice_25_begin_0"), val = tensor<int32, [3]>([0, 60, 0])];
            tensor<int32, [3]> slice_25_end_0 = const()[name = tensor<string, []>("slice_25_end_0"), val = tensor<int32, [3]>([1, 65, 801])];
            tensor<bool, [3]> slice_25_end_mask_0 = const()[name = tensor<string, []>("slice_25_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_25_cast_fp16 = slice_by_index(begin = slice_25_begin_0, end = slice_25_end_0, end_mask = slice_25_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_25_cast_fp16")];
            tensor<int32, [3]> slice_26_begin_0 = const()[name = tensor<string, []>("slice_26_begin_0"), val = tensor<int32, [3]>([0, 60, 0])];
            tensor<int32, [3]> slice_26_end_0 = const()[name = tensor<string, []>("slice_26_end_0"), val = tensor<int32, [3]>([1, 65, 801])];
            tensor<bool, [3]> slice_26_end_mask_0 = const()[name = tensor<string, []>("slice_26_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_26_cast_fp16 = slice_by_index(begin = slice_26_begin_0, end = slice_26_end_0, end_mask = slice_26_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_26_cast_fp16")];
            tensor<fp16, []> const_274_promoted_to_fp16 = const()[name = tensor<string, []>("const_274_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_37_cast_fp16 = pow(x = slice_25_cast_fp16, y = const_274_promoted_to_fp16)[name = tensor<string, []>("pow_37_cast_fp16")];
            tensor<fp16, []> const_275_promoted_to_fp16 = const()[name = tensor<string, []>("const_275_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_38_cast_fp16 = pow(x = slice_26_cast_fp16, y = const_275_promoted_to_fp16)[name = tensor<string, []>("pow_38_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_36_cast_fp16 = add(x = pow_37_cast_fp16, y = pow_38_cast_fp16)[name = tensor<string, []>("add_36_cast_fp16")];
            tensor<int32, [1]> sum_13_axes_0 = const()[name = tensor<string, []>("sum_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_13_keep_dims_0 = const()[name = tensor<string, []>("sum_13_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_13_cast_fp16 = reduce_sum(axes = sum_13_axes_0, keep_dims = sum_13_keep_dims_0, x = add_36_cast_fp16)[name = tensor<string, []>("sum_13_cast_fp16")];
            tensor<fp16, []> const_278_to_fp16 = const()[name = tensor<string, []>("const_278_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_37_cast_fp16 = add(x = sum_13_cast_fp16, y = const_278_to_fp16)[name = tensor<string, []>("add_37_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_12_cast_fp16 = sqrt(x = add_37_cast_fp16)[name = tensor<string, []>("sqrt_12_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_24_cast_fp16 = real_div(x = slice_25_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor<string, []>("div_24_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_25_cast_fp16 = real_div(x = slice_26_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor<string, []>("div_25_cast_fp16")];
            tensor<fp32, []> log_12_epsilon_0 = const()[name = tensor<string, []>("log_12_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_12_cast_fp16 = log(epsilon = log_12_epsilon_0, x = sqrt_12_cast_fp16)[name = tensor<string, []>("log_12_cast_fp16")];
            tensor<int32, []> const_279 = const()[name = tensor<string, []>("const_279"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_12_interleave_0 = const()[name = tensor<string, []>("cat_12_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_12_cast_fp16 = concat(axis = const_279, interleave = cat_12_interleave_0, values = (div_24_cast_fp16, div_25_cast_fp16, log_12_cast_fp16))[name = tensor<string, []>("cat_12_cast_fp16")];
            tensor<int32, [1]> unsqueeze_25_axes_0 = const()[name = tensor<string, []>("unsqueeze_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_25_cast_fp16 = expand_dims(axes = unsqueeze_25_axes_0, x = cat_12_cast_fp16)[name = tensor<string, []>("unsqueeze_25_cast_fp16")];
            tensor<fp16, []> const_284_promoted_to_fp16 = const()[name = tensor<string, []>("const_284_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_39_cast_fp16 = pow(x = unsqueeze_25_cast_fp16, y = const_284_promoted_to_fp16)[name = tensor<string, []>("pow_39_cast_fp16")];
            tensor<int32, [1]> mean_12_axes_0 = const()[name = tensor<string, []>("mean_12_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_12_keep_dims_0 = const()[name = tensor<string, []>("mean_12_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_12_cast_fp16 = reduce_mean(axes = mean_12_axes_0, keep_dims = mean_12_keep_dims_0, x = pow_39_cast_fp16)[name = tensor<string, []>("mean_12_cast_fp16")];
            tensor<fp16, []> const_287_to_fp16 = const()[name = tensor<string, []>("const_287_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_38_cast_fp16 = add(x = mean_12_cast_fp16, y = const_287_to_fp16)[name = tensor<string, []>("add_38_cast_fp16")];
            tensor<fp32, []> rsqrt_12_epsilon_0 = const()[name = tensor<string, []>("rsqrt_12_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_12_cast_fp16 = rsqrt(epsilon = rsqrt_12_epsilon_0, x = add_38_cast_fp16)[name = tensor<string, []>("rsqrt_12_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_24_cast_fp16 = mul(x = unsqueeze_25_cast_fp16, y = rsqrt_12_cast_fp16)[name = tensor<string, []>("mul_24_cast_fp16")];
            tensor<int32, [1]> squeeze_12_axes_0 = const()[name = tensor<string, []>("squeeze_12_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = mul_24_cast_fp16)[name = tensor<string, []>("squeeze_12_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_12_to_fp16 = const()[name = tensor<string, []>("view_12_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76864)))];
            tensor<fp16, [1, 11, 801]> mul_25_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = view_12_to_fp16)[name = tensor<string, []>("mul_25_cast_fp16")];
            tensor<string, []> conv1d_12_pad_type_0 = const()[name = tensor<string, []>("conv1d_12_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_12_strides_0 = const()[name = tensor<string, []>("conv1d_12_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_12_pad_0 = const()[name = tensor<string, []>("conv1d_12_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_12_dilations_0 = const()[name = tensor<string, []>("conv1d_12_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_12_groups_0 = const()[name = tensor<string, []>("conv1d_12_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_12_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_12_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76992)))];
            tensor<fp16, [256]> p_bn_12_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_12_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82688)))];
            tensor<fp16, [1, 256, 801]> conv1d_12_cast_fp16 = conv(bias = p_bn_12_1_bias_to_fp16, dilations = conv1d_12_dilations_0, groups = conv1d_12_groups_0, pad = conv1d_12_pad_0, pad_type = conv1d_12_pad_type_0, strides = conv1d_12_strides_0, weight = p_bn_12_1_weight_to_fp16, x = mul_25_cast_fp16)[name = tensor<string, []>("conv1d_12_cast_fp16")];
            tensor<int32, [3]> slice_27_begin_0 = const()[name = tensor<string, []>("slice_27_begin_0"), val = tensor<int32, [3]>([0, 65, 0])];
            tensor<int32, [3]> slice_27_end_0 = const()[name = tensor<string, []>("slice_27_end_0"), val = tensor<int32, [3]>([1, 70, 801])];
            tensor<bool, [3]> slice_27_end_mask_0 = const()[name = tensor<string, []>("slice_27_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_27_cast_fp16 = slice_by_index(begin = slice_27_begin_0, end = slice_27_end_0, end_mask = slice_27_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_27_cast_fp16")];
            tensor<int32, [3]> slice_28_begin_0 = const()[name = tensor<string, []>("slice_28_begin_0"), val = tensor<int32, [3]>([0, 65, 0])];
            tensor<int32, [3]> slice_28_end_0 = const()[name = tensor<string, []>("slice_28_end_0"), val = tensor<int32, [3]>([1, 70, 801])];
            tensor<bool, [3]> slice_28_end_mask_0 = const()[name = tensor<string, []>("slice_28_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_28_cast_fp16 = slice_by_index(begin = slice_28_begin_0, end = slice_28_end_0, end_mask = slice_28_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_28_cast_fp16")];
            tensor<fp16, []> const_296_promoted_to_fp16 = const()[name = tensor<string, []>("const_296_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_40_cast_fp16 = pow(x = slice_27_cast_fp16, y = const_296_promoted_to_fp16)[name = tensor<string, []>("pow_40_cast_fp16")];
            tensor<fp16, []> const_297_promoted_to_fp16 = const()[name = tensor<string, []>("const_297_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_41_cast_fp16 = pow(x = slice_28_cast_fp16, y = const_297_promoted_to_fp16)[name = tensor<string, []>("pow_41_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_39_cast_fp16 = add(x = pow_40_cast_fp16, y = pow_41_cast_fp16)[name = tensor<string, []>("add_39_cast_fp16")];
            tensor<int32, [1]> sum_14_axes_0 = const()[name = tensor<string, []>("sum_14_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_14_keep_dims_0 = const()[name = tensor<string, []>("sum_14_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_14_cast_fp16 = reduce_sum(axes = sum_14_axes_0, keep_dims = sum_14_keep_dims_0, x = add_39_cast_fp16)[name = tensor<string, []>("sum_14_cast_fp16")];
            tensor<fp16, []> const_300_to_fp16 = const()[name = tensor<string, []>("const_300_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_40_cast_fp16 = add(x = sum_14_cast_fp16, y = const_300_to_fp16)[name = tensor<string, []>("add_40_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_13_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor<string, []>("sqrt_13_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_26_cast_fp16 = real_div(x = slice_27_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor<string, []>("div_26_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_27_cast_fp16 = real_div(x = slice_28_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor<string, []>("div_27_cast_fp16")];
            tensor<fp32, []> log_13_epsilon_0 = const()[name = tensor<string, []>("log_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_13_cast_fp16 = log(epsilon = log_13_epsilon_0, x = sqrt_13_cast_fp16)[name = tensor<string, []>("log_13_cast_fp16")];
            tensor<int32, []> const_301 = const()[name = tensor<string, []>("const_301"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_13_interleave_0 = const()[name = tensor<string, []>("cat_13_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_13_cast_fp16 = concat(axis = const_301, interleave = cat_13_interleave_0, values = (div_26_cast_fp16, div_27_cast_fp16, log_13_cast_fp16))[name = tensor<string, []>("cat_13_cast_fp16")];
            tensor<int32, [1]> unsqueeze_27_axes_0 = const()[name = tensor<string, []>("unsqueeze_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_27_cast_fp16 = expand_dims(axes = unsqueeze_27_axes_0, x = cat_13_cast_fp16)[name = tensor<string, []>("unsqueeze_27_cast_fp16")];
            tensor<fp16, []> const_306_promoted_to_fp16 = const()[name = tensor<string, []>("const_306_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_42_cast_fp16 = pow(x = unsqueeze_27_cast_fp16, y = const_306_promoted_to_fp16)[name = tensor<string, []>("pow_42_cast_fp16")];
            tensor<int32, [1]> mean_13_axes_0 = const()[name = tensor<string, []>("mean_13_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_13_keep_dims_0 = const()[name = tensor<string, []>("mean_13_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_13_cast_fp16 = reduce_mean(axes = mean_13_axes_0, keep_dims = mean_13_keep_dims_0, x = pow_42_cast_fp16)[name = tensor<string, []>("mean_13_cast_fp16")];
            tensor<fp16, []> const_309_to_fp16 = const()[name = tensor<string, []>("const_309_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_41_cast_fp16 = add(x = mean_13_cast_fp16, y = const_309_to_fp16)[name = tensor<string, []>("add_41_cast_fp16")];
            tensor<fp32, []> rsqrt_13_epsilon_0 = const()[name = tensor<string, []>("rsqrt_13_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_13_cast_fp16 = rsqrt(epsilon = rsqrt_13_epsilon_0, x = add_41_cast_fp16)[name = tensor<string, []>("rsqrt_13_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_26_cast_fp16 = mul(x = unsqueeze_27_cast_fp16, y = rsqrt_13_cast_fp16)[name = tensor<string, []>("mul_26_cast_fp16")];
            tensor<int32, [1]> squeeze_13_axes_0 = const()[name = tensor<string, []>("squeeze_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = mul_26_cast_fp16)[name = tensor<string, []>("squeeze_13_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_13_to_fp16 = const()[name = tensor<string, []>("view_13_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(83264)))];
            tensor<fp16, [1, 11, 801]> mul_27_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = view_13_to_fp16)[name = tensor<string, []>("mul_27_cast_fp16")];
            tensor<string, []> conv1d_13_pad_type_0 = const()[name = tensor<string, []>("conv1d_13_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_13_strides_0 = const()[name = tensor<string, []>("conv1d_13_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_13_pad_0 = const()[name = tensor<string, []>("conv1d_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_13_dilations_0 = const()[name = tensor<string, []>("conv1d_13_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_13_groups_0 = const()[name = tensor<string, []>("conv1d_13_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_13_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_13_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(83392)))];
            tensor<fp16, [256]> p_bn_13_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_13_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89088)))];
            tensor<fp16, [1, 256, 801]> conv1d_13_cast_fp16 = conv(bias = p_bn_13_1_bias_to_fp16, dilations = conv1d_13_dilations_0, groups = conv1d_13_groups_0, pad = conv1d_13_pad_0, pad_type = conv1d_13_pad_type_0, strides = conv1d_13_strides_0, weight = p_bn_13_1_weight_to_fp16, x = mul_27_cast_fp16)[name = tensor<string, []>("conv1d_13_cast_fp16")];
            tensor<int32, [3]> slice_29_begin_0 = const()[name = tensor<string, []>("slice_29_begin_0"), val = tensor<int32, [3]>([0, 70, 0])];
            tensor<int32, [3]> slice_29_end_0 = const()[name = tensor<string, []>("slice_29_end_0"), val = tensor<int32, [3]>([1, 75, 801])];
            tensor<bool, [3]> slice_29_end_mask_0 = const()[name = tensor<string, []>("slice_29_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_29_cast_fp16 = slice_by_index(begin = slice_29_begin_0, end = slice_29_end_0, end_mask = slice_29_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_29_cast_fp16")];
            tensor<int32, [3]> slice_30_begin_0 = const()[name = tensor<string, []>("slice_30_begin_0"), val = tensor<int32, [3]>([0, 70, 0])];
            tensor<int32, [3]> slice_30_end_0 = const()[name = tensor<string, []>("slice_30_end_0"), val = tensor<int32, [3]>([1, 75, 801])];
            tensor<bool, [3]> slice_30_end_mask_0 = const()[name = tensor<string, []>("slice_30_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_30_cast_fp16 = slice_by_index(begin = slice_30_begin_0, end = slice_30_end_0, end_mask = slice_30_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_30_cast_fp16")];
            tensor<fp16, []> const_318_promoted_to_fp16 = const()[name = tensor<string, []>("const_318_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_43_cast_fp16 = pow(x = slice_29_cast_fp16, y = const_318_promoted_to_fp16)[name = tensor<string, []>("pow_43_cast_fp16")];
            tensor<fp16, []> const_319_promoted_to_fp16 = const()[name = tensor<string, []>("const_319_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_44_cast_fp16 = pow(x = slice_30_cast_fp16, y = const_319_promoted_to_fp16)[name = tensor<string, []>("pow_44_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_42_cast_fp16 = add(x = pow_43_cast_fp16, y = pow_44_cast_fp16)[name = tensor<string, []>("add_42_cast_fp16")];
            tensor<int32, [1]> sum_15_axes_0 = const()[name = tensor<string, []>("sum_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_15_keep_dims_0 = const()[name = tensor<string, []>("sum_15_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_15_cast_fp16 = reduce_sum(axes = sum_15_axes_0, keep_dims = sum_15_keep_dims_0, x = add_42_cast_fp16)[name = tensor<string, []>("sum_15_cast_fp16")];
            tensor<fp16, []> const_322_to_fp16 = const()[name = tensor<string, []>("const_322_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_43_cast_fp16 = add(x = sum_15_cast_fp16, y = const_322_to_fp16)[name = tensor<string, []>("add_43_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_14_cast_fp16 = sqrt(x = add_43_cast_fp16)[name = tensor<string, []>("sqrt_14_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_28_cast_fp16 = real_div(x = slice_29_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor<string, []>("div_28_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_29_cast_fp16 = real_div(x = slice_30_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor<string, []>("div_29_cast_fp16")];
            tensor<fp32, []> log_14_epsilon_0 = const()[name = tensor<string, []>("log_14_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_14_cast_fp16 = log(epsilon = log_14_epsilon_0, x = sqrt_14_cast_fp16)[name = tensor<string, []>("log_14_cast_fp16")];
            tensor<int32, []> const_323 = const()[name = tensor<string, []>("const_323"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_14_interleave_0 = const()[name = tensor<string, []>("cat_14_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_14_cast_fp16 = concat(axis = const_323, interleave = cat_14_interleave_0, values = (div_28_cast_fp16, div_29_cast_fp16, log_14_cast_fp16))[name = tensor<string, []>("cat_14_cast_fp16")];
            tensor<int32, [1]> unsqueeze_29_axes_0 = const()[name = tensor<string, []>("unsqueeze_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_29_cast_fp16 = expand_dims(axes = unsqueeze_29_axes_0, x = cat_14_cast_fp16)[name = tensor<string, []>("unsqueeze_29_cast_fp16")];
            tensor<fp16, []> const_328_promoted_to_fp16 = const()[name = tensor<string, []>("const_328_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_45_cast_fp16 = pow(x = unsqueeze_29_cast_fp16, y = const_328_promoted_to_fp16)[name = tensor<string, []>("pow_45_cast_fp16")];
            tensor<int32, [1]> mean_14_axes_0 = const()[name = tensor<string, []>("mean_14_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_14_keep_dims_0 = const()[name = tensor<string, []>("mean_14_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_14_cast_fp16 = reduce_mean(axes = mean_14_axes_0, keep_dims = mean_14_keep_dims_0, x = pow_45_cast_fp16)[name = tensor<string, []>("mean_14_cast_fp16")];
            tensor<fp16, []> const_331_to_fp16 = const()[name = tensor<string, []>("const_331_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_44_cast_fp16 = add(x = mean_14_cast_fp16, y = const_331_to_fp16)[name = tensor<string, []>("add_44_cast_fp16")];
            tensor<fp32, []> rsqrt_14_epsilon_0 = const()[name = tensor<string, []>("rsqrt_14_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_14_cast_fp16 = rsqrt(epsilon = rsqrt_14_epsilon_0, x = add_44_cast_fp16)[name = tensor<string, []>("rsqrt_14_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_28_cast_fp16 = mul(x = unsqueeze_29_cast_fp16, y = rsqrt_14_cast_fp16)[name = tensor<string, []>("mul_28_cast_fp16")];
            tensor<int32, [1]> squeeze_14_axes_0 = const()[name = tensor<string, []>("squeeze_14_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = mul_28_cast_fp16)[name = tensor<string, []>("squeeze_14_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_14_to_fp16 = const()[name = tensor<string, []>("view_14_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89664)))];
            tensor<fp16, [1, 11, 801]> mul_29_cast_fp16 = mul(x = squeeze_14_cast_fp16, y = view_14_to_fp16)[name = tensor<string, []>("mul_29_cast_fp16")];
            tensor<string, []> conv1d_14_pad_type_0 = const()[name = tensor<string, []>("conv1d_14_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_14_strides_0 = const()[name = tensor<string, []>("conv1d_14_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_14_pad_0 = const()[name = tensor<string, []>("conv1d_14_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_14_dilations_0 = const()[name = tensor<string, []>("conv1d_14_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_14_groups_0 = const()[name = tensor<string, []>("conv1d_14_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_14_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_14_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89792)))];
            tensor<fp16, [256]> p_bn_14_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_14_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95488)))];
            tensor<fp16, [1, 256, 801]> conv1d_14_cast_fp16 = conv(bias = p_bn_14_1_bias_to_fp16, dilations = conv1d_14_dilations_0, groups = conv1d_14_groups_0, pad = conv1d_14_pad_0, pad_type = conv1d_14_pad_type_0, strides = conv1d_14_strides_0, weight = p_bn_14_1_weight_to_fp16, x = mul_29_cast_fp16)[name = tensor<string, []>("conv1d_14_cast_fp16")];
            tensor<int32, [3]> slice_31_begin_0 = const()[name = tensor<string, []>("slice_31_begin_0"), val = tensor<int32, [3]>([0, 75, 0])];
            tensor<int32, [3]> slice_31_end_0 = const()[name = tensor<string, []>("slice_31_end_0"), val = tensor<int32, [3]>([1, 80, 801])];
            tensor<bool, [3]> slice_31_end_mask_0 = const()[name = tensor<string, []>("slice_31_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_31_cast_fp16 = slice_by_index(begin = slice_31_begin_0, end = slice_31_end_0, end_mask = slice_31_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_31_cast_fp16")];
            tensor<int32, [3]> slice_32_begin_0 = const()[name = tensor<string, []>("slice_32_begin_0"), val = tensor<int32, [3]>([0, 75, 0])];
            tensor<int32, [3]> slice_32_end_0 = const()[name = tensor<string, []>("slice_32_end_0"), val = tensor<int32, [3]>([1, 80, 801])];
            tensor<bool, [3]> slice_32_end_mask_0 = const()[name = tensor<string, []>("slice_32_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_32_cast_fp16 = slice_by_index(begin = slice_32_begin_0, end = slice_32_end_0, end_mask = slice_32_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_32_cast_fp16")];
            tensor<fp16, []> const_340_promoted_to_fp16 = const()[name = tensor<string, []>("const_340_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_46_cast_fp16 = pow(x = slice_31_cast_fp16, y = const_340_promoted_to_fp16)[name = tensor<string, []>("pow_46_cast_fp16")];
            tensor<fp16, []> const_341_promoted_to_fp16 = const()[name = tensor<string, []>("const_341_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_47_cast_fp16 = pow(x = slice_32_cast_fp16, y = const_341_promoted_to_fp16)[name = tensor<string, []>("pow_47_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_45_cast_fp16 = add(x = pow_46_cast_fp16, y = pow_47_cast_fp16)[name = tensor<string, []>("add_45_cast_fp16")];
            tensor<int32, [1]> sum_16_axes_0 = const()[name = tensor<string, []>("sum_16_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_16_keep_dims_0 = const()[name = tensor<string, []>("sum_16_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_16_cast_fp16 = reduce_sum(axes = sum_16_axes_0, keep_dims = sum_16_keep_dims_0, x = add_45_cast_fp16)[name = tensor<string, []>("sum_16_cast_fp16")];
            tensor<fp16, []> const_344_to_fp16 = const()[name = tensor<string, []>("const_344_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_46_cast_fp16 = add(x = sum_16_cast_fp16, y = const_344_to_fp16)[name = tensor<string, []>("add_46_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_15_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor<string, []>("sqrt_15_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_30_cast_fp16 = real_div(x = slice_31_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor<string, []>("div_30_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_31_cast_fp16 = real_div(x = slice_32_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor<string, []>("div_31_cast_fp16")];
            tensor<fp32, []> log_15_epsilon_0 = const()[name = tensor<string, []>("log_15_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_15_cast_fp16 = log(epsilon = log_15_epsilon_0, x = sqrt_15_cast_fp16)[name = tensor<string, []>("log_15_cast_fp16")];
            tensor<int32, []> const_345 = const()[name = tensor<string, []>("const_345"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_15_interleave_0 = const()[name = tensor<string, []>("cat_15_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_15_cast_fp16 = concat(axis = const_345, interleave = cat_15_interleave_0, values = (div_30_cast_fp16, div_31_cast_fp16, log_15_cast_fp16))[name = tensor<string, []>("cat_15_cast_fp16")];
            tensor<int32, [1]> unsqueeze_31_axes_0 = const()[name = tensor<string, []>("unsqueeze_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_31_cast_fp16 = expand_dims(axes = unsqueeze_31_axes_0, x = cat_15_cast_fp16)[name = tensor<string, []>("unsqueeze_31_cast_fp16")];
            tensor<fp16, []> const_350_promoted_to_fp16 = const()[name = tensor<string, []>("const_350_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_48_cast_fp16 = pow(x = unsqueeze_31_cast_fp16, y = const_350_promoted_to_fp16)[name = tensor<string, []>("pow_48_cast_fp16")];
            tensor<int32, [1]> mean_15_axes_0 = const()[name = tensor<string, []>("mean_15_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_15_keep_dims_0 = const()[name = tensor<string, []>("mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_15_cast_fp16 = reduce_mean(axes = mean_15_axes_0, keep_dims = mean_15_keep_dims_0, x = pow_48_cast_fp16)[name = tensor<string, []>("mean_15_cast_fp16")];
            tensor<fp16, []> const_353_to_fp16 = const()[name = tensor<string, []>("const_353_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_47_cast_fp16 = add(x = mean_15_cast_fp16, y = const_353_to_fp16)[name = tensor<string, []>("add_47_cast_fp16")];
            tensor<fp32, []> rsqrt_15_epsilon_0 = const()[name = tensor<string, []>("rsqrt_15_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_15_cast_fp16 = rsqrt(epsilon = rsqrt_15_epsilon_0, x = add_47_cast_fp16)[name = tensor<string, []>("rsqrt_15_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_30_cast_fp16 = mul(x = unsqueeze_31_cast_fp16, y = rsqrt_15_cast_fp16)[name = tensor<string, []>("mul_30_cast_fp16")];
            tensor<int32, [1]> squeeze_15_axes_0 = const()[name = tensor<string, []>("squeeze_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = mul_30_cast_fp16)[name = tensor<string, []>("squeeze_15_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_15_to_fp16 = const()[name = tensor<string, []>("view_15_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96064)))];
            tensor<fp16, [1, 11, 801]> mul_31_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = view_15_to_fp16)[name = tensor<string, []>("mul_31_cast_fp16")];
            tensor<string, []> conv1d_15_pad_type_0 = const()[name = tensor<string, []>("conv1d_15_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_15_strides_0 = const()[name = tensor<string, []>("conv1d_15_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_15_pad_0 = const()[name = tensor<string, []>("conv1d_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_15_dilations_0 = const()[name = tensor<string, []>("conv1d_15_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_15_groups_0 = const()[name = tensor<string, []>("conv1d_15_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_15_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_15_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(96192)))];
            tensor<fp16, [256]> p_bn_15_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_15_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101888)))];
            tensor<fp16, [1, 256, 801]> conv1d_15_cast_fp16 = conv(bias = p_bn_15_1_bias_to_fp16, dilations = conv1d_15_dilations_0, groups = conv1d_15_groups_0, pad = conv1d_15_pad_0, pad_type = conv1d_15_pad_type_0, strides = conv1d_15_strides_0, weight = p_bn_15_1_weight_to_fp16, x = mul_31_cast_fp16)[name = tensor<string, []>("conv1d_15_cast_fp16")];
            tensor<int32, [3]> slice_33_begin_0 = const()[name = tensor<string, []>("slice_33_begin_0"), val = tensor<int32, [3]>([0, 80, 0])];
            tensor<int32, [3]> slice_33_end_0 = const()[name = tensor<string, []>("slice_33_end_0"), val = tensor<int32, [3]>([1, 85, 801])];
            tensor<bool, [3]> slice_33_end_mask_0 = const()[name = tensor<string, []>("slice_33_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_33_cast_fp16 = slice_by_index(begin = slice_33_begin_0, end = slice_33_end_0, end_mask = slice_33_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_33_cast_fp16")];
            tensor<int32, [3]> slice_34_begin_0 = const()[name = tensor<string, []>("slice_34_begin_0"), val = tensor<int32, [3]>([0, 80, 0])];
            tensor<int32, [3]> slice_34_end_0 = const()[name = tensor<string, []>("slice_34_end_0"), val = tensor<int32, [3]>([1, 85, 801])];
            tensor<bool, [3]> slice_34_end_mask_0 = const()[name = tensor<string, []>("slice_34_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_34_cast_fp16 = slice_by_index(begin = slice_34_begin_0, end = slice_34_end_0, end_mask = slice_34_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_34_cast_fp16")];
            tensor<fp16, []> const_362_promoted_to_fp16 = const()[name = tensor<string, []>("const_362_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_49_cast_fp16 = pow(x = slice_33_cast_fp16, y = const_362_promoted_to_fp16)[name = tensor<string, []>("pow_49_cast_fp16")];
            tensor<fp16, []> const_363_promoted_to_fp16 = const()[name = tensor<string, []>("const_363_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_50_cast_fp16 = pow(x = slice_34_cast_fp16, y = const_363_promoted_to_fp16)[name = tensor<string, []>("pow_50_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_48_cast_fp16 = add(x = pow_49_cast_fp16, y = pow_50_cast_fp16)[name = tensor<string, []>("add_48_cast_fp16")];
            tensor<int32, [1]> sum_17_axes_0 = const()[name = tensor<string, []>("sum_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_17_keep_dims_0 = const()[name = tensor<string, []>("sum_17_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_17_cast_fp16 = reduce_sum(axes = sum_17_axes_0, keep_dims = sum_17_keep_dims_0, x = add_48_cast_fp16)[name = tensor<string, []>("sum_17_cast_fp16")];
            tensor<fp16, []> const_366_to_fp16 = const()[name = tensor<string, []>("const_366_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_49_cast_fp16 = add(x = sum_17_cast_fp16, y = const_366_to_fp16)[name = tensor<string, []>("add_49_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_16_cast_fp16 = sqrt(x = add_49_cast_fp16)[name = tensor<string, []>("sqrt_16_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_32_cast_fp16 = real_div(x = slice_33_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor<string, []>("div_32_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_33_cast_fp16 = real_div(x = slice_34_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor<string, []>("div_33_cast_fp16")];
            tensor<fp32, []> log_16_epsilon_0 = const()[name = tensor<string, []>("log_16_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_16_cast_fp16 = log(epsilon = log_16_epsilon_0, x = sqrt_16_cast_fp16)[name = tensor<string, []>("log_16_cast_fp16")];
            tensor<int32, []> const_367 = const()[name = tensor<string, []>("const_367"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_16_interleave_0 = const()[name = tensor<string, []>("cat_16_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_16_cast_fp16 = concat(axis = const_367, interleave = cat_16_interleave_0, values = (div_32_cast_fp16, div_33_cast_fp16, log_16_cast_fp16))[name = tensor<string, []>("cat_16_cast_fp16")];
            tensor<int32, [1]> unsqueeze_33_axes_0 = const()[name = tensor<string, []>("unsqueeze_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_33_cast_fp16 = expand_dims(axes = unsqueeze_33_axes_0, x = cat_16_cast_fp16)[name = tensor<string, []>("unsqueeze_33_cast_fp16")];
            tensor<fp16, []> const_372_promoted_to_fp16 = const()[name = tensor<string, []>("const_372_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_51_cast_fp16 = pow(x = unsqueeze_33_cast_fp16, y = const_372_promoted_to_fp16)[name = tensor<string, []>("pow_51_cast_fp16")];
            tensor<int32, [1]> mean_16_axes_0 = const()[name = tensor<string, []>("mean_16_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_16_keep_dims_0 = const()[name = tensor<string, []>("mean_16_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_16_cast_fp16 = reduce_mean(axes = mean_16_axes_0, keep_dims = mean_16_keep_dims_0, x = pow_51_cast_fp16)[name = tensor<string, []>("mean_16_cast_fp16")];
            tensor<fp16, []> const_375_to_fp16 = const()[name = tensor<string, []>("const_375_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_50_cast_fp16 = add(x = mean_16_cast_fp16, y = const_375_to_fp16)[name = tensor<string, []>("add_50_cast_fp16")];
            tensor<fp32, []> rsqrt_16_epsilon_0 = const()[name = tensor<string, []>("rsqrt_16_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_16_cast_fp16 = rsqrt(epsilon = rsqrt_16_epsilon_0, x = add_50_cast_fp16)[name = tensor<string, []>("rsqrt_16_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_32_cast_fp16 = mul(x = unsqueeze_33_cast_fp16, y = rsqrt_16_cast_fp16)[name = tensor<string, []>("mul_32_cast_fp16")];
            tensor<int32, [1]> squeeze_16_axes_0 = const()[name = tensor<string, []>("squeeze_16_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = mul_32_cast_fp16)[name = tensor<string, []>("squeeze_16_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_16_to_fp16 = const()[name = tensor<string, []>("view_16_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102464)))];
            tensor<fp16, [1, 11, 801]> mul_33_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = view_16_to_fp16)[name = tensor<string, []>("mul_33_cast_fp16")];
            tensor<string, []> conv1d_16_pad_type_0 = const()[name = tensor<string, []>("conv1d_16_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_16_strides_0 = const()[name = tensor<string, []>("conv1d_16_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_16_pad_0 = const()[name = tensor<string, []>("conv1d_16_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_16_dilations_0 = const()[name = tensor<string, []>("conv1d_16_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_16_groups_0 = const()[name = tensor<string, []>("conv1d_16_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_16_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_16_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102592)))];
            tensor<fp16, [256]> p_bn_16_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_16_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108288)))];
            tensor<fp16, [1, 256, 801]> conv1d_16_cast_fp16 = conv(bias = p_bn_16_1_bias_to_fp16, dilations = conv1d_16_dilations_0, groups = conv1d_16_groups_0, pad = conv1d_16_pad_0, pad_type = conv1d_16_pad_type_0, strides = conv1d_16_strides_0, weight = p_bn_16_1_weight_to_fp16, x = mul_33_cast_fp16)[name = tensor<string, []>("conv1d_16_cast_fp16")];
            tensor<int32, [3]> slice_35_begin_0 = const()[name = tensor<string, []>("slice_35_begin_0"), val = tensor<int32, [3]>([0, 85, 0])];
            tensor<int32, [3]> slice_35_end_0 = const()[name = tensor<string, []>("slice_35_end_0"), val = tensor<int32, [3]>([1, 90, 801])];
            tensor<bool, [3]> slice_35_end_mask_0 = const()[name = tensor<string, []>("slice_35_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_35_cast_fp16 = slice_by_index(begin = slice_35_begin_0, end = slice_35_end_0, end_mask = slice_35_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_35_cast_fp16")];
            tensor<int32, [3]> slice_36_begin_0 = const()[name = tensor<string, []>("slice_36_begin_0"), val = tensor<int32, [3]>([0, 85, 0])];
            tensor<int32, [3]> slice_36_end_0 = const()[name = tensor<string, []>("slice_36_end_0"), val = tensor<int32, [3]>([1, 90, 801])];
            tensor<bool, [3]> slice_36_end_mask_0 = const()[name = tensor<string, []>("slice_36_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_36_cast_fp16 = slice_by_index(begin = slice_36_begin_0, end = slice_36_end_0, end_mask = slice_36_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_36_cast_fp16")];
            tensor<fp16, []> const_384_promoted_to_fp16 = const()[name = tensor<string, []>("const_384_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_52_cast_fp16 = pow(x = slice_35_cast_fp16, y = const_384_promoted_to_fp16)[name = tensor<string, []>("pow_52_cast_fp16")];
            tensor<fp16, []> const_385_promoted_to_fp16 = const()[name = tensor<string, []>("const_385_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_53_cast_fp16 = pow(x = slice_36_cast_fp16, y = const_385_promoted_to_fp16)[name = tensor<string, []>("pow_53_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_51_cast_fp16 = add(x = pow_52_cast_fp16, y = pow_53_cast_fp16)[name = tensor<string, []>("add_51_cast_fp16")];
            tensor<int32, [1]> sum_18_axes_0 = const()[name = tensor<string, []>("sum_18_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_18_keep_dims_0 = const()[name = tensor<string, []>("sum_18_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_18_cast_fp16 = reduce_sum(axes = sum_18_axes_0, keep_dims = sum_18_keep_dims_0, x = add_51_cast_fp16)[name = tensor<string, []>("sum_18_cast_fp16")];
            tensor<fp16, []> const_388_to_fp16 = const()[name = tensor<string, []>("const_388_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_52_cast_fp16 = add(x = sum_18_cast_fp16, y = const_388_to_fp16)[name = tensor<string, []>("add_52_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_17_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor<string, []>("sqrt_17_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_34_cast_fp16 = real_div(x = slice_35_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor<string, []>("div_34_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_35_cast_fp16 = real_div(x = slice_36_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor<string, []>("div_35_cast_fp16")];
            tensor<fp32, []> log_17_epsilon_0 = const()[name = tensor<string, []>("log_17_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_17_cast_fp16 = log(epsilon = log_17_epsilon_0, x = sqrt_17_cast_fp16)[name = tensor<string, []>("log_17_cast_fp16")];
            tensor<int32, []> const_389 = const()[name = tensor<string, []>("const_389"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_17_interleave_0 = const()[name = tensor<string, []>("cat_17_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_17_cast_fp16 = concat(axis = const_389, interleave = cat_17_interleave_0, values = (div_34_cast_fp16, div_35_cast_fp16, log_17_cast_fp16))[name = tensor<string, []>("cat_17_cast_fp16")];
            tensor<int32, [1]> unsqueeze_35_axes_0 = const()[name = tensor<string, []>("unsqueeze_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_35_cast_fp16 = expand_dims(axes = unsqueeze_35_axes_0, x = cat_17_cast_fp16)[name = tensor<string, []>("unsqueeze_35_cast_fp16")];
            tensor<fp16, []> const_394_promoted_to_fp16 = const()[name = tensor<string, []>("const_394_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_54_cast_fp16 = pow(x = unsqueeze_35_cast_fp16, y = const_394_promoted_to_fp16)[name = tensor<string, []>("pow_54_cast_fp16")];
            tensor<int32, [1]> mean_17_axes_0 = const()[name = tensor<string, []>("mean_17_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_17_keep_dims_0 = const()[name = tensor<string, []>("mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_17_cast_fp16 = reduce_mean(axes = mean_17_axes_0, keep_dims = mean_17_keep_dims_0, x = pow_54_cast_fp16)[name = tensor<string, []>("mean_17_cast_fp16")];
            tensor<fp16, []> const_397_to_fp16 = const()[name = tensor<string, []>("const_397_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_53_cast_fp16 = add(x = mean_17_cast_fp16, y = const_397_to_fp16)[name = tensor<string, []>("add_53_cast_fp16")];
            tensor<fp32, []> rsqrt_17_epsilon_0 = const()[name = tensor<string, []>("rsqrt_17_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_17_cast_fp16 = rsqrt(epsilon = rsqrt_17_epsilon_0, x = add_53_cast_fp16)[name = tensor<string, []>("rsqrt_17_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_34_cast_fp16 = mul(x = unsqueeze_35_cast_fp16, y = rsqrt_17_cast_fp16)[name = tensor<string, []>("mul_34_cast_fp16")];
            tensor<int32, [1]> squeeze_17_axes_0 = const()[name = tensor<string, []>("squeeze_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = mul_34_cast_fp16)[name = tensor<string, []>("squeeze_17_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_17_to_fp16 = const()[name = tensor<string, []>("view_17_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108864)))];
            tensor<fp16, [1, 11, 801]> mul_35_cast_fp16 = mul(x = squeeze_17_cast_fp16, y = view_17_to_fp16)[name = tensor<string, []>("mul_35_cast_fp16")];
            tensor<string, []> conv1d_17_pad_type_0 = const()[name = tensor<string, []>("conv1d_17_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_17_strides_0 = const()[name = tensor<string, []>("conv1d_17_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_17_pad_0 = const()[name = tensor<string, []>("conv1d_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_17_dilations_0 = const()[name = tensor<string, []>("conv1d_17_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_17_groups_0 = const()[name = tensor<string, []>("conv1d_17_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_17_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_17_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108992)))];
            tensor<fp16, [256]> p_bn_17_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_17_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114688)))];
            tensor<fp16, [1, 256, 801]> conv1d_17_cast_fp16 = conv(bias = p_bn_17_1_bias_to_fp16, dilations = conv1d_17_dilations_0, groups = conv1d_17_groups_0, pad = conv1d_17_pad_0, pad_type = conv1d_17_pad_type_0, strides = conv1d_17_strides_0, weight = p_bn_17_1_weight_to_fp16, x = mul_35_cast_fp16)[name = tensor<string, []>("conv1d_17_cast_fp16")];
            tensor<int32, [3]> slice_37_begin_0 = const()[name = tensor<string, []>("slice_37_begin_0"), val = tensor<int32, [3]>([0, 90, 0])];
            tensor<int32, [3]> slice_37_end_0 = const()[name = tensor<string, []>("slice_37_end_0"), val = tensor<int32, [3]>([1, 95, 801])];
            tensor<bool, [3]> slice_37_end_mask_0 = const()[name = tensor<string, []>("slice_37_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_37_cast_fp16 = slice_by_index(begin = slice_37_begin_0, end = slice_37_end_0, end_mask = slice_37_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_37_cast_fp16")];
            tensor<int32, [3]> slice_38_begin_0 = const()[name = tensor<string, []>("slice_38_begin_0"), val = tensor<int32, [3]>([0, 90, 0])];
            tensor<int32, [3]> slice_38_end_0 = const()[name = tensor<string, []>("slice_38_end_0"), val = tensor<int32, [3]>([1, 95, 801])];
            tensor<bool, [3]> slice_38_end_mask_0 = const()[name = tensor<string, []>("slice_38_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_38_cast_fp16 = slice_by_index(begin = slice_38_begin_0, end = slice_38_end_0, end_mask = slice_38_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_38_cast_fp16")];
            tensor<fp16, []> const_406_promoted_to_fp16 = const()[name = tensor<string, []>("const_406_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_55_cast_fp16 = pow(x = slice_37_cast_fp16, y = const_406_promoted_to_fp16)[name = tensor<string, []>("pow_55_cast_fp16")];
            tensor<fp16, []> const_407_promoted_to_fp16 = const()[name = tensor<string, []>("const_407_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_56_cast_fp16 = pow(x = slice_38_cast_fp16, y = const_407_promoted_to_fp16)[name = tensor<string, []>("pow_56_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_54_cast_fp16 = add(x = pow_55_cast_fp16, y = pow_56_cast_fp16)[name = tensor<string, []>("add_54_cast_fp16")];
            tensor<int32, [1]> sum_19_axes_0 = const()[name = tensor<string, []>("sum_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_19_keep_dims_0 = const()[name = tensor<string, []>("sum_19_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_19_cast_fp16 = reduce_sum(axes = sum_19_axes_0, keep_dims = sum_19_keep_dims_0, x = add_54_cast_fp16)[name = tensor<string, []>("sum_19_cast_fp16")];
            tensor<fp16, []> const_410_to_fp16 = const()[name = tensor<string, []>("const_410_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_55_cast_fp16 = add(x = sum_19_cast_fp16, y = const_410_to_fp16)[name = tensor<string, []>("add_55_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_18_cast_fp16 = sqrt(x = add_55_cast_fp16)[name = tensor<string, []>("sqrt_18_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_36_cast_fp16 = real_div(x = slice_37_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor<string, []>("div_36_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_37_cast_fp16 = real_div(x = slice_38_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor<string, []>("div_37_cast_fp16")];
            tensor<fp32, []> log_18_epsilon_0 = const()[name = tensor<string, []>("log_18_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_18_cast_fp16 = log(epsilon = log_18_epsilon_0, x = sqrt_18_cast_fp16)[name = tensor<string, []>("log_18_cast_fp16")];
            tensor<int32, []> const_411 = const()[name = tensor<string, []>("const_411"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_18_interleave_0 = const()[name = tensor<string, []>("cat_18_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_18_cast_fp16 = concat(axis = const_411, interleave = cat_18_interleave_0, values = (div_36_cast_fp16, div_37_cast_fp16, log_18_cast_fp16))[name = tensor<string, []>("cat_18_cast_fp16")];
            tensor<int32, [1]> unsqueeze_37_axes_0 = const()[name = tensor<string, []>("unsqueeze_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_37_cast_fp16 = expand_dims(axes = unsqueeze_37_axes_0, x = cat_18_cast_fp16)[name = tensor<string, []>("unsqueeze_37_cast_fp16")];
            tensor<fp16, []> const_416_promoted_to_fp16 = const()[name = tensor<string, []>("const_416_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_57_cast_fp16 = pow(x = unsqueeze_37_cast_fp16, y = const_416_promoted_to_fp16)[name = tensor<string, []>("pow_57_cast_fp16")];
            tensor<int32, [1]> mean_18_axes_0 = const()[name = tensor<string, []>("mean_18_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_18_keep_dims_0 = const()[name = tensor<string, []>("mean_18_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_18_cast_fp16 = reduce_mean(axes = mean_18_axes_0, keep_dims = mean_18_keep_dims_0, x = pow_57_cast_fp16)[name = tensor<string, []>("mean_18_cast_fp16")];
            tensor<fp16, []> const_419_to_fp16 = const()[name = tensor<string, []>("const_419_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_56_cast_fp16 = add(x = mean_18_cast_fp16, y = const_419_to_fp16)[name = tensor<string, []>("add_56_cast_fp16")];
            tensor<fp32, []> rsqrt_18_epsilon_0 = const()[name = tensor<string, []>("rsqrt_18_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_18_cast_fp16 = rsqrt(epsilon = rsqrt_18_epsilon_0, x = add_56_cast_fp16)[name = tensor<string, []>("rsqrt_18_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_36_cast_fp16 = mul(x = unsqueeze_37_cast_fp16, y = rsqrt_18_cast_fp16)[name = tensor<string, []>("mul_36_cast_fp16")];
            tensor<int32, [1]> squeeze_18_axes_0 = const()[name = tensor<string, []>("squeeze_18_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = mul_36_cast_fp16)[name = tensor<string, []>("squeeze_18_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_18_to_fp16 = const()[name = tensor<string, []>("view_18_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115264)))];
            tensor<fp16, [1, 11, 801]> mul_37_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = view_18_to_fp16)[name = tensor<string, []>("mul_37_cast_fp16")];
            tensor<string, []> conv1d_18_pad_type_0 = const()[name = tensor<string, []>("conv1d_18_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_18_strides_0 = const()[name = tensor<string, []>("conv1d_18_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_18_pad_0 = const()[name = tensor<string, []>("conv1d_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_18_dilations_0 = const()[name = tensor<string, []>("conv1d_18_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_18_groups_0 = const()[name = tensor<string, []>("conv1d_18_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_18_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_18_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115392)))];
            tensor<fp16, [256]> p_bn_18_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_18_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121088)))];
            tensor<fp16, [1, 256, 801]> conv1d_18_cast_fp16 = conv(bias = p_bn_18_1_bias_to_fp16, dilations = conv1d_18_dilations_0, groups = conv1d_18_groups_0, pad = conv1d_18_pad_0, pad_type = conv1d_18_pad_type_0, strides = conv1d_18_strides_0, weight = p_bn_18_1_weight_to_fp16, x = mul_37_cast_fp16)[name = tensor<string, []>("conv1d_18_cast_fp16")];
            tensor<int32, [3]> slice_39_begin_0 = const()[name = tensor<string, []>("slice_39_begin_0"), val = tensor<int32, [3]>([0, 95, 0])];
            tensor<int32, [3]> slice_39_end_0 = const()[name = tensor<string, []>("slice_39_end_0"), val = tensor<int32, [3]>([1, 100, 801])];
            tensor<bool, [3]> slice_39_end_mask_0 = const()[name = tensor<string, []>("slice_39_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_39_cast_fp16 = slice_by_index(begin = slice_39_begin_0, end = slice_39_end_0, end_mask = slice_39_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_39_cast_fp16")];
            tensor<int32, [3]> slice_40_begin_0 = const()[name = tensor<string, []>("slice_40_begin_0"), val = tensor<int32, [3]>([0, 95, 0])];
            tensor<int32, [3]> slice_40_end_0 = const()[name = tensor<string, []>("slice_40_end_0"), val = tensor<int32, [3]>([1, 100, 801])];
            tensor<bool, [3]> slice_40_end_mask_0 = const()[name = tensor<string, []>("slice_40_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_40_cast_fp16 = slice_by_index(begin = slice_40_begin_0, end = slice_40_end_0, end_mask = slice_40_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_40_cast_fp16")];
            tensor<fp16, []> const_428_promoted_to_fp16 = const()[name = tensor<string, []>("const_428_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_58_cast_fp16 = pow(x = slice_39_cast_fp16, y = const_428_promoted_to_fp16)[name = tensor<string, []>("pow_58_cast_fp16")];
            tensor<fp16, []> const_429_promoted_to_fp16 = const()[name = tensor<string, []>("const_429_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_59_cast_fp16 = pow(x = slice_40_cast_fp16, y = const_429_promoted_to_fp16)[name = tensor<string, []>("pow_59_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_57_cast_fp16 = add(x = pow_58_cast_fp16, y = pow_59_cast_fp16)[name = tensor<string, []>("add_57_cast_fp16")];
            tensor<int32, [1]> sum_20_axes_0 = const()[name = tensor<string, []>("sum_20_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_20_keep_dims_0 = const()[name = tensor<string, []>("sum_20_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_20_cast_fp16 = reduce_sum(axes = sum_20_axes_0, keep_dims = sum_20_keep_dims_0, x = add_57_cast_fp16)[name = tensor<string, []>("sum_20_cast_fp16")];
            tensor<fp16, []> const_432_to_fp16 = const()[name = tensor<string, []>("const_432_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_58_cast_fp16 = add(x = sum_20_cast_fp16, y = const_432_to_fp16)[name = tensor<string, []>("add_58_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_19_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor<string, []>("sqrt_19_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_38_cast_fp16 = real_div(x = slice_39_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor<string, []>("div_38_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_39_cast_fp16 = real_div(x = slice_40_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor<string, []>("div_39_cast_fp16")];
            tensor<fp32, []> log_19_epsilon_0 = const()[name = tensor<string, []>("log_19_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_19_cast_fp16 = log(epsilon = log_19_epsilon_0, x = sqrt_19_cast_fp16)[name = tensor<string, []>("log_19_cast_fp16")];
            tensor<int32, []> const_433 = const()[name = tensor<string, []>("const_433"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_19_interleave_0 = const()[name = tensor<string, []>("cat_19_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_19_cast_fp16 = concat(axis = const_433, interleave = cat_19_interleave_0, values = (div_38_cast_fp16, div_39_cast_fp16, log_19_cast_fp16))[name = tensor<string, []>("cat_19_cast_fp16")];
            tensor<int32, [1]> unsqueeze_39_axes_0 = const()[name = tensor<string, []>("unsqueeze_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_39_cast_fp16 = expand_dims(axes = unsqueeze_39_axes_0, x = cat_19_cast_fp16)[name = tensor<string, []>("unsqueeze_39_cast_fp16")];
            tensor<fp16, []> const_438_promoted_to_fp16 = const()[name = tensor<string, []>("const_438_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_60_cast_fp16 = pow(x = unsqueeze_39_cast_fp16, y = const_438_promoted_to_fp16)[name = tensor<string, []>("pow_60_cast_fp16")];
            tensor<int32, [1]> mean_19_axes_0 = const()[name = tensor<string, []>("mean_19_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_19_keep_dims_0 = const()[name = tensor<string, []>("mean_19_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_19_cast_fp16 = reduce_mean(axes = mean_19_axes_0, keep_dims = mean_19_keep_dims_0, x = pow_60_cast_fp16)[name = tensor<string, []>("mean_19_cast_fp16")];
            tensor<fp16, []> const_441_to_fp16 = const()[name = tensor<string, []>("const_441_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_59_cast_fp16 = add(x = mean_19_cast_fp16, y = const_441_to_fp16)[name = tensor<string, []>("add_59_cast_fp16")];
            tensor<fp32, []> rsqrt_19_epsilon_0 = const()[name = tensor<string, []>("rsqrt_19_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_19_cast_fp16 = rsqrt(epsilon = rsqrt_19_epsilon_0, x = add_59_cast_fp16)[name = tensor<string, []>("rsqrt_19_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_38_cast_fp16 = mul(x = unsqueeze_39_cast_fp16, y = rsqrt_19_cast_fp16)[name = tensor<string, []>("mul_38_cast_fp16")];
            tensor<int32, [1]> squeeze_19_axes_0 = const()[name = tensor<string, []>("squeeze_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = mul_38_cast_fp16)[name = tensor<string, []>("squeeze_19_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_19_to_fp16 = const()[name = tensor<string, []>("view_19_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121664)))];
            tensor<fp16, [1, 11, 801]> mul_39_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = view_19_to_fp16)[name = tensor<string, []>("mul_39_cast_fp16")];
            tensor<string, []> conv1d_19_pad_type_0 = const()[name = tensor<string, []>("conv1d_19_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_19_strides_0 = const()[name = tensor<string, []>("conv1d_19_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_19_pad_0 = const()[name = tensor<string, []>("conv1d_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_19_dilations_0 = const()[name = tensor<string, []>("conv1d_19_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_19_groups_0 = const()[name = tensor<string, []>("conv1d_19_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_19_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_19_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121792)))];
            tensor<fp16, [256]> p_bn_19_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_19_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127488)))];
            tensor<fp16, [1, 256, 801]> conv1d_19_cast_fp16 = conv(bias = p_bn_19_1_bias_to_fp16, dilations = conv1d_19_dilations_0, groups = conv1d_19_groups_0, pad = conv1d_19_pad_0, pad_type = conv1d_19_pad_type_0, strides = conv1d_19_strides_0, weight = p_bn_19_1_weight_to_fp16, x = mul_39_cast_fp16)[name = tensor<string, []>("conv1d_19_cast_fp16")];
            tensor<int32, [3]> slice_41_begin_0 = const()[name = tensor<string, []>("slice_41_begin_0"), val = tensor<int32, [3]>([0, 100, 0])];
            tensor<int32, [3]> slice_41_end_0 = const()[name = tensor<string, []>("slice_41_end_0"), val = tensor<int32, [3]>([1, 105, 801])];
            tensor<bool, [3]> slice_41_end_mask_0 = const()[name = tensor<string, []>("slice_41_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_41_cast_fp16 = slice_by_index(begin = slice_41_begin_0, end = slice_41_end_0, end_mask = slice_41_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_41_cast_fp16")];
            tensor<int32, [3]> slice_42_begin_0 = const()[name = tensor<string, []>("slice_42_begin_0"), val = tensor<int32, [3]>([0, 100, 0])];
            tensor<int32, [3]> slice_42_end_0 = const()[name = tensor<string, []>("slice_42_end_0"), val = tensor<int32, [3]>([1, 105, 801])];
            tensor<bool, [3]> slice_42_end_mask_0 = const()[name = tensor<string, []>("slice_42_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_42_cast_fp16 = slice_by_index(begin = slice_42_begin_0, end = slice_42_end_0, end_mask = slice_42_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_42_cast_fp16")];
            tensor<fp16, []> const_450_promoted_to_fp16 = const()[name = tensor<string, []>("const_450_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_61_cast_fp16 = pow(x = slice_41_cast_fp16, y = const_450_promoted_to_fp16)[name = tensor<string, []>("pow_61_cast_fp16")];
            tensor<fp16, []> const_451_promoted_to_fp16 = const()[name = tensor<string, []>("const_451_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_62_cast_fp16 = pow(x = slice_42_cast_fp16, y = const_451_promoted_to_fp16)[name = tensor<string, []>("pow_62_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_60_cast_fp16 = add(x = pow_61_cast_fp16, y = pow_62_cast_fp16)[name = tensor<string, []>("add_60_cast_fp16")];
            tensor<int32, [1]> sum_21_axes_0 = const()[name = tensor<string, []>("sum_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_21_keep_dims_0 = const()[name = tensor<string, []>("sum_21_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_21_cast_fp16 = reduce_sum(axes = sum_21_axes_0, keep_dims = sum_21_keep_dims_0, x = add_60_cast_fp16)[name = tensor<string, []>("sum_21_cast_fp16")];
            tensor<fp16, []> const_454_to_fp16 = const()[name = tensor<string, []>("const_454_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_61_cast_fp16 = add(x = sum_21_cast_fp16, y = const_454_to_fp16)[name = tensor<string, []>("add_61_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_20_cast_fp16 = sqrt(x = add_61_cast_fp16)[name = tensor<string, []>("sqrt_20_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_40_cast_fp16 = real_div(x = slice_41_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor<string, []>("div_40_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_41_cast_fp16 = real_div(x = slice_42_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor<string, []>("div_41_cast_fp16")];
            tensor<fp32, []> log_20_epsilon_0 = const()[name = tensor<string, []>("log_20_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_20_cast_fp16 = log(epsilon = log_20_epsilon_0, x = sqrt_20_cast_fp16)[name = tensor<string, []>("log_20_cast_fp16")];
            tensor<int32, []> const_455 = const()[name = tensor<string, []>("const_455"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_20_interleave_0 = const()[name = tensor<string, []>("cat_20_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_20_cast_fp16 = concat(axis = const_455, interleave = cat_20_interleave_0, values = (div_40_cast_fp16, div_41_cast_fp16, log_20_cast_fp16))[name = tensor<string, []>("cat_20_cast_fp16")];
            tensor<int32, [1]> unsqueeze_41_axes_0 = const()[name = tensor<string, []>("unsqueeze_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_41_cast_fp16 = expand_dims(axes = unsqueeze_41_axes_0, x = cat_20_cast_fp16)[name = tensor<string, []>("unsqueeze_41_cast_fp16")];
            tensor<fp16, []> const_460_promoted_to_fp16 = const()[name = tensor<string, []>("const_460_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_63_cast_fp16 = pow(x = unsqueeze_41_cast_fp16, y = const_460_promoted_to_fp16)[name = tensor<string, []>("pow_63_cast_fp16")];
            tensor<int32, [1]> mean_20_axes_0 = const()[name = tensor<string, []>("mean_20_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_20_keep_dims_0 = const()[name = tensor<string, []>("mean_20_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_20_cast_fp16 = reduce_mean(axes = mean_20_axes_0, keep_dims = mean_20_keep_dims_0, x = pow_63_cast_fp16)[name = tensor<string, []>("mean_20_cast_fp16")];
            tensor<fp16, []> const_463_to_fp16 = const()[name = tensor<string, []>("const_463_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_62_cast_fp16 = add(x = mean_20_cast_fp16, y = const_463_to_fp16)[name = tensor<string, []>("add_62_cast_fp16")];
            tensor<fp32, []> rsqrt_20_epsilon_0 = const()[name = tensor<string, []>("rsqrt_20_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_20_cast_fp16 = rsqrt(epsilon = rsqrt_20_epsilon_0, x = add_62_cast_fp16)[name = tensor<string, []>("rsqrt_20_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_40_cast_fp16 = mul(x = unsqueeze_41_cast_fp16, y = rsqrt_20_cast_fp16)[name = tensor<string, []>("mul_40_cast_fp16")];
            tensor<int32, [1]> squeeze_20_axes_0 = const()[name = tensor<string, []>("squeeze_20_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = mul_40_cast_fp16)[name = tensor<string, []>("squeeze_20_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_20_to_fp16 = const()[name = tensor<string, []>("view_20_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128064)))];
            tensor<fp16, [1, 11, 801]> mul_41_cast_fp16 = mul(x = squeeze_20_cast_fp16, y = view_20_to_fp16)[name = tensor<string, []>("mul_41_cast_fp16")];
            tensor<string, []> conv1d_20_pad_type_0 = const()[name = tensor<string, []>("conv1d_20_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_20_strides_0 = const()[name = tensor<string, []>("conv1d_20_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_20_pad_0 = const()[name = tensor<string, []>("conv1d_20_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_20_dilations_0 = const()[name = tensor<string, []>("conv1d_20_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_20_groups_0 = const()[name = tensor<string, []>("conv1d_20_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_20_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_20_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128192)))];
            tensor<fp16, [256]> p_bn_20_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_20_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133888)))];
            tensor<fp16, [1, 256, 801]> conv1d_20_cast_fp16 = conv(bias = p_bn_20_1_bias_to_fp16, dilations = conv1d_20_dilations_0, groups = conv1d_20_groups_0, pad = conv1d_20_pad_0, pad_type = conv1d_20_pad_type_0, strides = conv1d_20_strides_0, weight = p_bn_20_1_weight_to_fp16, x = mul_41_cast_fp16)[name = tensor<string, []>("conv1d_20_cast_fp16")];
            tensor<int32, [3]> slice_43_begin_0 = const()[name = tensor<string, []>("slice_43_begin_0"), val = tensor<int32, [3]>([0, 105, 0])];
            tensor<int32, [3]> slice_43_end_0 = const()[name = tensor<string, []>("slice_43_end_0"), val = tensor<int32, [3]>([1, 110, 801])];
            tensor<bool, [3]> slice_43_end_mask_0 = const()[name = tensor<string, []>("slice_43_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_43_cast_fp16 = slice_by_index(begin = slice_43_begin_0, end = slice_43_end_0, end_mask = slice_43_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_43_cast_fp16")];
            tensor<int32, [3]> slice_44_begin_0 = const()[name = tensor<string, []>("slice_44_begin_0"), val = tensor<int32, [3]>([0, 105, 0])];
            tensor<int32, [3]> slice_44_end_0 = const()[name = tensor<string, []>("slice_44_end_0"), val = tensor<int32, [3]>([1, 110, 801])];
            tensor<bool, [3]> slice_44_end_mask_0 = const()[name = tensor<string, []>("slice_44_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_44_cast_fp16 = slice_by_index(begin = slice_44_begin_0, end = slice_44_end_0, end_mask = slice_44_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_44_cast_fp16")];
            tensor<fp16, []> const_472_promoted_to_fp16 = const()[name = tensor<string, []>("const_472_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_64_cast_fp16 = pow(x = slice_43_cast_fp16, y = const_472_promoted_to_fp16)[name = tensor<string, []>("pow_64_cast_fp16")];
            tensor<fp16, []> const_473_promoted_to_fp16 = const()[name = tensor<string, []>("const_473_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_65_cast_fp16 = pow(x = slice_44_cast_fp16, y = const_473_promoted_to_fp16)[name = tensor<string, []>("pow_65_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_63_cast_fp16 = add(x = pow_64_cast_fp16, y = pow_65_cast_fp16)[name = tensor<string, []>("add_63_cast_fp16")];
            tensor<int32, [1]> sum_22_axes_0 = const()[name = tensor<string, []>("sum_22_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_22_keep_dims_0 = const()[name = tensor<string, []>("sum_22_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_22_cast_fp16 = reduce_sum(axes = sum_22_axes_0, keep_dims = sum_22_keep_dims_0, x = add_63_cast_fp16)[name = tensor<string, []>("sum_22_cast_fp16")];
            tensor<fp16, []> const_476_to_fp16 = const()[name = tensor<string, []>("const_476_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_64_cast_fp16 = add(x = sum_22_cast_fp16, y = const_476_to_fp16)[name = tensor<string, []>("add_64_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_21_cast_fp16 = sqrt(x = add_64_cast_fp16)[name = tensor<string, []>("sqrt_21_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_42_cast_fp16 = real_div(x = slice_43_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor<string, []>("div_42_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_43_cast_fp16 = real_div(x = slice_44_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor<string, []>("div_43_cast_fp16")];
            tensor<fp32, []> log_21_epsilon_0 = const()[name = tensor<string, []>("log_21_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_21_cast_fp16 = log(epsilon = log_21_epsilon_0, x = sqrt_21_cast_fp16)[name = tensor<string, []>("log_21_cast_fp16")];
            tensor<int32, []> const_477 = const()[name = tensor<string, []>("const_477"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_21_interleave_0 = const()[name = tensor<string, []>("cat_21_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_21_cast_fp16 = concat(axis = const_477, interleave = cat_21_interleave_0, values = (div_42_cast_fp16, div_43_cast_fp16, log_21_cast_fp16))[name = tensor<string, []>("cat_21_cast_fp16")];
            tensor<int32, [1]> unsqueeze_43_axes_0 = const()[name = tensor<string, []>("unsqueeze_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_43_cast_fp16 = expand_dims(axes = unsqueeze_43_axes_0, x = cat_21_cast_fp16)[name = tensor<string, []>("unsqueeze_43_cast_fp16")];
            tensor<fp16, []> const_482_promoted_to_fp16 = const()[name = tensor<string, []>("const_482_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_66_cast_fp16 = pow(x = unsqueeze_43_cast_fp16, y = const_482_promoted_to_fp16)[name = tensor<string, []>("pow_66_cast_fp16")];
            tensor<int32, [1]> mean_21_axes_0 = const()[name = tensor<string, []>("mean_21_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_21_keep_dims_0 = const()[name = tensor<string, []>("mean_21_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_21_cast_fp16 = reduce_mean(axes = mean_21_axes_0, keep_dims = mean_21_keep_dims_0, x = pow_66_cast_fp16)[name = tensor<string, []>("mean_21_cast_fp16")];
            tensor<fp16, []> const_485_to_fp16 = const()[name = tensor<string, []>("const_485_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_65_cast_fp16 = add(x = mean_21_cast_fp16, y = const_485_to_fp16)[name = tensor<string, []>("add_65_cast_fp16")];
            tensor<fp32, []> rsqrt_21_epsilon_0 = const()[name = tensor<string, []>("rsqrt_21_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_21_cast_fp16 = rsqrt(epsilon = rsqrt_21_epsilon_0, x = add_65_cast_fp16)[name = tensor<string, []>("rsqrt_21_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_42_cast_fp16 = mul(x = unsqueeze_43_cast_fp16, y = rsqrt_21_cast_fp16)[name = tensor<string, []>("mul_42_cast_fp16")];
            tensor<int32, [1]> squeeze_21_axes_0 = const()[name = tensor<string, []>("squeeze_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = mul_42_cast_fp16)[name = tensor<string, []>("squeeze_21_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_21_to_fp16 = const()[name = tensor<string, []>("view_21_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134464)))];
            tensor<fp16, [1, 11, 801]> mul_43_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = view_21_to_fp16)[name = tensor<string, []>("mul_43_cast_fp16")];
            tensor<string, []> conv1d_21_pad_type_0 = const()[name = tensor<string, []>("conv1d_21_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_21_strides_0 = const()[name = tensor<string, []>("conv1d_21_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_21_pad_0 = const()[name = tensor<string, []>("conv1d_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_21_dilations_0 = const()[name = tensor<string, []>("conv1d_21_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_21_groups_0 = const()[name = tensor<string, []>("conv1d_21_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_21_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_21_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134592)))];
            tensor<fp16, [256]> p_bn_21_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_21_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140288)))];
            tensor<fp16, [1, 256, 801]> conv1d_21_cast_fp16 = conv(bias = p_bn_21_1_bias_to_fp16, dilations = conv1d_21_dilations_0, groups = conv1d_21_groups_0, pad = conv1d_21_pad_0, pad_type = conv1d_21_pad_type_0, strides = conv1d_21_strides_0, weight = p_bn_21_1_weight_to_fp16, x = mul_43_cast_fp16)[name = tensor<string, []>("conv1d_21_cast_fp16")];
            tensor<int32, [3]> slice_45_begin_0 = const()[name = tensor<string, []>("slice_45_begin_0"), val = tensor<int32, [3]>([0, 110, 0])];
            tensor<int32, [3]> slice_45_end_0 = const()[name = tensor<string, []>("slice_45_end_0"), val = tensor<int32, [3]>([1, 115, 801])];
            tensor<bool, [3]> slice_45_end_mask_0 = const()[name = tensor<string, []>("slice_45_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_45_cast_fp16 = slice_by_index(begin = slice_45_begin_0, end = slice_45_end_0, end_mask = slice_45_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_45_cast_fp16")];
            tensor<int32, [3]> slice_46_begin_0 = const()[name = tensor<string, []>("slice_46_begin_0"), val = tensor<int32, [3]>([0, 110, 0])];
            tensor<int32, [3]> slice_46_end_0 = const()[name = tensor<string, []>("slice_46_end_0"), val = tensor<int32, [3]>([1, 115, 801])];
            tensor<bool, [3]> slice_46_end_mask_0 = const()[name = tensor<string, []>("slice_46_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_46_cast_fp16 = slice_by_index(begin = slice_46_begin_0, end = slice_46_end_0, end_mask = slice_46_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_46_cast_fp16")];
            tensor<fp16, []> const_494_promoted_to_fp16 = const()[name = tensor<string, []>("const_494_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_67_cast_fp16 = pow(x = slice_45_cast_fp16, y = const_494_promoted_to_fp16)[name = tensor<string, []>("pow_67_cast_fp16")];
            tensor<fp16, []> const_495_promoted_to_fp16 = const()[name = tensor<string, []>("const_495_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_68_cast_fp16 = pow(x = slice_46_cast_fp16, y = const_495_promoted_to_fp16)[name = tensor<string, []>("pow_68_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_66_cast_fp16 = add(x = pow_67_cast_fp16, y = pow_68_cast_fp16)[name = tensor<string, []>("add_66_cast_fp16")];
            tensor<int32, [1]> sum_23_axes_0 = const()[name = tensor<string, []>("sum_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_23_keep_dims_0 = const()[name = tensor<string, []>("sum_23_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_23_cast_fp16 = reduce_sum(axes = sum_23_axes_0, keep_dims = sum_23_keep_dims_0, x = add_66_cast_fp16)[name = tensor<string, []>("sum_23_cast_fp16")];
            tensor<fp16, []> const_498_to_fp16 = const()[name = tensor<string, []>("const_498_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_67_cast_fp16 = add(x = sum_23_cast_fp16, y = const_498_to_fp16)[name = tensor<string, []>("add_67_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_22_cast_fp16 = sqrt(x = add_67_cast_fp16)[name = tensor<string, []>("sqrt_22_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_44_cast_fp16 = real_div(x = slice_45_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor<string, []>("div_44_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_45_cast_fp16 = real_div(x = slice_46_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor<string, []>("div_45_cast_fp16")];
            tensor<fp32, []> log_22_epsilon_0 = const()[name = tensor<string, []>("log_22_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_22_cast_fp16 = log(epsilon = log_22_epsilon_0, x = sqrt_22_cast_fp16)[name = tensor<string, []>("log_22_cast_fp16")];
            tensor<int32, []> const_499 = const()[name = tensor<string, []>("const_499"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_22_interleave_0 = const()[name = tensor<string, []>("cat_22_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_22_cast_fp16 = concat(axis = const_499, interleave = cat_22_interleave_0, values = (div_44_cast_fp16, div_45_cast_fp16, log_22_cast_fp16))[name = tensor<string, []>("cat_22_cast_fp16")];
            tensor<int32, [1]> unsqueeze_45_axes_0 = const()[name = tensor<string, []>("unsqueeze_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_45_cast_fp16 = expand_dims(axes = unsqueeze_45_axes_0, x = cat_22_cast_fp16)[name = tensor<string, []>("unsqueeze_45_cast_fp16")];
            tensor<fp16, []> const_504_promoted_to_fp16 = const()[name = tensor<string, []>("const_504_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_69_cast_fp16 = pow(x = unsqueeze_45_cast_fp16, y = const_504_promoted_to_fp16)[name = tensor<string, []>("pow_69_cast_fp16")];
            tensor<int32, [1]> mean_22_axes_0 = const()[name = tensor<string, []>("mean_22_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_22_keep_dims_0 = const()[name = tensor<string, []>("mean_22_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_22_cast_fp16 = reduce_mean(axes = mean_22_axes_0, keep_dims = mean_22_keep_dims_0, x = pow_69_cast_fp16)[name = tensor<string, []>("mean_22_cast_fp16")];
            tensor<fp16, []> const_507_to_fp16 = const()[name = tensor<string, []>("const_507_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_68_cast_fp16 = add(x = mean_22_cast_fp16, y = const_507_to_fp16)[name = tensor<string, []>("add_68_cast_fp16")];
            tensor<fp32, []> rsqrt_22_epsilon_0 = const()[name = tensor<string, []>("rsqrt_22_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_22_cast_fp16 = rsqrt(epsilon = rsqrt_22_epsilon_0, x = add_68_cast_fp16)[name = tensor<string, []>("rsqrt_22_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_44_cast_fp16 = mul(x = unsqueeze_45_cast_fp16, y = rsqrt_22_cast_fp16)[name = tensor<string, []>("mul_44_cast_fp16")];
            tensor<int32, [1]> squeeze_22_axes_0 = const()[name = tensor<string, []>("squeeze_22_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = mul_44_cast_fp16)[name = tensor<string, []>("squeeze_22_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_22_to_fp16 = const()[name = tensor<string, []>("view_22_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140864)))];
            tensor<fp16, [1, 11, 801]> mul_45_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = view_22_to_fp16)[name = tensor<string, []>("mul_45_cast_fp16")];
            tensor<string, []> conv1d_22_pad_type_0 = const()[name = tensor<string, []>("conv1d_22_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_22_strides_0 = const()[name = tensor<string, []>("conv1d_22_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_22_pad_0 = const()[name = tensor<string, []>("conv1d_22_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_22_dilations_0 = const()[name = tensor<string, []>("conv1d_22_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_22_groups_0 = const()[name = tensor<string, []>("conv1d_22_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_22_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_22_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140992)))];
            tensor<fp16, [256]> p_bn_22_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_22_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146688)))];
            tensor<fp16, [1, 256, 801]> conv1d_22_cast_fp16 = conv(bias = p_bn_22_1_bias_to_fp16, dilations = conv1d_22_dilations_0, groups = conv1d_22_groups_0, pad = conv1d_22_pad_0, pad_type = conv1d_22_pad_type_0, strides = conv1d_22_strides_0, weight = p_bn_22_1_weight_to_fp16, x = mul_45_cast_fp16)[name = tensor<string, []>("conv1d_22_cast_fp16")];
            tensor<int32, [3]> slice_47_begin_0 = const()[name = tensor<string, []>("slice_47_begin_0"), val = tensor<int32, [3]>([0, 115, 0])];
            tensor<int32, [3]> slice_47_end_0 = const()[name = tensor<string, []>("slice_47_end_0"), val = tensor<int32, [3]>([1, 120, 801])];
            tensor<bool, [3]> slice_47_end_mask_0 = const()[name = tensor<string, []>("slice_47_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_47_cast_fp16 = slice_by_index(begin = slice_47_begin_0, end = slice_47_end_0, end_mask = slice_47_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_47_cast_fp16")];
            tensor<int32, [3]> slice_48_begin_0 = const()[name = tensor<string, []>("slice_48_begin_0"), val = tensor<int32, [3]>([0, 115, 0])];
            tensor<int32, [3]> slice_48_end_0 = const()[name = tensor<string, []>("slice_48_end_0"), val = tensor<int32, [3]>([1, 120, 801])];
            tensor<bool, [3]> slice_48_end_mask_0 = const()[name = tensor<string, []>("slice_48_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_48_cast_fp16 = slice_by_index(begin = slice_48_begin_0, end = slice_48_end_0, end_mask = slice_48_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_48_cast_fp16")];
            tensor<fp16, []> const_516_promoted_to_fp16 = const()[name = tensor<string, []>("const_516_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_70_cast_fp16 = pow(x = slice_47_cast_fp16, y = const_516_promoted_to_fp16)[name = tensor<string, []>("pow_70_cast_fp16")];
            tensor<fp16, []> const_517_promoted_to_fp16 = const()[name = tensor<string, []>("const_517_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_71_cast_fp16 = pow(x = slice_48_cast_fp16, y = const_517_promoted_to_fp16)[name = tensor<string, []>("pow_71_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_69_cast_fp16 = add(x = pow_70_cast_fp16, y = pow_71_cast_fp16)[name = tensor<string, []>("add_69_cast_fp16")];
            tensor<int32, [1]> sum_24_axes_0 = const()[name = tensor<string, []>("sum_24_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_24_keep_dims_0 = const()[name = tensor<string, []>("sum_24_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_24_cast_fp16 = reduce_sum(axes = sum_24_axes_0, keep_dims = sum_24_keep_dims_0, x = add_69_cast_fp16)[name = tensor<string, []>("sum_24_cast_fp16")];
            tensor<fp16, []> const_520_to_fp16 = const()[name = tensor<string, []>("const_520_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_70_cast_fp16 = add(x = sum_24_cast_fp16, y = const_520_to_fp16)[name = tensor<string, []>("add_70_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_23_cast_fp16 = sqrt(x = add_70_cast_fp16)[name = tensor<string, []>("sqrt_23_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_46_cast_fp16 = real_div(x = slice_47_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor<string, []>("div_46_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_47_cast_fp16 = real_div(x = slice_48_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor<string, []>("div_47_cast_fp16")];
            tensor<fp32, []> log_23_epsilon_0 = const()[name = tensor<string, []>("log_23_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_23_cast_fp16 = log(epsilon = log_23_epsilon_0, x = sqrt_23_cast_fp16)[name = tensor<string, []>("log_23_cast_fp16")];
            tensor<int32, []> const_521 = const()[name = tensor<string, []>("const_521"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_23_interleave_0 = const()[name = tensor<string, []>("cat_23_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_23_cast_fp16 = concat(axis = const_521, interleave = cat_23_interleave_0, values = (div_46_cast_fp16, div_47_cast_fp16, log_23_cast_fp16))[name = tensor<string, []>("cat_23_cast_fp16")];
            tensor<int32, [1]> unsqueeze_47_axes_0 = const()[name = tensor<string, []>("unsqueeze_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_47_cast_fp16 = expand_dims(axes = unsqueeze_47_axes_0, x = cat_23_cast_fp16)[name = tensor<string, []>("unsqueeze_47_cast_fp16")];
            tensor<fp16, []> const_526_promoted_to_fp16 = const()[name = tensor<string, []>("const_526_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_72_cast_fp16 = pow(x = unsqueeze_47_cast_fp16, y = const_526_promoted_to_fp16)[name = tensor<string, []>("pow_72_cast_fp16")];
            tensor<int32, [1]> mean_23_axes_0 = const()[name = tensor<string, []>("mean_23_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_23_keep_dims_0 = const()[name = tensor<string, []>("mean_23_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_23_cast_fp16 = reduce_mean(axes = mean_23_axes_0, keep_dims = mean_23_keep_dims_0, x = pow_72_cast_fp16)[name = tensor<string, []>("mean_23_cast_fp16")];
            tensor<fp16, []> const_529_to_fp16 = const()[name = tensor<string, []>("const_529_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_71_cast_fp16 = add(x = mean_23_cast_fp16, y = const_529_to_fp16)[name = tensor<string, []>("add_71_cast_fp16")];
            tensor<fp32, []> rsqrt_23_epsilon_0 = const()[name = tensor<string, []>("rsqrt_23_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_23_cast_fp16 = rsqrt(epsilon = rsqrt_23_epsilon_0, x = add_71_cast_fp16)[name = tensor<string, []>("rsqrt_23_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_46_cast_fp16 = mul(x = unsqueeze_47_cast_fp16, y = rsqrt_23_cast_fp16)[name = tensor<string, []>("mul_46_cast_fp16")];
            tensor<int32, [1]> squeeze_23_axes_0 = const()[name = tensor<string, []>("squeeze_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = mul_46_cast_fp16)[name = tensor<string, []>("squeeze_23_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_23_to_fp16 = const()[name = tensor<string, []>("view_23_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147264)))];
            tensor<fp16, [1, 11, 801]> mul_47_cast_fp16 = mul(x = squeeze_23_cast_fp16, y = view_23_to_fp16)[name = tensor<string, []>("mul_47_cast_fp16")];
            tensor<string, []> conv1d_23_pad_type_0 = const()[name = tensor<string, []>("conv1d_23_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_23_strides_0 = const()[name = tensor<string, []>("conv1d_23_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_23_pad_0 = const()[name = tensor<string, []>("conv1d_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_23_dilations_0 = const()[name = tensor<string, []>("conv1d_23_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_23_groups_0 = const()[name = tensor<string, []>("conv1d_23_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_23_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_23_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147392)))];
            tensor<fp16, [256]> p_bn_23_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_23_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153088)))];
            tensor<fp16, [1, 256, 801]> conv1d_23_cast_fp16 = conv(bias = p_bn_23_1_bias_to_fp16, dilations = conv1d_23_dilations_0, groups = conv1d_23_groups_0, pad = conv1d_23_pad_0, pad_type = conv1d_23_pad_type_0, strides = conv1d_23_strides_0, weight = p_bn_23_1_weight_to_fp16, x = mul_47_cast_fp16)[name = tensor<string, []>("conv1d_23_cast_fp16")];
            tensor<int32, [3]> slice_49_begin_0 = const()[name = tensor<string, []>("slice_49_begin_0"), val = tensor<int32, [3]>([0, 120, 0])];
            tensor<int32, [3]> slice_49_end_0 = const()[name = tensor<string, []>("slice_49_end_0"), val = tensor<int32, [3]>([1, 125, 801])];
            tensor<bool, [3]> slice_49_end_mask_0 = const()[name = tensor<string, []>("slice_49_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_49_cast_fp16 = slice_by_index(begin = slice_49_begin_0, end = slice_49_end_0, end_mask = slice_49_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_49_cast_fp16")];
            tensor<int32, [3]> slice_50_begin_0 = const()[name = tensor<string, []>("slice_50_begin_0"), val = tensor<int32, [3]>([0, 120, 0])];
            tensor<int32, [3]> slice_50_end_0 = const()[name = tensor<string, []>("slice_50_end_0"), val = tensor<int32, [3]>([1, 125, 801])];
            tensor<bool, [3]> slice_50_end_mask_0 = const()[name = tensor<string, []>("slice_50_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_50_cast_fp16 = slice_by_index(begin = slice_50_begin_0, end = slice_50_end_0, end_mask = slice_50_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_50_cast_fp16")];
            tensor<fp16, []> const_538_promoted_to_fp16 = const()[name = tensor<string, []>("const_538_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_73_cast_fp16 = pow(x = slice_49_cast_fp16, y = const_538_promoted_to_fp16)[name = tensor<string, []>("pow_73_cast_fp16")];
            tensor<fp16, []> const_539_promoted_to_fp16 = const()[name = tensor<string, []>("const_539_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_74_cast_fp16 = pow(x = slice_50_cast_fp16, y = const_539_promoted_to_fp16)[name = tensor<string, []>("pow_74_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_72_cast_fp16 = add(x = pow_73_cast_fp16, y = pow_74_cast_fp16)[name = tensor<string, []>("add_72_cast_fp16")];
            tensor<int32, [1]> sum_25_axes_0 = const()[name = tensor<string, []>("sum_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_25_keep_dims_0 = const()[name = tensor<string, []>("sum_25_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_25_cast_fp16 = reduce_sum(axes = sum_25_axes_0, keep_dims = sum_25_keep_dims_0, x = add_72_cast_fp16)[name = tensor<string, []>("sum_25_cast_fp16")];
            tensor<fp16, []> const_542_to_fp16 = const()[name = tensor<string, []>("const_542_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_73_cast_fp16 = add(x = sum_25_cast_fp16, y = const_542_to_fp16)[name = tensor<string, []>("add_73_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_24_cast_fp16 = sqrt(x = add_73_cast_fp16)[name = tensor<string, []>("sqrt_24_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_48_cast_fp16 = real_div(x = slice_49_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor<string, []>("div_48_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_49_cast_fp16 = real_div(x = slice_50_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor<string, []>("div_49_cast_fp16")];
            tensor<fp32, []> log_24_epsilon_0 = const()[name = tensor<string, []>("log_24_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_24_cast_fp16 = log(epsilon = log_24_epsilon_0, x = sqrt_24_cast_fp16)[name = tensor<string, []>("log_24_cast_fp16")];
            tensor<int32, []> const_543 = const()[name = tensor<string, []>("const_543"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_24_interleave_0 = const()[name = tensor<string, []>("cat_24_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_24_cast_fp16 = concat(axis = const_543, interleave = cat_24_interleave_0, values = (div_48_cast_fp16, div_49_cast_fp16, log_24_cast_fp16))[name = tensor<string, []>("cat_24_cast_fp16")];
            tensor<int32, [1]> unsqueeze_49_axes_0 = const()[name = tensor<string, []>("unsqueeze_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_49_cast_fp16 = expand_dims(axes = unsqueeze_49_axes_0, x = cat_24_cast_fp16)[name = tensor<string, []>("unsqueeze_49_cast_fp16")];
            tensor<fp16, []> const_548_promoted_to_fp16 = const()[name = tensor<string, []>("const_548_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_75_cast_fp16 = pow(x = unsqueeze_49_cast_fp16, y = const_548_promoted_to_fp16)[name = tensor<string, []>("pow_75_cast_fp16")];
            tensor<int32, [1]> mean_24_axes_0 = const()[name = tensor<string, []>("mean_24_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_24_keep_dims_0 = const()[name = tensor<string, []>("mean_24_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_24_cast_fp16 = reduce_mean(axes = mean_24_axes_0, keep_dims = mean_24_keep_dims_0, x = pow_75_cast_fp16)[name = tensor<string, []>("mean_24_cast_fp16")];
            tensor<fp16, []> const_551_to_fp16 = const()[name = tensor<string, []>("const_551_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_74_cast_fp16 = add(x = mean_24_cast_fp16, y = const_551_to_fp16)[name = tensor<string, []>("add_74_cast_fp16")];
            tensor<fp32, []> rsqrt_24_epsilon_0 = const()[name = tensor<string, []>("rsqrt_24_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_24_cast_fp16 = rsqrt(epsilon = rsqrt_24_epsilon_0, x = add_74_cast_fp16)[name = tensor<string, []>("rsqrt_24_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_48_cast_fp16 = mul(x = unsqueeze_49_cast_fp16, y = rsqrt_24_cast_fp16)[name = tensor<string, []>("mul_48_cast_fp16")];
            tensor<int32, [1]> squeeze_24_axes_0 = const()[name = tensor<string, []>("squeeze_24_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = mul_48_cast_fp16)[name = tensor<string, []>("squeeze_24_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_24_to_fp16 = const()[name = tensor<string, []>("view_24_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153664)))];
            tensor<fp16, [1, 11, 801]> mul_49_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = view_24_to_fp16)[name = tensor<string, []>("mul_49_cast_fp16")];
            tensor<string, []> conv1d_24_pad_type_0 = const()[name = tensor<string, []>("conv1d_24_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_24_strides_0 = const()[name = tensor<string, []>("conv1d_24_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_24_pad_0 = const()[name = tensor<string, []>("conv1d_24_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_24_dilations_0 = const()[name = tensor<string, []>("conv1d_24_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_24_groups_0 = const()[name = tensor<string, []>("conv1d_24_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_24_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_24_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153792)))];
            tensor<fp16, [256]> p_bn_24_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_24_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159488)))];
            tensor<fp16, [1, 256, 801]> conv1d_24_cast_fp16 = conv(bias = p_bn_24_1_bias_to_fp16, dilations = conv1d_24_dilations_0, groups = conv1d_24_groups_0, pad = conv1d_24_pad_0, pad_type = conv1d_24_pad_type_0, strides = conv1d_24_strides_0, weight = p_bn_24_1_weight_to_fp16, x = mul_49_cast_fp16)[name = tensor<string, []>("conv1d_24_cast_fp16")];
            tensor<int32, [3]> slice_51_begin_0 = const()[name = tensor<string, []>("slice_51_begin_0"), val = tensor<int32, [3]>([0, 125, 0])];
            tensor<int32, [3]> slice_51_end_0 = const()[name = tensor<string, []>("slice_51_end_0"), val = tensor<int32, [3]>([1, 130, 801])];
            tensor<bool, [3]> slice_51_end_mask_0 = const()[name = tensor<string, []>("slice_51_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_51_cast_fp16 = slice_by_index(begin = slice_51_begin_0, end = slice_51_end_0, end_mask = slice_51_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_51_cast_fp16")];
            tensor<int32, [3]> slice_52_begin_0 = const()[name = tensor<string, []>("slice_52_begin_0"), val = tensor<int32, [3]>([0, 125, 0])];
            tensor<int32, [3]> slice_52_end_0 = const()[name = tensor<string, []>("slice_52_end_0"), val = tensor<int32, [3]>([1, 130, 801])];
            tensor<bool, [3]> slice_52_end_mask_0 = const()[name = tensor<string, []>("slice_52_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_52_cast_fp16 = slice_by_index(begin = slice_52_begin_0, end = slice_52_end_0, end_mask = slice_52_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_52_cast_fp16")];
            tensor<fp16, []> const_560_promoted_to_fp16 = const()[name = tensor<string, []>("const_560_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_76_cast_fp16 = pow(x = slice_51_cast_fp16, y = const_560_promoted_to_fp16)[name = tensor<string, []>("pow_76_cast_fp16")];
            tensor<fp16, []> const_561_promoted_to_fp16 = const()[name = tensor<string, []>("const_561_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_77_cast_fp16 = pow(x = slice_52_cast_fp16, y = const_561_promoted_to_fp16)[name = tensor<string, []>("pow_77_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_75_cast_fp16 = add(x = pow_76_cast_fp16, y = pow_77_cast_fp16)[name = tensor<string, []>("add_75_cast_fp16")];
            tensor<int32, [1]> sum_26_axes_0 = const()[name = tensor<string, []>("sum_26_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_26_keep_dims_0 = const()[name = tensor<string, []>("sum_26_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_26_cast_fp16 = reduce_sum(axes = sum_26_axes_0, keep_dims = sum_26_keep_dims_0, x = add_75_cast_fp16)[name = tensor<string, []>("sum_26_cast_fp16")];
            tensor<fp16, []> const_564_to_fp16 = const()[name = tensor<string, []>("const_564_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_76_cast_fp16 = add(x = sum_26_cast_fp16, y = const_564_to_fp16)[name = tensor<string, []>("add_76_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_25_cast_fp16 = sqrt(x = add_76_cast_fp16)[name = tensor<string, []>("sqrt_25_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_50_cast_fp16 = real_div(x = slice_51_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor<string, []>("div_50_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_51_cast_fp16 = real_div(x = slice_52_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor<string, []>("div_51_cast_fp16")];
            tensor<fp32, []> log_25_epsilon_0 = const()[name = tensor<string, []>("log_25_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_25_cast_fp16 = log(epsilon = log_25_epsilon_0, x = sqrt_25_cast_fp16)[name = tensor<string, []>("log_25_cast_fp16")];
            tensor<int32, []> const_565 = const()[name = tensor<string, []>("const_565"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_25_interleave_0 = const()[name = tensor<string, []>("cat_25_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_25_cast_fp16 = concat(axis = const_565, interleave = cat_25_interleave_0, values = (div_50_cast_fp16, div_51_cast_fp16, log_25_cast_fp16))[name = tensor<string, []>("cat_25_cast_fp16")];
            tensor<int32, [1]> unsqueeze_51_axes_0 = const()[name = tensor<string, []>("unsqueeze_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_51_cast_fp16 = expand_dims(axes = unsqueeze_51_axes_0, x = cat_25_cast_fp16)[name = tensor<string, []>("unsqueeze_51_cast_fp16")];
            tensor<fp16, []> const_570_promoted_to_fp16 = const()[name = tensor<string, []>("const_570_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_78_cast_fp16 = pow(x = unsqueeze_51_cast_fp16, y = const_570_promoted_to_fp16)[name = tensor<string, []>("pow_78_cast_fp16")];
            tensor<int32, [1]> mean_25_axes_0 = const()[name = tensor<string, []>("mean_25_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_25_keep_dims_0 = const()[name = tensor<string, []>("mean_25_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_25_cast_fp16 = reduce_mean(axes = mean_25_axes_0, keep_dims = mean_25_keep_dims_0, x = pow_78_cast_fp16)[name = tensor<string, []>("mean_25_cast_fp16")];
            tensor<fp16, []> const_573_to_fp16 = const()[name = tensor<string, []>("const_573_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_77_cast_fp16 = add(x = mean_25_cast_fp16, y = const_573_to_fp16)[name = tensor<string, []>("add_77_cast_fp16")];
            tensor<fp32, []> rsqrt_25_epsilon_0 = const()[name = tensor<string, []>("rsqrt_25_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_25_cast_fp16 = rsqrt(epsilon = rsqrt_25_epsilon_0, x = add_77_cast_fp16)[name = tensor<string, []>("rsqrt_25_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_50_cast_fp16 = mul(x = unsqueeze_51_cast_fp16, y = rsqrt_25_cast_fp16)[name = tensor<string, []>("mul_50_cast_fp16")];
            tensor<int32, [1]> squeeze_25_axes_0 = const()[name = tensor<string, []>("squeeze_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = mul_50_cast_fp16)[name = tensor<string, []>("squeeze_25_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_25_to_fp16 = const()[name = tensor<string, []>("view_25_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160064)))];
            tensor<fp16, [1, 11, 801]> mul_51_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = view_25_to_fp16)[name = tensor<string, []>("mul_51_cast_fp16")];
            tensor<string, []> conv1d_25_pad_type_0 = const()[name = tensor<string, []>("conv1d_25_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_25_strides_0 = const()[name = tensor<string, []>("conv1d_25_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_25_pad_0 = const()[name = tensor<string, []>("conv1d_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_25_dilations_0 = const()[name = tensor<string, []>("conv1d_25_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_25_groups_0 = const()[name = tensor<string, []>("conv1d_25_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_25_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_25_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160192)))];
            tensor<fp16, [256]> p_bn_25_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_25_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165888)))];
            tensor<fp16, [1, 256, 801]> conv1d_25_cast_fp16 = conv(bias = p_bn_25_1_bias_to_fp16, dilations = conv1d_25_dilations_0, groups = conv1d_25_groups_0, pad = conv1d_25_pad_0, pad_type = conv1d_25_pad_type_0, strides = conv1d_25_strides_0, weight = p_bn_25_1_weight_to_fp16, x = mul_51_cast_fp16)[name = tensor<string, []>("conv1d_25_cast_fp16")];
            tensor<int32, [3]> slice_53_begin_0 = const()[name = tensor<string, []>("slice_53_begin_0"), val = tensor<int32, [3]>([0, 130, 0])];
            tensor<int32, [3]> slice_53_end_0 = const()[name = tensor<string, []>("slice_53_end_0"), val = tensor<int32, [3]>([1, 135, 801])];
            tensor<bool, [3]> slice_53_end_mask_0 = const()[name = tensor<string, []>("slice_53_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_53_cast_fp16 = slice_by_index(begin = slice_53_begin_0, end = slice_53_end_0, end_mask = slice_53_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_53_cast_fp16")];
            tensor<int32, [3]> slice_54_begin_0 = const()[name = tensor<string, []>("slice_54_begin_0"), val = tensor<int32, [3]>([0, 130, 0])];
            tensor<int32, [3]> slice_54_end_0 = const()[name = tensor<string, []>("slice_54_end_0"), val = tensor<int32, [3]>([1, 135, 801])];
            tensor<bool, [3]> slice_54_end_mask_0 = const()[name = tensor<string, []>("slice_54_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_54_cast_fp16 = slice_by_index(begin = slice_54_begin_0, end = slice_54_end_0, end_mask = slice_54_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_54_cast_fp16")];
            tensor<fp16, []> const_582_promoted_to_fp16 = const()[name = tensor<string, []>("const_582_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_79_cast_fp16 = pow(x = slice_53_cast_fp16, y = const_582_promoted_to_fp16)[name = tensor<string, []>("pow_79_cast_fp16")];
            tensor<fp16, []> const_583_promoted_to_fp16 = const()[name = tensor<string, []>("const_583_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_80_cast_fp16 = pow(x = slice_54_cast_fp16, y = const_583_promoted_to_fp16)[name = tensor<string, []>("pow_80_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_78_cast_fp16 = add(x = pow_79_cast_fp16, y = pow_80_cast_fp16)[name = tensor<string, []>("add_78_cast_fp16")];
            tensor<int32, [1]> sum_27_axes_0 = const()[name = tensor<string, []>("sum_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_27_keep_dims_0 = const()[name = tensor<string, []>("sum_27_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_27_cast_fp16 = reduce_sum(axes = sum_27_axes_0, keep_dims = sum_27_keep_dims_0, x = add_78_cast_fp16)[name = tensor<string, []>("sum_27_cast_fp16")];
            tensor<fp16, []> const_586_to_fp16 = const()[name = tensor<string, []>("const_586_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_79_cast_fp16 = add(x = sum_27_cast_fp16, y = const_586_to_fp16)[name = tensor<string, []>("add_79_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_26_cast_fp16 = sqrt(x = add_79_cast_fp16)[name = tensor<string, []>("sqrt_26_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_52_cast_fp16 = real_div(x = slice_53_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor<string, []>("div_52_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_53_cast_fp16 = real_div(x = slice_54_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor<string, []>("div_53_cast_fp16")];
            tensor<fp32, []> log_26_epsilon_0 = const()[name = tensor<string, []>("log_26_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_26_cast_fp16 = log(epsilon = log_26_epsilon_0, x = sqrt_26_cast_fp16)[name = tensor<string, []>("log_26_cast_fp16")];
            tensor<int32, []> const_587 = const()[name = tensor<string, []>("const_587"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_26_interleave_0 = const()[name = tensor<string, []>("cat_26_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_26_cast_fp16 = concat(axis = const_587, interleave = cat_26_interleave_0, values = (div_52_cast_fp16, div_53_cast_fp16, log_26_cast_fp16))[name = tensor<string, []>("cat_26_cast_fp16")];
            tensor<int32, [1]> unsqueeze_53_axes_0 = const()[name = tensor<string, []>("unsqueeze_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_53_cast_fp16 = expand_dims(axes = unsqueeze_53_axes_0, x = cat_26_cast_fp16)[name = tensor<string, []>("unsqueeze_53_cast_fp16")];
            tensor<fp16, []> const_592_promoted_to_fp16 = const()[name = tensor<string, []>("const_592_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_81_cast_fp16 = pow(x = unsqueeze_53_cast_fp16, y = const_592_promoted_to_fp16)[name = tensor<string, []>("pow_81_cast_fp16")];
            tensor<int32, [1]> mean_26_axes_0 = const()[name = tensor<string, []>("mean_26_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_26_keep_dims_0 = const()[name = tensor<string, []>("mean_26_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_26_cast_fp16 = reduce_mean(axes = mean_26_axes_0, keep_dims = mean_26_keep_dims_0, x = pow_81_cast_fp16)[name = tensor<string, []>("mean_26_cast_fp16")];
            tensor<fp16, []> const_595_to_fp16 = const()[name = tensor<string, []>("const_595_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_80_cast_fp16 = add(x = mean_26_cast_fp16, y = const_595_to_fp16)[name = tensor<string, []>("add_80_cast_fp16")];
            tensor<fp32, []> rsqrt_26_epsilon_0 = const()[name = tensor<string, []>("rsqrt_26_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_26_cast_fp16 = rsqrt(epsilon = rsqrt_26_epsilon_0, x = add_80_cast_fp16)[name = tensor<string, []>("rsqrt_26_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_52_cast_fp16 = mul(x = unsqueeze_53_cast_fp16, y = rsqrt_26_cast_fp16)[name = tensor<string, []>("mul_52_cast_fp16")];
            tensor<int32, [1]> squeeze_26_axes_0 = const()[name = tensor<string, []>("squeeze_26_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = mul_52_cast_fp16)[name = tensor<string, []>("squeeze_26_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_26_to_fp16 = const()[name = tensor<string, []>("view_26_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166464)))];
            tensor<fp16, [1, 11, 801]> mul_53_cast_fp16 = mul(x = squeeze_26_cast_fp16, y = view_26_to_fp16)[name = tensor<string, []>("mul_53_cast_fp16")];
            tensor<string, []> conv1d_26_pad_type_0 = const()[name = tensor<string, []>("conv1d_26_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_26_strides_0 = const()[name = tensor<string, []>("conv1d_26_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_26_pad_0 = const()[name = tensor<string, []>("conv1d_26_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_26_dilations_0 = const()[name = tensor<string, []>("conv1d_26_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_26_groups_0 = const()[name = tensor<string, []>("conv1d_26_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_26_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_26_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166592)))];
            tensor<fp16, [256]> p_bn_26_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_26_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172288)))];
            tensor<fp16, [1, 256, 801]> conv1d_26_cast_fp16 = conv(bias = p_bn_26_1_bias_to_fp16, dilations = conv1d_26_dilations_0, groups = conv1d_26_groups_0, pad = conv1d_26_pad_0, pad_type = conv1d_26_pad_type_0, strides = conv1d_26_strides_0, weight = p_bn_26_1_weight_to_fp16, x = mul_53_cast_fp16)[name = tensor<string, []>("conv1d_26_cast_fp16")];
            tensor<int32, [3]> slice_55_begin_0 = const()[name = tensor<string, []>("slice_55_begin_0"), val = tensor<int32, [3]>([0, 135, 0])];
            tensor<int32, [3]> slice_55_end_0 = const()[name = tensor<string, []>("slice_55_end_0"), val = tensor<int32, [3]>([1, 140, 801])];
            tensor<bool, [3]> slice_55_end_mask_0 = const()[name = tensor<string, []>("slice_55_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_55_cast_fp16 = slice_by_index(begin = slice_55_begin_0, end = slice_55_end_0, end_mask = slice_55_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_55_cast_fp16")];
            tensor<int32, [3]> slice_56_begin_0 = const()[name = tensor<string, []>("slice_56_begin_0"), val = tensor<int32, [3]>([0, 135, 0])];
            tensor<int32, [3]> slice_56_end_0 = const()[name = tensor<string, []>("slice_56_end_0"), val = tensor<int32, [3]>([1, 140, 801])];
            tensor<bool, [3]> slice_56_end_mask_0 = const()[name = tensor<string, []>("slice_56_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_56_cast_fp16 = slice_by_index(begin = slice_56_begin_0, end = slice_56_end_0, end_mask = slice_56_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_56_cast_fp16")];
            tensor<fp16, []> const_604_promoted_to_fp16 = const()[name = tensor<string, []>("const_604_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_82_cast_fp16 = pow(x = slice_55_cast_fp16, y = const_604_promoted_to_fp16)[name = tensor<string, []>("pow_82_cast_fp16")];
            tensor<fp16, []> const_605_promoted_to_fp16 = const()[name = tensor<string, []>("const_605_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_83_cast_fp16 = pow(x = slice_56_cast_fp16, y = const_605_promoted_to_fp16)[name = tensor<string, []>("pow_83_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_81_cast_fp16 = add(x = pow_82_cast_fp16, y = pow_83_cast_fp16)[name = tensor<string, []>("add_81_cast_fp16")];
            tensor<int32, [1]> sum_28_axes_0 = const()[name = tensor<string, []>("sum_28_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_28_keep_dims_0 = const()[name = tensor<string, []>("sum_28_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_28_cast_fp16 = reduce_sum(axes = sum_28_axes_0, keep_dims = sum_28_keep_dims_0, x = add_81_cast_fp16)[name = tensor<string, []>("sum_28_cast_fp16")];
            tensor<fp16, []> const_608_to_fp16 = const()[name = tensor<string, []>("const_608_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_82_cast_fp16 = add(x = sum_28_cast_fp16, y = const_608_to_fp16)[name = tensor<string, []>("add_82_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_27_cast_fp16 = sqrt(x = add_82_cast_fp16)[name = tensor<string, []>("sqrt_27_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_54_cast_fp16 = real_div(x = slice_55_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor<string, []>("div_54_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_55_cast_fp16 = real_div(x = slice_56_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor<string, []>("div_55_cast_fp16")];
            tensor<fp32, []> log_27_epsilon_0 = const()[name = tensor<string, []>("log_27_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_27_cast_fp16 = log(epsilon = log_27_epsilon_0, x = sqrt_27_cast_fp16)[name = tensor<string, []>("log_27_cast_fp16")];
            tensor<int32, []> const_609 = const()[name = tensor<string, []>("const_609"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_27_interleave_0 = const()[name = tensor<string, []>("cat_27_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_27_cast_fp16 = concat(axis = const_609, interleave = cat_27_interleave_0, values = (div_54_cast_fp16, div_55_cast_fp16, log_27_cast_fp16))[name = tensor<string, []>("cat_27_cast_fp16")];
            tensor<int32, [1]> unsqueeze_55_axes_0 = const()[name = tensor<string, []>("unsqueeze_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_55_cast_fp16 = expand_dims(axes = unsqueeze_55_axes_0, x = cat_27_cast_fp16)[name = tensor<string, []>("unsqueeze_55_cast_fp16")];
            tensor<fp16, []> const_614_promoted_to_fp16 = const()[name = tensor<string, []>("const_614_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_84_cast_fp16 = pow(x = unsqueeze_55_cast_fp16, y = const_614_promoted_to_fp16)[name = tensor<string, []>("pow_84_cast_fp16")];
            tensor<int32, [1]> mean_27_axes_0 = const()[name = tensor<string, []>("mean_27_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_27_keep_dims_0 = const()[name = tensor<string, []>("mean_27_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_27_cast_fp16 = reduce_mean(axes = mean_27_axes_0, keep_dims = mean_27_keep_dims_0, x = pow_84_cast_fp16)[name = tensor<string, []>("mean_27_cast_fp16")];
            tensor<fp16, []> const_617_to_fp16 = const()[name = tensor<string, []>("const_617_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_83_cast_fp16 = add(x = mean_27_cast_fp16, y = const_617_to_fp16)[name = tensor<string, []>("add_83_cast_fp16")];
            tensor<fp32, []> rsqrt_27_epsilon_0 = const()[name = tensor<string, []>("rsqrt_27_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_27_cast_fp16 = rsqrt(epsilon = rsqrt_27_epsilon_0, x = add_83_cast_fp16)[name = tensor<string, []>("rsqrt_27_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_54_cast_fp16 = mul(x = unsqueeze_55_cast_fp16, y = rsqrt_27_cast_fp16)[name = tensor<string, []>("mul_54_cast_fp16")];
            tensor<int32, [1]> squeeze_27_axes_0 = const()[name = tensor<string, []>("squeeze_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = mul_54_cast_fp16)[name = tensor<string, []>("squeeze_27_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_27_to_fp16 = const()[name = tensor<string, []>("view_27_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172864)))];
            tensor<fp16, [1, 11, 801]> mul_55_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = view_27_to_fp16)[name = tensor<string, []>("mul_55_cast_fp16")];
            tensor<string, []> conv1d_27_pad_type_0 = const()[name = tensor<string, []>("conv1d_27_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_27_strides_0 = const()[name = tensor<string, []>("conv1d_27_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_27_pad_0 = const()[name = tensor<string, []>("conv1d_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_27_dilations_0 = const()[name = tensor<string, []>("conv1d_27_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_27_groups_0 = const()[name = tensor<string, []>("conv1d_27_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_27_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_27_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172992)))];
            tensor<fp16, [256]> p_bn_27_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_27_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178688)))];
            tensor<fp16, [1, 256, 801]> conv1d_27_cast_fp16 = conv(bias = p_bn_27_1_bias_to_fp16, dilations = conv1d_27_dilations_0, groups = conv1d_27_groups_0, pad = conv1d_27_pad_0, pad_type = conv1d_27_pad_type_0, strides = conv1d_27_strides_0, weight = p_bn_27_1_weight_to_fp16, x = mul_55_cast_fp16)[name = tensor<string, []>("conv1d_27_cast_fp16")];
            tensor<int32, [3]> slice_57_begin_0 = const()[name = tensor<string, []>("slice_57_begin_0"), val = tensor<int32, [3]>([0, 140, 0])];
            tensor<int32, [3]> slice_57_end_0 = const()[name = tensor<string, []>("slice_57_end_0"), val = tensor<int32, [3]>([1, 145, 801])];
            tensor<bool, [3]> slice_57_end_mask_0 = const()[name = tensor<string, []>("slice_57_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_57_cast_fp16 = slice_by_index(begin = slice_57_begin_0, end = slice_57_end_0, end_mask = slice_57_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_57_cast_fp16")];
            tensor<int32, [3]> slice_58_begin_0 = const()[name = tensor<string, []>("slice_58_begin_0"), val = tensor<int32, [3]>([0, 140, 0])];
            tensor<int32, [3]> slice_58_end_0 = const()[name = tensor<string, []>("slice_58_end_0"), val = tensor<int32, [3]>([1, 145, 801])];
            tensor<bool, [3]> slice_58_end_mask_0 = const()[name = tensor<string, []>("slice_58_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_58_cast_fp16 = slice_by_index(begin = slice_58_begin_0, end = slice_58_end_0, end_mask = slice_58_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_58_cast_fp16")];
            tensor<fp16, []> const_626_promoted_to_fp16 = const()[name = tensor<string, []>("const_626_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_85_cast_fp16 = pow(x = slice_57_cast_fp16, y = const_626_promoted_to_fp16)[name = tensor<string, []>("pow_85_cast_fp16")];
            tensor<fp16, []> const_627_promoted_to_fp16 = const()[name = tensor<string, []>("const_627_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_86_cast_fp16 = pow(x = slice_58_cast_fp16, y = const_627_promoted_to_fp16)[name = tensor<string, []>("pow_86_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_84_cast_fp16 = add(x = pow_85_cast_fp16, y = pow_86_cast_fp16)[name = tensor<string, []>("add_84_cast_fp16")];
            tensor<int32, [1]> sum_29_axes_0 = const()[name = tensor<string, []>("sum_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_29_keep_dims_0 = const()[name = tensor<string, []>("sum_29_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_29_cast_fp16 = reduce_sum(axes = sum_29_axes_0, keep_dims = sum_29_keep_dims_0, x = add_84_cast_fp16)[name = tensor<string, []>("sum_29_cast_fp16")];
            tensor<fp16, []> const_630_to_fp16 = const()[name = tensor<string, []>("const_630_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_85_cast_fp16 = add(x = sum_29_cast_fp16, y = const_630_to_fp16)[name = tensor<string, []>("add_85_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_28_cast_fp16 = sqrt(x = add_85_cast_fp16)[name = tensor<string, []>("sqrt_28_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_56_cast_fp16 = real_div(x = slice_57_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor<string, []>("div_56_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_57_cast_fp16 = real_div(x = slice_58_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor<string, []>("div_57_cast_fp16")];
            tensor<fp32, []> log_28_epsilon_0 = const()[name = tensor<string, []>("log_28_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_28_cast_fp16 = log(epsilon = log_28_epsilon_0, x = sqrt_28_cast_fp16)[name = tensor<string, []>("log_28_cast_fp16")];
            tensor<int32, []> const_631 = const()[name = tensor<string, []>("const_631"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_28_interleave_0 = const()[name = tensor<string, []>("cat_28_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_28_cast_fp16 = concat(axis = const_631, interleave = cat_28_interleave_0, values = (div_56_cast_fp16, div_57_cast_fp16, log_28_cast_fp16))[name = tensor<string, []>("cat_28_cast_fp16")];
            tensor<int32, [1]> unsqueeze_57_axes_0 = const()[name = tensor<string, []>("unsqueeze_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_57_cast_fp16 = expand_dims(axes = unsqueeze_57_axes_0, x = cat_28_cast_fp16)[name = tensor<string, []>("unsqueeze_57_cast_fp16")];
            tensor<fp16, []> const_636_promoted_to_fp16 = const()[name = tensor<string, []>("const_636_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_87_cast_fp16 = pow(x = unsqueeze_57_cast_fp16, y = const_636_promoted_to_fp16)[name = tensor<string, []>("pow_87_cast_fp16")];
            tensor<int32, [1]> mean_28_axes_0 = const()[name = tensor<string, []>("mean_28_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_28_keep_dims_0 = const()[name = tensor<string, []>("mean_28_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_28_cast_fp16 = reduce_mean(axes = mean_28_axes_0, keep_dims = mean_28_keep_dims_0, x = pow_87_cast_fp16)[name = tensor<string, []>("mean_28_cast_fp16")];
            tensor<fp16, []> const_639_to_fp16 = const()[name = tensor<string, []>("const_639_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_86_cast_fp16 = add(x = mean_28_cast_fp16, y = const_639_to_fp16)[name = tensor<string, []>("add_86_cast_fp16")];
            tensor<fp32, []> rsqrt_28_epsilon_0 = const()[name = tensor<string, []>("rsqrt_28_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_28_cast_fp16 = rsqrt(epsilon = rsqrt_28_epsilon_0, x = add_86_cast_fp16)[name = tensor<string, []>("rsqrt_28_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_56_cast_fp16 = mul(x = unsqueeze_57_cast_fp16, y = rsqrt_28_cast_fp16)[name = tensor<string, []>("mul_56_cast_fp16")];
            tensor<int32, [1]> squeeze_28_axes_0 = const()[name = tensor<string, []>("squeeze_28_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = mul_56_cast_fp16)[name = tensor<string, []>("squeeze_28_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_28_to_fp16 = const()[name = tensor<string, []>("view_28_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179264)))];
            tensor<fp16, [1, 11, 801]> mul_57_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = view_28_to_fp16)[name = tensor<string, []>("mul_57_cast_fp16")];
            tensor<string, []> conv1d_28_pad_type_0 = const()[name = tensor<string, []>("conv1d_28_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_28_strides_0 = const()[name = tensor<string, []>("conv1d_28_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_28_pad_0 = const()[name = tensor<string, []>("conv1d_28_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_28_dilations_0 = const()[name = tensor<string, []>("conv1d_28_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_28_groups_0 = const()[name = tensor<string, []>("conv1d_28_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_28_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_28_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179392)))];
            tensor<fp16, [256]> p_bn_28_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_28_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185088)))];
            tensor<fp16, [1, 256, 801]> conv1d_28_cast_fp16 = conv(bias = p_bn_28_1_bias_to_fp16, dilations = conv1d_28_dilations_0, groups = conv1d_28_groups_0, pad = conv1d_28_pad_0, pad_type = conv1d_28_pad_type_0, strides = conv1d_28_strides_0, weight = p_bn_28_1_weight_to_fp16, x = mul_57_cast_fp16)[name = tensor<string, []>("conv1d_28_cast_fp16")];
            tensor<int32, [3]> slice_59_begin_0 = const()[name = tensor<string, []>("slice_59_begin_0"), val = tensor<int32, [3]>([0, 145, 0])];
            tensor<int32, [3]> slice_59_end_0 = const()[name = tensor<string, []>("slice_59_end_0"), val = tensor<int32, [3]>([1, 150, 801])];
            tensor<bool, [3]> slice_59_end_mask_0 = const()[name = tensor<string, []>("slice_59_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_59_cast_fp16 = slice_by_index(begin = slice_59_begin_0, end = slice_59_end_0, end_mask = slice_59_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_59_cast_fp16")];
            tensor<int32, [3]> slice_60_begin_0 = const()[name = tensor<string, []>("slice_60_begin_0"), val = tensor<int32, [3]>([0, 145, 0])];
            tensor<int32, [3]> slice_60_end_0 = const()[name = tensor<string, []>("slice_60_end_0"), val = tensor<int32, [3]>([1, 150, 801])];
            tensor<bool, [3]> slice_60_end_mask_0 = const()[name = tensor<string, []>("slice_60_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_60_cast_fp16 = slice_by_index(begin = slice_60_begin_0, end = slice_60_end_0, end_mask = slice_60_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_60_cast_fp16")];
            tensor<fp16, []> const_648_promoted_to_fp16 = const()[name = tensor<string, []>("const_648_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_88_cast_fp16 = pow(x = slice_59_cast_fp16, y = const_648_promoted_to_fp16)[name = tensor<string, []>("pow_88_cast_fp16")];
            tensor<fp16, []> const_649_promoted_to_fp16 = const()[name = tensor<string, []>("const_649_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_89_cast_fp16 = pow(x = slice_60_cast_fp16, y = const_649_promoted_to_fp16)[name = tensor<string, []>("pow_89_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_87_cast_fp16 = add(x = pow_88_cast_fp16, y = pow_89_cast_fp16)[name = tensor<string, []>("add_87_cast_fp16")];
            tensor<int32, [1]> sum_30_axes_0 = const()[name = tensor<string, []>("sum_30_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_30_keep_dims_0 = const()[name = tensor<string, []>("sum_30_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_30_cast_fp16 = reduce_sum(axes = sum_30_axes_0, keep_dims = sum_30_keep_dims_0, x = add_87_cast_fp16)[name = tensor<string, []>("sum_30_cast_fp16")];
            tensor<fp16, []> const_652_to_fp16 = const()[name = tensor<string, []>("const_652_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_88_cast_fp16 = add(x = sum_30_cast_fp16, y = const_652_to_fp16)[name = tensor<string, []>("add_88_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_29_cast_fp16 = sqrt(x = add_88_cast_fp16)[name = tensor<string, []>("sqrt_29_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_58_cast_fp16 = real_div(x = slice_59_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor<string, []>("div_58_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_59_cast_fp16 = real_div(x = slice_60_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor<string, []>("div_59_cast_fp16")];
            tensor<fp32, []> log_29_epsilon_0 = const()[name = tensor<string, []>("log_29_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_29_cast_fp16 = log(epsilon = log_29_epsilon_0, x = sqrt_29_cast_fp16)[name = tensor<string, []>("log_29_cast_fp16")];
            tensor<int32, []> const_653 = const()[name = tensor<string, []>("const_653"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_29_interleave_0 = const()[name = tensor<string, []>("cat_29_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_29_cast_fp16 = concat(axis = const_653, interleave = cat_29_interleave_0, values = (div_58_cast_fp16, div_59_cast_fp16, log_29_cast_fp16))[name = tensor<string, []>("cat_29_cast_fp16")];
            tensor<int32, [1]> unsqueeze_59_axes_0 = const()[name = tensor<string, []>("unsqueeze_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_59_cast_fp16 = expand_dims(axes = unsqueeze_59_axes_0, x = cat_29_cast_fp16)[name = tensor<string, []>("unsqueeze_59_cast_fp16")];
            tensor<fp16, []> const_658_promoted_to_fp16 = const()[name = tensor<string, []>("const_658_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_90_cast_fp16 = pow(x = unsqueeze_59_cast_fp16, y = const_658_promoted_to_fp16)[name = tensor<string, []>("pow_90_cast_fp16")];
            tensor<int32, [1]> mean_29_axes_0 = const()[name = tensor<string, []>("mean_29_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_29_keep_dims_0 = const()[name = tensor<string, []>("mean_29_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_29_cast_fp16 = reduce_mean(axes = mean_29_axes_0, keep_dims = mean_29_keep_dims_0, x = pow_90_cast_fp16)[name = tensor<string, []>("mean_29_cast_fp16")];
            tensor<fp16, []> const_661_to_fp16 = const()[name = tensor<string, []>("const_661_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_89_cast_fp16 = add(x = mean_29_cast_fp16, y = const_661_to_fp16)[name = tensor<string, []>("add_89_cast_fp16")];
            tensor<fp32, []> rsqrt_29_epsilon_0 = const()[name = tensor<string, []>("rsqrt_29_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_29_cast_fp16 = rsqrt(epsilon = rsqrt_29_epsilon_0, x = add_89_cast_fp16)[name = tensor<string, []>("rsqrt_29_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_58_cast_fp16 = mul(x = unsqueeze_59_cast_fp16, y = rsqrt_29_cast_fp16)[name = tensor<string, []>("mul_58_cast_fp16")];
            tensor<int32, [1]> squeeze_29_axes_0 = const()[name = tensor<string, []>("squeeze_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = mul_58_cast_fp16)[name = tensor<string, []>("squeeze_29_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_29_to_fp16 = const()[name = tensor<string, []>("view_29_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185664)))];
            tensor<fp16, [1, 11, 801]> mul_59_cast_fp16 = mul(x = squeeze_29_cast_fp16, y = view_29_to_fp16)[name = tensor<string, []>("mul_59_cast_fp16")];
            tensor<string, []> conv1d_29_pad_type_0 = const()[name = tensor<string, []>("conv1d_29_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_29_strides_0 = const()[name = tensor<string, []>("conv1d_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_29_pad_0 = const()[name = tensor<string, []>("conv1d_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_29_dilations_0 = const()[name = tensor<string, []>("conv1d_29_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_29_groups_0 = const()[name = tensor<string, []>("conv1d_29_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_29_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_29_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185792)))];
            tensor<fp16, [256]> p_bn_29_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_29_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191488)))];
            tensor<fp16, [1, 256, 801]> conv1d_29_cast_fp16 = conv(bias = p_bn_29_1_bias_to_fp16, dilations = conv1d_29_dilations_0, groups = conv1d_29_groups_0, pad = conv1d_29_pad_0, pad_type = conv1d_29_pad_type_0, strides = conv1d_29_strides_0, weight = p_bn_29_1_weight_to_fp16, x = mul_59_cast_fp16)[name = tensor<string, []>("conv1d_29_cast_fp16")];
            tensor<int32, [3]> slice_61_begin_0 = const()[name = tensor<string, []>("slice_61_begin_0"), val = tensor<int32, [3]>([0, 150, 0])];
            tensor<int32, [3]> slice_61_end_0 = const()[name = tensor<string, []>("slice_61_end_0"), val = tensor<int32, [3]>([1, 155, 801])];
            tensor<bool, [3]> slice_61_end_mask_0 = const()[name = tensor<string, []>("slice_61_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_61_cast_fp16 = slice_by_index(begin = slice_61_begin_0, end = slice_61_end_0, end_mask = slice_61_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_61_cast_fp16")];
            tensor<int32, [3]> slice_62_begin_0 = const()[name = tensor<string, []>("slice_62_begin_0"), val = tensor<int32, [3]>([0, 150, 0])];
            tensor<int32, [3]> slice_62_end_0 = const()[name = tensor<string, []>("slice_62_end_0"), val = tensor<int32, [3]>([1, 155, 801])];
            tensor<bool, [3]> slice_62_end_mask_0 = const()[name = tensor<string, []>("slice_62_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_62_cast_fp16 = slice_by_index(begin = slice_62_begin_0, end = slice_62_end_0, end_mask = slice_62_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_62_cast_fp16")];
            tensor<fp16, []> const_670_promoted_to_fp16 = const()[name = tensor<string, []>("const_670_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_91_cast_fp16 = pow(x = slice_61_cast_fp16, y = const_670_promoted_to_fp16)[name = tensor<string, []>("pow_91_cast_fp16")];
            tensor<fp16, []> const_671_promoted_to_fp16 = const()[name = tensor<string, []>("const_671_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_92_cast_fp16 = pow(x = slice_62_cast_fp16, y = const_671_promoted_to_fp16)[name = tensor<string, []>("pow_92_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_90_cast_fp16 = add(x = pow_91_cast_fp16, y = pow_92_cast_fp16)[name = tensor<string, []>("add_90_cast_fp16")];
            tensor<int32, [1]> sum_31_axes_0 = const()[name = tensor<string, []>("sum_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_31_keep_dims_0 = const()[name = tensor<string, []>("sum_31_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_31_cast_fp16 = reduce_sum(axes = sum_31_axes_0, keep_dims = sum_31_keep_dims_0, x = add_90_cast_fp16)[name = tensor<string, []>("sum_31_cast_fp16")];
            tensor<fp16, []> const_674_to_fp16 = const()[name = tensor<string, []>("const_674_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_91_cast_fp16 = add(x = sum_31_cast_fp16, y = const_674_to_fp16)[name = tensor<string, []>("add_91_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_30_cast_fp16 = sqrt(x = add_91_cast_fp16)[name = tensor<string, []>("sqrt_30_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_60_cast_fp16 = real_div(x = slice_61_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor<string, []>("div_60_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_61_cast_fp16 = real_div(x = slice_62_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor<string, []>("div_61_cast_fp16")];
            tensor<fp32, []> log_30_epsilon_0 = const()[name = tensor<string, []>("log_30_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_30_cast_fp16 = log(epsilon = log_30_epsilon_0, x = sqrt_30_cast_fp16)[name = tensor<string, []>("log_30_cast_fp16")];
            tensor<int32, []> const_675 = const()[name = tensor<string, []>("const_675"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_30_interleave_0 = const()[name = tensor<string, []>("cat_30_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_30_cast_fp16 = concat(axis = const_675, interleave = cat_30_interleave_0, values = (div_60_cast_fp16, div_61_cast_fp16, log_30_cast_fp16))[name = tensor<string, []>("cat_30_cast_fp16")];
            tensor<int32, [1]> unsqueeze_61_axes_0 = const()[name = tensor<string, []>("unsqueeze_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_61_cast_fp16 = expand_dims(axes = unsqueeze_61_axes_0, x = cat_30_cast_fp16)[name = tensor<string, []>("unsqueeze_61_cast_fp16")];
            tensor<fp16, []> const_680_promoted_to_fp16 = const()[name = tensor<string, []>("const_680_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_93_cast_fp16 = pow(x = unsqueeze_61_cast_fp16, y = const_680_promoted_to_fp16)[name = tensor<string, []>("pow_93_cast_fp16")];
            tensor<int32, [1]> mean_30_axes_0 = const()[name = tensor<string, []>("mean_30_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_30_keep_dims_0 = const()[name = tensor<string, []>("mean_30_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_30_cast_fp16 = reduce_mean(axes = mean_30_axes_0, keep_dims = mean_30_keep_dims_0, x = pow_93_cast_fp16)[name = tensor<string, []>("mean_30_cast_fp16")];
            tensor<fp16, []> const_683_to_fp16 = const()[name = tensor<string, []>("const_683_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_92_cast_fp16 = add(x = mean_30_cast_fp16, y = const_683_to_fp16)[name = tensor<string, []>("add_92_cast_fp16")];
            tensor<fp32, []> rsqrt_30_epsilon_0 = const()[name = tensor<string, []>("rsqrt_30_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_30_cast_fp16 = rsqrt(epsilon = rsqrt_30_epsilon_0, x = add_92_cast_fp16)[name = tensor<string, []>("rsqrt_30_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_60_cast_fp16 = mul(x = unsqueeze_61_cast_fp16, y = rsqrt_30_cast_fp16)[name = tensor<string, []>("mul_60_cast_fp16")];
            tensor<int32, [1]> squeeze_30_axes_0 = const()[name = tensor<string, []>("squeeze_30_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = mul_60_cast_fp16)[name = tensor<string, []>("squeeze_30_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_30_to_fp16 = const()[name = tensor<string, []>("view_30_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192064)))];
            tensor<fp16, [1, 11, 801]> mul_61_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = view_30_to_fp16)[name = tensor<string, []>("mul_61_cast_fp16")];
            tensor<string, []> conv1d_30_pad_type_0 = const()[name = tensor<string, []>("conv1d_30_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_30_strides_0 = const()[name = tensor<string, []>("conv1d_30_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_30_pad_0 = const()[name = tensor<string, []>("conv1d_30_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_30_dilations_0 = const()[name = tensor<string, []>("conv1d_30_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_30_groups_0 = const()[name = tensor<string, []>("conv1d_30_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_30_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_30_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192192)))];
            tensor<fp16, [256]> p_bn_30_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_30_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197888)))];
            tensor<fp16, [1, 256, 801]> conv1d_30_cast_fp16 = conv(bias = p_bn_30_1_bias_to_fp16, dilations = conv1d_30_dilations_0, groups = conv1d_30_groups_0, pad = conv1d_30_pad_0, pad_type = conv1d_30_pad_type_0, strides = conv1d_30_strides_0, weight = p_bn_30_1_weight_to_fp16, x = mul_61_cast_fp16)[name = tensor<string, []>("conv1d_30_cast_fp16")];
            tensor<int32, [3]> slice_63_begin_0 = const()[name = tensor<string, []>("slice_63_begin_0"), val = tensor<int32, [3]>([0, 155, 0])];
            tensor<int32, [3]> slice_63_end_0 = const()[name = tensor<string, []>("slice_63_end_0"), val = tensor<int32, [3]>([1, 160, 801])];
            tensor<bool, [3]> slice_63_end_mask_0 = const()[name = tensor<string, []>("slice_63_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_63_cast_fp16 = slice_by_index(begin = slice_63_begin_0, end = slice_63_end_0, end_mask = slice_63_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_63_cast_fp16")];
            tensor<int32, [3]> slice_64_begin_0 = const()[name = tensor<string, []>("slice_64_begin_0"), val = tensor<int32, [3]>([0, 155, 0])];
            tensor<int32, [3]> slice_64_end_0 = const()[name = tensor<string, []>("slice_64_end_0"), val = tensor<int32, [3]>([1, 160, 801])];
            tensor<bool, [3]> slice_64_end_mask_0 = const()[name = tensor<string, []>("slice_64_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_64_cast_fp16 = slice_by_index(begin = slice_64_begin_0, end = slice_64_end_0, end_mask = slice_64_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_64_cast_fp16")];
            tensor<fp16, []> const_692_promoted_to_fp16 = const()[name = tensor<string, []>("const_692_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_94_cast_fp16 = pow(x = slice_63_cast_fp16, y = const_692_promoted_to_fp16)[name = tensor<string, []>("pow_94_cast_fp16")];
            tensor<fp16, []> const_693_promoted_to_fp16 = const()[name = tensor<string, []>("const_693_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_95_cast_fp16 = pow(x = slice_64_cast_fp16, y = const_693_promoted_to_fp16)[name = tensor<string, []>("pow_95_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_93_cast_fp16 = add(x = pow_94_cast_fp16, y = pow_95_cast_fp16)[name = tensor<string, []>("add_93_cast_fp16")];
            tensor<int32, [1]> sum_32_axes_0 = const()[name = tensor<string, []>("sum_32_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_32_keep_dims_0 = const()[name = tensor<string, []>("sum_32_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_32_cast_fp16 = reduce_sum(axes = sum_32_axes_0, keep_dims = sum_32_keep_dims_0, x = add_93_cast_fp16)[name = tensor<string, []>("sum_32_cast_fp16")];
            tensor<fp16, []> const_696_to_fp16 = const()[name = tensor<string, []>("const_696_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_94_cast_fp16 = add(x = sum_32_cast_fp16, y = const_696_to_fp16)[name = tensor<string, []>("add_94_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_31_cast_fp16 = sqrt(x = add_94_cast_fp16)[name = tensor<string, []>("sqrt_31_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_62_cast_fp16 = real_div(x = slice_63_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor<string, []>("div_62_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_63_cast_fp16 = real_div(x = slice_64_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor<string, []>("div_63_cast_fp16")];
            tensor<fp32, []> log_31_epsilon_0 = const()[name = tensor<string, []>("log_31_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_31_cast_fp16 = log(epsilon = log_31_epsilon_0, x = sqrt_31_cast_fp16)[name = tensor<string, []>("log_31_cast_fp16")];
            tensor<int32, []> const_697 = const()[name = tensor<string, []>("const_697"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_31_interleave_0 = const()[name = tensor<string, []>("cat_31_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_31_cast_fp16 = concat(axis = const_697, interleave = cat_31_interleave_0, values = (div_62_cast_fp16, div_63_cast_fp16, log_31_cast_fp16))[name = tensor<string, []>("cat_31_cast_fp16")];
            tensor<int32, [1]> unsqueeze_63_axes_0 = const()[name = tensor<string, []>("unsqueeze_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_63_cast_fp16 = expand_dims(axes = unsqueeze_63_axes_0, x = cat_31_cast_fp16)[name = tensor<string, []>("unsqueeze_63_cast_fp16")];
            tensor<fp16, []> const_702_promoted_to_fp16 = const()[name = tensor<string, []>("const_702_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_96_cast_fp16 = pow(x = unsqueeze_63_cast_fp16, y = const_702_promoted_to_fp16)[name = tensor<string, []>("pow_96_cast_fp16")];
            tensor<int32, [1]> mean_31_axes_0 = const()[name = tensor<string, []>("mean_31_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_31_keep_dims_0 = const()[name = tensor<string, []>("mean_31_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_31_cast_fp16 = reduce_mean(axes = mean_31_axes_0, keep_dims = mean_31_keep_dims_0, x = pow_96_cast_fp16)[name = tensor<string, []>("mean_31_cast_fp16")];
            tensor<fp16, []> const_705_to_fp16 = const()[name = tensor<string, []>("const_705_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_95_cast_fp16 = add(x = mean_31_cast_fp16, y = const_705_to_fp16)[name = tensor<string, []>("add_95_cast_fp16")];
            tensor<fp32, []> rsqrt_31_epsilon_0 = const()[name = tensor<string, []>("rsqrt_31_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_31_cast_fp16 = rsqrt(epsilon = rsqrt_31_epsilon_0, x = add_95_cast_fp16)[name = tensor<string, []>("rsqrt_31_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_62_cast_fp16 = mul(x = unsqueeze_63_cast_fp16, y = rsqrt_31_cast_fp16)[name = tensor<string, []>("mul_62_cast_fp16")];
            tensor<int32, [1]> squeeze_31_axes_0 = const()[name = tensor<string, []>("squeeze_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = mul_62_cast_fp16)[name = tensor<string, []>("squeeze_31_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_31_to_fp16 = const()[name = tensor<string, []>("view_31_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198464)))];
            tensor<fp16, [1, 11, 801]> mul_63_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = view_31_to_fp16)[name = tensor<string, []>("mul_63_cast_fp16")];
            tensor<string, []> conv1d_31_pad_type_0 = const()[name = tensor<string, []>("conv1d_31_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_31_strides_0 = const()[name = tensor<string, []>("conv1d_31_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_31_pad_0 = const()[name = tensor<string, []>("conv1d_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_31_dilations_0 = const()[name = tensor<string, []>("conv1d_31_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_31_groups_0 = const()[name = tensor<string, []>("conv1d_31_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_31_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_31_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198592)))];
            tensor<fp16, [256]> p_bn_31_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_31_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204288)))];
            tensor<fp16, [1, 256, 801]> conv1d_31_cast_fp16 = conv(bias = p_bn_31_1_bias_to_fp16, dilations = conv1d_31_dilations_0, groups = conv1d_31_groups_0, pad = conv1d_31_pad_0, pad_type = conv1d_31_pad_type_0, strides = conv1d_31_strides_0, weight = p_bn_31_1_weight_to_fp16, x = mul_63_cast_fp16)[name = tensor<string, []>("conv1d_31_cast_fp16")];
            tensor<int32, [3]> slice_65_begin_0 = const()[name = tensor<string, []>("slice_65_begin_0"), val = tensor<int32, [3]>([0, 160, 0])];
            tensor<int32, [3]> slice_65_end_0 = const()[name = tensor<string, []>("slice_65_end_0"), val = tensor<int32, [3]>([1, 165, 801])];
            tensor<bool, [3]> slice_65_end_mask_0 = const()[name = tensor<string, []>("slice_65_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_65_cast_fp16 = slice_by_index(begin = slice_65_begin_0, end = slice_65_end_0, end_mask = slice_65_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_65_cast_fp16")];
            tensor<int32, [3]> slice_66_begin_0 = const()[name = tensor<string, []>("slice_66_begin_0"), val = tensor<int32, [3]>([0, 160, 0])];
            tensor<int32, [3]> slice_66_end_0 = const()[name = tensor<string, []>("slice_66_end_0"), val = tensor<int32, [3]>([1, 165, 801])];
            tensor<bool, [3]> slice_66_end_mask_0 = const()[name = tensor<string, []>("slice_66_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_66_cast_fp16 = slice_by_index(begin = slice_66_begin_0, end = slice_66_end_0, end_mask = slice_66_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_66_cast_fp16")];
            tensor<fp16, []> const_714_promoted_to_fp16 = const()[name = tensor<string, []>("const_714_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_97_cast_fp16 = pow(x = slice_65_cast_fp16, y = const_714_promoted_to_fp16)[name = tensor<string, []>("pow_97_cast_fp16")];
            tensor<fp16, []> const_715_promoted_to_fp16 = const()[name = tensor<string, []>("const_715_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_98_cast_fp16 = pow(x = slice_66_cast_fp16, y = const_715_promoted_to_fp16)[name = tensor<string, []>("pow_98_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_96_cast_fp16 = add(x = pow_97_cast_fp16, y = pow_98_cast_fp16)[name = tensor<string, []>("add_96_cast_fp16")];
            tensor<int32, [1]> sum_33_axes_0 = const()[name = tensor<string, []>("sum_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_33_keep_dims_0 = const()[name = tensor<string, []>("sum_33_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_33_cast_fp16 = reduce_sum(axes = sum_33_axes_0, keep_dims = sum_33_keep_dims_0, x = add_96_cast_fp16)[name = tensor<string, []>("sum_33_cast_fp16")];
            tensor<fp16, []> const_718_to_fp16 = const()[name = tensor<string, []>("const_718_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_97_cast_fp16 = add(x = sum_33_cast_fp16, y = const_718_to_fp16)[name = tensor<string, []>("add_97_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_32_cast_fp16 = sqrt(x = add_97_cast_fp16)[name = tensor<string, []>("sqrt_32_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_64_cast_fp16 = real_div(x = slice_65_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor<string, []>("div_64_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_65_cast_fp16 = real_div(x = slice_66_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor<string, []>("div_65_cast_fp16")];
            tensor<fp32, []> log_32_epsilon_0 = const()[name = tensor<string, []>("log_32_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_32_cast_fp16 = log(epsilon = log_32_epsilon_0, x = sqrt_32_cast_fp16)[name = tensor<string, []>("log_32_cast_fp16")];
            tensor<int32, []> const_719 = const()[name = tensor<string, []>("const_719"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_32_interleave_0 = const()[name = tensor<string, []>("cat_32_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_32_cast_fp16 = concat(axis = const_719, interleave = cat_32_interleave_0, values = (div_64_cast_fp16, div_65_cast_fp16, log_32_cast_fp16))[name = tensor<string, []>("cat_32_cast_fp16")];
            tensor<int32, [1]> unsqueeze_65_axes_0 = const()[name = tensor<string, []>("unsqueeze_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_65_cast_fp16 = expand_dims(axes = unsqueeze_65_axes_0, x = cat_32_cast_fp16)[name = tensor<string, []>("unsqueeze_65_cast_fp16")];
            tensor<fp16, []> const_724_promoted_to_fp16 = const()[name = tensor<string, []>("const_724_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_99_cast_fp16 = pow(x = unsqueeze_65_cast_fp16, y = const_724_promoted_to_fp16)[name = tensor<string, []>("pow_99_cast_fp16")];
            tensor<int32, [1]> mean_32_axes_0 = const()[name = tensor<string, []>("mean_32_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_32_keep_dims_0 = const()[name = tensor<string, []>("mean_32_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_32_cast_fp16 = reduce_mean(axes = mean_32_axes_0, keep_dims = mean_32_keep_dims_0, x = pow_99_cast_fp16)[name = tensor<string, []>("mean_32_cast_fp16")];
            tensor<fp16, []> const_727_to_fp16 = const()[name = tensor<string, []>("const_727_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_98_cast_fp16 = add(x = mean_32_cast_fp16, y = const_727_to_fp16)[name = tensor<string, []>("add_98_cast_fp16")];
            tensor<fp32, []> rsqrt_32_epsilon_0 = const()[name = tensor<string, []>("rsqrt_32_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_32_cast_fp16 = rsqrt(epsilon = rsqrt_32_epsilon_0, x = add_98_cast_fp16)[name = tensor<string, []>("rsqrt_32_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_64_cast_fp16 = mul(x = unsqueeze_65_cast_fp16, y = rsqrt_32_cast_fp16)[name = tensor<string, []>("mul_64_cast_fp16")];
            tensor<int32, [1]> squeeze_32_axes_0 = const()[name = tensor<string, []>("squeeze_32_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = mul_64_cast_fp16)[name = tensor<string, []>("squeeze_32_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_32_to_fp16 = const()[name = tensor<string, []>("view_32_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204864)))];
            tensor<fp16, [1, 11, 801]> mul_65_cast_fp16 = mul(x = squeeze_32_cast_fp16, y = view_32_to_fp16)[name = tensor<string, []>("mul_65_cast_fp16")];
            tensor<string, []> conv1d_32_pad_type_0 = const()[name = tensor<string, []>("conv1d_32_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_32_strides_0 = const()[name = tensor<string, []>("conv1d_32_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_32_pad_0 = const()[name = tensor<string, []>("conv1d_32_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_32_dilations_0 = const()[name = tensor<string, []>("conv1d_32_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_32_groups_0 = const()[name = tensor<string, []>("conv1d_32_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_32_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_32_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204992)))];
            tensor<fp16, [256]> p_bn_32_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_32_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210688)))];
            tensor<fp16, [1, 256, 801]> conv1d_32_cast_fp16 = conv(bias = p_bn_32_1_bias_to_fp16, dilations = conv1d_32_dilations_0, groups = conv1d_32_groups_0, pad = conv1d_32_pad_0, pad_type = conv1d_32_pad_type_0, strides = conv1d_32_strides_0, weight = p_bn_32_1_weight_to_fp16, x = mul_65_cast_fp16)[name = tensor<string, []>("conv1d_32_cast_fp16")];
            tensor<int32, [3]> slice_67_begin_0 = const()[name = tensor<string, []>("slice_67_begin_0"), val = tensor<int32, [3]>([0, 165, 0])];
            tensor<int32, [3]> slice_67_end_0 = const()[name = tensor<string, []>("slice_67_end_0"), val = tensor<int32, [3]>([1, 170, 801])];
            tensor<bool, [3]> slice_67_end_mask_0 = const()[name = tensor<string, []>("slice_67_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_67_cast_fp16 = slice_by_index(begin = slice_67_begin_0, end = slice_67_end_0, end_mask = slice_67_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_67_cast_fp16")];
            tensor<int32, [3]> slice_68_begin_0 = const()[name = tensor<string, []>("slice_68_begin_0"), val = tensor<int32, [3]>([0, 165, 0])];
            tensor<int32, [3]> slice_68_end_0 = const()[name = tensor<string, []>("slice_68_end_0"), val = tensor<int32, [3]>([1, 170, 801])];
            tensor<bool, [3]> slice_68_end_mask_0 = const()[name = tensor<string, []>("slice_68_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_68_cast_fp16 = slice_by_index(begin = slice_68_begin_0, end = slice_68_end_0, end_mask = slice_68_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_68_cast_fp16")];
            tensor<fp16, []> const_736_promoted_to_fp16 = const()[name = tensor<string, []>("const_736_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_100_cast_fp16 = pow(x = slice_67_cast_fp16, y = const_736_promoted_to_fp16)[name = tensor<string, []>("pow_100_cast_fp16")];
            tensor<fp16, []> const_737_promoted_to_fp16 = const()[name = tensor<string, []>("const_737_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_101_cast_fp16 = pow(x = slice_68_cast_fp16, y = const_737_promoted_to_fp16)[name = tensor<string, []>("pow_101_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_99_cast_fp16 = add(x = pow_100_cast_fp16, y = pow_101_cast_fp16)[name = tensor<string, []>("add_99_cast_fp16")];
            tensor<int32, [1]> sum_34_axes_0 = const()[name = tensor<string, []>("sum_34_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_34_keep_dims_0 = const()[name = tensor<string, []>("sum_34_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_34_cast_fp16 = reduce_sum(axes = sum_34_axes_0, keep_dims = sum_34_keep_dims_0, x = add_99_cast_fp16)[name = tensor<string, []>("sum_34_cast_fp16")];
            tensor<fp16, []> const_740_to_fp16 = const()[name = tensor<string, []>("const_740_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_100_cast_fp16 = add(x = sum_34_cast_fp16, y = const_740_to_fp16)[name = tensor<string, []>("add_100_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_33_cast_fp16 = sqrt(x = add_100_cast_fp16)[name = tensor<string, []>("sqrt_33_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_66_cast_fp16 = real_div(x = slice_67_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor<string, []>("div_66_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_67_cast_fp16 = real_div(x = slice_68_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor<string, []>("div_67_cast_fp16")];
            tensor<fp32, []> log_33_epsilon_0 = const()[name = tensor<string, []>("log_33_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_33_cast_fp16 = log(epsilon = log_33_epsilon_0, x = sqrt_33_cast_fp16)[name = tensor<string, []>("log_33_cast_fp16")];
            tensor<int32, []> const_741 = const()[name = tensor<string, []>("const_741"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_33_interleave_0 = const()[name = tensor<string, []>("cat_33_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_33_cast_fp16 = concat(axis = const_741, interleave = cat_33_interleave_0, values = (div_66_cast_fp16, div_67_cast_fp16, log_33_cast_fp16))[name = tensor<string, []>("cat_33_cast_fp16")];
            tensor<int32, [1]> unsqueeze_67_axes_0 = const()[name = tensor<string, []>("unsqueeze_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_67_cast_fp16 = expand_dims(axes = unsqueeze_67_axes_0, x = cat_33_cast_fp16)[name = tensor<string, []>("unsqueeze_67_cast_fp16")];
            tensor<fp16, []> const_746_promoted_to_fp16 = const()[name = tensor<string, []>("const_746_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_102_cast_fp16 = pow(x = unsqueeze_67_cast_fp16, y = const_746_promoted_to_fp16)[name = tensor<string, []>("pow_102_cast_fp16")];
            tensor<int32, [1]> mean_33_axes_0 = const()[name = tensor<string, []>("mean_33_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_33_keep_dims_0 = const()[name = tensor<string, []>("mean_33_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_33_cast_fp16 = reduce_mean(axes = mean_33_axes_0, keep_dims = mean_33_keep_dims_0, x = pow_102_cast_fp16)[name = tensor<string, []>("mean_33_cast_fp16")];
            tensor<fp16, []> const_749_to_fp16 = const()[name = tensor<string, []>("const_749_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_101_cast_fp16 = add(x = mean_33_cast_fp16, y = const_749_to_fp16)[name = tensor<string, []>("add_101_cast_fp16")];
            tensor<fp32, []> rsqrt_33_epsilon_0 = const()[name = tensor<string, []>("rsqrt_33_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_33_cast_fp16 = rsqrt(epsilon = rsqrt_33_epsilon_0, x = add_101_cast_fp16)[name = tensor<string, []>("rsqrt_33_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_66_cast_fp16 = mul(x = unsqueeze_67_cast_fp16, y = rsqrt_33_cast_fp16)[name = tensor<string, []>("mul_66_cast_fp16")];
            tensor<int32, [1]> squeeze_33_axes_0 = const()[name = tensor<string, []>("squeeze_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = mul_66_cast_fp16)[name = tensor<string, []>("squeeze_33_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_33_to_fp16 = const()[name = tensor<string, []>("view_33_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211264)))];
            tensor<fp16, [1, 11, 801]> mul_67_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = view_33_to_fp16)[name = tensor<string, []>("mul_67_cast_fp16")];
            tensor<string, []> conv1d_33_pad_type_0 = const()[name = tensor<string, []>("conv1d_33_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_33_strides_0 = const()[name = tensor<string, []>("conv1d_33_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_33_pad_0 = const()[name = tensor<string, []>("conv1d_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_33_dilations_0 = const()[name = tensor<string, []>("conv1d_33_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_33_groups_0 = const()[name = tensor<string, []>("conv1d_33_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_33_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_33_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211392)))];
            tensor<fp16, [256]> p_bn_33_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_33_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217088)))];
            tensor<fp16, [1, 256, 801]> conv1d_33_cast_fp16 = conv(bias = p_bn_33_1_bias_to_fp16, dilations = conv1d_33_dilations_0, groups = conv1d_33_groups_0, pad = conv1d_33_pad_0, pad_type = conv1d_33_pad_type_0, strides = conv1d_33_strides_0, weight = p_bn_33_1_weight_to_fp16, x = mul_67_cast_fp16)[name = tensor<string, []>("conv1d_33_cast_fp16")];
            tensor<int32, [3]> slice_69_begin_0 = const()[name = tensor<string, []>("slice_69_begin_0"), val = tensor<int32, [3]>([0, 170, 0])];
            tensor<int32, [3]> slice_69_end_0 = const()[name = tensor<string, []>("slice_69_end_0"), val = tensor<int32, [3]>([1, 175, 801])];
            tensor<bool, [3]> slice_69_end_mask_0 = const()[name = tensor<string, []>("slice_69_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_69_cast_fp16 = slice_by_index(begin = slice_69_begin_0, end = slice_69_end_0, end_mask = slice_69_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_69_cast_fp16")];
            tensor<int32, [3]> slice_70_begin_0 = const()[name = tensor<string, []>("slice_70_begin_0"), val = tensor<int32, [3]>([0, 170, 0])];
            tensor<int32, [3]> slice_70_end_0 = const()[name = tensor<string, []>("slice_70_end_0"), val = tensor<int32, [3]>([1, 175, 801])];
            tensor<bool, [3]> slice_70_end_mask_0 = const()[name = tensor<string, []>("slice_70_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_70_cast_fp16 = slice_by_index(begin = slice_70_begin_0, end = slice_70_end_0, end_mask = slice_70_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_70_cast_fp16")];
            tensor<fp16, []> const_758_promoted_to_fp16 = const()[name = tensor<string, []>("const_758_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_103_cast_fp16 = pow(x = slice_69_cast_fp16, y = const_758_promoted_to_fp16)[name = tensor<string, []>("pow_103_cast_fp16")];
            tensor<fp16, []> const_759_promoted_to_fp16 = const()[name = tensor<string, []>("const_759_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_104_cast_fp16 = pow(x = slice_70_cast_fp16, y = const_759_promoted_to_fp16)[name = tensor<string, []>("pow_104_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_102_cast_fp16 = add(x = pow_103_cast_fp16, y = pow_104_cast_fp16)[name = tensor<string, []>("add_102_cast_fp16")];
            tensor<int32, [1]> sum_35_axes_0 = const()[name = tensor<string, []>("sum_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_35_keep_dims_0 = const()[name = tensor<string, []>("sum_35_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_35_cast_fp16 = reduce_sum(axes = sum_35_axes_0, keep_dims = sum_35_keep_dims_0, x = add_102_cast_fp16)[name = tensor<string, []>("sum_35_cast_fp16")];
            tensor<fp16, []> const_762_to_fp16 = const()[name = tensor<string, []>("const_762_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_103_cast_fp16 = add(x = sum_35_cast_fp16, y = const_762_to_fp16)[name = tensor<string, []>("add_103_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_34_cast_fp16 = sqrt(x = add_103_cast_fp16)[name = tensor<string, []>("sqrt_34_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_68_cast_fp16 = real_div(x = slice_69_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor<string, []>("div_68_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_69_cast_fp16 = real_div(x = slice_70_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor<string, []>("div_69_cast_fp16")];
            tensor<fp32, []> log_34_epsilon_0 = const()[name = tensor<string, []>("log_34_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_34_cast_fp16 = log(epsilon = log_34_epsilon_0, x = sqrt_34_cast_fp16)[name = tensor<string, []>("log_34_cast_fp16")];
            tensor<int32, []> const_763 = const()[name = tensor<string, []>("const_763"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_34_interleave_0 = const()[name = tensor<string, []>("cat_34_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_34_cast_fp16 = concat(axis = const_763, interleave = cat_34_interleave_0, values = (div_68_cast_fp16, div_69_cast_fp16, log_34_cast_fp16))[name = tensor<string, []>("cat_34_cast_fp16")];
            tensor<int32, [1]> unsqueeze_69_axes_0 = const()[name = tensor<string, []>("unsqueeze_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_69_cast_fp16 = expand_dims(axes = unsqueeze_69_axes_0, x = cat_34_cast_fp16)[name = tensor<string, []>("unsqueeze_69_cast_fp16")];
            tensor<fp16, []> const_768_promoted_to_fp16 = const()[name = tensor<string, []>("const_768_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_105_cast_fp16 = pow(x = unsqueeze_69_cast_fp16, y = const_768_promoted_to_fp16)[name = tensor<string, []>("pow_105_cast_fp16")];
            tensor<int32, [1]> mean_34_axes_0 = const()[name = tensor<string, []>("mean_34_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_34_keep_dims_0 = const()[name = tensor<string, []>("mean_34_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_34_cast_fp16 = reduce_mean(axes = mean_34_axes_0, keep_dims = mean_34_keep_dims_0, x = pow_105_cast_fp16)[name = tensor<string, []>("mean_34_cast_fp16")];
            tensor<fp16, []> const_771_to_fp16 = const()[name = tensor<string, []>("const_771_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_104_cast_fp16 = add(x = mean_34_cast_fp16, y = const_771_to_fp16)[name = tensor<string, []>("add_104_cast_fp16")];
            tensor<fp32, []> rsqrt_34_epsilon_0 = const()[name = tensor<string, []>("rsqrt_34_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_34_cast_fp16 = rsqrt(epsilon = rsqrt_34_epsilon_0, x = add_104_cast_fp16)[name = tensor<string, []>("rsqrt_34_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_68_cast_fp16 = mul(x = unsqueeze_69_cast_fp16, y = rsqrt_34_cast_fp16)[name = tensor<string, []>("mul_68_cast_fp16")];
            tensor<int32, [1]> squeeze_34_axes_0 = const()[name = tensor<string, []>("squeeze_34_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = mul_68_cast_fp16)[name = tensor<string, []>("squeeze_34_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_34_to_fp16 = const()[name = tensor<string, []>("view_34_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217664)))];
            tensor<fp16, [1, 11, 801]> mul_69_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = view_34_to_fp16)[name = tensor<string, []>("mul_69_cast_fp16")];
            tensor<string, []> conv1d_34_pad_type_0 = const()[name = tensor<string, []>("conv1d_34_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_34_strides_0 = const()[name = tensor<string, []>("conv1d_34_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_34_pad_0 = const()[name = tensor<string, []>("conv1d_34_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_34_dilations_0 = const()[name = tensor<string, []>("conv1d_34_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_34_groups_0 = const()[name = tensor<string, []>("conv1d_34_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_34_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_34_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217792)))];
            tensor<fp16, [256]> p_bn_34_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_34_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(223488)))];
            tensor<fp16, [1, 256, 801]> conv1d_34_cast_fp16 = conv(bias = p_bn_34_1_bias_to_fp16, dilations = conv1d_34_dilations_0, groups = conv1d_34_groups_0, pad = conv1d_34_pad_0, pad_type = conv1d_34_pad_type_0, strides = conv1d_34_strides_0, weight = p_bn_34_1_weight_to_fp16, x = mul_69_cast_fp16)[name = tensor<string, []>("conv1d_34_cast_fp16")];
            tensor<int32, [3]> slice_71_begin_0 = const()[name = tensor<string, []>("slice_71_begin_0"), val = tensor<int32, [3]>([0, 175, 0])];
            tensor<int32, [3]> slice_71_end_0 = const()[name = tensor<string, []>("slice_71_end_0"), val = tensor<int32, [3]>([1, 180, 801])];
            tensor<bool, [3]> slice_71_end_mask_0 = const()[name = tensor<string, []>("slice_71_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_71_cast_fp16 = slice_by_index(begin = slice_71_begin_0, end = slice_71_end_0, end_mask = slice_71_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_71_cast_fp16")];
            tensor<int32, [3]> slice_72_begin_0 = const()[name = tensor<string, []>("slice_72_begin_0"), val = tensor<int32, [3]>([0, 175, 0])];
            tensor<int32, [3]> slice_72_end_0 = const()[name = tensor<string, []>("slice_72_end_0"), val = tensor<int32, [3]>([1, 180, 801])];
            tensor<bool, [3]> slice_72_end_mask_0 = const()[name = tensor<string, []>("slice_72_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_72_cast_fp16 = slice_by_index(begin = slice_72_begin_0, end = slice_72_end_0, end_mask = slice_72_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_72_cast_fp16")];
            tensor<fp16, []> const_780_promoted_to_fp16 = const()[name = tensor<string, []>("const_780_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_106_cast_fp16 = pow(x = slice_71_cast_fp16, y = const_780_promoted_to_fp16)[name = tensor<string, []>("pow_106_cast_fp16")];
            tensor<fp16, []> const_781_promoted_to_fp16 = const()[name = tensor<string, []>("const_781_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_107_cast_fp16 = pow(x = slice_72_cast_fp16, y = const_781_promoted_to_fp16)[name = tensor<string, []>("pow_107_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_105_cast_fp16 = add(x = pow_106_cast_fp16, y = pow_107_cast_fp16)[name = tensor<string, []>("add_105_cast_fp16")];
            tensor<int32, [1]> sum_36_axes_0 = const()[name = tensor<string, []>("sum_36_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_36_keep_dims_0 = const()[name = tensor<string, []>("sum_36_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_36_cast_fp16 = reduce_sum(axes = sum_36_axes_0, keep_dims = sum_36_keep_dims_0, x = add_105_cast_fp16)[name = tensor<string, []>("sum_36_cast_fp16")];
            tensor<fp16, []> const_784_to_fp16 = const()[name = tensor<string, []>("const_784_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_106_cast_fp16 = add(x = sum_36_cast_fp16, y = const_784_to_fp16)[name = tensor<string, []>("add_106_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_35_cast_fp16 = sqrt(x = add_106_cast_fp16)[name = tensor<string, []>("sqrt_35_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_70_cast_fp16 = real_div(x = slice_71_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor<string, []>("div_70_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_71_cast_fp16 = real_div(x = slice_72_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor<string, []>("div_71_cast_fp16")];
            tensor<fp32, []> log_35_epsilon_0 = const()[name = tensor<string, []>("log_35_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_35_cast_fp16 = log(epsilon = log_35_epsilon_0, x = sqrt_35_cast_fp16)[name = tensor<string, []>("log_35_cast_fp16")];
            tensor<int32, []> const_785 = const()[name = tensor<string, []>("const_785"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_35_interleave_0 = const()[name = tensor<string, []>("cat_35_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_35_cast_fp16 = concat(axis = const_785, interleave = cat_35_interleave_0, values = (div_70_cast_fp16, div_71_cast_fp16, log_35_cast_fp16))[name = tensor<string, []>("cat_35_cast_fp16")];
            tensor<int32, [1]> unsqueeze_71_axes_0 = const()[name = tensor<string, []>("unsqueeze_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_71_cast_fp16 = expand_dims(axes = unsqueeze_71_axes_0, x = cat_35_cast_fp16)[name = tensor<string, []>("unsqueeze_71_cast_fp16")];
            tensor<fp16, []> const_790_promoted_to_fp16 = const()[name = tensor<string, []>("const_790_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_108_cast_fp16 = pow(x = unsqueeze_71_cast_fp16, y = const_790_promoted_to_fp16)[name = tensor<string, []>("pow_108_cast_fp16")];
            tensor<int32, [1]> mean_35_axes_0 = const()[name = tensor<string, []>("mean_35_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_35_keep_dims_0 = const()[name = tensor<string, []>("mean_35_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_35_cast_fp16 = reduce_mean(axes = mean_35_axes_0, keep_dims = mean_35_keep_dims_0, x = pow_108_cast_fp16)[name = tensor<string, []>("mean_35_cast_fp16")];
            tensor<fp16, []> const_793_to_fp16 = const()[name = tensor<string, []>("const_793_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_107_cast_fp16 = add(x = mean_35_cast_fp16, y = const_793_to_fp16)[name = tensor<string, []>("add_107_cast_fp16")];
            tensor<fp32, []> rsqrt_35_epsilon_0 = const()[name = tensor<string, []>("rsqrt_35_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_35_cast_fp16 = rsqrt(epsilon = rsqrt_35_epsilon_0, x = add_107_cast_fp16)[name = tensor<string, []>("rsqrt_35_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_70_cast_fp16 = mul(x = unsqueeze_71_cast_fp16, y = rsqrt_35_cast_fp16)[name = tensor<string, []>("mul_70_cast_fp16")];
            tensor<int32, [1]> squeeze_35_axes_0 = const()[name = tensor<string, []>("squeeze_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = mul_70_cast_fp16)[name = tensor<string, []>("squeeze_35_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_35_to_fp16 = const()[name = tensor<string, []>("view_35_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224064)))];
            tensor<fp16, [1, 11, 801]> mul_71_cast_fp16 = mul(x = squeeze_35_cast_fp16, y = view_35_to_fp16)[name = tensor<string, []>("mul_71_cast_fp16")];
            tensor<string, []> conv1d_35_pad_type_0 = const()[name = tensor<string, []>("conv1d_35_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_35_strides_0 = const()[name = tensor<string, []>("conv1d_35_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_35_pad_0 = const()[name = tensor<string, []>("conv1d_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_35_dilations_0 = const()[name = tensor<string, []>("conv1d_35_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_35_groups_0 = const()[name = tensor<string, []>("conv1d_35_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_35_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_35_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224192)))];
            tensor<fp16, [256]> p_bn_35_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_35_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(229888)))];
            tensor<fp16, [1, 256, 801]> conv1d_35_cast_fp16 = conv(bias = p_bn_35_1_bias_to_fp16, dilations = conv1d_35_dilations_0, groups = conv1d_35_groups_0, pad = conv1d_35_pad_0, pad_type = conv1d_35_pad_type_0, strides = conv1d_35_strides_0, weight = p_bn_35_1_weight_to_fp16, x = mul_71_cast_fp16)[name = tensor<string, []>("conv1d_35_cast_fp16")];
            tensor<int32, [3]> slice_73_begin_0 = const()[name = tensor<string, []>("slice_73_begin_0"), val = tensor<int32, [3]>([0, 180, 0])];
            tensor<int32, [3]> slice_73_end_0 = const()[name = tensor<string, []>("slice_73_end_0"), val = tensor<int32, [3]>([1, 185, 801])];
            tensor<bool, [3]> slice_73_end_mask_0 = const()[name = tensor<string, []>("slice_73_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_73_cast_fp16 = slice_by_index(begin = slice_73_begin_0, end = slice_73_end_0, end_mask = slice_73_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_73_cast_fp16")];
            tensor<int32, [3]> slice_74_begin_0 = const()[name = tensor<string, []>("slice_74_begin_0"), val = tensor<int32, [3]>([0, 180, 0])];
            tensor<int32, [3]> slice_74_end_0 = const()[name = tensor<string, []>("slice_74_end_0"), val = tensor<int32, [3]>([1, 185, 801])];
            tensor<bool, [3]> slice_74_end_mask_0 = const()[name = tensor<string, []>("slice_74_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_74_cast_fp16 = slice_by_index(begin = slice_74_begin_0, end = slice_74_end_0, end_mask = slice_74_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_74_cast_fp16")];
            tensor<fp16, []> const_802_promoted_to_fp16 = const()[name = tensor<string, []>("const_802_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_109_cast_fp16 = pow(x = slice_73_cast_fp16, y = const_802_promoted_to_fp16)[name = tensor<string, []>("pow_109_cast_fp16")];
            tensor<fp16, []> const_803_promoted_to_fp16 = const()[name = tensor<string, []>("const_803_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_110_cast_fp16 = pow(x = slice_74_cast_fp16, y = const_803_promoted_to_fp16)[name = tensor<string, []>("pow_110_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_108_cast_fp16 = add(x = pow_109_cast_fp16, y = pow_110_cast_fp16)[name = tensor<string, []>("add_108_cast_fp16")];
            tensor<int32, [1]> sum_37_axes_0 = const()[name = tensor<string, []>("sum_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_37_keep_dims_0 = const()[name = tensor<string, []>("sum_37_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_37_cast_fp16 = reduce_sum(axes = sum_37_axes_0, keep_dims = sum_37_keep_dims_0, x = add_108_cast_fp16)[name = tensor<string, []>("sum_37_cast_fp16")];
            tensor<fp16, []> const_806_to_fp16 = const()[name = tensor<string, []>("const_806_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_109_cast_fp16 = add(x = sum_37_cast_fp16, y = const_806_to_fp16)[name = tensor<string, []>("add_109_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_36_cast_fp16 = sqrt(x = add_109_cast_fp16)[name = tensor<string, []>("sqrt_36_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_72_cast_fp16 = real_div(x = slice_73_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor<string, []>("div_72_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_73_cast_fp16 = real_div(x = slice_74_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor<string, []>("div_73_cast_fp16")];
            tensor<fp32, []> log_36_epsilon_0 = const()[name = tensor<string, []>("log_36_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_36_cast_fp16 = log(epsilon = log_36_epsilon_0, x = sqrt_36_cast_fp16)[name = tensor<string, []>("log_36_cast_fp16")];
            tensor<int32, []> const_807 = const()[name = tensor<string, []>("const_807"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_36_interleave_0 = const()[name = tensor<string, []>("cat_36_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_36_cast_fp16 = concat(axis = const_807, interleave = cat_36_interleave_0, values = (div_72_cast_fp16, div_73_cast_fp16, log_36_cast_fp16))[name = tensor<string, []>("cat_36_cast_fp16")];
            tensor<int32, [1]> unsqueeze_73_axes_0 = const()[name = tensor<string, []>("unsqueeze_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_73_cast_fp16 = expand_dims(axes = unsqueeze_73_axes_0, x = cat_36_cast_fp16)[name = tensor<string, []>("unsqueeze_73_cast_fp16")];
            tensor<fp16, []> const_812_promoted_to_fp16 = const()[name = tensor<string, []>("const_812_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_111_cast_fp16 = pow(x = unsqueeze_73_cast_fp16, y = const_812_promoted_to_fp16)[name = tensor<string, []>("pow_111_cast_fp16")];
            tensor<int32, [1]> mean_36_axes_0 = const()[name = tensor<string, []>("mean_36_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_36_keep_dims_0 = const()[name = tensor<string, []>("mean_36_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_36_cast_fp16 = reduce_mean(axes = mean_36_axes_0, keep_dims = mean_36_keep_dims_0, x = pow_111_cast_fp16)[name = tensor<string, []>("mean_36_cast_fp16")];
            tensor<fp16, []> const_815_to_fp16 = const()[name = tensor<string, []>("const_815_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_110_cast_fp16 = add(x = mean_36_cast_fp16, y = const_815_to_fp16)[name = tensor<string, []>("add_110_cast_fp16")];
            tensor<fp32, []> rsqrt_36_epsilon_0 = const()[name = tensor<string, []>("rsqrt_36_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_36_cast_fp16 = rsqrt(epsilon = rsqrt_36_epsilon_0, x = add_110_cast_fp16)[name = tensor<string, []>("rsqrt_36_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_72_cast_fp16 = mul(x = unsqueeze_73_cast_fp16, y = rsqrt_36_cast_fp16)[name = tensor<string, []>("mul_72_cast_fp16")];
            tensor<int32, [1]> squeeze_36_axes_0 = const()[name = tensor<string, []>("squeeze_36_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_36_cast_fp16 = squeeze(axes = squeeze_36_axes_0, x = mul_72_cast_fp16)[name = tensor<string, []>("squeeze_36_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_36_to_fp16 = const()[name = tensor<string, []>("view_36_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230464)))];
            tensor<fp16, [1, 11, 801]> mul_73_cast_fp16 = mul(x = squeeze_36_cast_fp16, y = view_36_to_fp16)[name = tensor<string, []>("mul_73_cast_fp16")];
            tensor<string, []> conv1d_36_pad_type_0 = const()[name = tensor<string, []>("conv1d_36_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_36_strides_0 = const()[name = tensor<string, []>("conv1d_36_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_36_pad_0 = const()[name = tensor<string, []>("conv1d_36_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_36_dilations_0 = const()[name = tensor<string, []>("conv1d_36_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_36_groups_0 = const()[name = tensor<string, []>("conv1d_36_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_36_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_36_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(230592)))];
            tensor<fp16, [256]> p_bn_36_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_36_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236288)))];
            tensor<fp16, [1, 256, 801]> conv1d_36_cast_fp16 = conv(bias = p_bn_36_1_bias_to_fp16, dilations = conv1d_36_dilations_0, groups = conv1d_36_groups_0, pad = conv1d_36_pad_0, pad_type = conv1d_36_pad_type_0, strides = conv1d_36_strides_0, weight = p_bn_36_1_weight_to_fp16, x = mul_73_cast_fp16)[name = tensor<string, []>("conv1d_36_cast_fp16")];
            tensor<int32, [3]> slice_75_begin_0 = const()[name = tensor<string, []>("slice_75_begin_0"), val = tensor<int32, [3]>([0, 185, 0])];
            tensor<int32, [3]> slice_75_end_0 = const()[name = tensor<string, []>("slice_75_end_0"), val = tensor<int32, [3]>([1, 190, 801])];
            tensor<bool, [3]> slice_75_end_mask_0 = const()[name = tensor<string, []>("slice_75_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_75_cast_fp16 = slice_by_index(begin = slice_75_begin_0, end = slice_75_end_0, end_mask = slice_75_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_75_cast_fp16")];
            tensor<int32, [3]> slice_76_begin_0 = const()[name = tensor<string, []>("slice_76_begin_0"), val = tensor<int32, [3]>([0, 185, 0])];
            tensor<int32, [3]> slice_76_end_0 = const()[name = tensor<string, []>("slice_76_end_0"), val = tensor<int32, [3]>([1, 190, 801])];
            tensor<bool, [3]> slice_76_end_mask_0 = const()[name = tensor<string, []>("slice_76_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_76_cast_fp16 = slice_by_index(begin = slice_76_begin_0, end = slice_76_end_0, end_mask = slice_76_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_76_cast_fp16")];
            tensor<fp16, []> const_824_promoted_to_fp16 = const()[name = tensor<string, []>("const_824_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_112_cast_fp16 = pow(x = slice_75_cast_fp16, y = const_824_promoted_to_fp16)[name = tensor<string, []>("pow_112_cast_fp16")];
            tensor<fp16, []> const_825_promoted_to_fp16 = const()[name = tensor<string, []>("const_825_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_113_cast_fp16 = pow(x = slice_76_cast_fp16, y = const_825_promoted_to_fp16)[name = tensor<string, []>("pow_113_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_111_cast_fp16 = add(x = pow_112_cast_fp16, y = pow_113_cast_fp16)[name = tensor<string, []>("add_111_cast_fp16")];
            tensor<int32, [1]> sum_38_axes_0 = const()[name = tensor<string, []>("sum_38_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_38_keep_dims_0 = const()[name = tensor<string, []>("sum_38_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_38_cast_fp16 = reduce_sum(axes = sum_38_axes_0, keep_dims = sum_38_keep_dims_0, x = add_111_cast_fp16)[name = tensor<string, []>("sum_38_cast_fp16")];
            tensor<fp16, []> const_828_to_fp16 = const()[name = tensor<string, []>("const_828_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_112_cast_fp16 = add(x = sum_38_cast_fp16, y = const_828_to_fp16)[name = tensor<string, []>("add_112_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_37_cast_fp16 = sqrt(x = add_112_cast_fp16)[name = tensor<string, []>("sqrt_37_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_74_cast_fp16 = real_div(x = slice_75_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor<string, []>("div_74_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_75_cast_fp16 = real_div(x = slice_76_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor<string, []>("div_75_cast_fp16")];
            tensor<fp32, []> log_37_epsilon_0 = const()[name = tensor<string, []>("log_37_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_37_cast_fp16 = log(epsilon = log_37_epsilon_0, x = sqrt_37_cast_fp16)[name = tensor<string, []>("log_37_cast_fp16")];
            tensor<int32, []> const_829 = const()[name = tensor<string, []>("const_829"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_37_interleave_0 = const()[name = tensor<string, []>("cat_37_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_37_cast_fp16 = concat(axis = const_829, interleave = cat_37_interleave_0, values = (div_74_cast_fp16, div_75_cast_fp16, log_37_cast_fp16))[name = tensor<string, []>("cat_37_cast_fp16")];
            tensor<int32, [1]> unsqueeze_75_axes_0 = const()[name = tensor<string, []>("unsqueeze_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_75_cast_fp16 = expand_dims(axes = unsqueeze_75_axes_0, x = cat_37_cast_fp16)[name = tensor<string, []>("unsqueeze_75_cast_fp16")];
            tensor<fp16, []> const_834_promoted_to_fp16 = const()[name = tensor<string, []>("const_834_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_114_cast_fp16 = pow(x = unsqueeze_75_cast_fp16, y = const_834_promoted_to_fp16)[name = tensor<string, []>("pow_114_cast_fp16")];
            tensor<int32, [1]> mean_37_axes_0 = const()[name = tensor<string, []>("mean_37_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_37_keep_dims_0 = const()[name = tensor<string, []>("mean_37_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_37_cast_fp16 = reduce_mean(axes = mean_37_axes_0, keep_dims = mean_37_keep_dims_0, x = pow_114_cast_fp16)[name = tensor<string, []>("mean_37_cast_fp16")];
            tensor<fp16, []> const_837_to_fp16 = const()[name = tensor<string, []>("const_837_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_113_cast_fp16 = add(x = mean_37_cast_fp16, y = const_837_to_fp16)[name = tensor<string, []>("add_113_cast_fp16")];
            tensor<fp32, []> rsqrt_37_epsilon_0 = const()[name = tensor<string, []>("rsqrt_37_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_37_cast_fp16 = rsqrt(epsilon = rsqrt_37_epsilon_0, x = add_113_cast_fp16)[name = tensor<string, []>("rsqrt_37_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_74_cast_fp16 = mul(x = unsqueeze_75_cast_fp16, y = rsqrt_37_cast_fp16)[name = tensor<string, []>("mul_74_cast_fp16")];
            tensor<int32, [1]> squeeze_37_axes_0 = const()[name = tensor<string, []>("squeeze_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_37_cast_fp16 = squeeze(axes = squeeze_37_axes_0, x = mul_74_cast_fp16)[name = tensor<string, []>("squeeze_37_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_37_to_fp16 = const()[name = tensor<string, []>("view_37_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236864)))];
            tensor<fp16, [1, 11, 801]> mul_75_cast_fp16 = mul(x = squeeze_37_cast_fp16, y = view_37_to_fp16)[name = tensor<string, []>("mul_75_cast_fp16")];
            tensor<string, []> conv1d_37_pad_type_0 = const()[name = tensor<string, []>("conv1d_37_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_37_strides_0 = const()[name = tensor<string, []>("conv1d_37_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_37_pad_0 = const()[name = tensor<string, []>("conv1d_37_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_37_dilations_0 = const()[name = tensor<string, []>("conv1d_37_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_37_groups_0 = const()[name = tensor<string, []>("conv1d_37_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_37_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_37_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236992)))];
            tensor<fp16, [256]> p_bn_37_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_37_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242688)))];
            tensor<fp16, [1, 256, 801]> conv1d_37_cast_fp16 = conv(bias = p_bn_37_1_bias_to_fp16, dilations = conv1d_37_dilations_0, groups = conv1d_37_groups_0, pad = conv1d_37_pad_0, pad_type = conv1d_37_pad_type_0, strides = conv1d_37_strides_0, weight = p_bn_37_1_weight_to_fp16, x = mul_75_cast_fp16)[name = tensor<string, []>("conv1d_37_cast_fp16")];
            tensor<int32, [3]> slice_77_begin_0 = const()[name = tensor<string, []>("slice_77_begin_0"), val = tensor<int32, [3]>([0, 190, 0])];
            tensor<int32, [3]> slice_77_end_0 = const()[name = tensor<string, []>("slice_77_end_0"), val = tensor<int32, [3]>([1, 195, 801])];
            tensor<bool, [3]> slice_77_end_mask_0 = const()[name = tensor<string, []>("slice_77_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_77_cast_fp16 = slice_by_index(begin = slice_77_begin_0, end = slice_77_end_0, end_mask = slice_77_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_77_cast_fp16")];
            tensor<int32, [3]> slice_78_begin_0 = const()[name = tensor<string, []>("slice_78_begin_0"), val = tensor<int32, [3]>([0, 190, 0])];
            tensor<int32, [3]> slice_78_end_0 = const()[name = tensor<string, []>("slice_78_end_0"), val = tensor<int32, [3]>([1, 195, 801])];
            tensor<bool, [3]> slice_78_end_mask_0 = const()[name = tensor<string, []>("slice_78_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_78_cast_fp16 = slice_by_index(begin = slice_78_begin_0, end = slice_78_end_0, end_mask = slice_78_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_78_cast_fp16")];
            tensor<fp16, []> const_846_promoted_to_fp16 = const()[name = tensor<string, []>("const_846_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_115_cast_fp16 = pow(x = slice_77_cast_fp16, y = const_846_promoted_to_fp16)[name = tensor<string, []>("pow_115_cast_fp16")];
            tensor<fp16, []> const_847_promoted_to_fp16 = const()[name = tensor<string, []>("const_847_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_116_cast_fp16 = pow(x = slice_78_cast_fp16, y = const_847_promoted_to_fp16)[name = tensor<string, []>("pow_116_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_114_cast_fp16 = add(x = pow_115_cast_fp16, y = pow_116_cast_fp16)[name = tensor<string, []>("add_114_cast_fp16")];
            tensor<int32, [1]> sum_39_axes_0 = const()[name = tensor<string, []>("sum_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_39_keep_dims_0 = const()[name = tensor<string, []>("sum_39_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_39_cast_fp16 = reduce_sum(axes = sum_39_axes_0, keep_dims = sum_39_keep_dims_0, x = add_114_cast_fp16)[name = tensor<string, []>("sum_39_cast_fp16")];
            tensor<fp16, []> const_850_to_fp16 = const()[name = tensor<string, []>("const_850_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_115_cast_fp16 = add(x = sum_39_cast_fp16, y = const_850_to_fp16)[name = tensor<string, []>("add_115_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_38_cast_fp16 = sqrt(x = add_115_cast_fp16)[name = tensor<string, []>("sqrt_38_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_76_cast_fp16 = real_div(x = slice_77_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor<string, []>("div_76_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_77_cast_fp16 = real_div(x = slice_78_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor<string, []>("div_77_cast_fp16")];
            tensor<fp32, []> log_38_epsilon_0 = const()[name = tensor<string, []>("log_38_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_38_cast_fp16 = log(epsilon = log_38_epsilon_0, x = sqrt_38_cast_fp16)[name = tensor<string, []>("log_38_cast_fp16")];
            tensor<int32, []> const_851 = const()[name = tensor<string, []>("const_851"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_38_interleave_0 = const()[name = tensor<string, []>("cat_38_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_38_cast_fp16 = concat(axis = const_851, interleave = cat_38_interleave_0, values = (div_76_cast_fp16, div_77_cast_fp16, log_38_cast_fp16))[name = tensor<string, []>("cat_38_cast_fp16")];
            tensor<int32, [1]> unsqueeze_77_axes_0 = const()[name = tensor<string, []>("unsqueeze_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_77_cast_fp16 = expand_dims(axes = unsqueeze_77_axes_0, x = cat_38_cast_fp16)[name = tensor<string, []>("unsqueeze_77_cast_fp16")];
            tensor<fp16, []> const_856_promoted_to_fp16 = const()[name = tensor<string, []>("const_856_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_117_cast_fp16 = pow(x = unsqueeze_77_cast_fp16, y = const_856_promoted_to_fp16)[name = tensor<string, []>("pow_117_cast_fp16")];
            tensor<int32, [1]> mean_38_axes_0 = const()[name = tensor<string, []>("mean_38_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_38_keep_dims_0 = const()[name = tensor<string, []>("mean_38_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_38_cast_fp16 = reduce_mean(axes = mean_38_axes_0, keep_dims = mean_38_keep_dims_0, x = pow_117_cast_fp16)[name = tensor<string, []>("mean_38_cast_fp16")];
            tensor<fp16, []> const_859_to_fp16 = const()[name = tensor<string, []>("const_859_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_116_cast_fp16 = add(x = mean_38_cast_fp16, y = const_859_to_fp16)[name = tensor<string, []>("add_116_cast_fp16")];
            tensor<fp32, []> rsqrt_38_epsilon_0 = const()[name = tensor<string, []>("rsqrt_38_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_38_cast_fp16 = rsqrt(epsilon = rsqrt_38_epsilon_0, x = add_116_cast_fp16)[name = tensor<string, []>("rsqrt_38_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_76_cast_fp16 = mul(x = unsqueeze_77_cast_fp16, y = rsqrt_38_cast_fp16)[name = tensor<string, []>("mul_76_cast_fp16")];
            tensor<int32, [1]> squeeze_38_axes_0 = const()[name = tensor<string, []>("squeeze_38_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_38_cast_fp16 = squeeze(axes = squeeze_38_axes_0, x = mul_76_cast_fp16)[name = tensor<string, []>("squeeze_38_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_38_to_fp16 = const()[name = tensor<string, []>("view_38_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243264)))];
            tensor<fp16, [1, 11, 801]> mul_77_cast_fp16 = mul(x = squeeze_38_cast_fp16, y = view_38_to_fp16)[name = tensor<string, []>("mul_77_cast_fp16")];
            tensor<string, []> conv1d_38_pad_type_0 = const()[name = tensor<string, []>("conv1d_38_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_38_strides_0 = const()[name = tensor<string, []>("conv1d_38_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_38_pad_0 = const()[name = tensor<string, []>("conv1d_38_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_38_dilations_0 = const()[name = tensor<string, []>("conv1d_38_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_38_groups_0 = const()[name = tensor<string, []>("conv1d_38_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_38_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_38_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243392)))];
            tensor<fp16, [256]> p_bn_38_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_38_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249088)))];
            tensor<fp16, [1, 256, 801]> conv1d_38_cast_fp16 = conv(bias = p_bn_38_1_bias_to_fp16, dilations = conv1d_38_dilations_0, groups = conv1d_38_groups_0, pad = conv1d_38_pad_0, pad_type = conv1d_38_pad_type_0, strides = conv1d_38_strides_0, weight = p_bn_38_1_weight_to_fp16, x = mul_77_cast_fp16)[name = tensor<string, []>("conv1d_38_cast_fp16")];
            tensor<int32, [3]> slice_79_begin_0 = const()[name = tensor<string, []>("slice_79_begin_0"), val = tensor<int32, [3]>([0, 195, 0])];
            tensor<int32, [3]> slice_79_end_0 = const()[name = tensor<string, []>("slice_79_end_0"), val = tensor<int32, [3]>([1, 200, 801])];
            tensor<bool, [3]> slice_79_end_mask_0 = const()[name = tensor<string, []>("slice_79_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_79_cast_fp16 = slice_by_index(begin = slice_79_begin_0, end = slice_79_end_0, end_mask = slice_79_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_79_cast_fp16")];
            tensor<int32, [3]> slice_80_begin_0 = const()[name = tensor<string, []>("slice_80_begin_0"), val = tensor<int32, [3]>([0, 195, 0])];
            tensor<int32, [3]> slice_80_end_0 = const()[name = tensor<string, []>("slice_80_end_0"), val = tensor<int32, [3]>([1, 200, 801])];
            tensor<bool, [3]> slice_80_end_mask_0 = const()[name = tensor<string, []>("slice_80_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_80_cast_fp16 = slice_by_index(begin = slice_80_begin_0, end = slice_80_end_0, end_mask = slice_80_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_80_cast_fp16")];
            tensor<fp16, []> const_868_promoted_to_fp16 = const()[name = tensor<string, []>("const_868_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_118_cast_fp16 = pow(x = slice_79_cast_fp16, y = const_868_promoted_to_fp16)[name = tensor<string, []>("pow_118_cast_fp16")];
            tensor<fp16, []> const_869_promoted_to_fp16 = const()[name = tensor<string, []>("const_869_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_119_cast_fp16 = pow(x = slice_80_cast_fp16, y = const_869_promoted_to_fp16)[name = tensor<string, []>("pow_119_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_117_cast_fp16 = add(x = pow_118_cast_fp16, y = pow_119_cast_fp16)[name = tensor<string, []>("add_117_cast_fp16")];
            tensor<int32, [1]> sum_40_axes_0 = const()[name = tensor<string, []>("sum_40_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_40_keep_dims_0 = const()[name = tensor<string, []>("sum_40_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_40_cast_fp16 = reduce_sum(axes = sum_40_axes_0, keep_dims = sum_40_keep_dims_0, x = add_117_cast_fp16)[name = tensor<string, []>("sum_40_cast_fp16")];
            tensor<fp16, []> const_872_to_fp16 = const()[name = tensor<string, []>("const_872_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_118_cast_fp16 = add(x = sum_40_cast_fp16, y = const_872_to_fp16)[name = tensor<string, []>("add_118_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_39_cast_fp16 = sqrt(x = add_118_cast_fp16)[name = tensor<string, []>("sqrt_39_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_78_cast_fp16 = real_div(x = slice_79_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor<string, []>("div_78_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_79_cast_fp16 = real_div(x = slice_80_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor<string, []>("div_79_cast_fp16")];
            tensor<fp32, []> log_39_epsilon_0 = const()[name = tensor<string, []>("log_39_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_39_cast_fp16 = log(epsilon = log_39_epsilon_0, x = sqrt_39_cast_fp16)[name = tensor<string, []>("log_39_cast_fp16")];
            tensor<int32, []> const_873 = const()[name = tensor<string, []>("const_873"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_39_interleave_0 = const()[name = tensor<string, []>("cat_39_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_39_cast_fp16 = concat(axis = const_873, interleave = cat_39_interleave_0, values = (div_78_cast_fp16, div_79_cast_fp16, log_39_cast_fp16))[name = tensor<string, []>("cat_39_cast_fp16")];
            tensor<int32, [1]> unsqueeze_79_axes_0 = const()[name = tensor<string, []>("unsqueeze_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_79_cast_fp16 = expand_dims(axes = unsqueeze_79_axes_0, x = cat_39_cast_fp16)[name = tensor<string, []>("unsqueeze_79_cast_fp16")];
            tensor<fp16, []> const_878_promoted_to_fp16 = const()[name = tensor<string, []>("const_878_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_120_cast_fp16 = pow(x = unsqueeze_79_cast_fp16, y = const_878_promoted_to_fp16)[name = tensor<string, []>("pow_120_cast_fp16")];
            tensor<int32, [1]> mean_39_axes_0 = const()[name = tensor<string, []>("mean_39_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_39_keep_dims_0 = const()[name = tensor<string, []>("mean_39_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_39_cast_fp16 = reduce_mean(axes = mean_39_axes_0, keep_dims = mean_39_keep_dims_0, x = pow_120_cast_fp16)[name = tensor<string, []>("mean_39_cast_fp16")];
            tensor<fp16, []> const_881_to_fp16 = const()[name = tensor<string, []>("const_881_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_119_cast_fp16 = add(x = mean_39_cast_fp16, y = const_881_to_fp16)[name = tensor<string, []>("add_119_cast_fp16")];
            tensor<fp32, []> rsqrt_39_epsilon_0 = const()[name = tensor<string, []>("rsqrt_39_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_39_cast_fp16 = rsqrt(epsilon = rsqrt_39_epsilon_0, x = add_119_cast_fp16)[name = tensor<string, []>("rsqrt_39_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_78_cast_fp16 = mul(x = unsqueeze_79_cast_fp16, y = rsqrt_39_cast_fp16)[name = tensor<string, []>("mul_78_cast_fp16")];
            tensor<int32, [1]> squeeze_39_axes_0 = const()[name = tensor<string, []>("squeeze_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_39_cast_fp16 = squeeze(axes = squeeze_39_axes_0, x = mul_78_cast_fp16)[name = tensor<string, []>("squeeze_39_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_39_to_fp16 = const()[name = tensor<string, []>("view_39_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249664)))];
            tensor<fp16, [1, 11, 801]> mul_79_cast_fp16 = mul(x = squeeze_39_cast_fp16, y = view_39_to_fp16)[name = tensor<string, []>("mul_79_cast_fp16")];
            tensor<string, []> conv1d_39_pad_type_0 = const()[name = tensor<string, []>("conv1d_39_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_39_strides_0 = const()[name = tensor<string, []>("conv1d_39_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_39_pad_0 = const()[name = tensor<string, []>("conv1d_39_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_39_dilations_0 = const()[name = tensor<string, []>("conv1d_39_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_39_groups_0 = const()[name = tensor<string, []>("conv1d_39_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_39_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_39_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(249792)))];
            tensor<fp16, [256]> p_bn_39_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_39_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255488)))];
            tensor<fp16, [1, 256, 801]> conv1d_39_cast_fp16 = conv(bias = p_bn_39_1_bias_to_fp16, dilations = conv1d_39_dilations_0, groups = conv1d_39_groups_0, pad = conv1d_39_pad_0, pad_type = conv1d_39_pad_type_0, strides = conv1d_39_strides_0, weight = p_bn_39_1_weight_to_fp16, x = mul_79_cast_fp16)[name = tensor<string, []>("conv1d_39_cast_fp16")];
            tensor<int32, [3]> slice_81_begin_0 = const()[name = tensor<string, []>("slice_81_begin_0"), val = tensor<int32, [3]>([0, 200, 0])];
            tensor<int32, [3]> slice_81_end_0 = const()[name = tensor<string, []>("slice_81_end_0"), val = tensor<int32, [3]>([1, 205, 801])];
            tensor<bool, [3]> slice_81_end_mask_0 = const()[name = tensor<string, []>("slice_81_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_81_cast_fp16 = slice_by_index(begin = slice_81_begin_0, end = slice_81_end_0, end_mask = slice_81_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_81_cast_fp16")];
            tensor<int32, [3]> slice_82_begin_0 = const()[name = tensor<string, []>("slice_82_begin_0"), val = tensor<int32, [3]>([0, 200, 0])];
            tensor<int32, [3]> slice_82_end_0 = const()[name = tensor<string, []>("slice_82_end_0"), val = tensor<int32, [3]>([1, 205, 801])];
            tensor<bool, [3]> slice_82_end_mask_0 = const()[name = tensor<string, []>("slice_82_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_82_cast_fp16 = slice_by_index(begin = slice_82_begin_0, end = slice_82_end_0, end_mask = slice_82_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_82_cast_fp16")];
            tensor<fp16, []> const_890_promoted_to_fp16 = const()[name = tensor<string, []>("const_890_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_121_cast_fp16 = pow(x = slice_81_cast_fp16, y = const_890_promoted_to_fp16)[name = tensor<string, []>("pow_121_cast_fp16")];
            tensor<fp16, []> const_891_promoted_to_fp16 = const()[name = tensor<string, []>("const_891_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_122_cast_fp16 = pow(x = slice_82_cast_fp16, y = const_891_promoted_to_fp16)[name = tensor<string, []>("pow_122_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_120_cast_fp16 = add(x = pow_121_cast_fp16, y = pow_122_cast_fp16)[name = tensor<string, []>("add_120_cast_fp16")];
            tensor<int32, [1]> sum_41_axes_0 = const()[name = tensor<string, []>("sum_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_41_keep_dims_0 = const()[name = tensor<string, []>("sum_41_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_41_cast_fp16 = reduce_sum(axes = sum_41_axes_0, keep_dims = sum_41_keep_dims_0, x = add_120_cast_fp16)[name = tensor<string, []>("sum_41_cast_fp16")];
            tensor<fp16, []> const_894_to_fp16 = const()[name = tensor<string, []>("const_894_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_121_cast_fp16 = add(x = sum_41_cast_fp16, y = const_894_to_fp16)[name = tensor<string, []>("add_121_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_40_cast_fp16 = sqrt(x = add_121_cast_fp16)[name = tensor<string, []>("sqrt_40_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_80_cast_fp16 = real_div(x = slice_81_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor<string, []>("div_80_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_81_cast_fp16 = real_div(x = slice_82_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor<string, []>("div_81_cast_fp16")];
            tensor<fp32, []> log_40_epsilon_0 = const()[name = tensor<string, []>("log_40_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_40_cast_fp16 = log(epsilon = log_40_epsilon_0, x = sqrt_40_cast_fp16)[name = tensor<string, []>("log_40_cast_fp16")];
            tensor<int32, []> const_895 = const()[name = tensor<string, []>("const_895"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_40_interleave_0 = const()[name = tensor<string, []>("cat_40_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_40_cast_fp16 = concat(axis = const_895, interleave = cat_40_interleave_0, values = (div_80_cast_fp16, div_81_cast_fp16, log_40_cast_fp16))[name = tensor<string, []>("cat_40_cast_fp16")];
            tensor<int32, [1]> unsqueeze_81_axes_0 = const()[name = tensor<string, []>("unsqueeze_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_81_cast_fp16 = expand_dims(axes = unsqueeze_81_axes_0, x = cat_40_cast_fp16)[name = tensor<string, []>("unsqueeze_81_cast_fp16")];
            tensor<fp16, []> const_900_promoted_to_fp16 = const()[name = tensor<string, []>("const_900_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_123_cast_fp16 = pow(x = unsqueeze_81_cast_fp16, y = const_900_promoted_to_fp16)[name = tensor<string, []>("pow_123_cast_fp16")];
            tensor<int32, [1]> mean_40_axes_0 = const()[name = tensor<string, []>("mean_40_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_40_keep_dims_0 = const()[name = tensor<string, []>("mean_40_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_40_cast_fp16 = reduce_mean(axes = mean_40_axes_0, keep_dims = mean_40_keep_dims_0, x = pow_123_cast_fp16)[name = tensor<string, []>("mean_40_cast_fp16")];
            tensor<fp16, []> const_903_to_fp16 = const()[name = tensor<string, []>("const_903_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_122_cast_fp16 = add(x = mean_40_cast_fp16, y = const_903_to_fp16)[name = tensor<string, []>("add_122_cast_fp16")];
            tensor<fp32, []> rsqrt_40_epsilon_0 = const()[name = tensor<string, []>("rsqrt_40_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_40_cast_fp16 = rsqrt(epsilon = rsqrt_40_epsilon_0, x = add_122_cast_fp16)[name = tensor<string, []>("rsqrt_40_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_80_cast_fp16 = mul(x = unsqueeze_81_cast_fp16, y = rsqrt_40_cast_fp16)[name = tensor<string, []>("mul_80_cast_fp16")];
            tensor<int32, [1]> squeeze_40_axes_0 = const()[name = tensor<string, []>("squeeze_40_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_40_cast_fp16 = squeeze(axes = squeeze_40_axes_0, x = mul_80_cast_fp16)[name = tensor<string, []>("squeeze_40_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_40_to_fp16 = const()[name = tensor<string, []>("view_40_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256064)))];
            tensor<fp16, [1, 11, 801]> mul_81_cast_fp16 = mul(x = squeeze_40_cast_fp16, y = view_40_to_fp16)[name = tensor<string, []>("mul_81_cast_fp16")];
            tensor<string, []> conv1d_40_pad_type_0 = const()[name = tensor<string, []>("conv1d_40_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_40_strides_0 = const()[name = tensor<string, []>("conv1d_40_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_40_pad_0 = const()[name = tensor<string, []>("conv1d_40_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_40_dilations_0 = const()[name = tensor<string, []>("conv1d_40_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_40_groups_0 = const()[name = tensor<string, []>("conv1d_40_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_40_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_40_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256192)))];
            tensor<fp16, [256]> p_bn_40_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_40_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261888)))];
            tensor<fp16, [1, 256, 801]> conv1d_40_cast_fp16 = conv(bias = p_bn_40_1_bias_to_fp16, dilations = conv1d_40_dilations_0, groups = conv1d_40_groups_0, pad = conv1d_40_pad_0, pad_type = conv1d_40_pad_type_0, strides = conv1d_40_strides_0, weight = p_bn_40_1_weight_to_fp16, x = mul_81_cast_fp16)[name = tensor<string, []>("conv1d_40_cast_fp16")];
            tensor<int32, [3]> slice_83_begin_0 = const()[name = tensor<string, []>("slice_83_begin_0"), val = tensor<int32, [3]>([0, 205, 0])];
            tensor<int32, [3]> slice_83_end_0 = const()[name = tensor<string, []>("slice_83_end_0"), val = tensor<int32, [3]>([1, 210, 801])];
            tensor<bool, [3]> slice_83_end_mask_0 = const()[name = tensor<string, []>("slice_83_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_83_cast_fp16 = slice_by_index(begin = slice_83_begin_0, end = slice_83_end_0, end_mask = slice_83_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_83_cast_fp16")];
            tensor<int32, [3]> slice_84_begin_0 = const()[name = tensor<string, []>("slice_84_begin_0"), val = tensor<int32, [3]>([0, 205, 0])];
            tensor<int32, [3]> slice_84_end_0 = const()[name = tensor<string, []>("slice_84_end_0"), val = tensor<int32, [3]>([1, 210, 801])];
            tensor<bool, [3]> slice_84_end_mask_0 = const()[name = tensor<string, []>("slice_84_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_84_cast_fp16 = slice_by_index(begin = slice_84_begin_0, end = slice_84_end_0, end_mask = slice_84_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_84_cast_fp16")];
            tensor<fp16, []> const_912_promoted_to_fp16 = const()[name = tensor<string, []>("const_912_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_124_cast_fp16 = pow(x = slice_83_cast_fp16, y = const_912_promoted_to_fp16)[name = tensor<string, []>("pow_124_cast_fp16")];
            tensor<fp16, []> const_913_promoted_to_fp16 = const()[name = tensor<string, []>("const_913_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_125_cast_fp16 = pow(x = slice_84_cast_fp16, y = const_913_promoted_to_fp16)[name = tensor<string, []>("pow_125_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_123_cast_fp16 = add(x = pow_124_cast_fp16, y = pow_125_cast_fp16)[name = tensor<string, []>("add_123_cast_fp16")];
            tensor<int32, [1]> sum_42_axes_0 = const()[name = tensor<string, []>("sum_42_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_42_keep_dims_0 = const()[name = tensor<string, []>("sum_42_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_42_cast_fp16 = reduce_sum(axes = sum_42_axes_0, keep_dims = sum_42_keep_dims_0, x = add_123_cast_fp16)[name = tensor<string, []>("sum_42_cast_fp16")];
            tensor<fp16, []> const_916_to_fp16 = const()[name = tensor<string, []>("const_916_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_124_cast_fp16 = add(x = sum_42_cast_fp16, y = const_916_to_fp16)[name = tensor<string, []>("add_124_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_41_cast_fp16 = sqrt(x = add_124_cast_fp16)[name = tensor<string, []>("sqrt_41_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_82_cast_fp16 = real_div(x = slice_83_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor<string, []>("div_82_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_83_cast_fp16 = real_div(x = slice_84_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor<string, []>("div_83_cast_fp16")];
            tensor<fp32, []> log_41_epsilon_0 = const()[name = tensor<string, []>("log_41_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_41_cast_fp16 = log(epsilon = log_41_epsilon_0, x = sqrt_41_cast_fp16)[name = tensor<string, []>("log_41_cast_fp16")];
            tensor<int32, []> const_917 = const()[name = tensor<string, []>("const_917"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_41_interleave_0 = const()[name = tensor<string, []>("cat_41_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_41_cast_fp16 = concat(axis = const_917, interleave = cat_41_interleave_0, values = (div_82_cast_fp16, div_83_cast_fp16, log_41_cast_fp16))[name = tensor<string, []>("cat_41_cast_fp16")];
            tensor<int32, [1]> unsqueeze_83_axes_0 = const()[name = tensor<string, []>("unsqueeze_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_83_cast_fp16 = expand_dims(axes = unsqueeze_83_axes_0, x = cat_41_cast_fp16)[name = tensor<string, []>("unsqueeze_83_cast_fp16")];
            tensor<fp16, []> const_922_promoted_to_fp16 = const()[name = tensor<string, []>("const_922_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_126_cast_fp16 = pow(x = unsqueeze_83_cast_fp16, y = const_922_promoted_to_fp16)[name = tensor<string, []>("pow_126_cast_fp16")];
            tensor<int32, [1]> mean_41_axes_0 = const()[name = tensor<string, []>("mean_41_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_41_keep_dims_0 = const()[name = tensor<string, []>("mean_41_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_41_cast_fp16 = reduce_mean(axes = mean_41_axes_0, keep_dims = mean_41_keep_dims_0, x = pow_126_cast_fp16)[name = tensor<string, []>("mean_41_cast_fp16")];
            tensor<fp16, []> const_925_to_fp16 = const()[name = tensor<string, []>("const_925_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_125_cast_fp16 = add(x = mean_41_cast_fp16, y = const_925_to_fp16)[name = tensor<string, []>("add_125_cast_fp16")];
            tensor<fp32, []> rsqrt_41_epsilon_0 = const()[name = tensor<string, []>("rsqrt_41_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_41_cast_fp16 = rsqrt(epsilon = rsqrt_41_epsilon_0, x = add_125_cast_fp16)[name = tensor<string, []>("rsqrt_41_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_82_cast_fp16 = mul(x = unsqueeze_83_cast_fp16, y = rsqrt_41_cast_fp16)[name = tensor<string, []>("mul_82_cast_fp16")];
            tensor<int32, [1]> squeeze_41_axes_0 = const()[name = tensor<string, []>("squeeze_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_41_cast_fp16 = squeeze(axes = squeeze_41_axes_0, x = mul_82_cast_fp16)[name = tensor<string, []>("squeeze_41_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_41_to_fp16 = const()[name = tensor<string, []>("view_41_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262464)))];
            tensor<fp16, [1, 11, 801]> mul_83_cast_fp16 = mul(x = squeeze_41_cast_fp16, y = view_41_to_fp16)[name = tensor<string, []>("mul_83_cast_fp16")];
            tensor<string, []> conv1d_41_pad_type_0 = const()[name = tensor<string, []>("conv1d_41_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_41_strides_0 = const()[name = tensor<string, []>("conv1d_41_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_41_pad_0 = const()[name = tensor<string, []>("conv1d_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_41_dilations_0 = const()[name = tensor<string, []>("conv1d_41_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_41_groups_0 = const()[name = tensor<string, []>("conv1d_41_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_41_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_41_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(262592)))];
            tensor<fp16, [256]> p_bn_41_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_41_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268288)))];
            tensor<fp16, [1, 256, 801]> conv1d_41_cast_fp16 = conv(bias = p_bn_41_1_bias_to_fp16, dilations = conv1d_41_dilations_0, groups = conv1d_41_groups_0, pad = conv1d_41_pad_0, pad_type = conv1d_41_pad_type_0, strides = conv1d_41_strides_0, weight = p_bn_41_1_weight_to_fp16, x = mul_83_cast_fp16)[name = tensor<string, []>("conv1d_41_cast_fp16")];
            tensor<int32, [3]> slice_85_begin_0 = const()[name = tensor<string, []>("slice_85_begin_0"), val = tensor<int32, [3]>([0, 210, 0])];
            tensor<int32, [3]> slice_85_end_0 = const()[name = tensor<string, []>("slice_85_end_0"), val = tensor<int32, [3]>([1, 215, 801])];
            tensor<bool, [3]> slice_85_end_mask_0 = const()[name = tensor<string, []>("slice_85_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_85_cast_fp16 = slice_by_index(begin = slice_85_begin_0, end = slice_85_end_0, end_mask = slice_85_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_85_cast_fp16")];
            tensor<int32, [3]> slice_86_begin_0 = const()[name = tensor<string, []>("slice_86_begin_0"), val = tensor<int32, [3]>([0, 210, 0])];
            tensor<int32, [3]> slice_86_end_0 = const()[name = tensor<string, []>("slice_86_end_0"), val = tensor<int32, [3]>([1, 215, 801])];
            tensor<bool, [3]> slice_86_end_mask_0 = const()[name = tensor<string, []>("slice_86_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_86_cast_fp16 = slice_by_index(begin = slice_86_begin_0, end = slice_86_end_0, end_mask = slice_86_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_86_cast_fp16")];
            tensor<fp16, []> const_934_promoted_to_fp16 = const()[name = tensor<string, []>("const_934_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_127_cast_fp16 = pow(x = slice_85_cast_fp16, y = const_934_promoted_to_fp16)[name = tensor<string, []>("pow_127_cast_fp16")];
            tensor<fp16, []> const_935_promoted_to_fp16 = const()[name = tensor<string, []>("const_935_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_128_cast_fp16 = pow(x = slice_86_cast_fp16, y = const_935_promoted_to_fp16)[name = tensor<string, []>("pow_128_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_126_cast_fp16 = add(x = pow_127_cast_fp16, y = pow_128_cast_fp16)[name = tensor<string, []>("add_126_cast_fp16")];
            tensor<int32, [1]> sum_43_axes_0 = const()[name = tensor<string, []>("sum_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_43_keep_dims_0 = const()[name = tensor<string, []>("sum_43_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_43_cast_fp16 = reduce_sum(axes = sum_43_axes_0, keep_dims = sum_43_keep_dims_0, x = add_126_cast_fp16)[name = tensor<string, []>("sum_43_cast_fp16")];
            tensor<fp16, []> const_938_to_fp16 = const()[name = tensor<string, []>("const_938_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_127_cast_fp16 = add(x = sum_43_cast_fp16, y = const_938_to_fp16)[name = tensor<string, []>("add_127_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_42_cast_fp16 = sqrt(x = add_127_cast_fp16)[name = tensor<string, []>("sqrt_42_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_84_cast_fp16 = real_div(x = slice_85_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor<string, []>("div_84_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_85_cast_fp16 = real_div(x = slice_86_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor<string, []>("div_85_cast_fp16")];
            tensor<fp32, []> log_42_epsilon_0 = const()[name = tensor<string, []>("log_42_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_42_cast_fp16 = log(epsilon = log_42_epsilon_0, x = sqrt_42_cast_fp16)[name = tensor<string, []>("log_42_cast_fp16")];
            tensor<int32, []> const_939 = const()[name = tensor<string, []>("const_939"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_42_interleave_0 = const()[name = tensor<string, []>("cat_42_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_42_cast_fp16 = concat(axis = const_939, interleave = cat_42_interleave_0, values = (div_84_cast_fp16, div_85_cast_fp16, log_42_cast_fp16))[name = tensor<string, []>("cat_42_cast_fp16")];
            tensor<int32, [1]> unsqueeze_85_axes_0 = const()[name = tensor<string, []>("unsqueeze_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_85_cast_fp16 = expand_dims(axes = unsqueeze_85_axes_0, x = cat_42_cast_fp16)[name = tensor<string, []>("unsqueeze_85_cast_fp16")];
            tensor<fp16, []> const_944_promoted_to_fp16 = const()[name = tensor<string, []>("const_944_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_129_cast_fp16 = pow(x = unsqueeze_85_cast_fp16, y = const_944_promoted_to_fp16)[name = tensor<string, []>("pow_129_cast_fp16")];
            tensor<int32, [1]> mean_42_axes_0 = const()[name = tensor<string, []>("mean_42_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_42_keep_dims_0 = const()[name = tensor<string, []>("mean_42_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_42_cast_fp16 = reduce_mean(axes = mean_42_axes_0, keep_dims = mean_42_keep_dims_0, x = pow_129_cast_fp16)[name = tensor<string, []>("mean_42_cast_fp16")];
            tensor<fp16, []> const_947_to_fp16 = const()[name = tensor<string, []>("const_947_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_128_cast_fp16 = add(x = mean_42_cast_fp16, y = const_947_to_fp16)[name = tensor<string, []>("add_128_cast_fp16")];
            tensor<fp32, []> rsqrt_42_epsilon_0 = const()[name = tensor<string, []>("rsqrt_42_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_42_cast_fp16 = rsqrt(epsilon = rsqrt_42_epsilon_0, x = add_128_cast_fp16)[name = tensor<string, []>("rsqrt_42_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_84_cast_fp16 = mul(x = unsqueeze_85_cast_fp16, y = rsqrt_42_cast_fp16)[name = tensor<string, []>("mul_84_cast_fp16")];
            tensor<int32, [1]> squeeze_42_axes_0 = const()[name = tensor<string, []>("squeeze_42_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_42_cast_fp16 = squeeze(axes = squeeze_42_axes_0, x = mul_84_cast_fp16)[name = tensor<string, []>("squeeze_42_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_42_to_fp16 = const()[name = tensor<string, []>("view_42_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268864)))];
            tensor<fp16, [1, 11, 801]> mul_85_cast_fp16 = mul(x = squeeze_42_cast_fp16, y = view_42_to_fp16)[name = tensor<string, []>("mul_85_cast_fp16")];
            tensor<string, []> conv1d_42_pad_type_0 = const()[name = tensor<string, []>("conv1d_42_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_42_strides_0 = const()[name = tensor<string, []>("conv1d_42_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_42_pad_0 = const()[name = tensor<string, []>("conv1d_42_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_42_dilations_0 = const()[name = tensor<string, []>("conv1d_42_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_42_groups_0 = const()[name = tensor<string, []>("conv1d_42_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_42_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_42_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(268992)))];
            tensor<fp16, [256]> p_bn_42_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_42_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274688)))];
            tensor<fp16, [1, 256, 801]> conv1d_42_cast_fp16 = conv(bias = p_bn_42_1_bias_to_fp16, dilations = conv1d_42_dilations_0, groups = conv1d_42_groups_0, pad = conv1d_42_pad_0, pad_type = conv1d_42_pad_type_0, strides = conv1d_42_strides_0, weight = p_bn_42_1_weight_to_fp16, x = mul_85_cast_fp16)[name = tensor<string, []>("conv1d_42_cast_fp16")];
            tensor<int32, [3]> slice_87_begin_0 = const()[name = tensor<string, []>("slice_87_begin_0"), val = tensor<int32, [3]>([0, 215, 0])];
            tensor<int32, [3]> slice_87_end_0 = const()[name = tensor<string, []>("slice_87_end_0"), val = tensor<int32, [3]>([1, 220, 801])];
            tensor<bool, [3]> slice_87_end_mask_0 = const()[name = tensor<string, []>("slice_87_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_87_cast_fp16 = slice_by_index(begin = slice_87_begin_0, end = slice_87_end_0, end_mask = slice_87_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_87_cast_fp16")];
            tensor<int32, [3]> slice_88_begin_0 = const()[name = tensor<string, []>("slice_88_begin_0"), val = tensor<int32, [3]>([0, 215, 0])];
            tensor<int32, [3]> slice_88_end_0 = const()[name = tensor<string, []>("slice_88_end_0"), val = tensor<int32, [3]>([1, 220, 801])];
            tensor<bool, [3]> slice_88_end_mask_0 = const()[name = tensor<string, []>("slice_88_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_88_cast_fp16 = slice_by_index(begin = slice_88_begin_0, end = slice_88_end_0, end_mask = slice_88_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_88_cast_fp16")];
            tensor<fp16, []> const_956_promoted_to_fp16 = const()[name = tensor<string, []>("const_956_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_130_cast_fp16 = pow(x = slice_87_cast_fp16, y = const_956_promoted_to_fp16)[name = tensor<string, []>("pow_130_cast_fp16")];
            tensor<fp16, []> const_957_promoted_to_fp16 = const()[name = tensor<string, []>("const_957_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_131_cast_fp16 = pow(x = slice_88_cast_fp16, y = const_957_promoted_to_fp16)[name = tensor<string, []>("pow_131_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_129_cast_fp16 = add(x = pow_130_cast_fp16, y = pow_131_cast_fp16)[name = tensor<string, []>("add_129_cast_fp16")];
            tensor<int32, [1]> sum_44_axes_0 = const()[name = tensor<string, []>("sum_44_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_44_keep_dims_0 = const()[name = tensor<string, []>("sum_44_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_44_cast_fp16 = reduce_sum(axes = sum_44_axes_0, keep_dims = sum_44_keep_dims_0, x = add_129_cast_fp16)[name = tensor<string, []>("sum_44_cast_fp16")];
            tensor<fp16, []> const_960_to_fp16 = const()[name = tensor<string, []>("const_960_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_130_cast_fp16 = add(x = sum_44_cast_fp16, y = const_960_to_fp16)[name = tensor<string, []>("add_130_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_43_cast_fp16 = sqrt(x = add_130_cast_fp16)[name = tensor<string, []>("sqrt_43_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_86_cast_fp16 = real_div(x = slice_87_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor<string, []>("div_86_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_87_cast_fp16 = real_div(x = slice_88_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor<string, []>("div_87_cast_fp16")];
            tensor<fp32, []> log_43_epsilon_0 = const()[name = tensor<string, []>("log_43_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_43_cast_fp16 = log(epsilon = log_43_epsilon_0, x = sqrt_43_cast_fp16)[name = tensor<string, []>("log_43_cast_fp16")];
            tensor<int32, []> const_961 = const()[name = tensor<string, []>("const_961"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_43_interleave_0 = const()[name = tensor<string, []>("cat_43_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_43_cast_fp16 = concat(axis = const_961, interleave = cat_43_interleave_0, values = (div_86_cast_fp16, div_87_cast_fp16, log_43_cast_fp16))[name = tensor<string, []>("cat_43_cast_fp16")];
            tensor<int32, [1]> unsqueeze_87_axes_0 = const()[name = tensor<string, []>("unsqueeze_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_87_cast_fp16 = expand_dims(axes = unsqueeze_87_axes_0, x = cat_43_cast_fp16)[name = tensor<string, []>("unsqueeze_87_cast_fp16")];
            tensor<fp16, []> const_966_promoted_to_fp16 = const()[name = tensor<string, []>("const_966_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_132_cast_fp16 = pow(x = unsqueeze_87_cast_fp16, y = const_966_promoted_to_fp16)[name = tensor<string, []>("pow_132_cast_fp16")];
            tensor<int32, [1]> mean_43_axes_0 = const()[name = tensor<string, []>("mean_43_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_43_keep_dims_0 = const()[name = tensor<string, []>("mean_43_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_43_cast_fp16 = reduce_mean(axes = mean_43_axes_0, keep_dims = mean_43_keep_dims_0, x = pow_132_cast_fp16)[name = tensor<string, []>("mean_43_cast_fp16")];
            tensor<fp16, []> const_969_to_fp16 = const()[name = tensor<string, []>("const_969_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_131_cast_fp16 = add(x = mean_43_cast_fp16, y = const_969_to_fp16)[name = tensor<string, []>("add_131_cast_fp16")];
            tensor<fp32, []> rsqrt_43_epsilon_0 = const()[name = tensor<string, []>("rsqrt_43_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_43_cast_fp16 = rsqrt(epsilon = rsqrt_43_epsilon_0, x = add_131_cast_fp16)[name = tensor<string, []>("rsqrt_43_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_86_cast_fp16 = mul(x = unsqueeze_87_cast_fp16, y = rsqrt_43_cast_fp16)[name = tensor<string, []>("mul_86_cast_fp16")];
            tensor<int32, [1]> squeeze_43_axes_0 = const()[name = tensor<string, []>("squeeze_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_43_cast_fp16 = squeeze(axes = squeeze_43_axes_0, x = mul_86_cast_fp16)[name = tensor<string, []>("squeeze_43_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_43_to_fp16 = const()[name = tensor<string, []>("view_43_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(275264)))];
            tensor<fp16, [1, 11, 801]> mul_87_cast_fp16 = mul(x = squeeze_43_cast_fp16, y = view_43_to_fp16)[name = tensor<string, []>("mul_87_cast_fp16")];
            tensor<string, []> conv1d_43_pad_type_0 = const()[name = tensor<string, []>("conv1d_43_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_43_strides_0 = const()[name = tensor<string, []>("conv1d_43_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_43_pad_0 = const()[name = tensor<string, []>("conv1d_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_43_dilations_0 = const()[name = tensor<string, []>("conv1d_43_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_43_groups_0 = const()[name = tensor<string, []>("conv1d_43_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_43_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_43_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(275392)))];
            tensor<fp16, [256]> p_bn_43_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_43_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281088)))];
            tensor<fp16, [1, 256, 801]> conv1d_43_cast_fp16 = conv(bias = p_bn_43_1_bias_to_fp16, dilations = conv1d_43_dilations_0, groups = conv1d_43_groups_0, pad = conv1d_43_pad_0, pad_type = conv1d_43_pad_type_0, strides = conv1d_43_strides_0, weight = p_bn_43_1_weight_to_fp16, x = mul_87_cast_fp16)[name = tensor<string, []>("conv1d_43_cast_fp16")];
            tensor<int32, [3]> slice_89_begin_0 = const()[name = tensor<string, []>("slice_89_begin_0"), val = tensor<int32, [3]>([0, 220, 0])];
            tensor<int32, [3]> slice_89_end_0 = const()[name = tensor<string, []>("slice_89_end_0"), val = tensor<int32, [3]>([1, 225, 801])];
            tensor<bool, [3]> slice_89_end_mask_0 = const()[name = tensor<string, []>("slice_89_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_89_cast_fp16 = slice_by_index(begin = slice_89_begin_0, end = slice_89_end_0, end_mask = slice_89_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_89_cast_fp16")];
            tensor<int32, [3]> slice_90_begin_0 = const()[name = tensor<string, []>("slice_90_begin_0"), val = tensor<int32, [3]>([0, 220, 0])];
            tensor<int32, [3]> slice_90_end_0 = const()[name = tensor<string, []>("slice_90_end_0"), val = tensor<int32, [3]>([1, 225, 801])];
            tensor<bool, [3]> slice_90_end_mask_0 = const()[name = tensor<string, []>("slice_90_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_90_cast_fp16 = slice_by_index(begin = slice_90_begin_0, end = slice_90_end_0, end_mask = slice_90_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_90_cast_fp16")];
            tensor<fp16, []> const_978_promoted_to_fp16 = const()[name = tensor<string, []>("const_978_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_133_cast_fp16 = pow(x = slice_89_cast_fp16, y = const_978_promoted_to_fp16)[name = tensor<string, []>("pow_133_cast_fp16")];
            tensor<fp16, []> const_979_promoted_to_fp16 = const()[name = tensor<string, []>("const_979_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_134_cast_fp16 = pow(x = slice_90_cast_fp16, y = const_979_promoted_to_fp16)[name = tensor<string, []>("pow_134_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_132_cast_fp16 = add(x = pow_133_cast_fp16, y = pow_134_cast_fp16)[name = tensor<string, []>("add_132_cast_fp16")];
            tensor<int32, [1]> sum_45_axes_0 = const()[name = tensor<string, []>("sum_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_45_keep_dims_0 = const()[name = tensor<string, []>("sum_45_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_45_cast_fp16 = reduce_sum(axes = sum_45_axes_0, keep_dims = sum_45_keep_dims_0, x = add_132_cast_fp16)[name = tensor<string, []>("sum_45_cast_fp16")];
            tensor<fp16, []> const_982_to_fp16 = const()[name = tensor<string, []>("const_982_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_133_cast_fp16 = add(x = sum_45_cast_fp16, y = const_982_to_fp16)[name = tensor<string, []>("add_133_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_44_cast_fp16 = sqrt(x = add_133_cast_fp16)[name = tensor<string, []>("sqrt_44_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_88_cast_fp16 = real_div(x = slice_89_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor<string, []>("div_88_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_89_cast_fp16 = real_div(x = slice_90_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor<string, []>("div_89_cast_fp16")];
            tensor<fp32, []> log_44_epsilon_0 = const()[name = tensor<string, []>("log_44_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_44_cast_fp16 = log(epsilon = log_44_epsilon_0, x = sqrt_44_cast_fp16)[name = tensor<string, []>("log_44_cast_fp16")];
            tensor<int32, []> const_983 = const()[name = tensor<string, []>("const_983"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_44_interleave_0 = const()[name = tensor<string, []>("cat_44_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_44_cast_fp16 = concat(axis = const_983, interleave = cat_44_interleave_0, values = (div_88_cast_fp16, div_89_cast_fp16, log_44_cast_fp16))[name = tensor<string, []>("cat_44_cast_fp16")];
            tensor<int32, [1]> unsqueeze_89_axes_0 = const()[name = tensor<string, []>("unsqueeze_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_89_cast_fp16 = expand_dims(axes = unsqueeze_89_axes_0, x = cat_44_cast_fp16)[name = tensor<string, []>("unsqueeze_89_cast_fp16")];
            tensor<fp16, []> const_988_promoted_to_fp16 = const()[name = tensor<string, []>("const_988_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_135_cast_fp16 = pow(x = unsqueeze_89_cast_fp16, y = const_988_promoted_to_fp16)[name = tensor<string, []>("pow_135_cast_fp16")];
            tensor<int32, [1]> mean_44_axes_0 = const()[name = tensor<string, []>("mean_44_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_44_keep_dims_0 = const()[name = tensor<string, []>("mean_44_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_44_cast_fp16 = reduce_mean(axes = mean_44_axes_0, keep_dims = mean_44_keep_dims_0, x = pow_135_cast_fp16)[name = tensor<string, []>("mean_44_cast_fp16")];
            tensor<fp16, []> const_991_to_fp16 = const()[name = tensor<string, []>("const_991_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_134_cast_fp16 = add(x = mean_44_cast_fp16, y = const_991_to_fp16)[name = tensor<string, []>("add_134_cast_fp16")];
            tensor<fp32, []> rsqrt_44_epsilon_0 = const()[name = tensor<string, []>("rsqrt_44_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_44_cast_fp16 = rsqrt(epsilon = rsqrt_44_epsilon_0, x = add_134_cast_fp16)[name = tensor<string, []>("rsqrt_44_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_88_cast_fp16 = mul(x = unsqueeze_89_cast_fp16, y = rsqrt_44_cast_fp16)[name = tensor<string, []>("mul_88_cast_fp16")];
            tensor<int32, [1]> squeeze_44_axes_0 = const()[name = tensor<string, []>("squeeze_44_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_44_cast_fp16 = squeeze(axes = squeeze_44_axes_0, x = mul_88_cast_fp16)[name = tensor<string, []>("squeeze_44_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_44_to_fp16 = const()[name = tensor<string, []>("view_44_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281664)))];
            tensor<fp16, [1, 11, 801]> mul_89_cast_fp16 = mul(x = squeeze_44_cast_fp16, y = view_44_to_fp16)[name = tensor<string, []>("mul_89_cast_fp16")];
            tensor<string, []> conv1d_44_pad_type_0 = const()[name = tensor<string, []>("conv1d_44_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_44_strides_0 = const()[name = tensor<string, []>("conv1d_44_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_44_pad_0 = const()[name = tensor<string, []>("conv1d_44_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_44_dilations_0 = const()[name = tensor<string, []>("conv1d_44_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_44_groups_0 = const()[name = tensor<string, []>("conv1d_44_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_44_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_44_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281792)))];
            tensor<fp16, [256]> p_bn_44_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_44_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287488)))];
            tensor<fp16, [1, 256, 801]> conv1d_44_cast_fp16 = conv(bias = p_bn_44_1_bias_to_fp16, dilations = conv1d_44_dilations_0, groups = conv1d_44_groups_0, pad = conv1d_44_pad_0, pad_type = conv1d_44_pad_type_0, strides = conv1d_44_strides_0, weight = p_bn_44_1_weight_to_fp16, x = mul_89_cast_fp16)[name = tensor<string, []>("conv1d_44_cast_fp16")];
            tensor<int32, [3]> slice_91_begin_0 = const()[name = tensor<string, []>("slice_91_begin_0"), val = tensor<int32, [3]>([0, 225, 0])];
            tensor<int32, [3]> slice_91_end_0 = const()[name = tensor<string, []>("slice_91_end_0"), val = tensor<int32, [3]>([1, 230, 801])];
            tensor<bool, [3]> slice_91_end_mask_0 = const()[name = tensor<string, []>("slice_91_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_91_cast_fp16 = slice_by_index(begin = slice_91_begin_0, end = slice_91_end_0, end_mask = slice_91_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_91_cast_fp16")];
            tensor<int32, [3]> slice_92_begin_0 = const()[name = tensor<string, []>("slice_92_begin_0"), val = tensor<int32, [3]>([0, 225, 0])];
            tensor<int32, [3]> slice_92_end_0 = const()[name = tensor<string, []>("slice_92_end_0"), val = tensor<int32, [3]>([1, 230, 801])];
            tensor<bool, [3]> slice_92_end_mask_0 = const()[name = tensor<string, []>("slice_92_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_92_cast_fp16 = slice_by_index(begin = slice_92_begin_0, end = slice_92_end_0, end_mask = slice_92_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_92_cast_fp16")];
            tensor<fp16, []> const_1000_promoted_to_fp16 = const()[name = tensor<string, []>("const_1000_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_136_cast_fp16 = pow(x = slice_91_cast_fp16, y = const_1000_promoted_to_fp16)[name = tensor<string, []>("pow_136_cast_fp16")];
            tensor<fp16, []> const_1001_promoted_to_fp16 = const()[name = tensor<string, []>("const_1001_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_137_cast_fp16 = pow(x = slice_92_cast_fp16, y = const_1001_promoted_to_fp16)[name = tensor<string, []>("pow_137_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_135_cast_fp16 = add(x = pow_136_cast_fp16, y = pow_137_cast_fp16)[name = tensor<string, []>("add_135_cast_fp16")];
            tensor<int32, [1]> sum_46_axes_0 = const()[name = tensor<string, []>("sum_46_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_46_keep_dims_0 = const()[name = tensor<string, []>("sum_46_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_46_cast_fp16 = reduce_sum(axes = sum_46_axes_0, keep_dims = sum_46_keep_dims_0, x = add_135_cast_fp16)[name = tensor<string, []>("sum_46_cast_fp16")];
            tensor<fp16, []> const_1004_to_fp16 = const()[name = tensor<string, []>("const_1004_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_136_cast_fp16 = add(x = sum_46_cast_fp16, y = const_1004_to_fp16)[name = tensor<string, []>("add_136_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_45_cast_fp16 = sqrt(x = add_136_cast_fp16)[name = tensor<string, []>("sqrt_45_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_90_cast_fp16 = real_div(x = slice_91_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor<string, []>("div_90_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_91_cast_fp16 = real_div(x = slice_92_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor<string, []>("div_91_cast_fp16")];
            tensor<fp32, []> log_45_epsilon_0 = const()[name = tensor<string, []>("log_45_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_45_cast_fp16 = log(epsilon = log_45_epsilon_0, x = sqrt_45_cast_fp16)[name = tensor<string, []>("log_45_cast_fp16")];
            tensor<int32, []> const_1005 = const()[name = tensor<string, []>("const_1005"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_45_interleave_0 = const()[name = tensor<string, []>("cat_45_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_45_cast_fp16 = concat(axis = const_1005, interleave = cat_45_interleave_0, values = (div_90_cast_fp16, div_91_cast_fp16, log_45_cast_fp16))[name = tensor<string, []>("cat_45_cast_fp16")];
            tensor<int32, [1]> unsqueeze_91_axes_0 = const()[name = tensor<string, []>("unsqueeze_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_91_cast_fp16 = expand_dims(axes = unsqueeze_91_axes_0, x = cat_45_cast_fp16)[name = tensor<string, []>("unsqueeze_91_cast_fp16")];
            tensor<fp16, []> const_1010_promoted_to_fp16 = const()[name = tensor<string, []>("const_1010_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_138_cast_fp16 = pow(x = unsqueeze_91_cast_fp16, y = const_1010_promoted_to_fp16)[name = tensor<string, []>("pow_138_cast_fp16")];
            tensor<int32, [1]> mean_45_axes_0 = const()[name = tensor<string, []>("mean_45_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_45_keep_dims_0 = const()[name = tensor<string, []>("mean_45_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_45_cast_fp16 = reduce_mean(axes = mean_45_axes_0, keep_dims = mean_45_keep_dims_0, x = pow_138_cast_fp16)[name = tensor<string, []>("mean_45_cast_fp16")];
            tensor<fp16, []> const_1013_to_fp16 = const()[name = tensor<string, []>("const_1013_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_137_cast_fp16 = add(x = mean_45_cast_fp16, y = const_1013_to_fp16)[name = tensor<string, []>("add_137_cast_fp16")];
            tensor<fp32, []> rsqrt_45_epsilon_0 = const()[name = tensor<string, []>("rsqrt_45_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_45_cast_fp16 = rsqrt(epsilon = rsqrt_45_epsilon_0, x = add_137_cast_fp16)[name = tensor<string, []>("rsqrt_45_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_90_cast_fp16 = mul(x = unsqueeze_91_cast_fp16, y = rsqrt_45_cast_fp16)[name = tensor<string, []>("mul_90_cast_fp16")];
            tensor<int32, [1]> squeeze_45_axes_0 = const()[name = tensor<string, []>("squeeze_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_45_cast_fp16 = squeeze(axes = squeeze_45_axes_0, x = mul_90_cast_fp16)[name = tensor<string, []>("squeeze_45_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_45_to_fp16 = const()[name = tensor<string, []>("view_45_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288064)))];
            tensor<fp16, [1, 11, 801]> mul_91_cast_fp16 = mul(x = squeeze_45_cast_fp16, y = view_45_to_fp16)[name = tensor<string, []>("mul_91_cast_fp16")];
            tensor<string, []> conv1d_45_pad_type_0 = const()[name = tensor<string, []>("conv1d_45_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_45_strides_0 = const()[name = tensor<string, []>("conv1d_45_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_45_pad_0 = const()[name = tensor<string, []>("conv1d_45_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_45_dilations_0 = const()[name = tensor<string, []>("conv1d_45_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_45_groups_0 = const()[name = tensor<string, []>("conv1d_45_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_45_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_45_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288192)))];
            tensor<fp16, [256]> p_bn_45_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_45_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293888)))];
            tensor<fp16, [1, 256, 801]> conv1d_45_cast_fp16 = conv(bias = p_bn_45_1_bias_to_fp16, dilations = conv1d_45_dilations_0, groups = conv1d_45_groups_0, pad = conv1d_45_pad_0, pad_type = conv1d_45_pad_type_0, strides = conv1d_45_strides_0, weight = p_bn_45_1_weight_to_fp16, x = mul_91_cast_fp16)[name = tensor<string, []>("conv1d_45_cast_fp16")];
            tensor<int32, [3]> slice_93_begin_0 = const()[name = tensor<string, []>("slice_93_begin_0"), val = tensor<int32, [3]>([0, 230, 0])];
            tensor<int32, [3]> slice_93_end_0 = const()[name = tensor<string, []>("slice_93_end_0"), val = tensor<int32, [3]>([1, 235, 801])];
            tensor<bool, [3]> slice_93_end_mask_0 = const()[name = tensor<string, []>("slice_93_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_93_cast_fp16 = slice_by_index(begin = slice_93_begin_0, end = slice_93_end_0, end_mask = slice_93_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_93_cast_fp16")];
            tensor<int32, [3]> slice_94_begin_0 = const()[name = tensor<string, []>("slice_94_begin_0"), val = tensor<int32, [3]>([0, 230, 0])];
            tensor<int32, [3]> slice_94_end_0 = const()[name = tensor<string, []>("slice_94_end_0"), val = tensor<int32, [3]>([1, 235, 801])];
            tensor<bool, [3]> slice_94_end_mask_0 = const()[name = tensor<string, []>("slice_94_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_94_cast_fp16 = slice_by_index(begin = slice_94_begin_0, end = slice_94_end_0, end_mask = slice_94_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_94_cast_fp16")];
            tensor<fp16, []> const_1022_promoted_to_fp16 = const()[name = tensor<string, []>("const_1022_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_139_cast_fp16 = pow(x = slice_93_cast_fp16, y = const_1022_promoted_to_fp16)[name = tensor<string, []>("pow_139_cast_fp16")];
            tensor<fp16, []> const_1023_promoted_to_fp16 = const()[name = tensor<string, []>("const_1023_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_140_cast_fp16 = pow(x = slice_94_cast_fp16, y = const_1023_promoted_to_fp16)[name = tensor<string, []>("pow_140_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_138_cast_fp16 = add(x = pow_139_cast_fp16, y = pow_140_cast_fp16)[name = tensor<string, []>("add_138_cast_fp16")];
            tensor<int32, [1]> sum_47_axes_0 = const()[name = tensor<string, []>("sum_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_47_keep_dims_0 = const()[name = tensor<string, []>("sum_47_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_47_cast_fp16 = reduce_sum(axes = sum_47_axes_0, keep_dims = sum_47_keep_dims_0, x = add_138_cast_fp16)[name = tensor<string, []>("sum_47_cast_fp16")];
            tensor<fp16, []> const_1026_to_fp16 = const()[name = tensor<string, []>("const_1026_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_139_cast_fp16 = add(x = sum_47_cast_fp16, y = const_1026_to_fp16)[name = tensor<string, []>("add_139_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_46_cast_fp16 = sqrt(x = add_139_cast_fp16)[name = tensor<string, []>("sqrt_46_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_92_cast_fp16 = real_div(x = slice_93_cast_fp16, y = sqrt_46_cast_fp16)[name = tensor<string, []>("div_92_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_93_cast_fp16 = real_div(x = slice_94_cast_fp16, y = sqrt_46_cast_fp16)[name = tensor<string, []>("div_93_cast_fp16")];
            tensor<fp32, []> log_46_epsilon_0 = const()[name = tensor<string, []>("log_46_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_46_cast_fp16 = log(epsilon = log_46_epsilon_0, x = sqrt_46_cast_fp16)[name = tensor<string, []>("log_46_cast_fp16")];
            tensor<int32, []> const_1027 = const()[name = tensor<string, []>("const_1027"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_46_interleave_0 = const()[name = tensor<string, []>("cat_46_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_46_cast_fp16 = concat(axis = const_1027, interleave = cat_46_interleave_0, values = (div_92_cast_fp16, div_93_cast_fp16, log_46_cast_fp16))[name = tensor<string, []>("cat_46_cast_fp16")];
            tensor<int32, [1]> unsqueeze_93_axes_0 = const()[name = tensor<string, []>("unsqueeze_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_93_cast_fp16 = expand_dims(axes = unsqueeze_93_axes_0, x = cat_46_cast_fp16)[name = tensor<string, []>("unsqueeze_93_cast_fp16")];
            tensor<fp16, []> const_1032_promoted_to_fp16 = const()[name = tensor<string, []>("const_1032_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_141_cast_fp16 = pow(x = unsqueeze_93_cast_fp16, y = const_1032_promoted_to_fp16)[name = tensor<string, []>("pow_141_cast_fp16")];
            tensor<int32, [1]> mean_46_axes_0 = const()[name = tensor<string, []>("mean_46_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_46_keep_dims_0 = const()[name = tensor<string, []>("mean_46_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_46_cast_fp16 = reduce_mean(axes = mean_46_axes_0, keep_dims = mean_46_keep_dims_0, x = pow_141_cast_fp16)[name = tensor<string, []>("mean_46_cast_fp16")];
            tensor<fp16, []> const_1035_to_fp16 = const()[name = tensor<string, []>("const_1035_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_140_cast_fp16 = add(x = mean_46_cast_fp16, y = const_1035_to_fp16)[name = tensor<string, []>("add_140_cast_fp16")];
            tensor<fp32, []> rsqrt_46_epsilon_0 = const()[name = tensor<string, []>("rsqrt_46_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_46_cast_fp16 = rsqrt(epsilon = rsqrt_46_epsilon_0, x = add_140_cast_fp16)[name = tensor<string, []>("rsqrt_46_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_92_cast_fp16 = mul(x = unsqueeze_93_cast_fp16, y = rsqrt_46_cast_fp16)[name = tensor<string, []>("mul_92_cast_fp16")];
            tensor<int32, [1]> squeeze_46_axes_0 = const()[name = tensor<string, []>("squeeze_46_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_46_cast_fp16 = squeeze(axes = squeeze_46_axes_0, x = mul_92_cast_fp16)[name = tensor<string, []>("squeeze_46_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_46_to_fp16 = const()[name = tensor<string, []>("view_46_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(294464)))];
            tensor<fp16, [1, 11, 801]> mul_93_cast_fp16 = mul(x = squeeze_46_cast_fp16, y = view_46_to_fp16)[name = tensor<string, []>("mul_93_cast_fp16")];
            tensor<string, []> conv1d_46_pad_type_0 = const()[name = tensor<string, []>("conv1d_46_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_46_strides_0 = const()[name = tensor<string, []>("conv1d_46_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_46_pad_0 = const()[name = tensor<string, []>("conv1d_46_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_46_dilations_0 = const()[name = tensor<string, []>("conv1d_46_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_46_groups_0 = const()[name = tensor<string, []>("conv1d_46_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_46_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_46_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(294592)))];
            tensor<fp16, [256]> p_bn_46_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_46_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300288)))];
            tensor<fp16, [1, 256, 801]> conv1d_46_cast_fp16 = conv(bias = p_bn_46_1_bias_to_fp16, dilations = conv1d_46_dilations_0, groups = conv1d_46_groups_0, pad = conv1d_46_pad_0, pad_type = conv1d_46_pad_type_0, strides = conv1d_46_strides_0, weight = p_bn_46_1_weight_to_fp16, x = mul_93_cast_fp16)[name = tensor<string, []>("conv1d_46_cast_fp16")];
            tensor<int32, [3]> slice_95_begin_0 = const()[name = tensor<string, []>("slice_95_begin_0"), val = tensor<int32, [3]>([0, 235, 0])];
            tensor<int32, [3]> slice_95_end_0 = const()[name = tensor<string, []>("slice_95_end_0"), val = tensor<int32, [3]>([1, 240, 801])];
            tensor<bool, [3]> slice_95_end_mask_0 = const()[name = tensor<string, []>("slice_95_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_95_cast_fp16 = slice_by_index(begin = slice_95_begin_0, end = slice_95_end_0, end_mask = slice_95_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_95_cast_fp16")];
            tensor<int32, [3]> slice_96_begin_0 = const()[name = tensor<string, []>("slice_96_begin_0"), val = tensor<int32, [3]>([0, 235, 0])];
            tensor<int32, [3]> slice_96_end_0 = const()[name = tensor<string, []>("slice_96_end_0"), val = tensor<int32, [3]>([1, 240, 801])];
            tensor<bool, [3]> slice_96_end_mask_0 = const()[name = tensor<string, []>("slice_96_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_96_cast_fp16 = slice_by_index(begin = slice_96_begin_0, end = slice_96_end_0, end_mask = slice_96_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_96_cast_fp16")];
            tensor<fp16, []> const_1044_promoted_to_fp16 = const()[name = tensor<string, []>("const_1044_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_142_cast_fp16 = pow(x = slice_95_cast_fp16, y = const_1044_promoted_to_fp16)[name = tensor<string, []>("pow_142_cast_fp16")];
            tensor<fp16, []> const_1045_promoted_to_fp16 = const()[name = tensor<string, []>("const_1045_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_143_cast_fp16 = pow(x = slice_96_cast_fp16, y = const_1045_promoted_to_fp16)[name = tensor<string, []>("pow_143_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_141_cast_fp16 = add(x = pow_142_cast_fp16, y = pow_143_cast_fp16)[name = tensor<string, []>("add_141_cast_fp16")];
            tensor<int32, [1]> sum_48_axes_0 = const()[name = tensor<string, []>("sum_48_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_48_keep_dims_0 = const()[name = tensor<string, []>("sum_48_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_48_cast_fp16 = reduce_sum(axes = sum_48_axes_0, keep_dims = sum_48_keep_dims_0, x = add_141_cast_fp16)[name = tensor<string, []>("sum_48_cast_fp16")];
            tensor<fp16, []> const_1048_to_fp16 = const()[name = tensor<string, []>("const_1048_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_142_cast_fp16 = add(x = sum_48_cast_fp16, y = const_1048_to_fp16)[name = tensor<string, []>("add_142_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_47_cast_fp16 = sqrt(x = add_142_cast_fp16)[name = tensor<string, []>("sqrt_47_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_94_cast_fp16 = real_div(x = slice_95_cast_fp16, y = sqrt_47_cast_fp16)[name = tensor<string, []>("div_94_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_95_cast_fp16 = real_div(x = slice_96_cast_fp16, y = sqrt_47_cast_fp16)[name = tensor<string, []>("div_95_cast_fp16")];
            tensor<fp32, []> log_47_epsilon_0 = const()[name = tensor<string, []>("log_47_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_47_cast_fp16 = log(epsilon = log_47_epsilon_0, x = sqrt_47_cast_fp16)[name = tensor<string, []>("log_47_cast_fp16")];
            tensor<int32, []> const_1049 = const()[name = tensor<string, []>("const_1049"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_47_interleave_0 = const()[name = tensor<string, []>("cat_47_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_47_cast_fp16 = concat(axis = const_1049, interleave = cat_47_interleave_0, values = (div_94_cast_fp16, div_95_cast_fp16, log_47_cast_fp16))[name = tensor<string, []>("cat_47_cast_fp16")];
            tensor<int32, [1]> unsqueeze_95_axes_0 = const()[name = tensor<string, []>("unsqueeze_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_95_cast_fp16 = expand_dims(axes = unsqueeze_95_axes_0, x = cat_47_cast_fp16)[name = tensor<string, []>("unsqueeze_95_cast_fp16")];
            tensor<fp16, []> const_1054_promoted_to_fp16 = const()[name = tensor<string, []>("const_1054_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_144_cast_fp16 = pow(x = unsqueeze_95_cast_fp16, y = const_1054_promoted_to_fp16)[name = tensor<string, []>("pow_144_cast_fp16")];
            tensor<int32, [1]> mean_47_axes_0 = const()[name = tensor<string, []>("mean_47_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_47_keep_dims_0 = const()[name = tensor<string, []>("mean_47_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_47_cast_fp16 = reduce_mean(axes = mean_47_axes_0, keep_dims = mean_47_keep_dims_0, x = pow_144_cast_fp16)[name = tensor<string, []>("mean_47_cast_fp16")];
            tensor<fp16, []> const_1057_to_fp16 = const()[name = tensor<string, []>("const_1057_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_143_cast_fp16 = add(x = mean_47_cast_fp16, y = const_1057_to_fp16)[name = tensor<string, []>("add_143_cast_fp16")];
            tensor<fp32, []> rsqrt_47_epsilon_0 = const()[name = tensor<string, []>("rsqrt_47_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_47_cast_fp16 = rsqrt(epsilon = rsqrt_47_epsilon_0, x = add_143_cast_fp16)[name = tensor<string, []>("rsqrt_47_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_94_cast_fp16 = mul(x = unsqueeze_95_cast_fp16, y = rsqrt_47_cast_fp16)[name = tensor<string, []>("mul_94_cast_fp16")];
            tensor<int32, [1]> squeeze_47_axes_0 = const()[name = tensor<string, []>("squeeze_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_47_cast_fp16 = squeeze(axes = squeeze_47_axes_0, x = mul_94_cast_fp16)[name = tensor<string, []>("squeeze_47_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_47_to_fp16 = const()[name = tensor<string, []>("view_47_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300864)))];
            tensor<fp16, [1, 11, 801]> mul_95_cast_fp16 = mul(x = squeeze_47_cast_fp16, y = view_47_to_fp16)[name = tensor<string, []>("mul_95_cast_fp16")];
            tensor<string, []> conv1d_47_pad_type_0 = const()[name = tensor<string, []>("conv1d_47_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_47_strides_0 = const()[name = tensor<string, []>("conv1d_47_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_47_pad_0 = const()[name = tensor<string, []>("conv1d_47_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_47_dilations_0 = const()[name = tensor<string, []>("conv1d_47_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_47_groups_0 = const()[name = tensor<string, []>("conv1d_47_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_47_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_47_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300992)))];
            tensor<fp16, [256]> p_bn_47_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_47_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306688)))];
            tensor<fp16, [1, 256, 801]> conv1d_47_cast_fp16 = conv(bias = p_bn_47_1_bias_to_fp16, dilations = conv1d_47_dilations_0, groups = conv1d_47_groups_0, pad = conv1d_47_pad_0, pad_type = conv1d_47_pad_type_0, strides = conv1d_47_strides_0, weight = p_bn_47_1_weight_to_fp16, x = mul_95_cast_fp16)[name = tensor<string, []>("conv1d_47_cast_fp16")];
            tensor<int32, [3]> slice_97_begin_0 = const()[name = tensor<string, []>("slice_97_begin_0"), val = tensor<int32, [3]>([0, 240, 0])];
            tensor<int32, [3]> slice_97_end_0 = const()[name = tensor<string, []>("slice_97_end_0"), val = tensor<int32, [3]>([1, 245, 801])];
            tensor<bool, [3]> slice_97_end_mask_0 = const()[name = tensor<string, []>("slice_97_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_97_cast_fp16 = slice_by_index(begin = slice_97_begin_0, end = slice_97_end_0, end_mask = slice_97_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_97_cast_fp16")];
            tensor<int32, [3]> slice_98_begin_0 = const()[name = tensor<string, []>("slice_98_begin_0"), val = tensor<int32, [3]>([0, 240, 0])];
            tensor<int32, [3]> slice_98_end_0 = const()[name = tensor<string, []>("slice_98_end_0"), val = tensor<int32, [3]>([1, 245, 801])];
            tensor<bool, [3]> slice_98_end_mask_0 = const()[name = tensor<string, []>("slice_98_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_98_cast_fp16 = slice_by_index(begin = slice_98_begin_0, end = slice_98_end_0, end_mask = slice_98_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_98_cast_fp16")];
            tensor<fp16, []> const_1066_promoted_to_fp16 = const()[name = tensor<string, []>("const_1066_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_145_cast_fp16 = pow(x = slice_97_cast_fp16, y = const_1066_promoted_to_fp16)[name = tensor<string, []>("pow_145_cast_fp16")];
            tensor<fp16, []> const_1067_promoted_to_fp16 = const()[name = tensor<string, []>("const_1067_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_146_cast_fp16 = pow(x = slice_98_cast_fp16, y = const_1067_promoted_to_fp16)[name = tensor<string, []>("pow_146_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_144_cast_fp16 = add(x = pow_145_cast_fp16, y = pow_146_cast_fp16)[name = tensor<string, []>("add_144_cast_fp16")];
            tensor<int32, [1]> sum_49_axes_0 = const()[name = tensor<string, []>("sum_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_49_keep_dims_0 = const()[name = tensor<string, []>("sum_49_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_49_cast_fp16 = reduce_sum(axes = sum_49_axes_0, keep_dims = sum_49_keep_dims_0, x = add_144_cast_fp16)[name = tensor<string, []>("sum_49_cast_fp16")];
            tensor<fp16, []> const_1070_to_fp16 = const()[name = tensor<string, []>("const_1070_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_145_cast_fp16 = add(x = sum_49_cast_fp16, y = const_1070_to_fp16)[name = tensor<string, []>("add_145_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_48_cast_fp16 = sqrt(x = add_145_cast_fp16)[name = tensor<string, []>("sqrt_48_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_96_cast_fp16 = real_div(x = slice_97_cast_fp16, y = sqrt_48_cast_fp16)[name = tensor<string, []>("div_96_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_97_cast_fp16 = real_div(x = slice_98_cast_fp16, y = sqrt_48_cast_fp16)[name = tensor<string, []>("div_97_cast_fp16")];
            tensor<fp32, []> log_48_epsilon_0 = const()[name = tensor<string, []>("log_48_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_48_cast_fp16 = log(epsilon = log_48_epsilon_0, x = sqrt_48_cast_fp16)[name = tensor<string, []>("log_48_cast_fp16")];
            tensor<int32, []> const_1071 = const()[name = tensor<string, []>("const_1071"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_48_interleave_0 = const()[name = tensor<string, []>("cat_48_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_48_cast_fp16 = concat(axis = const_1071, interleave = cat_48_interleave_0, values = (div_96_cast_fp16, div_97_cast_fp16, log_48_cast_fp16))[name = tensor<string, []>("cat_48_cast_fp16")];
            tensor<int32, [1]> unsqueeze_97_axes_0 = const()[name = tensor<string, []>("unsqueeze_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_97_cast_fp16 = expand_dims(axes = unsqueeze_97_axes_0, x = cat_48_cast_fp16)[name = tensor<string, []>("unsqueeze_97_cast_fp16")];
            tensor<fp16, []> const_1076_promoted_to_fp16 = const()[name = tensor<string, []>("const_1076_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_147_cast_fp16 = pow(x = unsqueeze_97_cast_fp16, y = const_1076_promoted_to_fp16)[name = tensor<string, []>("pow_147_cast_fp16")];
            tensor<int32, [1]> mean_48_axes_0 = const()[name = tensor<string, []>("mean_48_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_48_keep_dims_0 = const()[name = tensor<string, []>("mean_48_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_48_cast_fp16 = reduce_mean(axes = mean_48_axes_0, keep_dims = mean_48_keep_dims_0, x = pow_147_cast_fp16)[name = tensor<string, []>("mean_48_cast_fp16")];
            tensor<fp16, []> const_1079_to_fp16 = const()[name = tensor<string, []>("const_1079_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_146_cast_fp16 = add(x = mean_48_cast_fp16, y = const_1079_to_fp16)[name = tensor<string, []>("add_146_cast_fp16")];
            tensor<fp32, []> rsqrt_48_epsilon_0 = const()[name = tensor<string, []>("rsqrt_48_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_48_cast_fp16 = rsqrt(epsilon = rsqrt_48_epsilon_0, x = add_146_cast_fp16)[name = tensor<string, []>("rsqrt_48_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_96_cast_fp16 = mul(x = unsqueeze_97_cast_fp16, y = rsqrt_48_cast_fp16)[name = tensor<string, []>("mul_96_cast_fp16")];
            tensor<int32, [1]> squeeze_48_axes_0 = const()[name = tensor<string, []>("squeeze_48_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_48_cast_fp16 = squeeze(axes = squeeze_48_axes_0, x = mul_96_cast_fp16)[name = tensor<string, []>("squeeze_48_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_48_to_fp16 = const()[name = tensor<string, []>("view_48_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307264)))];
            tensor<fp16, [1, 11, 801]> mul_97_cast_fp16 = mul(x = squeeze_48_cast_fp16, y = view_48_to_fp16)[name = tensor<string, []>("mul_97_cast_fp16")];
            tensor<string, []> conv1d_48_pad_type_0 = const()[name = tensor<string, []>("conv1d_48_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_48_strides_0 = const()[name = tensor<string, []>("conv1d_48_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_48_pad_0 = const()[name = tensor<string, []>("conv1d_48_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_48_dilations_0 = const()[name = tensor<string, []>("conv1d_48_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_48_groups_0 = const()[name = tensor<string, []>("conv1d_48_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_48_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_48_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307392)))];
            tensor<fp16, [256]> p_bn_48_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_48_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313088)))];
            tensor<fp16, [1, 256, 801]> conv1d_48_cast_fp16 = conv(bias = p_bn_48_1_bias_to_fp16, dilations = conv1d_48_dilations_0, groups = conv1d_48_groups_0, pad = conv1d_48_pad_0, pad_type = conv1d_48_pad_type_0, strides = conv1d_48_strides_0, weight = p_bn_48_1_weight_to_fp16, x = mul_97_cast_fp16)[name = tensor<string, []>("conv1d_48_cast_fp16")];
            tensor<int32, [3]> slice_99_begin_0 = const()[name = tensor<string, []>("slice_99_begin_0"), val = tensor<int32, [3]>([0, 245, 0])];
            tensor<int32, [3]> slice_99_end_0 = const()[name = tensor<string, []>("slice_99_end_0"), val = tensor<int32, [3]>([1, 250, 801])];
            tensor<bool, [3]> slice_99_end_mask_0 = const()[name = tensor<string, []>("slice_99_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_99_cast_fp16 = slice_by_index(begin = slice_99_begin_0, end = slice_99_end_0, end_mask = slice_99_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_99_cast_fp16")];
            tensor<int32, [3]> slice_100_begin_0 = const()[name = tensor<string, []>("slice_100_begin_0"), val = tensor<int32, [3]>([0, 245, 0])];
            tensor<int32, [3]> slice_100_end_0 = const()[name = tensor<string, []>("slice_100_end_0"), val = tensor<int32, [3]>([1, 250, 801])];
            tensor<bool, [3]> slice_100_end_mask_0 = const()[name = tensor<string, []>("slice_100_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_100_cast_fp16 = slice_by_index(begin = slice_100_begin_0, end = slice_100_end_0, end_mask = slice_100_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_100_cast_fp16")];
            tensor<fp16, []> const_1088_promoted_to_fp16 = const()[name = tensor<string, []>("const_1088_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_148_cast_fp16 = pow(x = slice_99_cast_fp16, y = const_1088_promoted_to_fp16)[name = tensor<string, []>("pow_148_cast_fp16")];
            tensor<fp16, []> const_1089_promoted_to_fp16 = const()[name = tensor<string, []>("const_1089_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_149_cast_fp16 = pow(x = slice_100_cast_fp16, y = const_1089_promoted_to_fp16)[name = tensor<string, []>("pow_149_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_147_cast_fp16 = add(x = pow_148_cast_fp16, y = pow_149_cast_fp16)[name = tensor<string, []>("add_147_cast_fp16")];
            tensor<int32, [1]> sum_50_axes_0 = const()[name = tensor<string, []>("sum_50_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_50_keep_dims_0 = const()[name = tensor<string, []>("sum_50_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_50_cast_fp16 = reduce_sum(axes = sum_50_axes_0, keep_dims = sum_50_keep_dims_0, x = add_147_cast_fp16)[name = tensor<string, []>("sum_50_cast_fp16")];
            tensor<fp16, []> const_1092_to_fp16 = const()[name = tensor<string, []>("const_1092_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_148_cast_fp16 = add(x = sum_50_cast_fp16, y = const_1092_to_fp16)[name = tensor<string, []>("add_148_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_49_cast_fp16 = sqrt(x = add_148_cast_fp16)[name = tensor<string, []>("sqrt_49_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_98_cast_fp16 = real_div(x = slice_99_cast_fp16, y = sqrt_49_cast_fp16)[name = tensor<string, []>("div_98_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_99_cast_fp16 = real_div(x = slice_100_cast_fp16, y = sqrt_49_cast_fp16)[name = tensor<string, []>("div_99_cast_fp16")];
            tensor<fp32, []> log_49_epsilon_0 = const()[name = tensor<string, []>("log_49_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_49_cast_fp16 = log(epsilon = log_49_epsilon_0, x = sqrt_49_cast_fp16)[name = tensor<string, []>("log_49_cast_fp16")];
            tensor<int32, []> const_1093 = const()[name = tensor<string, []>("const_1093"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_49_interleave_0 = const()[name = tensor<string, []>("cat_49_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_49_cast_fp16 = concat(axis = const_1093, interleave = cat_49_interleave_0, values = (div_98_cast_fp16, div_99_cast_fp16, log_49_cast_fp16))[name = tensor<string, []>("cat_49_cast_fp16")];
            tensor<int32, [1]> unsqueeze_99_axes_0 = const()[name = tensor<string, []>("unsqueeze_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_99_cast_fp16 = expand_dims(axes = unsqueeze_99_axes_0, x = cat_49_cast_fp16)[name = tensor<string, []>("unsqueeze_99_cast_fp16")];
            tensor<fp16, []> const_1098_promoted_to_fp16 = const()[name = tensor<string, []>("const_1098_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_150_cast_fp16 = pow(x = unsqueeze_99_cast_fp16, y = const_1098_promoted_to_fp16)[name = tensor<string, []>("pow_150_cast_fp16")];
            tensor<int32, [1]> mean_49_axes_0 = const()[name = tensor<string, []>("mean_49_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_49_keep_dims_0 = const()[name = tensor<string, []>("mean_49_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_49_cast_fp16 = reduce_mean(axes = mean_49_axes_0, keep_dims = mean_49_keep_dims_0, x = pow_150_cast_fp16)[name = tensor<string, []>("mean_49_cast_fp16")];
            tensor<fp16, []> const_1101_to_fp16 = const()[name = tensor<string, []>("const_1101_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_149_cast_fp16 = add(x = mean_49_cast_fp16, y = const_1101_to_fp16)[name = tensor<string, []>("add_149_cast_fp16")];
            tensor<fp32, []> rsqrt_49_epsilon_0 = const()[name = tensor<string, []>("rsqrt_49_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_49_cast_fp16 = rsqrt(epsilon = rsqrt_49_epsilon_0, x = add_149_cast_fp16)[name = tensor<string, []>("rsqrt_49_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_98_cast_fp16 = mul(x = unsqueeze_99_cast_fp16, y = rsqrt_49_cast_fp16)[name = tensor<string, []>("mul_98_cast_fp16")];
            tensor<int32, [1]> squeeze_49_axes_0 = const()[name = tensor<string, []>("squeeze_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_49_cast_fp16 = squeeze(axes = squeeze_49_axes_0, x = mul_98_cast_fp16)[name = tensor<string, []>("squeeze_49_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_49_to_fp16 = const()[name = tensor<string, []>("view_49_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313664)))];
            tensor<fp16, [1, 11, 801]> mul_99_cast_fp16 = mul(x = squeeze_49_cast_fp16, y = view_49_to_fp16)[name = tensor<string, []>("mul_99_cast_fp16")];
            tensor<string, []> conv1d_49_pad_type_0 = const()[name = tensor<string, []>("conv1d_49_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_49_strides_0 = const()[name = tensor<string, []>("conv1d_49_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_49_pad_0 = const()[name = tensor<string, []>("conv1d_49_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_49_dilations_0 = const()[name = tensor<string, []>("conv1d_49_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_49_groups_0 = const()[name = tensor<string, []>("conv1d_49_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_49_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_49_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313792)))];
            tensor<fp16, [256]> p_bn_49_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_49_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(319488)))];
            tensor<fp16, [1, 256, 801]> conv1d_49_cast_fp16 = conv(bias = p_bn_49_1_bias_to_fp16, dilations = conv1d_49_dilations_0, groups = conv1d_49_groups_0, pad = conv1d_49_pad_0, pad_type = conv1d_49_pad_type_0, strides = conv1d_49_strides_0, weight = p_bn_49_1_weight_to_fp16, x = mul_99_cast_fp16)[name = tensor<string, []>("conv1d_49_cast_fp16")];
            tensor<int32, [3]> slice_101_begin_0 = const()[name = tensor<string, []>("slice_101_begin_0"), val = tensor<int32, [3]>([0, 250, 0])];
            tensor<int32, [3]> slice_101_end_0 = const()[name = tensor<string, []>("slice_101_end_0"), val = tensor<int32, [3]>([1, 255, 801])];
            tensor<bool, [3]> slice_101_end_mask_0 = const()[name = tensor<string, []>("slice_101_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_101_cast_fp16 = slice_by_index(begin = slice_101_begin_0, end = slice_101_end_0, end_mask = slice_101_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_101_cast_fp16")];
            tensor<int32, [3]> slice_102_begin_0 = const()[name = tensor<string, []>("slice_102_begin_0"), val = tensor<int32, [3]>([0, 250, 0])];
            tensor<int32, [3]> slice_102_end_0 = const()[name = tensor<string, []>("slice_102_end_0"), val = tensor<int32, [3]>([1, 255, 801])];
            tensor<bool, [3]> slice_102_end_mask_0 = const()[name = tensor<string, []>("slice_102_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_102_cast_fp16 = slice_by_index(begin = slice_102_begin_0, end = slice_102_end_0, end_mask = slice_102_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_102_cast_fp16")];
            tensor<fp16, []> const_1110_promoted_to_fp16 = const()[name = tensor<string, []>("const_1110_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_151_cast_fp16 = pow(x = slice_101_cast_fp16, y = const_1110_promoted_to_fp16)[name = tensor<string, []>("pow_151_cast_fp16")];
            tensor<fp16, []> const_1111_promoted_to_fp16 = const()[name = tensor<string, []>("const_1111_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_152_cast_fp16 = pow(x = slice_102_cast_fp16, y = const_1111_promoted_to_fp16)[name = tensor<string, []>("pow_152_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_150_cast_fp16 = add(x = pow_151_cast_fp16, y = pow_152_cast_fp16)[name = tensor<string, []>("add_150_cast_fp16")];
            tensor<int32, [1]> sum_51_axes_0 = const()[name = tensor<string, []>("sum_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_51_keep_dims_0 = const()[name = tensor<string, []>("sum_51_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_51_cast_fp16 = reduce_sum(axes = sum_51_axes_0, keep_dims = sum_51_keep_dims_0, x = add_150_cast_fp16)[name = tensor<string, []>("sum_51_cast_fp16")];
            tensor<fp16, []> const_1114_to_fp16 = const()[name = tensor<string, []>("const_1114_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_151_cast_fp16 = add(x = sum_51_cast_fp16, y = const_1114_to_fp16)[name = tensor<string, []>("add_151_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_50_cast_fp16 = sqrt(x = add_151_cast_fp16)[name = tensor<string, []>("sqrt_50_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_100_cast_fp16 = real_div(x = slice_101_cast_fp16, y = sqrt_50_cast_fp16)[name = tensor<string, []>("div_100_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_101_cast_fp16 = real_div(x = slice_102_cast_fp16, y = sqrt_50_cast_fp16)[name = tensor<string, []>("div_101_cast_fp16")];
            tensor<fp32, []> log_50_epsilon_0 = const()[name = tensor<string, []>("log_50_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_50_cast_fp16 = log(epsilon = log_50_epsilon_0, x = sqrt_50_cast_fp16)[name = tensor<string, []>("log_50_cast_fp16")];
            tensor<int32, []> const_1115 = const()[name = tensor<string, []>("const_1115"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_50_interleave_0 = const()[name = tensor<string, []>("cat_50_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_50_cast_fp16 = concat(axis = const_1115, interleave = cat_50_interleave_0, values = (div_100_cast_fp16, div_101_cast_fp16, log_50_cast_fp16))[name = tensor<string, []>("cat_50_cast_fp16")];
            tensor<int32, [1]> unsqueeze_101_axes_0 = const()[name = tensor<string, []>("unsqueeze_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_101_cast_fp16 = expand_dims(axes = unsqueeze_101_axes_0, x = cat_50_cast_fp16)[name = tensor<string, []>("unsqueeze_101_cast_fp16")];
            tensor<fp16, []> const_1120_promoted_to_fp16 = const()[name = tensor<string, []>("const_1120_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_153_cast_fp16 = pow(x = unsqueeze_101_cast_fp16, y = const_1120_promoted_to_fp16)[name = tensor<string, []>("pow_153_cast_fp16")];
            tensor<int32, [1]> mean_50_axes_0 = const()[name = tensor<string, []>("mean_50_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_50_keep_dims_0 = const()[name = tensor<string, []>("mean_50_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_50_cast_fp16 = reduce_mean(axes = mean_50_axes_0, keep_dims = mean_50_keep_dims_0, x = pow_153_cast_fp16)[name = tensor<string, []>("mean_50_cast_fp16")];
            tensor<fp16, []> const_1123_to_fp16 = const()[name = tensor<string, []>("const_1123_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_152_cast_fp16 = add(x = mean_50_cast_fp16, y = const_1123_to_fp16)[name = tensor<string, []>("add_152_cast_fp16")];
            tensor<fp32, []> rsqrt_50_epsilon_0 = const()[name = tensor<string, []>("rsqrt_50_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_50_cast_fp16 = rsqrt(epsilon = rsqrt_50_epsilon_0, x = add_152_cast_fp16)[name = tensor<string, []>("rsqrt_50_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_100_cast_fp16 = mul(x = unsqueeze_101_cast_fp16, y = rsqrt_50_cast_fp16)[name = tensor<string, []>("mul_100_cast_fp16")];
            tensor<int32, [1]> squeeze_50_axes_0 = const()[name = tensor<string, []>("squeeze_50_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_50_cast_fp16 = squeeze(axes = squeeze_50_axes_0, x = mul_100_cast_fp16)[name = tensor<string, []>("squeeze_50_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_50_to_fp16 = const()[name = tensor<string, []>("view_50_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320064)))];
            tensor<fp16, [1, 11, 801]> mul_101_cast_fp16 = mul(x = squeeze_50_cast_fp16, y = view_50_to_fp16)[name = tensor<string, []>("mul_101_cast_fp16")];
            tensor<string, []> conv1d_50_pad_type_0 = const()[name = tensor<string, []>("conv1d_50_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_50_strides_0 = const()[name = tensor<string, []>("conv1d_50_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_50_pad_0 = const()[name = tensor<string, []>("conv1d_50_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_50_dilations_0 = const()[name = tensor<string, []>("conv1d_50_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_50_groups_0 = const()[name = tensor<string, []>("conv1d_50_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_50_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_50_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320192)))];
            tensor<fp16, [256]> p_bn_50_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_50_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325888)))];
            tensor<fp16, [1, 256, 801]> conv1d_50_cast_fp16 = conv(bias = p_bn_50_1_bias_to_fp16, dilations = conv1d_50_dilations_0, groups = conv1d_50_groups_0, pad = conv1d_50_pad_0, pad_type = conv1d_50_pad_type_0, strides = conv1d_50_strides_0, weight = p_bn_50_1_weight_to_fp16, x = mul_101_cast_fp16)[name = tensor<string, []>("conv1d_50_cast_fp16")];
            tensor<int32, [3]> slice_103_begin_0 = const()[name = tensor<string, []>("slice_103_begin_0"), val = tensor<int32, [3]>([0, 255, 0])];
            tensor<int32, [3]> slice_103_end_0 = const()[name = tensor<string, []>("slice_103_end_0"), val = tensor<int32, [3]>([1, 260, 801])];
            tensor<bool, [3]> slice_103_end_mask_0 = const()[name = tensor<string, []>("slice_103_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_103_cast_fp16 = slice_by_index(begin = slice_103_begin_0, end = slice_103_end_0, end_mask = slice_103_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_103_cast_fp16")];
            tensor<int32, [3]> slice_104_begin_0 = const()[name = tensor<string, []>("slice_104_begin_0"), val = tensor<int32, [3]>([0, 255, 0])];
            tensor<int32, [3]> slice_104_end_0 = const()[name = tensor<string, []>("slice_104_end_0"), val = tensor<int32, [3]>([1, 260, 801])];
            tensor<bool, [3]> slice_104_end_mask_0 = const()[name = tensor<string, []>("slice_104_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_104_cast_fp16 = slice_by_index(begin = slice_104_begin_0, end = slice_104_end_0, end_mask = slice_104_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_104_cast_fp16")];
            tensor<fp16, []> const_1132_promoted_to_fp16 = const()[name = tensor<string, []>("const_1132_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_154_cast_fp16 = pow(x = slice_103_cast_fp16, y = const_1132_promoted_to_fp16)[name = tensor<string, []>("pow_154_cast_fp16")];
            tensor<fp16, []> const_1133_promoted_to_fp16 = const()[name = tensor<string, []>("const_1133_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_155_cast_fp16 = pow(x = slice_104_cast_fp16, y = const_1133_promoted_to_fp16)[name = tensor<string, []>("pow_155_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_153_cast_fp16 = add(x = pow_154_cast_fp16, y = pow_155_cast_fp16)[name = tensor<string, []>("add_153_cast_fp16")];
            tensor<int32, [1]> sum_52_axes_0 = const()[name = tensor<string, []>("sum_52_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_52_keep_dims_0 = const()[name = tensor<string, []>("sum_52_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_52_cast_fp16 = reduce_sum(axes = sum_52_axes_0, keep_dims = sum_52_keep_dims_0, x = add_153_cast_fp16)[name = tensor<string, []>("sum_52_cast_fp16")];
            tensor<fp16, []> const_1136_to_fp16 = const()[name = tensor<string, []>("const_1136_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_154_cast_fp16 = add(x = sum_52_cast_fp16, y = const_1136_to_fp16)[name = tensor<string, []>("add_154_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_51_cast_fp16 = sqrt(x = add_154_cast_fp16)[name = tensor<string, []>("sqrt_51_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_102_cast_fp16 = real_div(x = slice_103_cast_fp16, y = sqrt_51_cast_fp16)[name = tensor<string, []>("div_102_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_103_cast_fp16 = real_div(x = slice_104_cast_fp16, y = sqrt_51_cast_fp16)[name = tensor<string, []>("div_103_cast_fp16")];
            tensor<fp32, []> log_51_epsilon_0 = const()[name = tensor<string, []>("log_51_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_51_cast_fp16 = log(epsilon = log_51_epsilon_0, x = sqrt_51_cast_fp16)[name = tensor<string, []>("log_51_cast_fp16")];
            tensor<int32, []> const_1137 = const()[name = tensor<string, []>("const_1137"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_51_interleave_0 = const()[name = tensor<string, []>("cat_51_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_51_cast_fp16 = concat(axis = const_1137, interleave = cat_51_interleave_0, values = (div_102_cast_fp16, div_103_cast_fp16, log_51_cast_fp16))[name = tensor<string, []>("cat_51_cast_fp16")];
            tensor<int32, [1]> unsqueeze_103_axes_0 = const()[name = tensor<string, []>("unsqueeze_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_103_cast_fp16 = expand_dims(axes = unsqueeze_103_axes_0, x = cat_51_cast_fp16)[name = tensor<string, []>("unsqueeze_103_cast_fp16")];
            tensor<fp16, []> const_1142_promoted_to_fp16 = const()[name = tensor<string, []>("const_1142_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_156_cast_fp16 = pow(x = unsqueeze_103_cast_fp16, y = const_1142_promoted_to_fp16)[name = tensor<string, []>("pow_156_cast_fp16")];
            tensor<int32, [1]> mean_51_axes_0 = const()[name = tensor<string, []>("mean_51_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_51_keep_dims_0 = const()[name = tensor<string, []>("mean_51_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_51_cast_fp16 = reduce_mean(axes = mean_51_axes_0, keep_dims = mean_51_keep_dims_0, x = pow_156_cast_fp16)[name = tensor<string, []>("mean_51_cast_fp16")];
            tensor<fp16, []> const_1145_to_fp16 = const()[name = tensor<string, []>("const_1145_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_155_cast_fp16 = add(x = mean_51_cast_fp16, y = const_1145_to_fp16)[name = tensor<string, []>("add_155_cast_fp16")];
            tensor<fp32, []> rsqrt_51_epsilon_0 = const()[name = tensor<string, []>("rsqrt_51_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_51_cast_fp16 = rsqrt(epsilon = rsqrt_51_epsilon_0, x = add_155_cast_fp16)[name = tensor<string, []>("rsqrt_51_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_102_cast_fp16 = mul(x = unsqueeze_103_cast_fp16, y = rsqrt_51_cast_fp16)[name = tensor<string, []>("mul_102_cast_fp16")];
            tensor<int32, [1]> squeeze_51_axes_0 = const()[name = tensor<string, []>("squeeze_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_51_cast_fp16 = squeeze(axes = squeeze_51_axes_0, x = mul_102_cast_fp16)[name = tensor<string, []>("squeeze_51_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_51_to_fp16 = const()[name = tensor<string, []>("view_51_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326464)))];
            tensor<fp16, [1, 11, 801]> mul_103_cast_fp16 = mul(x = squeeze_51_cast_fp16, y = view_51_to_fp16)[name = tensor<string, []>("mul_103_cast_fp16")];
            tensor<string, []> conv1d_51_pad_type_0 = const()[name = tensor<string, []>("conv1d_51_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_51_strides_0 = const()[name = tensor<string, []>("conv1d_51_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_51_pad_0 = const()[name = tensor<string, []>("conv1d_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_51_dilations_0 = const()[name = tensor<string, []>("conv1d_51_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_51_groups_0 = const()[name = tensor<string, []>("conv1d_51_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_51_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_51_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326592)))];
            tensor<fp16, [256]> p_bn_51_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_51_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332288)))];
            tensor<fp16, [1, 256, 801]> conv1d_51_cast_fp16 = conv(bias = p_bn_51_1_bias_to_fp16, dilations = conv1d_51_dilations_0, groups = conv1d_51_groups_0, pad = conv1d_51_pad_0, pad_type = conv1d_51_pad_type_0, strides = conv1d_51_strides_0, weight = p_bn_51_1_weight_to_fp16, x = mul_103_cast_fp16)[name = tensor<string, []>("conv1d_51_cast_fp16")];
            tensor<int32, [3]> slice_105_begin_0 = const()[name = tensor<string, []>("slice_105_begin_0"), val = tensor<int32, [3]>([0, 260, 0])];
            tensor<int32, [3]> slice_105_end_0 = const()[name = tensor<string, []>("slice_105_end_0"), val = tensor<int32, [3]>([1, 265, 801])];
            tensor<bool, [3]> slice_105_end_mask_0 = const()[name = tensor<string, []>("slice_105_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_105_cast_fp16 = slice_by_index(begin = slice_105_begin_0, end = slice_105_end_0, end_mask = slice_105_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_105_cast_fp16")];
            tensor<int32, [3]> slice_106_begin_0 = const()[name = tensor<string, []>("slice_106_begin_0"), val = tensor<int32, [3]>([0, 260, 0])];
            tensor<int32, [3]> slice_106_end_0 = const()[name = tensor<string, []>("slice_106_end_0"), val = tensor<int32, [3]>([1, 265, 801])];
            tensor<bool, [3]> slice_106_end_mask_0 = const()[name = tensor<string, []>("slice_106_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_106_cast_fp16 = slice_by_index(begin = slice_106_begin_0, end = slice_106_end_0, end_mask = slice_106_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_106_cast_fp16")];
            tensor<fp16, []> const_1154_promoted_to_fp16 = const()[name = tensor<string, []>("const_1154_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_157_cast_fp16 = pow(x = slice_105_cast_fp16, y = const_1154_promoted_to_fp16)[name = tensor<string, []>("pow_157_cast_fp16")];
            tensor<fp16, []> const_1155_promoted_to_fp16 = const()[name = tensor<string, []>("const_1155_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_158_cast_fp16 = pow(x = slice_106_cast_fp16, y = const_1155_promoted_to_fp16)[name = tensor<string, []>("pow_158_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_156_cast_fp16 = add(x = pow_157_cast_fp16, y = pow_158_cast_fp16)[name = tensor<string, []>("add_156_cast_fp16")];
            tensor<int32, [1]> sum_53_axes_0 = const()[name = tensor<string, []>("sum_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_53_keep_dims_0 = const()[name = tensor<string, []>("sum_53_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_53_cast_fp16 = reduce_sum(axes = sum_53_axes_0, keep_dims = sum_53_keep_dims_0, x = add_156_cast_fp16)[name = tensor<string, []>("sum_53_cast_fp16")];
            tensor<fp16, []> const_1158_to_fp16 = const()[name = tensor<string, []>("const_1158_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_157_cast_fp16 = add(x = sum_53_cast_fp16, y = const_1158_to_fp16)[name = tensor<string, []>("add_157_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_52_cast_fp16 = sqrt(x = add_157_cast_fp16)[name = tensor<string, []>("sqrt_52_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_104_cast_fp16 = real_div(x = slice_105_cast_fp16, y = sqrt_52_cast_fp16)[name = tensor<string, []>("div_104_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_105_cast_fp16 = real_div(x = slice_106_cast_fp16, y = sqrt_52_cast_fp16)[name = tensor<string, []>("div_105_cast_fp16")];
            tensor<fp32, []> log_52_epsilon_0 = const()[name = tensor<string, []>("log_52_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_52_cast_fp16 = log(epsilon = log_52_epsilon_0, x = sqrt_52_cast_fp16)[name = tensor<string, []>("log_52_cast_fp16")];
            tensor<int32, []> const_1159 = const()[name = tensor<string, []>("const_1159"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_52_interleave_0 = const()[name = tensor<string, []>("cat_52_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_52_cast_fp16 = concat(axis = const_1159, interleave = cat_52_interleave_0, values = (div_104_cast_fp16, div_105_cast_fp16, log_52_cast_fp16))[name = tensor<string, []>("cat_52_cast_fp16")];
            tensor<int32, [1]> unsqueeze_105_axes_0 = const()[name = tensor<string, []>("unsqueeze_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_105_cast_fp16 = expand_dims(axes = unsqueeze_105_axes_0, x = cat_52_cast_fp16)[name = tensor<string, []>("unsqueeze_105_cast_fp16")];
            tensor<fp16, []> const_1164_promoted_to_fp16 = const()[name = tensor<string, []>("const_1164_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_159_cast_fp16 = pow(x = unsqueeze_105_cast_fp16, y = const_1164_promoted_to_fp16)[name = tensor<string, []>("pow_159_cast_fp16")];
            tensor<int32, [1]> mean_52_axes_0 = const()[name = tensor<string, []>("mean_52_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_52_keep_dims_0 = const()[name = tensor<string, []>("mean_52_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_52_cast_fp16 = reduce_mean(axes = mean_52_axes_0, keep_dims = mean_52_keep_dims_0, x = pow_159_cast_fp16)[name = tensor<string, []>("mean_52_cast_fp16")];
            tensor<fp16, []> const_1167_to_fp16 = const()[name = tensor<string, []>("const_1167_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_158_cast_fp16 = add(x = mean_52_cast_fp16, y = const_1167_to_fp16)[name = tensor<string, []>("add_158_cast_fp16")];
            tensor<fp32, []> rsqrt_52_epsilon_0 = const()[name = tensor<string, []>("rsqrt_52_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_52_cast_fp16 = rsqrt(epsilon = rsqrt_52_epsilon_0, x = add_158_cast_fp16)[name = tensor<string, []>("rsqrt_52_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_104_cast_fp16 = mul(x = unsqueeze_105_cast_fp16, y = rsqrt_52_cast_fp16)[name = tensor<string, []>("mul_104_cast_fp16")];
            tensor<int32, [1]> squeeze_52_axes_0 = const()[name = tensor<string, []>("squeeze_52_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_52_cast_fp16 = squeeze(axes = squeeze_52_axes_0, x = mul_104_cast_fp16)[name = tensor<string, []>("squeeze_52_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_52_to_fp16 = const()[name = tensor<string, []>("view_52_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332864)))];
            tensor<fp16, [1, 11, 801]> mul_105_cast_fp16 = mul(x = squeeze_52_cast_fp16, y = view_52_to_fp16)[name = tensor<string, []>("mul_105_cast_fp16")];
            tensor<string, []> conv1d_52_pad_type_0 = const()[name = tensor<string, []>("conv1d_52_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_52_strides_0 = const()[name = tensor<string, []>("conv1d_52_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_52_pad_0 = const()[name = tensor<string, []>("conv1d_52_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_52_dilations_0 = const()[name = tensor<string, []>("conv1d_52_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_52_groups_0 = const()[name = tensor<string, []>("conv1d_52_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_52_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_52_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332992)))];
            tensor<fp16, [256]> p_bn_52_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_52_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338688)))];
            tensor<fp16, [1, 256, 801]> conv1d_52_cast_fp16 = conv(bias = p_bn_52_1_bias_to_fp16, dilations = conv1d_52_dilations_0, groups = conv1d_52_groups_0, pad = conv1d_52_pad_0, pad_type = conv1d_52_pad_type_0, strides = conv1d_52_strides_0, weight = p_bn_52_1_weight_to_fp16, x = mul_105_cast_fp16)[name = tensor<string, []>("conv1d_52_cast_fp16")];
            tensor<int32, [3]> slice_107_begin_0 = const()[name = tensor<string, []>("slice_107_begin_0"), val = tensor<int32, [3]>([0, 265, 0])];
            tensor<int32, [3]> slice_107_end_0 = const()[name = tensor<string, []>("slice_107_end_0"), val = tensor<int32, [3]>([1, 270, 801])];
            tensor<bool, [3]> slice_107_end_mask_0 = const()[name = tensor<string, []>("slice_107_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_107_cast_fp16 = slice_by_index(begin = slice_107_begin_0, end = slice_107_end_0, end_mask = slice_107_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_107_cast_fp16")];
            tensor<int32, [3]> slice_108_begin_0 = const()[name = tensor<string, []>("slice_108_begin_0"), val = tensor<int32, [3]>([0, 265, 0])];
            tensor<int32, [3]> slice_108_end_0 = const()[name = tensor<string, []>("slice_108_end_0"), val = tensor<int32, [3]>([1, 270, 801])];
            tensor<bool, [3]> slice_108_end_mask_0 = const()[name = tensor<string, []>("slice_108_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_108_cast_fp16 = slice_by_index(begin = slice_108_begin_0, end = slice_108_end_0, end_mask = slice_108_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_108_cast_fp16")];
            tensor<fp16, []> const_1176_promoted_to_fp16 = const()[name = tensor<string, []>("const_1176_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_160_cast_fp16 = pow(x = slice_107_cast_fp16, y = const_1176_promoted_to_fp16)[name = tensor<string, []>("pow_160_cast_fp16")];
            tensor<fp16, []> const_1177_promoted_to_fp16 = const()[name = tensor<string, []>("const_1177_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_161_cast_fp16 = pow(x = slice_108_cast_fp16, y = const_1177_promoted_to_fp16)[name = tensor<string, []>("pow_161_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_159_cast_fp16 = add(x = pow_160_cast_fp16, y = pow_161_cast_fp16)[name = tensor<string, []>("add_159_cast_fp16")];
            tensor<int32, [1]> sum_54_axes_0 = const()[name = tensor<string, []>("sum_54_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_54_keep_dims_0 = const()[name = tensor<string, []>("sum_54_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_54_cast_fp16 = reduce_sum(axes = sum_54_axes_0, keep_dims = sum_54_keep_dims_0, x = add_159_cast_fp16)[name = tensor<string, []>("sum_54_cast_fp16")];
            tensor<fp16, []> const_1180_to_fp16 = const()[name = tensor<string, []>("const_1180_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_160_cast_fp16 = add(x = sum_54_cast_fp16, y = const_1180_to_fp16)[name = tensor<string, []>("add_160_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_53_cast_fp16 = sqrt(x = add_160_cast_fp16)[name = tensor<string, []>("sqrt_53_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_106_cast_fp16 = real_div(x = slice_107_cast_fp16, y = sqrt_53_cast_fp16)[name = tensor<string, []>("div_106_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_107_cast_fp16 = real_div(x = slice_108_cast_fp16, y = sqrt_53_cast_fp16)[name = tensor<string, []>("div_107_cast_fp16")];
            tensor<fp32, []> log_53_epsilon_0 = const()[name = tensor<string, []>("log_53_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_53_cast_fp16 = log(epsilon = log_53_epsilon_0, x = sqrt_53_cast_fp16)[name = tensor<string, []>("log_53_cast_fp16")];
            tensor<int32, []> const_1181 = const()[name = tensor<string, []>("const_1181"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_53_interleave_0 = const()[name = tensor<string, []>("cat_53_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_53_cast_fp16 = concat(axis = const_1181, interleave = cat_53_interleave_0, values = (div_106_cast_fp16, div_107_cast_fp16, log_53_cast_fp16))[name = tensor<string, []>("cat_53_cast_fp16")];
            tensor<int32, [1]> unsqueeze_107_axes_0 = const()[name = tensor<string, []>("unsqueeze_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_107_cast_fp16 = expand_dims(axes = unsqueeze_107_axes_0, x = cat_53_cast_fp16)[name = tensor<string, []>("unsqueeze_107_cast_fp16")];
            tensor<fp16, []> const_1186_promoted_to_fp16 = const()[name = tensor<string, []>("const_1186_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_162_cast_fp16 = pow(x = unsqueeze_107_cast_fp16, y = const_1186_promoted_to_fp16)[name = tensor<string, []>("pow_162_cast_fp16")];
            tensor<int32, [1]> mean_53_axes_0 = const()[name = tensor<string, []>("mean_53_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_53_keep_dims_0 = const()[name = tensor<string, []>("mean_53_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_53_cast_fp16 = reduce_mean(axes = mean_53_axes_0, keep_dims = mean_53_keep_dims_0, x = pow_162_cast_fp16)[name = tensor<string, []>("mean_53_cast_fp16")];
            tensor<fp16, []> const_1189_to_fp16 = const()[name = tensor<string, []>("const_1189_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_161_cast_fp16 = add(x = mean_53_cast_fp16, y = const_1189_to_fp16)[name = tensor<string, []>("add_161_cast_fp16")];
            tensor<fp32, []> rsqrt_53_epsilon_0 = const()[name = tensor<string, []>("rsqrt_53_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_53_cast_fp16 = rsqrt(epsilon = rsqrt_53_epsilon_0, x = add_161_cast_fp16)[name = tensor<string, []>("rsqrt_53_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_106_cast_fp16 = mul(x = unsqueeze_107_cast_fp16, y = rsqrt_53_cast_fp16)[name = tensor<string, []>("mul_106_cast_fp16")];
            tensor<int32, [1]> squeeze_53_axes_0 = const()[name = tensor<string, []>("squeeze_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_53_cast_fp16 = squeeze(axes = squeeze_53_axes_0, x = mul_106_cast_fp16)[name = tensor<string, []>("squeeze_53_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_53_to_fp16 = const()[name = tensor<string, []>("view_53_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(339264)))];
            tensor<fp16, [1, 11, 801]> mul_107_cast_fp16 = mul(x = squeeze_53_cast_fp16, y = view_53_to_fp16)[name = tensor<string, []>("mul_107_cast_fp16")];
            tensor<string, []> conv1d_53_pad_type_0 = const()[name = tensor<string, []>("conv1d_53_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_53_strides_0 = const()[name = tensor<string, []>("conv1d_53_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_53_pad_0 = const()[name = tensor<string, []>("conv1d_53_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_53_dilations_0 = const()[name = tensor<string, []>("conv1d_53_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_53_groups_0 = const()[name = tensor<string, []>("conv1d_53_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_53_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_53_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(339392)))];
            tensor<fp16, [256]> p_bn_53_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_53_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(345088)))];
            tensor<fp16, [1, 256, 801]> conv1d_53_cast_fp16 = conv(bias = p_bn_53_1_bias_to_fp16, dilations = conv1d_53_dilations_0, groups = conv1d_53_groups_0, pad = conv1d_53_pad_0, pad_type = conv1d_53_pad_type_0, strides = conv1d_53_strides_0, weight = p_bn_53_1_weight_to_fp16, x = mul_107_cast_fp16)[name = tensor<string, []>("conv1d_53_cast_fp16")];
            tensor<int32, [3]> slice_109_begin_0 = const()[name = tensor<string, []>("slice_109_begin_0"), val = tensor<int32, [3]>([0, 270, 0])];
            tensor<int32, [3]> slice_109_end_0 = const()[name = tensor<string, []>("slice_109_end_0"), val = tensor<int32, [3]>([1, 275, 801])];
            tensor<bool, [3]> slice_109_end_mask_0 = const()[name = tensor<string, []>("slice_109_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_109_cast_fp16 = slice_by_index(begin = slice_109_begin_0, end = slice_109_end_0, end_mask = slice_109_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_109_cast_fp16")];
            tensor<int32, [3]> slice_110_begin_0 = const()[name = tensor<string, []>("slice_110_begin_0"), val = tensor<int32, [3]>([0, 270, 0])];
            tensor<int32, [3]> slice_110_end_0 = const()[name = tensor<string, []>("slice_110_end_0"), val = tensor<int32, [3]>([1, 275, 801])];
            tensor<bool, [3]> slice_110_end_mask_0 = const()[name = tensor<string, []>("slice_110_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_110_cast_fp16 = slice_by_index(begin = slice_110_begin_0, end = slice_110_end_0, end_mask = slice_110_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_110_cast_fp16")];
            tensor<fp16, []> const_1198_promoted_to_fp16 = const()[name = tensor<string, []>("const_1198_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_163_cast_fp16 = pow(x = slice_109_cast_fp16, y = const_1198_promoted_to_fp16)[name = tensor<string, []>("pow_163_cast_fp16")];
            tensor<fp16, []> const_1199_promoted_to_fp16 = const()[name = tensor<string, []>("const_1199_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_164_cast_fp16 = pow(x = slice_110_cast_fp16, y = const_1199_promoted_to_fp16)[name = tensor<string, []>("pow_164_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_162_cast_fp16 = add(x = pow_163_cast_fp16, y = pow_164_cast_fp16)[name = tensor<string, []>("add_162_cast_fp16")];
            tensor<int32, [1]> sum_55_axes_0 = const()[name = tensor<string, []>("sum_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_55_keep_dims_0 = const()[name = tensor<string, []>("sum_55_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_55_cast_fp16 = reduce_sum(axes = sum_55_axes_0, keep_dims = sum_55_keep_dims_0, x = add_162_cast_fp16)[name = tensor<string, []>("sum_55_cast_fp16")];
            tensor<fp16, []> const_1202_to_fp16 = const()[name = tensor<string, []>("const_1202_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_163_cast_fp16 = add(x = sum_55_cast_fp16, y = const_1202_to_fp16)[name = tensor<string, []>("add_163_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_54_cast_fp16 = sqrt(x = add_163_cast_fp16)[name = tensor<string, []>("sqrt_54_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_108_cast_fp16 = real_div(x = slice_109_cast_fp16, y = sqrt_54_cast_fp16)[name = tensor<string, []>("div_108_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_109_cast_fp16 = real_div(x = slice_110_cast_fp16, y = sqrt_54_cast_fp16)[name = tensor<string, []>("div_109_cast_fp16")];
            tensor<fp32, []> log_54_epsilon_0 = const()[name = tensor<string, []>("log_54_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_54_cast_fp16 = log(epsilon = log_54_epsilon_0, x = sqrt_54_cast_fp16)[name = tensor<string, []>("log_54_cast_fp16")];
            tensor<int32, []> const_1203 = const()[name = tensor<string, []>("const_1203"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_54_interleave_0 = const()[name = tensor<string, []>("cat_54_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_54_cast_fp16 = concat(axis = const_1203, interleave = cat_54_interleave_0, values = (div_108_cast_fp16, div_109_cast_fp16, log_54_cast_fp16))[name = tensor<string, []>("cat_54_cast_fp16")];
            tensor<int32, [1]> unsqueeze_109_axes_0 = const()[name = tensor<string, []>("unsqueeze_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_109_cast_fp16 = expand_dims(axes = unsqueeze_109_axes_0, x = cat_54_cast_fp16)[name = tensor<string, []>("unsqueeze_109_cast_fp16")];
            tensor<fp16, []> const_1208_promoted_to_fp16 = const()[name = tensor<string, []>("const_1208_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_165_cast_fp16 = pow(x = unsqueeze_109_cast_fp16, y = const_1208_promoted_to_fp16)[name = tensor<string, []>("pow_165_cast_fp16")];
            tensor<int32, [1]> mean_54_axes_0 = const()[name = tensor<string, []>("mean_54_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_54_keep_dims_0 = const()[name = tensor<string, []>("mean_54_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_54_cast_fp16 = reduce_mean(axes = mean_54_axes_0, keep_dims = mean_54_keep_dims_0, x = pow_165_cast_fp16)[name = tensor<string, []>("mean_54_cast_fp16")];
            tensor<fp16, []> const_1211_to_fp16 = const()[name = tensor<string, []>("const_1211_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_164_cast_fp16 = add(x = mean_54_cast_fp16, y = const_1211_to_fp16)[name = tensor<string, []>("add_164_cast_fp16")];
            tensor<fp32, []> rsqrt_54_epsilon_0 = const()[name = tensor<string, []>("rsqrt_54_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_54_cast_fp16 = rsqrt(epsilon = rsqrt_54_epsilon_0, x = add_164_cast_fp16)[name = tensor<string, []>("rsqrt_54_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_108_cast_fp16 = mul(x = unsqueeze_109_cast_fp16, y = rsqrt_54_cast_fp16)[name = tensor<string, []>("mul_108_cast_fp16")];
            tensor<int32, [1]> squeeze_54_axes_0 = const()[name = tensor<string, []>("squeeze_54_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_54_cast_fp16 = squeeze(axes = squeeze_54_axes_0, x = mul_108_cast_fp16)[name = tensor<string, []>("squeeze_54_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_54_to_fp16 = const()[name = tensor<string, []>("view_54_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(345664)))];
            tensor<fp16, [1, 11, 801]> mul_109_cast_fp16 = mul(x = squeeze_54_cast_fp16, y = view_54_to_fp16)[name = tensor<string, []>("mul_109_cast_fp16")];
            tensor<string, []> conv1d_54_pad_type_0 = const()[name = tensor<string, []>("conv1d_54_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_54_strides_0 = const()[name = tensor<string, []>("conv1d_54_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_54_pad_0 = const()[name = tensor<string, []>("conv1d_54_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_54_dilations_0 = const()[name = tensor<string, []>("conv1d_54_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_54_groups_0 = const()[name = tensor<string, []>("conv1d_54_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_54_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_54_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(345792)))];
            tensor<fp16, [256]> p_bn_54_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_54_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(351488)))];
            tensor<fp16, [1, 256, 801]> conv1d_54_cast_fp16 = conv(bias = p_bn_54_1_bias_to_fp16, dilations = conv1d_54_dilations_0, groups = conv1d_54_groups_0, pad = conv1d_54_pad_0, pad_type = conv1d_54_pad_type_0, strides = conv1d_54_strides_0, weight = p_bn_54_1_weight_to_fp16, x = mul_109_cast_fp16)[name = tensor<string, []>("conv1d_54_cast_fp16")];
            tensor<int32, [3]> slice_111_begin_0 = const()[name = tensor<string, []>("slice_111_begin_0"), val = tensor<int32, [3]>([0, 275, 0])];
            tensor<int32, [3]> slice_111_end_0 = const()[name = tensor<string, []>("slice_111_end_0"), val = tensor<int32, [3]>([1, 280, 801])];
            tensor<bool, [3]> slice_111_end_mask_0 = const()[name = tensor<string, []>("slice_111_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_111_cast_fp16 = slice_by_index(begin = slice_111_begin_0, end = slice_111_end_0, end_mask = slice_111_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_111_cast_fp16")];
            tensor<int32, [3]> slice_112_begin_0 = const()[name = tensor<string, []>("slice_112_begin_0"), val = tensor<int32, [3]>([0, 275, 0])];
            tensor<int32, [3]> slice_112_end_0 = const()[name = tensor<string, []>("slice_112_end_0"), val = tensor<int32, [3]>([1, 280, 801])];
            tensor<bool, [3]> slice_112_end_mask_0 = const()[name = tensor<string, []>("slice_112_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_112_cast_fp16 = slice_by_index(begin = slice_112_begin_0, end = slice_112_end_0, end_mask = slice_112_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_112_cast_fp16")];
            tensor<fp16, []> const_1220_promoted_to_fp16 = const()[name = tensor<string, []>("const_1220_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_166_cast_fp16 = pow(x = slice_111_cast_fp16, y = const_1220_promoted_to_fp16)[name = tensor<string, []>("pow_166_cast_fp16")];
            tensor<fp16, []> const_1221_promoted_to_fp16 = const()[name = tensor<string, []>("const_1221_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_167_cast_fp16 = pow(x = slice_112_cast_fp16, y = const_1221_promoted_to_fp16)[name = tensor<string, []>("pow_167_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_165_cast_fp16 = add(x = pow_166_cast_fp16, y = pow_167_cast_fp16)[name = tensor<string, []>("add_165_cast_fp16")];
            tensor<int32, [1]> sum_56_axes_0 = const()[name = tensor<string, []>("sum_56_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_56_keep_dims_0 = const()[name = tensor<string, []>("sum_56_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_56_cast_fp16 = reduce_sum(axes = sum_56_axes_0, keep_dims = sum_56_keep_dims_0, x = add_165_cast_fp16)[name = tensor<string, []>("sum_56_cast_fp16")];
            tensor<fp16, []> const_1224_to_fp16 = const()[name = tensor<string, []>("const_1224_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_166_cast_fp16 = add(x = sum_56_cast_fp16, y = const_1224_to_fp16)[name = tensor<string, []>("add_166_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_55_cast_fp16 = sqrt(x = add_166_cast_fp16)[name = tensor<string, []>("sqrt_55_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_110_cast_fp16 = real_div(x = slice_111_cast_fp16, y = sqrt_55_cast_fp16)[name = tensor<string, []>("div_110_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_111_cast_fp16 = real_div(x = slice_112_cast_fp16, y = sqrt_55_cast_fp16)[name = tensor<string, []>("div_111_cast_fp16")];
            tensor<fp32, []> log_55_epsilon_0 = const()[name = tensor<string, []>("log_55_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_55_cast_fp16 = log(epsilon = log_55_epsilon_0, x = sqrt_55_cast_fp16)[name = tensor<string, []>("log_55_cast_fp16")];
            tensor<int32, []> const_1225 = const()[name = tensor<string, []>("const_1225"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_55_interleave_0 = const()[name = tensor<string, []>("cat_55_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_55_cast_fp16 = concat(axis = const_1225, interleave = cat_55_interleave_0, values = (div_110_cast_fp16, div_111_cast_fp16, log_55_cast_fp16))[name = tensor<string, []>("cat_55_cast_fp16")];
            tensor<int32, [1]> unsqueeze_111_axes_0 = const()[name = tensor<string, []>("unsqueeze_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_111_cast_fp16 = expand_dims(axes = unsqueeze_111_axes_0, x = cat_55_cast_fp16)[name = tensor<string, []>("unsqueeze_111_cast_fp16")];
            tensor<fp16, []> const_1230_promoted_to_fp16 = const()[name = tensor<string, []>("const_1230_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_168_cast_fp16 = pow(x = unsqueeze_111_cast_fp16, y = const_1230_promoted_to_fp16)[name = tensor<string, []>("pow_168_cast_fp16")];
            tensor<int32, [1]> mean_55_axes_0 = const()[name = tensor<string, []>("mean_55_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_55_keep_dims_0 = const()[name = tensor<string, []>("mean_55_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_55_cast_fp16 = reduce_mean(axes = mean_55_axes_0, keep_dims = mean_55_keep_dims_0, x = pow_168_cast_fp16)[name = tensor<string, []>("mean_55_cast_fp16")];
            tensor<fp16, []> const_1233_to_fp16 = const()[name = tensor<string, []>("const_1233_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_167_cast_fp16 = add(x = mean_55_cast_fp16, y = const_1233_to_fp16)[name = tensor<string, []>("add_167_cast_fp16")];
            tensor<fp32, []> rsqrt_55_epsilon_0 = const()[name = tensor<string, []>("rsqrt_55_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_55_cast_fp16 = rsqrt(epsilon = rsqrt_55_epsilon_0, x = add_167_cast_fp16)[name = tensor<string, []>("rsqrt_55_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_110_cast_fp16 = mul(x = unsqueeze_111_cast_fp16, y = rsqrt_55_cast_fp16)[name = tensor<string, []>("mul_110_cast_fp16")];
            tensor<int32, [1]> squeeze_55_axes_0 = const()[name = tensor<string, []>("squeeze_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_55_cast_fp16 = squeeze(axes = squeeze_55_axes_0, x = mul_110_cast_fp16)[name = tensor<string, []>("squeeze_55_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_55_to_fp16 = const()[name = tensor<string, []>("view_55_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352064)))];
            tensor<fp16, [1, 11, 801]> mul_111_cast_fp16 = mul(x = squeeze_55_cast_fp16, y = view_55_to_fp16)[name = tensor<string, []>("mul_111_cast_fp16")];
            tensor<string, []> conv1d_55_pad_type_0 = const()[name = tensor<string, []>("conv1d_55_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_55_strides_0 = const()[name = tensor<string, []>("conv1d_55_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_55_pad_0 = const()[name = tensor<string, []>("conv1d_55_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_55_dilations_0 = const()[name = tensor<string, []>("conv1d_55_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_55_groups_0 = const()[name = tensor<string, []>("conv1d_55_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_55_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_55_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352192)))];
            tensor<fp16, [256]> p_bn_55_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_55_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357888)))];
            tensor<fp16, [1, 256, 801]> conv1d_55_cast_fp16 = conv(bias = p_bn_55_1_bias_to_fp16, dilations = conv1d_55_dilations_0, groups = conv1d_55_groups_0, pad = conv1d_55_pad_0, pad_type = conv1d_55_pad_type_0, strides = conv1d_55_strides_0, weight = p_bn_55_1_weight_to_fp16, x = mul_111_cast_fp16)[name = tensor<string, []>("conv1d_55_cast_fp16")];
            tensor<int32, [3]> slice_113_begin_0 = const()[name = tensor<string, []>("slice_113_begin_0"), val = tensor<int32, [3]>([0, 280, 0])];
            tensor<int32, [3]> slice_113_end_0 = const()[name = tensor<string, []>("slice_113_end_0"), val = tensor<int32, [3]>([1, 285, 801])];
            tensor<bool, [3]> slice_113_end_mask_0 = const()[name = tensor<string, []>("slice_113_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_113_cast_fp16 = slice_by_index(begin = slice_113_begin_0, end = slice_113_end_0, end_mask = slice_113_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_113_cast_fp16")];
            tensor<int32, [3]> slice_114_begin_0 = const()[name = tensor<string, []>("slice_114_begin_0"), val = tensor<int32, [3]>([0, 280, 0])];
            tensor<int32, [3]> slice_114_end_0 = const()[name = tensor<string, []>("slice_114_end_0"), val = tensor<int32, [3]>([1, 285, 801])];
            tensor<bool, [3]> slice_114_end_mask_0 = const()[name = tensor<string, []>("slice_114_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_114_cast_fp16 = slice_by_index(begin = slice_114_begin_0, end = slice_114_end_0, end_mask = slice_114_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_114_cast_fp16")];
            tensor<fp16, []> const_1242_promoted_to_fp16 = const()[name = tensor<string, []>("const_1242_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_169_cast_fp16 = pow(x = slice_113_cast_fp16, y = const_1242_promoted_to_fp16)[name = tensor<string, []>("pow_169_cast_fp16")];
            tensor<fp16, []> const_1243_promoted_to_fp16 = const()[name = tensor<string, []>("const_1243_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_170_cast_fp16 = pow(x = slice_114_cast_fp16, y = const_1243_promoted_to_fp16)[name = tensor<string, []>("pow_170_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_168_cast_fp16 = add(x = pow_169_cast_fp16, y = pow_170_cast_fp16)[name = tensor<string, []>("add_168_cast_fp16")];
            tensor<int32, [1]> sum_57_axes_0 = const()[name = tensor<string, []>("sum_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_57_keep_dims_0 = const()[name = tensor<string, []>("sum_57_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_57_cast_fp16 = reduce_sum(axes = sum_57_axes_0, keep_dims = sum_57_keep_dims_0, x = add_168_cast_fp16)[name = tensor<string, []>("sum_57_cast_fp16")];
            tensor<fp16, []> const_1246_to_fp16 = const()[name = tensor<string, []>("const_1246_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_169_cast_fp16 = add(x = sum_57_cast_fp16, y = const_1246_to_fp16)[name = tensor<string, []>("add_169_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_56_cast_fp16 = sqrt(x = add_169_cast_fp16)[name = tensor<string, []>("sqrt_56_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_112_cast_fp16 = real_div(x = slice_113_cast_fp16, y = sqrt_56_cast_fp16)[name = tensor<string, []>("div_112_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_113_cast_fp16 = real_div(x = slice_114_cast_fp16, y = sqrt_56_cast_fp16)[name = tensor<string, []>("div_113_cast_fp16")];
            tensor<fp32, []> log_56_epsilon_0 = const()[name = tensor<string, []>("log_56_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_56_cast_fp16 = log(epsilon = log_56_epsilon_0, x = sqrt_56_cast_fp16)[name = tensor<string, []>("log_56_cast_fp16")];
            tensor<int32, []> const_1247 = const()[name = tensor<string, []>("const_1247"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_56_interleave_0 = const()[name = tensor<string, []>("cat_56_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_56_cast_fp16 = concat(axis = const_1247, interleave = cat_56_interleave_0, values = (div_112_cast_fp16, div_113_cast_fp16, log_56_cast_fp16))[name = tensor<string, []>("cat_56_cast_fp16")];
            tensor<int32, [1]> unsqueeze_113_axes_0 = const()[name = tensor<string, []>("unsqueeze_113_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_113_cast_fp16 = expand_dims(axes = unsqueeze_113_axes_0, x = cat_56_cast_fp16)[name = tensor<string, []>("unsqueeze_113_cast_fp16")];
            tensor<fp16, []> const_1252_promoted_to_fp16 = const()[name = tensor<string, []>("const_1252_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_171_cast_fp16 = pow(x = unsqueeze_113_cast_fp16, y = const_1252_promoted_to_fp16)[name = tensor<string, []>("pow_171_cast_fp16")];
            tensor<int32, [1]> mean_56_axes_0 = const()[name = tensor<string, []>("mean_56_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_56_keep_dims_0 = const()[name = tensor<string, []>("mean_56_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_56_cast_fp16 = reduce_mean(axes = mean_56_axes_0, keep_dims = mean_56_keep_dims_0, x = pow_171_cast_fp16)[name = tensor<string, []>("mean_56_cast_fp16")];
            tensor<fp16, []> const_1255_to_fp16 = const()[name = tensor<string, []>("const_1255_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_170_cast_fp16 = add(x = mean_56_cast_fp16, y = const_1255_to_fp16)[name = tensor<string, []>("add_170_cast_fp16")];
            tensor<fp32, []> rsqrt_56_epsilon_0 = const()[name = tensor<string, []>("rsqrt_56_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_56_cast_fp16 = rsqrt(epsilon = rsqrt_56_epsilon_0, x = add_170_cast_fp16)[name = tensor<string, []>("rsqrt_56_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_112_cast_fp16 = mul(x = unsqueeze_113_cast_fp16, y = rsqrt_56_cast_fp16)[name = tensor<string, []>("mul_112_cast_fp16")];
            tensor<int32, [1]> squeeze_56_axes_0 = const()[name = tensor<string, []>("squeeze_56_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_56_cast_fp16 = squeeze(axes = squeeze_56_axes_0, x = mul_112_cast_fp16)[name = tensor<string, []>("squeeze_56_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_56_to_fp16 = const()[name = tensor<string, []>("view_56_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358464)))];
            tensor<fp16, [1, 11, 801]> mul_113_cast_fp16 = mul(x = squeeze_56_cast_fp16, y = view_56_to_fp16)[name = tensor<string, []>("mul_113_cast_fp16")];
            tensor<string, []> conv1d_56_pad_type_0 = const()[name = tensor<string, []>("conv1d_56_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_56_strides_0 = const()[name = tensor<string, []>("conv1d_56_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_56_pad_0 = const()[name = tensor<string, []>("conv1d_56_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_56_dilations_0 = const()[name = tensor<string, []>("conv1d_56_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_56_groups_0 = const()[name = tensor<string, []>("conv1d_56_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_56_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_56_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358592)))];
            tensor<fp16, [256]> p_bn_56_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_56_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364288)))];
            tensor<fp16, [1, 256, 801]> conv1d_56_cast_fp16 = conv(bias = p_bn_56_1_bias_to_fp16, dilations = conv1d_56_dilations_0, groups = conv1d_56_groups_0, pad = conv1d_56_pad_0, pad_type = conv1d_56_pad_type_0, strides = conv1d_56_strides_0, weight = p_bn_56_1_weight_to_fp16, x = mul_113_cast_fp16)[name = tensor<string, []>("conv1d_56_cast_fp16")];
            tensor<int32, [3]> slice_115_begin_0 = const()[name = tensor<string, []>("slice_115_begin_0"), val = tensor<int32, [3]>([0, 285, 0])];
            tensor<int32, [3]> slice_115_end_0 = const()[name = tensor<string, []>("slice_115_end_0"), val = tensor<int32, [3]>([1, 290, 801])];
            tensor<bool, [3]> slice_115_end_mask_0 = const()[name = tensor<string, []>("slice_115_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_115_cast_fp16 = slice_by_index(begin = slice_115_begin_0, end = slice_115_end_0, end_mask = slice_115_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_115_cast_fp16")];
            tensor<int32, [3]> slice_116_begin_0 = const()[name = tensor<string, []>("slice_116_begin_0"), val = tensor<int32, [3]>([0, 285, 0])];
            tensor<int32, [3]> slice_116_end_0 = const()[name = tensor<string, []>("slice_116_end_0"), val = tensor<int32, [3]>([1, 290, 801])];
            tensor<bool, [3]> slice_116_end_mask_0 = const()[name = tensor<string, []>("slice_116_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_116_cast_fp16 = slice_by_index(begin = slice_116_begin_0, end = slice_116_end_0, end_mask = slice_116_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_116_cast_fp16")];
            tensor<fp16, []> const_1264_promoted_to_fp16 = const()[name = tensor<string, []>("const_1264_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_172_cast_fp16 = pow(x = slice_115_cast_fp16, y = const_1264_promoted_to_fp16)[name = tensor<string, []>("pow_172_cast_fp16")];
            tensor<fp16, []> const_1265_promoted_to_fp16 = const()[name = tensor<string, []>("const_1265_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_173_cast_fp16 = pow(x = slice_116_cast_fp16, y = const_1265_promoted_to_fp16)[name = tensor<string, []>("pow_173_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_171_cast_fp16 = add(x = pow_172_cast_fp16, y = pow_173_cast_fp16)[name = tensor<string, []>("add_171_cast_fp16")];
            tensor<int32, [1]> sum_58_axes_0 = const()[name = tensor<string, []>("sum_58_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_58_keep_dims_0 = const()[name = tensor<string, []>("sum_58_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_58_cast_fp16 = reduce_sum(axes = sum_58_axes_0, keep_dims = sum_58_keep_dims_0, x = add_171_cast_fp16)[name = tensor<string, []>("sum_58_cast_fp16")];
            tensor<fp16, []> const_1268_to_fp16 = const()[name = tensor<string, []>("const_1268_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_172_cast_fp16 = add(x = sum_58_cast_fp16, y = const_1268_to_fp16)[name = tensor<string, []>("add_172_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_57_cast_fp16 = sqrt(x = add_172_cast_fp16)[name = tensor<string, []>("sqrt_57_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_114_cast_fp16 = real_div(x = slice_115_cast_fp16, y = sqrt_57_cast_fp16)[name = tensor<string, []>("div_114_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_115_cast_fp16 = real_div(x = slice_116_cast_fp16, y = sqrt_57_cast_fp16)[name = tensor<string, []>("div_115_cast_fp16")];
            tensor<fp32, []> log_57_epsilon_0 = const()[name = tensor<string, []>("log_57_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_57_cast_fp16 = log(epsilon = log_57_epsilon_0, x = sqrt_57_cast_fp16)[name = tensor<string, []>("log_57_cast_fp16")];
            tensor<int32, []> const_1269 = const()[name = tensor<string, []>("const_1269"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_57_interleave_0 = const()[name = tensor<string, []>("cat_57_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_57_cast_fp16 = concat(axis = const_1269, interleave = cat_57_interleave_0, values = (div_114_cast_fp16, div_115_cast_fp16, log_57_cast_fp16))[name = tensor<string, []>("cat_57_cast_fp16")];
            tensor<int32, [1]> unsqueeze_115_axes_0 = const()[name = tensor<string, []>("unsqueeze_115_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_115_cast_fp16 = expand_dims(axes = unsqueeze_115_axes_0, x = cat_57_cast_fp16)[name = tensor<string, []>("unsqueeze_115_cast_fp16")];
            tensor<fp16, []> const_1274_promoted_to_fp16 = const()[name = tensor<string, []>("const_1274_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_174_cast_fp16 = pow(x = unsqueeze_115_cast_fp16, y = const_1274_promoted_to_fp16)[name = tensor<string, []>("pow_174_cast_fp16")];
            tensor<int32, [1]> mean_57_axes_0 = const()[name = tensor<string, []>("mean_57_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_57_keep_dims_0 = const()[name = tensor<string, []>("mean_57_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_57_cast_fp16 = reduce_mean(axes = mean_57_axes_0, keep_dims = mean_57_keep_dims_0, x = pow_174_cast_fp16)[name = tensor<string, []>("mean_57_cast_fp16")];
            tensor<fp16, []> const_1277_to_fp16 = const()[name = tensor<string, []>("const_1277_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_173_cast_fp16 = add(x = mean_57_cast_fp16, y = const_1277_to_fp16)[name = tensor<string, []>("add_173_cast_fp16")];
            tensor<fp32, []> rsqrt_57_epsilon_0 = const()[name = tensor<string, []>("rsqrt_57_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_57_cast_fp16 = rsqrt(epsilon = rsqrt_57_epsilon_0, x = add_173_cast_fp16)[name = tensor<string, []>("rsqrt_57_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_114_cast_fp16 = mul(x = unsqueeze_115_cast_fp16, y = rsqrt_57_cast_fp16)[name = tensor<string, []>("mul_114_cast_fp16")];
            tensor<int32, [1]> squeeze_57_axes_0 = const()[name = tensor<string, []>("squeeze_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_57_cast_fp16 = squeeze(axes = squeeze_57_axes_0, x = mul_114_cast_fp16)[name = tensor<string, []>("squeeze_57_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_57_to_fp16 = const()[name = tensor<string, []>("view_57_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364864)))];
            tensor<fp16, [1, 11, 801]> mul_115_cast_fp16 = mul(x = squeeze_57_cast_fp16, y = view_57_to_fp16)[name = tensor<string, []>("mul_115_cast_fp16")];
            tensor<string, []> conv1d_57_pad_type_0 = const()[name = tensor<string, []>("conv1d_57_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_57_strides_0 = const()[name = tensor<string, []>("conv1d_57_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_57_pad_0 = const()[name = tensor<string, []>("conv1d_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_57_dilations_0 = const()[name = tensor<string, []>("conv1d_57_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_57_groups_0 = const()[name = tensor<string, []>("conv1d_57_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_57_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_57_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(364992)))];
            tensor<fp16, [256]> p_bn_57_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_57_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370688)))];
            tensor<fp16, [1, 256, 801]> conv1d_57_cast_fp16 = conv(bias = p_bn_57_1_bias_to_fp16, dilations = conv1d_57_dilations_0, groups = conv1d_57_groups_0, pad = conv1d_57_pad_0, pad_type = conv1d_57_pad_type_0, strides = conv1d_57_strides_0, weight = p_bn_57_1_weight_to_fp16, x = mul_115_cast_fp16)[name = tensor<string, []>("conv1d_57_cast_fp16")];
            tensor<int32, [3]> slice_117_begin_0 = const()[name = tensor<string, []>("slice_117_begin_0"), val = tensor<int32, [3]>([0, 290, 0])];
            tensor<int32, [3]> slice_117_end_0 = const()[name = tensor<string, []>("slice_117_end_0"), val = tensor<int32, [3]>([1, 295, 801])];
            tensor<bool, [3]> slice_117_end_mask_0 = const()[name = tensor<string, []>("slice_117_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_117_cast_fp16 = slice_by_index(begin = slice_117_begin_0, end = slice_117_end_0, end_mask = slice_117_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_117_cast_fp16")];
            tensor<int32, [3]> slice_118_begin_0 = const()[name = tensor<string, []>("slice_118_begin_0"), val = tensor<int32, [3]>([0, 290, 0])];
            tensor<int32, [3]> slice_118_end_0 = const()[name = tensor<string, []>("slice_118_end_0"), val = tensor<int32, [3]>([1, 295, 801])];
            tensor<bool, [3]> slice_118_end_mask_0 = const()[name = tensor<string, []>("slice_118_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_118_cast_fp16 = slice_by_index(begin = slice_118_begin_0, end = slice_118_end_0, end_mask = slice_118_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_118_cast_fp16")];
            tensor<fp16, []> const_1286_promoted_to_fp16 = const()[name = tensor<string, []>("const_1286_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_175_cast_fp16 = pow(x = slice_117_cast_fp16, y = const_1286_promoted_to_fp16)[name = tensor<string, []>("pow_175_cast_fp16")];
            tensor<fp16, []> const_1287_promoted_to_fp16 = const()[name = tensor<string, []>("const_1287_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_176_cast_fp16 = pow(x = slice_118_cast_fp16, y = const_1287_promoted_to_fp16)[name = tensor<string, []>("pow_176_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_174_cast_fp16 = add(x = pow_175_cast_fp16, y = pow_176_cast_fp16)[name = tensor<string, []>("add_174_cast_fp16")];
            tensor<int32, [1]> sum_59_axes_0 = const()[name = tensor<string, []>("sum_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_59_keep_dims_0 = const()[name = tensor<string, []>("sum_59_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_59_cast_fp16 = reduce_sum(axes = sum_59_axes_0, keep_dims = sum_59_keep_dims_0, x = add_174_cast_fp16)[name = tensor<string, []>("sum_59_cast_fp16")];
            tensor<fp16, []> const_1290_to_fp16 = const()[name = tensor<string, []>("const_1290_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_175_cast_fp16 = add(x = sum_59_cast_fp16, y = const_1290_to_fp16)[name = tensor<string, []>("add_175_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_58_cast_fp16 = sqrt(x = add_175_cast_fp16)[name = tensor<string, []>("sqrt_58_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_116_cast_fp16 = real_div(x = slice_117_cast_fp16, y = sqrt_58_cast_fp16)[name = tensor<string, []>("div_116_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_117_cast_fp16 = real_div(x = slice_118_cast_fp16, y = sqrt_58_cast_fp16)[name = tensor<string, []>("div_117_cast_fp16")];
            tensor<fp32, []> log_58_epsilon_0 = const()[name = tensor<string, []>("log_58_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_58_cast_fp16 = log(epsilon = log_58_epsilon_0, x = sqrt_58_cast_fp16)[name = tensor<string, []>("log_58_cast_fp16")];
            tensor<int32, []> const_1291 = const()[name = tensor<string, []>("const_1291"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_58_interleave_0 = const()[name = tensor<string, []>("cat_58_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_58_cast_fp16 = concat(axis = const_1291, interleave = cat_58_interleave_0, values = (div_116_cast_fp16, div_117_cast_fp16, log_58_cast_fp16))[name = tensor<string, []>("cat_58_cast_fp16")];
            tensor<int32, [1]> unsqueeze_117_axes_0 = const()[name = tensor<string, []>("unsqueeze_117_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_117_cast_fp16 = expand_dims(axes = unsqueeze_117_axes_0, x = cat_58_cast_fp16)[name = tensor<string, []>("unsqueeze_117_cast_fp16")];
            tensor<fp16, []> const_1296_promoted_to_fp16 = const()[name = tensor<string, []>("const_1296_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_177_cast_fp16 = pow(x = unsqueeze_117_cast_fp16, y = const_1296_promoted_to_fp16)[name = tensor<string, []>("pow_177_cast_fp16")];
            tensor<int32, [1]> mean_58_axes_0 = const()[name = tensor<string, []>("mean_58_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_58_keep_dims_0 = const()[name = tensor<string, []>("mean_58_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_58_cast_fp16 = reduce_mean(axes = mean_58_axes_0, keep_dims = mean_58_keep_dims_0, x = pow_177_cast_fp16)[name = tensor<string, []>("mean_58_cast_fp16")];
            tensor<fp16, []> const_1299_to_fp16 = const()[name = tensor<string, []>("const_1299_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_176_cast_fp16 = add(x = mean_58_cast_fp16, y = const_1299_to_fp16)[name = tensor<string, []>("add_176_cast_fp16")];
            tensor<fp32, []> rsqrt_58_epsilon_0 = const()[name = tensor<string, []>("rsqrt_58_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_58_cast_fp16 = rsqrt(epsilon = rsqrt_58_epsilon_0, x = add_176_cast_fp16)[name = tensor<string, []>("rsqrt_58_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_116_cast_fp16 = mul(x = unsqueeze_117_cast_fp16, y = rsqrt_58_cast_fp16)[name = tensor<string, []>("mul_116_cast_fp16")];
            tensor<int32, [1]> squeeze_58_axes_0 = const()[name = tensor<string, []>("squeeze_58_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_58_cast_fp16 = squeeze(axes = squeeze_58_axes_0, x = mul_116_cast_fp16)[name = tensor<string, []>("squeeze_58_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_58_to_fp16 = const()[name = tensor<string, []>("view_58_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371264)))];
            tensor<fp16, [1, 11, 801]> mul_117_cast_fp16 = mul(x = squeeze_58_cast_fp16, y = view_58_to_fp16)[name = tensor<string, []>("mul_117_cast_fp16")];
            tensor<string, []> conv1d_58_pad_type_0 = const()[name = tensor<string, []>("conv1d_58_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_58_strides_0 = const()[name = tensor<string, []>("conv1d_58_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_58_pad_0 = const()[name = tensor<string, []>("conv1d_58_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_58_dilations_0 = const()[name = tensor<string, []>("conv1d_58_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_58_groups_0 = const()[name = tensor<string, []>("conv1d_58_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_58_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_58_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371392)))];
            tensor<fp16, [256]> p_bn_58_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_58_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377088)))];
            tensor<fp16, [1, 256, 801]> conv1d_58_cast_fp16 = conv(bias = p_bn_58_1_bias_to_fp16, dilations = conv1d_58_dilations_0, groups = conv1d_58_groups_0, pad = conv1d_58_pad_0, pad_type = conv1d_58_pad_type_0, strides = conv1d_58_strides_0, weight = p_bn_58_1_weight_to_fp16, x = mul_117_cast_fp16)[name = tensor<string, []>("conv1d_58_cast_fp16")];
            tensor<int32, [3]> slice_119_begin_0 = const()[name = tensor<string, []>("slice_119_begin_0"), val = tensor<int32, [3]>([0, 295, 0])];
            tensor<int32, [3]> slice_119_end_0 = const()[name = tensor<string, []>("slice_119_end_0"), val = tensor<int32, [3]>([1, 300, 801])];
            tensor<bool, [3]> slice_119_end_mask_0 = const()[name = tensor<string, []>("slice_119_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_119_cast_fp16 = slice_by_index(begin = slice_119_begin_0, end = slice_119_end_0, end_mask = slice_119_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_119_cast_fp16")];
            tensor<int32, [3]> slice_120_begin_0 = const()[name = tensor<string, []>("slice_120_begin_0"), val = tensor<int32, [3]>([0, 295, 0])];
            tensor<int32, [3]> slice_120_end_0 = const()[name = tensor<string, []>("slice_120_end_0"), val = tensor<int32, [3]>([1, 300, 801])];
            tensor<bool, [3]> slice_120_end_mask_0 = const()[name = tensor<string, []>("slice_120_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_120_cast_fp16 = slice_by_index(begin = slice_120_begin_0, end = slice_120_end_0, end_mask = slice_120_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_120_cast_fp16")];
            tensor<fp16, []> const_1308_promoted_to_fp16 = const()[name = tensor<string, []>("const_1308_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_178_cast_fp16 = pow(x = slice_119_cast_fp16, y = const_1308_promoted_to_fp16)[name = tensor<string, []>("pow_178_cast_fp16")];
            tensor<fp16, []> const_1309_promoted_to_fp16 = const()[name = tensor<string, []>("const_1309_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_179_cast_fp16 = pow(x = slice_120_cast_fp16, y = const_1309_promoted_to_fp16)[name = tensor<string, []>("pow_179_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_177_cast_fp16 = add(x = pow_178_cast_fp16, y = pow_179_cast_fp16)[name = tensor<string, []>("add_177_cast_fp16")];
            tensor<int32, [1]> sum_60_axes_0 = const()[name = tensor<string, []>("sum_60_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_60_keep_dims_0 = const()[name = tensor<string, []>("sum_60_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_60_cast_fp16 = reduce_sum(axes = sum_60_axes_0, keep_dims = sum_60_keep_dims_0, x = add_177_cast_fp16)[name = tensor<string, []>("sum_60_cast_fp16")];
            tensor<fp16, []> const_1312_to_fp16 = const()[name = tensor<string, []>("const_1312_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_178_cast_fp16 = add(x = sum_60_cast_fp16, y = const_1312_to_fp16)[name = tensor<string, []>("add_178_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_59_cast_fp16 = sqrt(x = add_178_cast_fp16)[name = tensor<string, []>("sqrt_59_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_118_cast_fp16 = real_div(x = slice_119_cast_fp16, y = sqrt_59_cast_fp16)[name = tensor<string, []>("div_118_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_119_cast_fp16 = real_div(x = slice_120_cast_fp16, y = sqrt_59_cast_fp16)[name = tensor<string, []>("div_119_cast_fp16")];
            tensor<fp32, []> log_59_epsilon_0 = const()[name = tensor<string, []>("log_59_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_59_cast_fp16 = log(epsilon = log_59_epsilon_0, x = sqrt_59_cast_fp16)[name = tensor<string, []>("log_59_cast_fp16")];
            tensor<int32, []> const_1313 = const()[name = tensor<string, []>("const_1313"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_59_interleave_0 = const()[name = tensor<string, []>("cat_59_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_59_cast_fp16 = concat(axis = const_1313, interleave = cat_59_interleave_0, values = (div_118_cast_fp16, div_119_cast_fp16, log_59_cast_fp16))[name = tensor<string, []>("cat_59_cast_fp16")];
            tensor<int32, [1]> unsqueeze_119_axes_0 = const()[name = tensor<string, []>("unsqueeze_119_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_119_cast_fp16 = expand_dims(axes = unsqueeze_119_axes_0, x = cat_59_cast_fp16)[name = tensor<string, []>("unsqueeze_119_cast_fp16")];
            tensor<fp16, []> const_1318_promoted_to_fp16 = const()[name = tensor<string, []>("const_1318_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_180_cast_fp16 = pow(x = unsqueeze_119_cast_fp16, y = const_1318_promoted_to_fp16)[name = tensor<string, []>("pow_180_cast_fp16")];
            tensor<int32, [1]> mean_59_axes_0 = const()[name = tensor<string, []>("mean_59_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_59_keep_dims_0 = const()[name = tensor<string, []>("mean_59_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_59_cast_fp16 = reduce_mean(axes = mean_59_axes_0, keep_dims = mean_59_keep_dims_0, x = pow_180_cast_fp16)[name = tensor<string, []>("mean_59_cast_fp16")];
            tensor<fp16, []> const_1321_to_fp16 = const()[name = tensor<string, []>("const_1321_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_179_cast_fp16 = add(x = mean_59_cast_fp16, y = const_1321_to_fp16)[name = tensor<string, []>("add_179_cast_fp16")];
            tensor<fp32, []> rsqrt_59_epsilon_0 = const()[name = tensor<string, []>("rsqrt_59_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_59_cast_fp16 = rsqrt(epsilon = rsqrt_59_epsilon_0, x = add_179_cast_fp16)[name = tensor<string, []>("rsqrt_59_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_118_cast_fp16 = mul(x = unsqueeze_119_cast_fp16, y = rsqrt_59_cast_fp16)[name = tensor<string, []>("mul_118_cast_fp16")];
            tensor<int32, [1]> squeeze_59_axes_0 = const()[name = tensor<string, []>("squeeze_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_59_cast_fp16 = squeeze(axes = squeeze_59_axes_0, x = mul_118_cast_fp16)[name = tensor<string, []>("squeeze_59_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_59_to_fp16 = const()[name = tensor<string, []>("view_59_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377664)))];
            tensor<fp16, [1, 11, 801]> mul_119_cast_fp16 = mul(x = squeeze_59_cast_fp16, y = view_59_to_fp16)[name = tensor<string, []>("mul_119_cast_fp16")];
            tensor<string, []> conv1d_59_pad_type_0 = const()[name = tensor<string, []>("conv1d_59_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_59_strides_0 = const()[name = tensor<string, []>("conv1d_59_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_59_pad_0 = const()[name = tensor<string, []>("conv1d_59_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_59_dilations_0 = const()[name = tensor<string, []>("conv1d_59_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_59_groups_0 = const()[name = tensor<string, []>("conv1d_59_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_59_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_59_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(377792)))];
            tensor<fp16, [256]> p_bn_59_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_59_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383488)))];
            tensor<fp16, [1, 256, 801]> conv1d_59_cast_fp16 = conv(bias = p_bn_59_1_bias_to_fp16, dilations = conv1d_59_dilations_0, groups = conv1d_59_groups_0, pad = conv1d_59_pad_0, pad_type = conv1d_59_pad_type_0, strides = conv1d_59_strides_0, weight = p_bn_59_1_weight_to_fp16, x = mul_119_cast_fp16)[name = tensor<string, []>("conv1d_59_cast_fp16")];
            tensor<int32, [3]> slice_121_begin_0 = const()[name = tensor<string, []>("slice_121_begin_0"), val = tensor<int32, [3]>([0, 300, 0])];
            tensor<int32, [3]> slice_121_end_0 = const()[name = tensor<string, []>("slice_121_end_0"), val = tensor<int32, [3]>([1, 305, 801])];
            tensor<bool, [3]> slice_121_end_mask_0 = const()[name = tensor<string, []>("slice_121_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_121_cast_fp16 = slice_by_index(begin = slice_121_begin_0, end = slice_121_end_0, end_mask = slice_121_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_121_cast_fp16")];
            tensor<int32, [3]> slice_122_begin_0 = const()[name = tensor<string, []>("slice_122_begin_0"), val = tensor<int32, [3]>([0, 300, 0])];
            tensor<int32, [3]> slice_122_end_0 = const()[name = tensor<string, []>("slice_122_end_0"), val = tensor<int32, [3]>([1, 305, 801])];
            tensor<bool, [3]> slice_122_end_mask_0 = const()[name = tensor<string, []>("slice_122_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_122_cast_fp16 = slice_by_index(begin = slice_122_begin_0, end = slice_122_end_0, end_mask = slice_122_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_122_cast_fp16")];
            tensor<fp16, []> const_1330_promoted_to_fp16 = const()[name = tensor<string, []>("const_1330_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_181_cast_fp16 = pow(x = slice_121_cast_fp16, y = const_1330_promoted_to_fp16)[name = tensor<string, []>("pow_181_cast_fp16")];
            tensor<fp16, []> const_1331_promoted_to_fp16 = const()[name = tensor<string, []>("const_1331_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_182_cast_fp16 = pow(x = slice_122_cast_fp16, y = const_1331_promoted_to_fp16)[name = tensor<string, []>("pow_182_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_180_cast_fp16 = add(x = pow_181_cast_fp16, y = pow_182_cast_fp16)[name = tensor<string, []>("add_180_cast_fp16")];
            tensor<int32, [1]> sum_61_axes_0 = const()[name = tensor<string, []>("sum_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_61_keep_dims_0 = const()[name = tensor<string, []>("sum_61_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_61_cast_fp16 = reduce_sum(axes = sum_61_axes_0, keep_dims = sum_61_keep_dims_0, x = add_180_cast_fp16)[name = tensor<string, []>("sum_61_cast_fp16")];
            tensor<fp16, []> const_1334_to_fp16 = const()[name = tensor<string, []>("const_1334_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_181_cast_fp16 = add(x = sum_61_cast_fp16, y = const_1334_to_fp16)[name = tensor<string, []>("add_181_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_60_cast_fp16 = sqrt(x = add_181_cast_fp16)[name = tensor<string, []>("sqrt_60_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_120_cast_fp16 = real_div(x = slice_121_cast_fp16, y = sqrt_60_cast_fp16)[name = tensor<string, []>("div_120_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_121_cast_fp16 = real_div(x = slice_122_cast_fp16, y = sqrt_60_cast_fp16)[name = tensor<string, []>("div_121_cast_fp16")];
            tensor<fp32, []> log_60_epsilon_0 = const()[name = tensor<string, []>("log_60_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_60_cast_fp16 = log(epsilon = log_60_epsilon_0, x = sqrt_60_cast_fp16)[name = tensor<string, []>("log_60_cast_fp16")];
            tensor<int32, []> const_1335 = const()[name = tensor<string, []>("const_1335"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_60_interleave_0 = const()[name = tensor<string, []>("cat_60_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_60_cast_fp16 = concat(axis = const_1335, interleave = cat_60_interleave_0, values = (div_120_cast_fp16, div_121_cast_fp16, log_60_cast_fp16))[name = tensor<string, []>("cat_60_cast_fp16")];
            tensor<int32, [1]> unsqueeze_121_axes_0 = const()[name = tensor<string, []>("unsqueeze_121_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_121_cast_fp16 = expand_dims(axes = unsqueeze_121_axes_0, x = cat_60_cast_fp16)[name = tensor<string, []>("unsqueeze_121_cast_fp16")];
            tensor<fp16, []> const_1340_promoted_to_fp16 = const()[name = tensor<string, []>("const_1340_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_183_cast_fp16 = pow(x = unsqueeze_121_cast_fp16, y = const_1340_promoted_to_fp16)[name = tensor<string, []>("pow_183_cast_fp16")];
            tensor<int32, [1]> mean_60_axes_0 = const()[name = tensor<string, []>("mean_60_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_60_keep_dims_0 = const()[name = tensor<string, []>("mean_60_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_60_cast_fp16 = reduce_mean(axes = mean_60_axes_0, keep_dims = mean_60_keep_dims_0, x = pow_183_cast_fp16)[name = tensor<string, []>("mean_60_cast_fp16")];
            tensor<fp16, []> const_1343_to_fp16 = const()[name = tensor<string, []>("const_1343_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_182_cast_fp16 = add(x = mean_60_cast_fp16, y = const_1343_to_fp16)[name = tensor<string, []>("add_182_cast_fp16")];
            tensor<fp32, []> rsqrt_60_epsilon_0 = const()[name = tensor<string, []>("rsqrt_60_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_60_cast_fp16 = rsqrt(epsilon = rsqrt_60_epsilon_0, x = add_182_cast_fp16)[name = tensor<string, []>("rsqrt_60_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_120_cast_fp16 = mul(x = unsqueeze_121_cast_fp16, y = rsqrt_60_cast_fp16)[name = tensor<string, []>("mul_120_cast_fp16")];
            tensor<int32, [1]> squeeze_60_axes_0 = const()[name = tensor<string, []>("squeeze_60_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_60_cast_fp16 = squeeze(axes = squeeze_60_axes_0, x = mul_120_cast_fp16)[name = tensor<string, []>("squeeze_60_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_60_to_fp16 = const()[name = tensor<string, []>("view_60_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384064)))];
            tensor<fp16, [1, 11, 801]> mul_121_cast_fp16 = mul(x = squeeze_60_cast_fp16, y = view_60_to_fp16)[name = tensor<string, []>("mul_121_cast_fp16")];
            tensor<string, []> conv1d_60_pad_type_0 = const()[name = tensor<string, []>("conv1d_60_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_60_strides_0 = const()[name = tensor<string, []>("conv1d_60_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_60_pad_0 = const()[name = tensor<string, []>("conv1d_60_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_60_dilations_0 = const()[name = tensor<string, []>("conv1d_60_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_60_groups_0 = const()[name = tensor<string, []>("conv1d_60_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_60_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_60_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384192)))];
            tensor<fp16, [256]> p_bn_60_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_60_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389888)))];
            tensor<fp16, [1, 256, 801]> conv1d_60_cast_fp16 = conv(bias = p_bn_60_1_bias_to_fp16, dilations = conv1d_60_dilations_0, groups = conv1d_60_groups_0, pad = conv1d_60_pad_0, pad_type = conv1d_60_pad_type_0, strides = conv1d_60_strides_0, weight = p_bn_60_1_weight_to_fp16, x = mul_121_cast_fp16)[name = tensor<string, []>("conv1d_60_cast_fp16")];
            tensor<int32, [3]> slice_123_begin_0 = const()[name = tensor<string, []>("slice_123_begin_0"), val = tensor<int32, [3]>([0, 305, 0])];
            tensor<int32, [3]> slice_123_end_0 = const()[name = tensor<string, []>("slice_123_end_0"), val = tensor<int32, [3]>([1, 310, 801])];
            tensor<bool, [3]> slice_123_end_mask_0 = const()[name = tensor<string, []>("slice_123_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_123_cast_fp16 = slice_by_index(begin = slice_123_begin_0, end = slice_123_end_0, end_mask = slice_123_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_123_cast_fp16")];
            tensor<int32, [3]> slice_124_begin_0 = const()[name = tensor<string, []>("slice_124_begin_0"), val = tensor<int32, [3]>([0, 305, 0])];
            tensor<int32, [3]> slice_124_end_0 = const()[name = tensor<string, []>("slice_124_end_0"), val = tensor<int32, [3]>([1, 310, 801])];
            tensor<bool, [3]> slice_124_end_mask_0 = const()[name = tensor<string, []>("slice_124_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_124_cast_fp16 = slice_by_index(begin = slice_124_begin_0, end = slice_124_end_0, end_mask = slice_124_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_124_cast_fp16")];
            tensor<fp16, []> const_1352_promoted_to_fp16 = const()[name = tensor<string, []>("const_1352_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_184_cast_fp16 = pow(x = slice_123_cast_fp16, y = const_1352_promoted_to_fp16)[name = tensor<string, []>("pow_184_cast_fp16")];
            tensor<fp16, []> const_1353_promoted_to_fp16 = const()[name = tensor<string, []>("const_1353_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_185_cast_fp16 = pow(x = slice_124_cast_fp16, y = const_1353_promoted_to_fp16)[name = tensor<string, []>("pow_185_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_183_cast_fp16 = add(x = pow_184_cast_fp16, y = pow_185_cast_fp16)[name = tensor<string, []>("add_183_cast_fp16")];
            tensor<int32, [1]> sum_62_axes_0 = const()[name = tensor<string, []>("sum_62_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_62_keep_dims_0 = const()[name = tensor<string, []>("sum_62_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_62_cast_fp16 = reduce_sum(axes = sum_62_axes_0, keep_dims = sum_62_keep_dims_0, x = add_183_cast_fp16)[name = tensor<string, []>("sum_62_cast_fp16")];
            tensor<fp16, []> const_1356_to_fp16 = const()[name = tensor<string, []>("const_1356_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_184_cast_fp16 = add(x = sum_62_cast_fp16, y = const_1356_to_fp16)[name = tensor<string, []>("add_184_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_61_cast_fp16 = sqrt(x = add_184_cast_fp16)[name = tensor<string, []>("sqrt_61_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_122_cast_fp16 = real_div(x = slice_123_cast_fp16, y = sqrt_61_cast_fp16)[name = tensor<string, []>("div_122_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_123_cast_fp16 = real_div(x = slice_124_cast_fp16, y = sqrt_61_cast_fp16)[name = tensor<string, []>("div_123_cast_fp16")];
            tensor<fp32, []> log_61_epsilon_0 = const()[name = tensor<string, []>("log_61_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_61_cast_fp16 = log(epsilon = log_61_epsilon_0, x = sqrt_61_cast_fp16)[name = tensor<string, []>("log_61_cast_fp16")];
            tensor<int32, []> const_1357 = const()[name = tensor<string, []>("const_1357"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_61_interleave_0 = const()[name = tensor<string, []>("cat_61_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_61_cast_fp16 = concat(axis = const_1357, interleave = cat_61_interleave_0, values = (div_122_cast_fp16, div_123_cast_fp16, log_61_cast_fp16))[name = tensor<string, []>("cat_61_cast_fp16")];
            tensor<int32, [1]> unsqueeze_123_axes_0 = const()[name = tensor<string, []>("unsqueeze_123_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_123_cast_fp16 = expand_dims(axes = unsqueeze_123_axes_0, x = cat_61_cast_fp16)[name = tensor<string, []>("unsqueeze_123_cast_fp16")];
            tensor<fp16, []> const_1362_promoted_to_fp16 = const()[name = tensor<string, []>("const_1362_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_186_cast_fp16 = pow(x = unsqueeze_123_cast_fp16, y = const_1362_promoted_to_fp16)[name = tensor<string, []>("pow_186_cast_fp16")];
            tensor<int32, [1]> mean_61_axes_0 = const()[name = tensor<string, []>("mean_61_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_61_keep_dims_0 = const()[name = tensor<string, []>("mean_61_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_61_cast_fp16 = reduce_mean(axes = mean_61_axes_0, keep_dims = mean_61_keep_dims_0, x = pow_186_cast_fp16)[name = tensor<string, []>("mean_61_cast_fp16")];
            tensor<fp16, []> const_1365_to_fp16 = const()[name = tensor<string, []>("const_1365_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_185_cast_fp16 = add(x = mean_61_cast_fp16, y = const_1365_to_fp16)[name = tensor<string, []>("add_185_cast_fp16")];
            tensor<fp32, []> rsqrt_61_epsilon_0 = const()[name = tensor<string, []>("rsqrt_61_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_61_cast_fp16 = rsqrt(epsilon = rsqrt_61_epsilon_0, x = add_185_cast_fp16)[name = tensor<string, []>("rsqrt_61_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_122_cast_fp16 = mul(x = unsqueeze_123_cast_fp16, y = rsqrt_61_cast_fp16)[name = tensor<string, []>("mul_122_cast_fp16")];
            tensor<int32, [1]> squeeze_61_axes_0 = const()[name = tensor<string, []>("squeeze_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_61_cast_fp16 = squeeze(axes = squeeze_61_axes_0, x = mul_122_cast_fp16)[name = tensor<string, []>("squeeze_61_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_61_to_fp16 = const()[name = tensor<string, []>("view_61_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(390464)))];
            tensor<fp16, [1, 11, 801]> mul_123_cast_fp16 = mul(x = squeeze_61_cast_fp16, y = view_61_to_fp16)[name = tensor<string, []>("mul_123_cast_fp16")];
            tensor<string, []> conv1d_61_pad_type_0 = const()[name = tensor<string, []>("conv1d_61_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_61_strides_0 = const()[name = tensor<string, []>("conv1d_61_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_61_pad_0 = const()[name = tensor<string, []>("conv1d_61_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_61_dilations_0 = const()[name = tensor<string, []>("conv1d_61_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_61_groups_0 = const()[name = tensor<string, []>("conv1d_61_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_61_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_61_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(390592)))];
            tensor<fp16, [256]> p_bn_61_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_61_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396288)))];
            tensor<fp16, [1, 256, 801]> conv1d_61_cast_fp16 = conv(bias = p_bn_61_1_bias_to_fp16, dilations = conv1d_61_dilations_0, groups = conv1d_61_groups_0, pad = conv1d_61_pad_0, pad_type = conv1d_61_pad_type_0, strides = conv1d_61_strides_0, weight = p_bn_61_1_weight_to_fp16, x = mul_123_cast_fp16)[name = tensor<string, []>("conv1d_61_cast_fp16")];
            tensor<int32, [3]> slice_125_begin_0 = const()[name = tensor<string, []>("slice_125_begin_0"), val = tensor<int32, [3]>([0, 310, 0])];
            tensor<int32, [3]> slice_125_end_0 = const()[name = tensor<string, []>("slice_125_end_0"), val = tensor<int32, [3]>([1, 315, 801])];
            tensor<bool, [3]> slice_125_end_mask_0 = const()[name = tensor<string, []>("slice_125_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_125_cast_fp16 = slice_by_index(begin = slice_125_begin_0, end = slice_125_end_0, end_mask = slice_125_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_125_cast_fp16")];
            tensor<int32, [3]> slice_126_begin_0 = const()[name = tensor<string, []>("slice_126_begin_0"), val = tensor<int32, [3]>([0, 310, 0])];
            tensor<int32, [3]> slice_126_end_0 = const()[name = tensor<string, []>("slice_126_end_0"), val = tensor<int32, [3]>([1, 315, 801])];
            tensor<bool, [3]> slice_126_end_mask_0 = const()[name = tensor<string, []>("slice_126_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_126_cast_fp16 = slice_by_index(begin = slice_126_begin_0, end = slice_126_end_0, end_mask = slice_126_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_126_cast_fp16")];
            tensor<fp16, []> const_1374_promoted_to_fp16 = const()[name = tensor<string, []>("const_1374_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_187_cast_fp16 = pow(x = slice_125_cast_fp16, y = const_1374_promoted_to_fp16)[name = tensor<string, []>("pow_187_cast_fp16")];
            tensor<fp16, []> const_1375_promoted_to_fp16 = const()[name = tensor<string, []>("const_1375_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_188_cast_fp16 = pow(x = slice_126_cast_fp16, y = const_1375_promoted_to_fp16)[name = tensor<string, []>("pow_188_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_186_cast_fp16 = add(x = pow_187_cast_fp16, y = pow_188_cast_fp16)[name = tensor<string, []>("add_186_cast_fp16")];
            tensor<int32, [1]> sum_63_axes_0 = const()[name = tensor<string, []>("sum_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_63_keep_dims_0 = const()[name = tensor<string, []>("sum_63_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_63_cast_fp16 = reduce_sum(axes = sum_63_axes_0, keep_dims = sum_63_keep_dims_0, x = add_186_cast_fp16)[name = tensor<string, []>("sum_63_cast_fp16")];
            tensor<fp16, []> const_1378_to_fp16 = const()[name = tensor<string, []>("const_1378_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_187_cast_fp16 = add(x = sum_63_cast_fp16, y = const_1378_to_fp16)[name = tensor<string, []>("add_187_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_62_cast_fp16 = sqrt(x = add_187_cast_fp16)[name = tensor<string, []>("sqrt_62_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_124_cast_fp16 = real_div(x = slice_125_cast_fp16, y = sqrt_62_cast_fp16)[name = tensor<string, []>("div_124_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_125_cast_fp16 = real_div(x = slice_126_cast_fp16, y = sqrt_62_cast_fp16)[name = tensor<string, []>("div_125_cast_fp16")];
            tensor<fp32, []> log_62_epsilon_0 = const()[name = tensor<string, []>("log_62_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_62_cast_fp16 = log(epsilon = log_62_epsilon_0, x = sqrt_62_cast_fp16)[name = tensor<string, []>("log_62_cast_fp16")];
            tensor<int32, []> const_1379 = const()[name = tensor<string, []>("const_1379"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_62_interleave_0 = const()[name = tensor<string, []>("cat_62_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_62_cast_fp16 = concat(axis = const_1379, interleave = cat_62_interleave_0, values = (div_124_cast_fp16, div_125_cast_fp16, log_62_cast_fp16))[name = tensor<string, []>("cat_62_cast_fp16")];
            tensor<int32, [1]> unsqueeze_125_axes_0 = const()[name = tensor<string, []>("unsqueeze_125_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_125_cast_fp16 = expand_dims(axes = unsqueeze_125_axes_0, x = cat_62_cast_fp16)[name = tensor<string, []>("unsqueeze_125_cast_fp16")];
            tensor<fp16, []> const_1384_promoted_to_fp16 = const()[name = tensor<string, []>("const_1384_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_189_cast_fp16 = pow(x = unsqueeze_125_cast_fp16, y = const_1384_promoted_to_fp16)[name = tensor<string, []>("pow_189_cast_fp16")];
            tensor<int32, [1]> mean_62_axes_0 = const()[name = tensor<string, []>("mean_62_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_62_keep_dims_0 = const()[name = tensor<string, []>("mean_62_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_62_cast_fp16 = reduce_mean(axes = mean_62_axes_0, keep_dims = mean_62_keep_dims_0, x = pow_189_cast_fp16)[name = tensor<string, []>("mean_62_cast_fp16")];
            tensor<fp16, []> const_1387_to_fp16 = const()[name = tensor<string, []>("const_1387_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_188_cast_fp16 = add(x = mean_62_cast_fp16, y = const_1387_to_fp16)[name = tensor<string, []>("add_188_cast_fp16")];
            tensor<fp32, []> rsqrt_62_epsilon_0 = const()[name = tensor<string, []>("rsqrt_62_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_62_cast_fp16 = rsqrt(epsilon = rsqrt_62_epsilon_0, x = add_188_cast_fp16)[name = tensor<string, []>("rsqrt_62_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_124_cast_fp16 = mul(x = unsqueeze_125_cast_fp16, y = rsqrt_62_cast_fp16)[name = tensor<string, []>("mul_124_cast_fp16")];
            tensor<int32, [1]> squeeze_62_axes_0 = const()[name = tensor<string, []>("squeeze_62_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_62_cast_fp16 = squeeze(axes = squeeze_62_axes_0, x = mul_124_cast_fp16)[name = tensor<string, []>("squeeze_62_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_62_to_fp16 = const()[name = tensor<string, []>("view_62_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396864)))];
            tensor<fp16, [1, 11, 801]> mul_125_cast_fp16 = mul(x = squeeze_62_cast_fp16, y = view_62_to_fp16)[name = tensor<string, []>("mul_125_cast_fp16")];
            tensor<string, []> conv1d_62_pad_type_0 = const()[name = tensor<string, []>("conv1d_62_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_62_strides_0 = const()[name = tensor<string, []>("conv1d_62_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_62_pad_0 = const()[name = tensor<string, []>("conv1d_62_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_62_dilations_0 = const()[name = tensor<string, []>("conv1d_62_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_62_groups_0 = const()[name = tensor<string, []>("conv1d_62_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_62_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_62_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396992)))];
            tensor<fp16, [256]> p_bn_62_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_62_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402688)))];
            tensor<fp16, [1, 256, 801]> conv1d_62_cast_fp16 = conv(bias = p_bn_62_1_bias_to_fp16, dilations = conv1d_62_dilations_0, groups = conv1d_62_groups_0, pad = conv1d_62_pad_0, pad_type = conv1d_62_pad_type_0, strides = conv1d_62_strides_0, weight = p_bn_62_1_weight_to_fp16, x = mul_125_cast_fp16)[name = tensor<string, []>("conv1d_62_cast_fp16")];
            tensor<int32, [3]> slice_127_begin_0 = const()[name = tensor<string, []>("slice_127_begin_0"), val = tensor<int32, [3]>([0, 315, 0])];
            tensor<int32, [3]> slice_127_end_0 = const()[name = tensor<string, []>("slice_127_end_0"), val = tensor<int32, [3]>([1, 320, 801])];
            tensor<bool, [3]> slice_127_end_mask_0 = const()[name = tensor<string, []>("slice_127_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_127_cast_fp16 = slice_by_index(begin = slice_127_begin_0, end = slice_127_end_0, end_mask = slice_127_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_127_cast_fp16")];
            tensor<int32, [3]> slice_128_begin_0 = const()[name = tensor<string, []>("slice_128_begin_0"), val = tensor<int32, [3]>([0, 315, 0])];
            tensor<int32, [3]> slice_128_end_0 = const()[name = tensor<string, []>("slice_128_end_0"), val = tensor<int32, [3]>([1, 320, 801])];
            tensor<bool, [3]> slice_128_end_mask_0 = const()[name = tensor<string, []>("slice_128_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_128_cast_fp16 = slice_by_index(begin = slice_128_begin_0, end = slice_128_end_0, end_mask = slice_128_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_128_cast_fp16")];
            tensor<fp16, []> const_1396_promoted_to_fp16 = const()[name = tensor<string, []>("const_1396_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_190_cast_fp16 = pow(x = slice_127_cast_fp16, y = const_1396_promoted_to_fp16)[name = tensor<string, []>("pow_190_cast_fp16")];
            tensor<fp16, []> const_1397_promoted_to_fp16 = const()[name = tensor<string, []>("const_1397_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_191_cast_fp16 = pow(x = slice_128_cast_fp16, y = const_1397_promoted_to_fp16)[name = tensor<string, []>("pow_191_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_189_cast_fp16 = add(x = pow_190_cast_fp16, y = pow_191_cast_fp16)[name = tensor<string, []>("add_189_cast_fp16")];
            tensor<int32, [1]> sum_64_axes_0 = const()[name = tensor<string, []>("sum_64_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_64_keep_dims_0 = const()[name = tensor<string, []>("sum_64_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_64_cast_fp16 = reduce_sum(axes = sum_64_axes_0, keep_dims = sum_64_keep_dims_0, x = add_189_cast_fp16)[name = tensor<string, []>("sum_64_cast_fp16")];
            tensor<fp16, []> const_1400_to_fp16 = const()[name = tensor<string, []>("const_1400_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_190_cast_fp16 = add(x = sum_64_cast_fp16, y = const_1400_to_fp16)[name = tensor<string, []>("add_190_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_63_cast_fp16 = sqrt(x = add_190_cast_fp16)[name = tensor<string, []>("sqrt_63_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_126_cast_fp16 = real_div(x = slice_127_cast_fp16, y = sqrt_63_cast_fp16)[name = tensor<string, []>("div_126_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_127_cast_fp16 = real_div(x = slice_128_cast_fp16, y = sqrt_63_cast_fp16)[name = tensor<string, []>("div_127_cast_fp16")];
            tensor<fp32, []> log_63_epsilon_0 = const()[name = tensor<string, []>("log_63_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_63_cast_fp16 = log(epsilon = log_63_epsilon_0, x = sqrt_63_cast_fp16)[name = tensor<string, []>("log_63_cast_fp16")];
            tensor<int32, []> const_1401 = const()[name = tensor<string, []>("const_1401"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_63_interleave_0 = const()[name = tensor<string, []>("cat_63_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_63_cast_fp16 = concat(axis = const_1401, interleave = cat_63_interleave_0, values = (div_126_cast_fp16, div_127_cast_fp16, log_63_cast_fp16))[name = tensor<string, []>("cat_63_cast_fp16")];
            tensor<int32, [1]> unsqueeze_127_axes_0 = const()[name = tensor<string, []>("unsqueeze_127_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_127_cast_fp16 = expand_dims(axes = unsqueeze_127_axes_0, x = cat_63_cast_fp16)[name = tensor<string, []>("unsqueeze_127_cast_fp16")];
            tensor<fp16, []> const_1406_promoted_to_fp16 = const()[name = tensor<string, []>("const_1406_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_192_cast_fp16 = pow(x = unsqueeze_127_cast_fp16, y = const_1406_promoted_to_fp16)[name = tensor<string, []>("pow_192_cast_fp16")];
            tensor<int32, [1]> mean_63_axes_0 = const()[name = tensor<string, []>("mean_63_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_63_keep_dims_0 = const()[name = tensor<string, []>("mean_63_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_63_cast_fp16 = reduce_mean(axes = mean_63_axes_0, keep_dims = mean_63_keep_dims_0, x = pow_192_cast_fp16)[name = tensor<string, []>("mean_63_cast_fp16")];
            tensor<fp16, []> const_1409_to_fp16 = const()[name = tensor<string, []>("const_1409_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_191_cast_fp16 = add(x = mean_63_cast_fp16, y = const_1409_to_fp16)[name = tensor<string, []>("add_191_cast_fp16")];
            tensor<fp32, []> rsqrt_63_epsilon_0 = const()[name = tensor<string, []>("rsqrt_63_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_63_cast_fp16 = rsqrt(epsilon = rsqrt_63_epsilon_0, x = add_191_cast_fp16)[name = tensor<string, []>("rsqrt_63_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_126_cast_fp16 = mul(x = unsqueeze_127_cast_fp16, y = rsqrt_63_cast_fp16)[name = tensor<string, []>("mul_126_cast_fp16")];
            tensor<int32, [1]> squeeze_63_axes_0 = const()[name = tensor<string, []>("squeeze_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_63_cast_fp16 = squeeze(axes = squeeze_63_axes_0, x = mul_126_cast_fp16)[name = tensor<string, []>("squeeze_63_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_63_to_fp16 = const()[name = tensor<string, []>("view_63_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403264)))];
            tensor<fp16, [1, 11, 801]> mul_127_cast_fp16 = mul(x = squeeze_63_cast_fp16, y = view_63_to_fp16)[name = tensor<string, []>("mul_127_cast_fp16")];
            tensor<string, []> conv1d_63_pad_type_0 = const()[name = tensor<string, []>("conv1d_63_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_63_strides_0 = const()[name = tensor<string, []>("conv1d_63_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_63_pad_0 = const()[name = tensor<string, []>("conv1d_63_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_63_dilations_0 = const()[name = tensor<string, []>("conv1d_63_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_63_groups_0 = const()[name = tensor<string, []>("conv1d_63_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_63_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_63_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(403392)))];
            tensor<fp16, [256]> p_bn_63_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_63_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409088)))];
            tensor<fp16, [1, 256, 801]> conv1d_63_cast_fp16 = conv(bias = p_bn_63_1_bias_to_fp16, dilations = conv1d_63_dilations_0, groups = conv1d_63_groups_0, pad = conv1d_63_pad_0, pad_type = conv1d_63_pad_type_0, strides = conv1d_63_strides_0, weight = p_bn_63_1_weight_to_fp16, x = mul_127_cast_fp16)[name = tensor<string, []>("conv1d_63_cast_fp16")];
            tensor<int32, [3]> slice_129_begin_0 = const()[name = tensor<string, []>("slice_129_begin_0"), val = tensor<int32, [3]>([0, 320, 0])];
            tensor<int32, [3]> slice_129_end_0 = const()[name = tensor<string, []>("slice_129_end_0"), val = tensor<int32, [3]>([1, 325, 801])];
            tensor<bool, [3]> slice_129_end_mask_0 = const()[name = tensor<string, []>("slice_129_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_129_cast_fp16 = slice_by_index(begin = slice_129_begin_0, end = slice_129_end_0, end_mask = slice_129_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_129_cast_fp16")];
            tensor<int32, [3]> slice_130_begin_0 = const()[name = tensor<string, []>("slice_130_begin_0"), val = tensor<int32, [3]>([0, 320, 0])];
            tensor<int32, [3]> slice_130_end_0 = const()[name = tensor<string, []>("slice_130_end_0"), val = tensor<int32, [3]>([1, 325, 801])];
            tensor<bool, [3]> slice_130_end_mask_0 = const()[name = tensor<string, []>("slice_130_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_130_cast_fp16 = slice_by_index(begin = slice_130_begin_0, end = slice_130_end_0, end_mask = slice_130_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_130_cast_fp16")];
            tensor<fp16, []> const_1418_promoted_to_fp16 = const()[name = tensor<string, []>("const_1418_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_193_cast_fp16 = pow(x = slice_129_cast_fp16, y = const_1418_promoted_to_fp16)[name = tensor<string, []>("pow_193_cast_fp16")];
            tensor<fp16, []> const_1419_promoted_to_fp16 = const()[name = tensor<string, []>("const_1419_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_194_cast_fp16 = pow(x = slice_130_cast_fp16, y = const_1419_promoted_to_fp16)[name = tensor<string, []>("pow_194_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_192_cast_fp16 = add(x = pow_193_cast_fp16, y = pow_194_cast_fp16)[name = tensor<string, []>("add_192_cast_fp16")];
            tensor<int32, [1]> sum_65_axes_0 = const()[name = tensor<string, []>("sum_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_65_keep_dims_0 = const()[name = tensor<string, []>("sum_65_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_65_cast_fp16 = reduce_sum(axes = sum_65_axes_0, keep_dims = sum_65_keep_dims_0, x = add_192_cast_fp16)[name = tensor<string, []>("sum_65_cast_fp16")];
            tensor<fp16, []> const_1422_to_fp16 = const()[name = tensor<string, []>("const_1422_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_193_cast_fp16 = add(x = sum_65_cast_fp16, y = const_1422_to_fp16)[name = tensor<string, []>("add_193_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_64_cast_fp16 = sqrt(x = add_193_cast_fp16)[name = tensor<string, []>("sqrt_64_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_128_cast_fp16 = real_div(x = slice_129_cast_fp16, y = sqrt_64_cast_fp16)[name = tensor<string, []>("div_128_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_129_cast_fp16 = real_div(x = slice_130_cast_fp16, y = sqrt_64_cast_fp16)[name = tensor<string, []>("div_129_cast_fp16")];
            tensor<fp32, []> log_64_epsilon_0 = const()[name = tensor<string, []>("log_64_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_64_cast_fp16 = log(epsilon = log_64_epsilon_0, x = sqrt_64_cast_fp16)[name = tensor<string, []>("log_64_cast_fp16")];
            tensor<int32, []> const_1423 = const()[name = tensor<string, []>("const_1423"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_64_interleave_0 = const()[name = tensor<string, []>("cat_64_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_64_cast_fp16 = concat(axis = const_1423, interleave = cat_64_interleave_0, values = (div_128_cast_fp16, div_129_cast_fp16, log_64_cast_fp16))[name = tensor<string, []>("cat_64_cast_fp16")];
            tensor<int32, [1]> unsqueeze_129_axes_0 = const()[name = tensor<string, []>("unsqueeze_129_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_129_cast_fp16 = expand_dims(axes = unsqueeze_129_axes_0, x = cat_64_cast_fp16)[name = tensor<string, []>("unsqueeze_129_cast_fp16")];
            tensor<fp16, []> const_1428_promoted_to_fp16 = const()[name = tensor<string, []>("const_1428_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_195_cast_fp16 = pow(x = unsqueeze_129_cast_fp16, y = const_1428_promoted_to_fp16)[name = tensor<string, []>("pow_195_cast_fp16")];
            tensor<int32, [1]> mean_64_axes_0 = const()[name = tensor<string, []>("mean_64_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_64_keep_dims_0 = const()[name = tensor<string, []>("mean_64_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_64_cast_fp16 = reduce_mean(axes = mean_64_axes_0, keep_dims = mean_64_keep_dims_0, x = pow_195_cast_fp16)[name = tensor<string, []>("mean_64_cast_fp16")];
            tensor<fp16, []> const_1431_to_fp16 = const()[name = tensor<string, []>("const_1431_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_194_cast_fp16 = add(x = mean_64_cast_fp16, y = const_1431_to_fp16)[name = tensor<string, []>("add_194_cast_fp16")];
            tensor<fp32, []> rsqrt_64_epsilon_0 = const()[name = tensor<string, []>("rsqrt_64_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_64_cast_fp16 = rsqrt(epsilon = rsqrt_64_epsilon_0, x = add_194_cast_fp16)[name = tensor<string, []>("rsqrt_64_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_128_cast_fp16 = mul(x = unsqueeze_129_cast_fp16, y = rsqrt_64_cast_fp16)[name = tensor<string, []>("mul_128_cast_fp16")];
            tensor<int32, [1]> squeeze_64_axes_0 = const()[name = tensor<string, []>("squeeze_64_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_64_cast_fp16 = squeeze(axes = squeeze_64_axes_0, x = mul_128_cast_fp16)[name = tensor<string, []>("squeeze_64_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_64_to_fp16 = const()[name = tensor<string, []>("view_64_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409664)))];
            tensor<fp16, [1, 11, 801]> mul_129_cast_fp16 = mul(x = squeeze_64_cast_fp16, y = view_64_to_fp16)[name = tensor<string, []>("mul_129_cast_fp16")];
            tensor<string, []> conv1d_64_pad_type_0 = const()[name = tensor<string, []>("conv1d_64_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_64_strides_0 = const()[name = tensor<string, []>("conv1d_64_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_64_pad_0 = const()[name = tensor<string, []>("conv1d_64_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_64_dilations_0 = const()[name = tensor<string, []>("conv1d_64_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_64_groups_0 = const()[name = tensor<string, []>("conv1d_64_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_64_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_64_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409792)))];
            tensor<fp16, [256]> p_bn_64_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_64_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415488)))];
            tensor<fp16, [1, 256, 801]> conv1d_64_cast_fp16 = conv(bias = p_bn_64_1_bias_to_fp16, dilations = conv1d_64_dilations_0, groups = conv1d_64_groups_0, pad = conv1d_64_pad_0, pad_type = conv1d_64_pad_type_0, strides = conv1d_64_strides_0, weight = p_bn_64_1_weight_to_fp16, x = mul_129_cast_fp16)[name = tensor<string, []>("conv1d_64_cast_fp16")];
            tensor<int32, [3]> slice_131_begin_0 = const()[name = tensor<string, []>("slice_131_begin_0"), val = tensor<int32, [3]>([0, 325, 0])];
            tensor<int32, [3]> slice_131_end_0 = const()[name = tensor<string, []>("slice_131_end_0"), val = tensor<int32, [3]>([1, 330, 801])];
            tensor<bool, [3]> slice_131_end_mask_0 = const()[name = tensor<string, []>("slice_131_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_131_cast_fp16 = slice_by_index(begin = slice_131_begin_0, end = slice_131_end_0, end_mask = slice_131_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_131_cast_fp16")];
            tensor<int32, [3]> slice_132_begin_0 = const()[name = tensor<string, []>("slice_132_begin_0"), val = tensor<int32, [3]>([0, 325, 0])];
            tensor<int32, [3]> slice_132_end_0 = const()[name = tensor<string, []>("slice_132_end_0"), val = tensor<int32, [3]>([1, 330, 801])];
            tensor<bool, [3]> slice_132_end_mask_0 = const()[name = tensor<string, []>("slice_132_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_132_cast_fp16 = slice_by_index(begin = slice_132_begin_0, end = slice_132_end_0, end_mask = slice_132_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_132_cast_fp16")];
            tensor<fp16, []> const_1440_promoted_to_fp16 = const()[name = tensor<string, []>("const_1440_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_196_cast_fp16 = pow(x = slice_131_cast_fp16, y = const_1440_promoted_to_fp16)[name = tensor<string, []>("pow_196_cast_fp16")];
            tensor<fp16, []> const_1441_promoted_to_fp16 = const()[name = tensor<string, []>("const_1441_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_197_cast_fp16 = pow(x = slice_132_cast_fp16, y = const_1441_promoted_to_fp16)[name = tensor<string, []>("pow_197_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_195_cast_fp16 = add(x = pow_196_cast_fp16, y = pow_197_cast_fp16)[name = tensor<string, []>("add_195_cast_fp16")];
            tensor<int32, [1]> sum_66_axes_0 = const()[name = tensor<string, []>("sum_66_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_66_keep_dims_0 = const()[name = tensor<string, []>("sum_66_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_66_cast_fp16 = reduce_sum(axes = sum_66_axes_0, keep_dims = sum_66_keep_dims_0, x = add_195_cast_fp16)[name = tensor<string, []>("sum_66_cast_fp16")];
            tensor<fp16, []> const_1444_to_fp16 = const()[name = tensor<string, []>("const_1444_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_196_cast_fp16 = add(x = sum_66_cast_fp16, y = const_1444_to_fp16)[name = tensor<string, []>("add_196_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_65_cast_fp16 = sqrt(x = add_196_cast_fp16)[name = tensor<string, []>("sqrt_65_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_130_cast_fp16 = real_div(x = slice_131_cast_fp16, y = sqrt_65_cast_fp16)[name = tensor<string, []>("div_130_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_131_cast_fp16 = real_div(x = slice_132_cast_fp16, y = sqrt_65_cast_fp16)[name = tensor<string, []>("div_131_cast_fp16")];
            tensor<fp32, []> log_65_epsilon_0 = const()[name = tensor<string, []>("log_65_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_65_cast_fp16 = log(epsilon = log_65_epsilon_0, x = sqrt_65_cast_fp16)[name = tensor<string, []>("log_65_cast_fp16")];
            tensor<int32, []> const_1445 = const()[name = tensor<string, []>("const_1445"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_65_interleave_0 = const()[name = tensor<string, []>("cat_65_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_65_cast_fp16 = concat(axis = const_1445, interleave = cat_65_interleave_0, values = (div_130_cast_fp16, div_131_cast_fp16, log_65_cast_fp16))[name = tensor<string, []>("cat_65_cast_fp16")];
            tensor<int32, [1]> unsqueeze_131_axes_0 = const()[name = tensor<string, []>("unsqueeze_131_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_131_cast_fp16 = expand_dims(axes = unsqueeze_131_axes_0, x = cat_65_cast_fp16)[name = tensor<string, []>("unsqueeze_131_cast_fp16")];
            tensor<fp16, []> const_1450_promoted_to_fp16 = const()[name = tensor<string, []>("const_1450_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_198_cast_fp16 = pow(x = unsqueeze_131_cast_fp16, y = const_1450_promoted_to_fp16)[name = tensor<string, []>("pow_198_cast_fp16")];
            tensor<int32, [1]> mean_65_axes_0 = const()[name = tensor<string, []>("mean_65_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_65_keep_dims_0 = const()[name = tensor<string, []>("mean_65_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_65_cast_fp16 = reduce_mean(axes = mean_65_axes_0, keep_dims = mean_65_keep_dims_0, x = pow_198_cast_fp16)[name = tensor<string, []>("mean_65_cast_fp16")];
            tensor<fp16, []> const_1453_to_fp16 = const()[name = tensor<string, []>("const_1453_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_197_cast_fp16 = add(x = mean_65_cast_fp16, y = const_1453_to_fp16)[name = tensor<string, []>("add_197_cast_fp16")];
            tensor<fp32, []> rsqrt_65_epsilon_0 = const()[name = tensor<string, []>("rsqrt_65_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_65_cast_fp16 = rsqrt(epsilon = rsqrt_65_epsilon_0, x = add_197_cast_fp16)[name = tensor<string, []>("rsqrt_65_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_130_cast_fp16 = mul(x = unsqueeze_131_cast_fp16, y = rsqrt_65_cast_fp16)[name = tensor<string, []>("mul_130_cast_fp16")];
            tensor<int32, [1]> squeeze_65_axes_0 = const()[name = tensor<string, []>("squeeze_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_65_cast_fp16 = squeeze(axes = squeeze_65_axes_0, x = mul_130_cast_fp16)[name = tensor<string, []>("squeeze_65_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_65_to_fp16 = const()[name = tensor<string, []>("view_65_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416064)))];
            tensor<fp16, [1, 11, 801]> mul_131_cast_fp16 = mul(x = squeeze_65_cast_fp16, y = view_65_to_fp16)[name = tensor<string, []>("mul_131_cast_fp16")];
            tensor<string, []> conv1d_65_pad_type_0 = const()[name = tensor<string, []>("conv1d_65_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_65_strides_0 = const()[name = tensor<string, []>("conv1d_65_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_65_pad_0 = const()[name = tensor<string, []>("conv1d_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_65_dilations_0 = const()[name = tensor<string, []>("conv1d_65_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_65_groups_0 = const()[name = tensor<string, []>("conv1d_65_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_65_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_65_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416192)))];
            tensor<fp16, [256]> p_bn_65_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_65_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421888)))];
            tensor<fp16, [1, 256, 801]> conv1d_65_cast_fp16 = conv(bias = p_bn_65_1_bias_to_fp16, dilations = conv1d_65_dilations_0, groups = conv1d_65_groups_0, pad = conv1d_65_pad_0, pad_type = conv1d_65_pad_type_0, strides = conv1d_65_strides_0, weight = p_bn_65_1_weight_to_fp16, x = mul_131_cast_fp16)[name = tensor<string, []>("conv1d_65_cast_fp16")];
            tensor<int32, [3]> slice_133_begin_0 = const()[name = tensor<string, []>("slice_133_begin_0"), val = tensor<int32, [3]>([0, 330, 0])];
            tensor<int32, [3]> slice_133_end_0 = const()[name = tensor<string, []>("slice_133_end_0"), val = tensor<int32, [3]>([1, 335, 801])];
            tensor<bool, [3]> slice_133_end_mask_0 = const()[name = tensor<string, []>("slice_133_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_133_cast_fp16 = slice_by_index(begin = slice_133_begin_0, end = slice_133_end_0, end_mask = slice_133_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_133_cast_fp16")];
            tensor<int32, [3]> slice_134_begin_0 = const()[name = tensor<string, []>("slice_134_begin_0"), val = tensor<int32, [3]>([0, 330, 0])];
            tensor<int32, [3]> slice_134_end_0 = const()[name = tensor<string, []>("slice_134_end_0"), val = tensor<int32, [3]>([1, 335, 801])];
            tensor<bool, [3]> slice_134_end_mask_0 = const()[name = tensor<string, []>("slice_134_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_134_cast_fp16 = slice_by_index(begin = slice_134_begin_0, end = slice_134_end_0, end_mask = slice_134_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_134_cast_fp16")];
            tensor<fp16, []> const_1462_promoted_to_fp16 = const()[name = tensor<string, []>("const_1462_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_199_cast_fp16 = pow(x = slice_133_cast_fp16, y = const_1462_promoted_to_fp16)[name = tensor<string, []>("pow_199_cast_fp16")];
            tensor<fp16, []> const_1463_promoted_to_fp16 = const()[name = tensor<string, []>("const_1463_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_200_cast_fp16 = pow(x = slice_134_cast_fp16, y = const_1463_promoted_to_fp16)[name = tensor<string, []>("pow_200_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_198_cast_fp16 = add(x = pow_199_cast_fp16, y = pow_200_cast_fp16)[name = tensor<string, []>("add_198_cast_fp16")];
            tensor<int32, [1]> sum_67_axes_0 = const()[name = tensor<string, []>("sum_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_67_keep_dims_0 = const()[name = tensor<string, []>("sum_67_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_67_cast_fp16 = reduce_sum(axes = sum_67_axes_0, keep_dims = sum_67_keep_dims_0, x = add_198_cast_fp16)[name = tensor<string, []>("sum_67_cast_fp16")];
            tensor<fp16, []> const_1466_to_fp16 = const()[name = tensor<string, []>("const_1466_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_199_cast_fp16 = add(x = sum_67_cast_fp16, y = const_1466_to_fp16)[name = tensor<string, []>("add_199_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_66_cast_fp16 = sqrt(x = add_199_cast_fp16)[name = tensor<string, []>("sqrt_66_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_132_cast_fp16 = real_div(x = slice_133_cast_fp16, y = sqrt_66_cast_fp16)[name = tensor<string, []>("div_132_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_133_cast_fp16 = real_div(x = slice_134_cast_fp16, y = sqrt_66_cast_fp16)[name = tensor<string, []>("div_133_cast_fp16")];
            tensor<fp32, []> log_66_epsilon_0 = const()[name = tensor<string, []>("log_66_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_66_cast_fp16 = log(epsilon = log_66_epsilon_0, x = sqrt_66_cast_fp16)[name = tensor<string, []>("log_66_cast_fp16")];
            tensor<int32, []> const_1467 = const()[name = tensor<string, []>("const_1467"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_66_interleave_0 = const()[name = tensor<string, []>("cat_66_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_66_cast_fp16 = concat(axis = const_1467, interleave = cat_66_interleave_0, values = (div_132_cast_fp16, div_133_cast_fp16, log_66_cast_fp16))[name = tensor<string, []>("cat_66_cast_fp16")];
            tensor<int32, [1]> unsqueeze_133_axes_0 = const()[name = tensor<string, []>("unsqueeze_133_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_133_cast_fp16 = expand_dims(axes = unsqueeze_133_axes_0, x = cat_66_cast_fp16)[name = tensor<string, []>("unsqueeze_133_cast_fp16")];
            tensor<fp16, []> const_1472_promoted_to_fp16 = const()[name = tensor<string, []>("const_1472_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_201_cast_fp16 = pow(x = unsqueeze_133_cast_fp16, y = const_1472_promoted_to_fp16)[name = tensor<string, []>("pow_201_cast_fp16")];
            tensor<int32, [1]> mean_66_axes_0 = const()[name = tensor<string, []>("mean_66_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_66_keep_dims_0 = const()[name = tensor<string, []>("mean_66_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_66_cast_fp16 = reduce_mean(axes = mean_66_axes_0, keep_dims = mean_66_keep_dims_0, x = pow_201_cast_fp16)[name = tensor<string, []>("mean_66_cast_fp16")];
            tensor<fp16, []> const_1475_to_fp16 = const()[name = tensor<string, []>("const_1475_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_200_cast_fp16 = add(x = mean_66_cast_fp16, y = const_1475_to_fp16)[name = tensor<string, []>("add_200_cast_fp16")];
            tensor<fp32, []> rsqrt_66_epsilon_0 = const()[name = tensor<string, []>("rsqrt_66_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_66_cast_fp16 = rsqrt(epsilon = rsqrt_66_epsilon_0, x = add_200_cast_fp16)[name = tensor<string, []>("rsqrt_66_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_132_cast_fp16 = mul(x = unsqueeze_133_cast_fp16, y = rsqrt_66_cast_fp16)[name = tensor<string, []>("mul_132_cast_fp16")];
            tensor<int32, [1]> squeeze_66_axes_0 = const()[name = tensor<string, []>("squeeze_66_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_66_cast_fp16 = squeeze(axes = squeeze_66_axes_0, x = mul_132_cast_fp16)[name = tensor<string, []>("squeeze_66_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_66_to_fp16 = const()[name = tensor<string, []>("view_66_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422464)))];
            tensor<fp16, [1, 11, 801]> mul_133_cast_fp16 = mul(x = squeeze_66_cast_fp16, y = view_66_to_fp16)[name = tensor<string, []>("mul_133_cast_fp16")];
            tensor<string, []> conv1d_66_pad_type_0 = const()[name = tensor<string, []>("conv1d_66_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_66_strides_0 = const()[name = tensor<string, []>("conv1d_66_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_66_pad_0 = const()[name = tensor<string, []>("conv1d_66_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_66_dilations_0 = const()[name = tensor<string, []>("conv1d_66_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_66_groups_0 = const()[name = tensor<string, []>("conv1d_66_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_66_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_66_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422592)))];
            tensor<fp16, [256]> p_bn_66_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_66_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428288)))];
            tensor<fp16, [1, 256, 801]> conv1d_66_cast_fp16 = conv(bias = p_bn_66_1_bias_to_fp16, dilations = conv1d_66_dilations_0, groups = conv1d_66_groups_0, pad = conv1d_66_pad_0, pad_type = conv1d_66_pad_type_0, strides = conv1d_66_strides_0, weight = p_bn_66_1_weight_to_fp16, x = mul_133_cast_fp16)[name = tensor<string, []>("conv1d_66_cast_fp16")];
            tensor<int32, [3]> slice_135_begin_0 = const()[name = tensor<string, []>("slice_135_begin_0"), val = tensor<int32, [3]>([0, 335, 0])];
            tensor<int32, [3]> slice_135_end_0 = const()[name = tensor<string, []>("slice_135_end_0"), val = tensor<int32, [3]>([1, 340, 801])];
            tensor<bool, [3]> slice_135_end_mask_0 = const()[name = tensor<string, []>("slice_135_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_135_cast_fp16 = slice_by_index(begin = slice_135_begin_0, end = slice_135_end_0, end_mask = slice_135_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_135_cast_fp16")];
            tensor<int32, [3]> slice_136_begin_0 = const()[name = tensor<string, []>("slice_136_begin_0"), val = tensor<int32, [3]>([0, 335, 0])];
            tensor<int32, [3]> slice_136_end_0 = const()[name = tensor<string, []>("slice_136_end_0"), val = tensor<int32, [3]>([1, 340, 801])];
            tensor<bool, [3]> slice_136_end_mask_0 = const()[name = tensor<string, []>("slice_136_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_136_cast_fp16 = slice_by_index(begin = slice_136_begin_0, end = slice_136_end_0, end_mask = slice_136_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_136_cast_fp16")];
            tensor<fp16, []> const_1484_promoted_to_fp16 = const()[name = tensor<string, []>("const_1484_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_202_cast_fp16 = pow(x = slice_135_cast_fp16, y = const_1484_promoted_to_fp16)[name = tensor<string, []>("pow_202_cast_fp16")];
            tensor<fp16, []> const_1485_promoted_to_fp16 = const()[name = tensor<string, []>("const_1485_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_203_cast_fp16 = pow(x = slice_136_cast_fp16, y = const_1485_promoted_to_fp16)[name = tensor<string, []>("pow_203_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_201_cast_fp16 = add(x = pow_202_cast_fp16, y = pow_203_cast_fp16)[name = tensor<string, []>("add_201_cast_fp16")];
            tensor<int32, [1]> sum_68_axes_0 = const()[name = tensor<string, []>("sum_68_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_68_keep_dims_0 = const()[name = tensor<string, []>("sum_68_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_68_cast_fp16 = reduce_sum(axes = sum_68_axes_0, keep_dims = sum_68_keep_dims_0, x = add_201_cast_fp16)[name = tensor<string, []>("sum_68_cast_fp16")];
            tensor<fp16, []> const_1488_to_fp16 = const()[name = tensor<string, []>("const_1488_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_202_cast_fp16 = add(x = sum_68_cast_fp16, y = const_1488_to_fp16)[name = tensor<string, []>("add_202_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_67_cast_fp16 = sqrt(x = add_202_cast_fp16)[name = tensor<string, []>("sqrt_67_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_134_cast_fp16 = real_div(x = slice_135_cast_fp16, y = sqrt_67_cast_fp16)[name = tensor<string, []>("div_134_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_135_cast_fp16 = real_div(x = slice_136_cast_fp16, y = sqrt_67_cast_fp16)[name = tensor<string, []>("div_135_cast_fp16")];
            tensor<fp32, []> log_67_epsilon_0 = const()[name = tensor<string, []>("log_67_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_67_cast_fp16 = log(epsilon = log_67_epsilon_0, x = sqrt_67_cast_fp16)[name = tensor<string, []>("log_67_cast_fp16")];
            tensor<int32, []> const_1489 = const()[name = tensor<string, []>("const_1489"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_67_interleave_0 = const()[name = tensor<string, []>("cat_67_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_67_cast_fp16 = concat(axis = const_1489, interleave = cat_67_interleave_0, values = (div_134_cast_fp16, div_135_cast_fp16, log_67_cast_fp16))[name = tensor<string, []>("cat_67_cast_fp16")];
            tensor<int32, [1]> unsqueeze_135_axes_0 = const()[name = tensor<string, []>("unsqueeze_135_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_135_cast_fp16 = expand_dims(axes = unsqueeze_135_axes_0, x = cat_67_cast_fp16)[name = tensor<string, []>("unsqueeze_135_cast_fp16")];
            tensor<fp16, []> const_1494_promoted_to_fp16 = const()[name = tensor<string, []>("const_1494_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_204_cast_fp16 = pow(x = unsqueeze_135_cast_fp16, y = const_1494_promoted_to_fp16)[name = tensor<string, []>("pow_204_cast_fp16")];
            tensor<int32, [1]> mean_67_axes_0 = const()[name = tensor<string, []>("mean_67_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_67_keep_dims_0 = const()[name = tensor<string, []>("mean_67_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_67_cast_fp16 = reduce_mean(axes = mean_67_axes_0, keep_dims = mean_67_keep_dims_0, x = pow_204_cast_fp16)[name = tensor<string, []>("mean_67_cast_fp16")];
            tensor<fp16, []> const_1497_to_fp16 = const()[name = tensor<string, []>("const_1497_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_203_cast_fp16 = add(x = mean_67_cast_fp16, y = const_1497_to_fp16)[name = tensor<string, []>("add_203_cast_fp16")];
            tensor<fp32, []> rsqrt_67_epsilon_0 = const()[name = tensor<string, []>("rsqrt_67_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_67_cast_fp16 = rsqrt(epsilon = rsqrt_67_epsilon_0, x = add_203_cast_fp16)[name = tensor<string, []>("rsqrt_67_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_134_cast_fp16 = mul(x = unsqueeze_135_cast_fp16, y = rsqrt_67_cast_fp16)[name = tensor<string, []>("mul_134_cast_fp16")];
            tensor<int32, [1]> squeeze_67_axes_0 = const()[name = tensor<string, []>("squeeze_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_67_cast_fp16 = squeeze(axes = squeeze_67_axes_0, x = mul_134_cast_fp16)[name = tensor<string, []>("squeeze_67_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_67_to_fp16 = const()[name = tensor<string, []>("view_67_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428864)))];
            tensor<fp16, [1, 11, 801]> mul_135_cast_fp16 = mul(x = squeeze_67_cast_fp16, y = view_67_to_fp16)[name = tensor<string, []>("mul_135_cast_fp16")];
            tensor<string, []> conv1d_67_pad_type_0 = const()[name = tensor<string, []>("conv1d_67_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_67_strides_0 = const()[name = tensor<string, []>("conv1d_67_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_67_pad_0 = const()[name = tensor<string, []>("conv1d_67_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_67_dilations_0 = const()[name = tensor<string, []>("conv1d_67_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_67_groups_0 = const()[name = tensor<string, []>("conv1d_67_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_67_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_67_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428992)))];
            tensor<fp16, [256]> p_bn_67_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_67_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434688)))];
            tensor<fp16, [1, 256, 801]> conv1d_67_cast_fp16 = conv(bias = p_bn_67_1_bias_to_fp16, dilations = conv1d_67_dilations_0, groups = conv1d_67_groups_0, pad = conv1d_67_pad_0, pad_type = conv1d_67_pad_type_0, strides = conv1d_67_strides_0, weight = p_bn_67_1_weight_to_fp16, x = mul_135_cast_fp16)[name = tensor<string, []>("conv1d_67_cast_fp16")];
            tensor<int32, [3]> slice_137_begin_0 = const()[name = tensor<string, []>("slice_137_begin_0"), val = tensor<int32, [3]>([0, 340, 0])];
            tensor<int32, [3]> slice_137_end_0 = const()[name = tensor<string, []>("slice_137_end_0"), val = tensor<int32, [3]>([1, 345, 801])];
            tensor<bool, [3]> slice_137_end_mask_0 = const()[name = tensor<string, []>("slice_137_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_137_cast_fp16 = slice_by_index(begin = slice_137_begin_0, end = slice_137_end_0, end_mask = slice_137_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_137_cast_fp16")];
            tensor<int32, [3]> slice_138_begin_0 = const()[name = tensor<string, []>("slice_138_begin_0"), val = tensor<int32, [3]>([0, 340, 0])];
            tensor<int32, [3]> slice_138_end_0 = const()[name = tensor<string, []>("slice_138_end_0"), val = tensor<int32, [3]>([1, 345, 801])];
            tensor<bool, [3]> slice_138_end_mask_0 = const()[name = tensor<string, []>("slice_138_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_138_cast_fp16 = slice_by_index(begin = slice_138_begin_0, end = slice_138_end_0, end_mask = slice_138_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_138_cast_fp16")];
            tensor<fp16, []> const_1506_promoted_to_fp16 = const()[name = tensor<string, []>("const_1506_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_205_cast_fp16 = pow(x = slice_137_cast_fp16, y = const_1506_promoted_to_fp16)[name = tensor<string, []>("pow_205_cast_fp16")];
            tensor<fp16, []> const_1507_promoted_to_fp16 = const()[name = tensor<string, []>("const_1507_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_206_cast_fp16 = pow(x = slice_138_cast_fp16, y = const_1507_promoted_to_fp16)[name = tensor<string, []>("pow_206_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_204_cast_fp16 = add(x = pow_205_cast_fp16, y = pow_206_cast_fp16)[name = tensor<string, []>("add_204_cast_fp16")];
            tensor<int32, [1]> sum_69_axes_0 = const()[name = tensor<string, []>("sum_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_69_keep_dims_0 = const()[name = tensor<string, []>("sum_69_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_69_cast_fp16 = reduce_sum(axes = sum_69_axes_0, keep_dims = sum_69_keep_dims_0, x = add_204_cast_fp16)[name = tensor<string, []>("sum_69_cast_fp16")];
            tensor<fp16, []> const_1510_to_fp16 = const()[name = tensor<string, []>("const_1510_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_205_cast_fp16 = add(x = sum_69_cast_fp16, y = const_1510_to_fp16)[name = tensor<string, []>("add_205_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_68_cast_fp16 = sqrt(x = add_205_cast_fp16)[name = tensor<string, []>("sqrt_68_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_136_cast_fp16 = real_div(x = slice_137_cast_fp16, y = sqrt_68_cast_fp16)[name = tensor<string, []>("div_136_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_137_cast_fp16 = real_div(x = slice_138_cast_fp16, y = sqrt_68_cast_fp16)[name = tensor<string, []>("div_137_cast_fp16")];
            tensor<fp32, []> log_68_epsilon_0 = const()[name = tensor<string, []>("log_68_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_68_cast_fp16 = log(epsilon = log_68_epsilon_0, x = sqrt_68_cast_fp16)[name = tensor<string, []>("log_68_cast_fp16")];
            tensor<int32, []> const_1511 = const()[name = tensor<string, []>("const_1511"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_68_interleave_0 = const()[name = tensor<string, []>("cat_68_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_68_cast_fp16 = concat(axis = const_1511, interleave = cat_68_interleave_0, values = (div_136_cast_fp16, div_137_cast_fp16, log_68_cast_fp16))[name = tensor<string, []>("cat_68_cast_fp16")];
            tensor<int32, [1]> unsqueeze_137_axes_0 = const()[name = tensor<string, []>("unsqueeze_137_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_137_cast_fp16 = expand_dims(axes = unsqueeze_137_axes_0, x = cat_68_cast_fp16)[name = tensor<string, []>("unsqueeze_137_cast_fp16")];
            tensor<fp16, []> const_1516_promoted_to_fp16 = const()[name = tensor<string, []>("const_1516_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_207_cast_fp16 = pow(x = unsqueeze_137_cast_fp16, y = const_1516_promoted_to_fp16)[name = tensor<string, []>("pow_207_cast_fp16")];
            tensor<int32, [1]> mean_68_axes_0 = const()[name = tensor<string, []>("mean_68_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_68_keep_dims_0 = const()[name = tensor<string, []>("mean_68_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_68_cast_fp16 = reduce_mean(axes = mean_68_axes_0, keep_dims = mean_68_keep_dims_0, x = pow_207_cast_fp16)[name = tensor<string, []>("mean_68_cast_fp16")];
            tensor<fp16, []> const_1519_to_fp16 = const()[name = tensor<string, []>("const_1519_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_206_cast_fp16 = add(x = mean_68_cast_fp16, y = const_1519_to_fp16)[name = tensor<string, []>("add_206_cast_fp16")];
            tensor<fp32, []> rsqrt_68_epsilon_0 = const()[name = tensor<string, []>("rsqrt_68_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_68_cast_fp16 = rsqrt(epsilon = rsqrt_68_epsilon_0, x = add_206_cast_fp16)[name = tensor<string, []>("rsqrt_68_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_136_cast_fp16 = mul(x = unsqueeze_137_cast_fp16, y = rsqrt_68_cast_fp16)[name = tensor<string, []>("mul_136_cast_fp16")];
            tensor<int32, [1]> squeeze_68_axes_0 = const()[name = tensor<string, []>("squeeze_68_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_68_cast_fp16 = squeeze(axes = squeeze_68_axes_0, x = mul_136_cast_fp16)[name = tensor<string, []>("squeeze_68_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_68_to_fp16 = const()[name = tensor<string, []>("view_68_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435264)))];
            tensor<fp16, [1, 11, 801]> mul_137_cast_fp16 = mul(x = squeeze_68_cast_fp16, y = view_68_to_fp16)[name = tensor<string, []>("mul_137_cast_fp16")];
            tensor<string, []> conv1d_68_pad_type_0 = const()[name = tensor<string, []>("conv1d_68_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_68_strides_0 = const()[name = tensor<string, []>("conv1d_68_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_68_pad_0 = const()[name = tensor<string, []>("conv1d_68_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_68_dilations_0 = const()[name = tensor<string, []>("conv1d_68_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_68_groups_0 = const()[name = tensor<string, []>("conv1d_68_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_68_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_68_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435392)))];
            tensor<fp16, [256]> p_bn_68_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_68_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441088)))];
            tensor<fp16, [1, 256, 801]> conv1d_68_cast_fp16 = conv(bias = p_bn_68_1_bias_to_fp16, dilations = conv1d_68_dilations_0, groups = conv1d_68_groups_0, pad = conv1d_68_pad_0, pad_type = conv1d_68_pad_type_0, strides = conv1d_68_strides_0, weight = p_bn_68_1_weight_to_fp16, x = mul_137_cast_fp16)[name = tensor<string, []>("conv1d_68_cast_fp16")];
            tensor<int32, [3]> slice_139_begin_0 = const()[name = tensor<string, []>("slice_139_begin_0"), val = tensor<int32, [3]>([0, 345, 0])];
            tensor<int32, [3]> slice_139_end_0 = const()[name = tensor<string, []>("slice_139_end_0"), val = tensor<int32, [3]>([1, 350, 801])];
            tensor<bool, [3]> slice_139_end_mask_0 = const()[name = tensor<string, []>("slice_139_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_139_cast_fp16 = slice_by_index(begin = slice_139_begin_0, end = slice_139_end_0, end_mask = slice_139_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_139_cast_fp16")];
            tensor<int32, [3]> slice_140_begin_0 = const()[name = tensor<string, []>("slice_140_begin_0"), val = tensor<int32, [3]>([0, 345, 0])];
            tensor<int32, [3]> slice_140_end_0 = const()[name = tensor<string, []>("slice_140_end_0"), val = tensor<int32, [3]>([1, 350, 801])];
            tensor<bool, [3]> slice_140_end_mask_0 = const()[name = tensor<string, []>("slice_140_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_140_cast_fp16 = slice_by_index(begin = slice_140_begin_0, end = slice_140_end_0, end_mask = slice_140_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_140_cast_fp16")];
            tensor<fp16, []> const_1528_promoted_to_fp16 = const()[name = tensor<string, []>("const_1528_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_208_cast_fp16 = pow(x = slice_139_cast_fp16, y = const_1528_promoted_to_fp16)[name = tensor<string, []>("pow_208_cast_fp16")];
            tensor<fp16, []> const_1529_promoted_to_fp16 = const()[name = tensor<string, []>("const_1529_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_209_cast_fp16 = pow(x = slice_140_cast_fp16, y = const_1529_promoted_to_fp16)[name = tensor<string, []>("pow_209_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_207_cast_fp16 = add(x = pow_208_cast_fp16, y = pow_209_cast_fp16)[name = tensor<string, []>("add_207_cast_fp16")];
            tensor<int32, [1]> sum_70_axes_0 = const()[name = tensor<string, []>("sum_70_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_70_keep_dims_0 = const()[name = tensor<string, []>("sum_70_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_70_cast_fp16 = reduce_sum(axes = sum_70_axes_0, keep_dims = sum_70_keep_dims_0, x = add_207_cast_fp16)[name = tensor<string, []>("sum_70_cast_fp16")];
            tensor<fp16, []> const_1532_to_fp16 = const()[name = tensor<string, []>("const_1532_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_208_cast_fp16 = add(x = sum_70_cast_fp16, y = const_1532_to_fp16)[name = tensor<string, []>("add_208_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_69_cast_fp16 = sqrt(x = add_208_cast_fp16)[name = tensor<string, []>("sqrt_69_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_138_cast_fp16 = real_div(x = slice_139_cast_fp16, y = sqrt_69_cast_fp16)[name = tensor<string, []>("div_138_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_139_cast_fp16 = real_div(x = slice_140_cast_fp16, y = sqrt_69_cast_fp16)[name = tensor<string, []>("div_139_cast_fp16")];
            tensor<fp32, []> log_69_epsilon_0 = const()[name = tensor<string, []>("log_69_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_69_cast_fp16 = log(epsilon = log_69_epsilon_0, x = sqrt_69_cast_fp16)[name = tensor<string, []>("log_69_cast_fp16")];
            tensor<int32, []> const_1533 = const()[name = tensor<string, []>("const_1533"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_69_interleave_0 = const()[name = tensor<string, []>("cat_69_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_69_cast_fp16 = concat(axis = const_1533, interleave = cat_69_interleave_0, values = (div_138_cast_fp16, div_139_cast_fp16, log_69_cast_fp16))[name = tensor<string, []>("cat_69_cast_fp16")];
            tensor<int32, [1]> unsqueeze_139_axes_0 = const()[name = tensor<string, []>("unsqueeze_139_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_139_cast_fp16 = expand_dims(axes = unsqueeze_139_axes_0, x = cat_69_cast_fp16)[name = tensor<string, []>("unsqueeze_139_cast_fp16")];
            tensor<fp16, []> const_1538_promoted_to_fp16 = const()[name = tensor<string, []>("const_1538_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_210_cast_fp16 = pow(x = unsqueeze_139_cast_fp16, y = const_1538_promoted_to_fp16)[name = tensor<string, []>("pow_210_cast_fp16")];
            tensor<int32, [1]> mean_69_axes_0 = const()[name = tensor<string, []>("mean_69_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_69_keep_dims_0 = const()[name = tensor<string, []>("mean_69_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_69_cast_fp16 = reduce_mean(axes = mean_69_axes_0, keep_dims = mean_69_keep_dims_0, x = pow_210_cast_fp16)[name = tensor<string, []>("mean_69_cast_fp16")];
            tensor<fp16, []> const_1541_to_fp16 = const()[name = tensor<string, []>("const_1541_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_209_cast_fp16 = add(x = mean_69_cast_fp16, y = const_1541_to_fp16)[name = tensor<string, []>("add_209_cast_fp16")];
            tensor<fp32, []> rsqrt_69_epsilon_0 = const()[name = tensor<string, []>("rsqrt_69_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_69_cast_fp16 = rsqrt(epsilon = rsqrt_69_epsilon_0, x = add_209_cast_fp16)[name = tensor<string, []>("rsqrt_69_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_138_cast_fp16 = mul(x = unsqueeze_139_cast_fp16, y = rsqrt_69_cast_fp16)[name = tensor<string, []>("mul_138_cast_fp16")];
            tensor<int32, [1]> squeeze_69_axes_0 = const()[name = tensor<string, []>("squeeze_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_69_cast_fp16 = squeeze(axes = squeeze_69_axes_0, x = mul_138_cast_fp16)[name = tensor<string, []>("squeeze_69_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_69_to_fp16 = const()[name = tensor<string, []>("view_69_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441664)))];
            tensor<fp16, [1, 11, 801]> mul_139_cast_fp16 = mul(x = squeeze_69_cast_fp16, y = view_69_to_fp16)[name = tensor<string, []>("mul_139_cast_fp16")];
            tensor<string, []> conv1d_69_pad_type_0 = const()[name = tensor<string, []>("conv1d_69_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_69_strides_0 = const()[name = tensor<string, []>("conv1d_69_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_69_pad_0 = const()[name = tensor<string, []>("conv1d_69_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_69_dilations_0 = const()[name = tensor<string, []>("conv1d_69_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_69_groups_0 = const()[name = tensor<string, []>("conv1d_69_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_69_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_69_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441792)))];
            tensor<fp16, [256]> p_bn_69_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_69_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(447488)))];
            tensor<fp16, [1, 256, 801]> conv1d_69_cast_fp16 = conv(bias = p_bn_69_1_bias_to_fp16, dilations = conv1d_69_dilations_0, groups = conv1d_69_groups_0, pad = conv1d_69_pad_0, pad_type = conv1d_69_pad_type_0, strides = conv1d_69_strides_0, weight = p_bn_69_1_weight_to_fp16, x = mul_139_cast_fp16)[name = tensor<string, []>("conv1d_69_cast_fp16")];
            tensor<int32, [3]> slice_141_begin_0 = const()[name = tensor<string, []>("slice_141_begin_0"), val = tensor<int32, [3]>([0, 350, 0])];
            tensor<int32, [3]> slice_141_end_0 = const()[name = tensor<string, []>("slice_141_end_0"), val = tensor<int32, [3]>([1, 355, 801])];
            tensor<bool, [3]> slice_141_end_mask_0 = const()[name = tensor<string, []>("slice_141_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_141_cast_fp16 = slice_by_index(begin = slice_141_begin_0, end = slice_141_end_0, end_mask = slice_141_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_141_cast_fp16")];
            tensor<int32, [3]> slice_142_begin_0 = const()[name = tensor<string, []>("slice_142_begin_0"), val = tensor<int32, [3]>([0, 350, 0])];
            tensor<int32, [3]> slice_142_end_0 = const()[name = tensor<string, []>("slice_142_end_0"), val = tensor<int32, [3]>([1, 355, 801])];
            tensor<bool, [3]> slice_142_end_mask_0 = const()[name = tensor<string, []>("slice_142_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_142_cast_fp16 = slice_by_index(begin = slice_142_begin_0, end = slice_142_end_0, end_mask = slice_142_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_142_cast_fp16")];
            tensor<fp16, []> const_1550_promoted_to_fp16 = const()[name = tensor<string, []>("const_1550_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_211_cast_fp16 = pow(x = slice_141_cast_fp16, y = const_1550_promoted_to_fp16)[name = tensor<string, []>("pow_211_cast_fp16")];
            tensor<fp16, []> const_1551_promoted_to_fp16 = const()[name = tensor<string, []>("const_1551_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_212_cast_fp16 = pow(x = slice_142_cast_fp16, y = const_1551_promoted_to_fp16)[name = tensor<string, []>("pow_212_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_210_cast_fp16 = add(x = pow_211_cast_fp16, y = pow_212_cast_fp16)[name = tensor<string, []>("add_210_cast_fp16")];
            tensor<int32, [1]> sum_71_axes_0 = const()[name = tensor<string, []>("sum_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_71_keep_dims_0 = const()[name = tensor<string, []>("sum_71_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_71_cast_fp16 = reduce_sum(axes = sum_71_axes_0, keep_dims = sum_71_keep_dims_0, x = add_210_cast_fp16)[name = tensor<string, []>("sum_71_cast_fp16")];
            tensor<fp16, []> const_1554_to_fp16 = const()[name = tensor<string, []>("const_1554_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_211_cast_fp16 = add(x = sum_71_cast_fp16, y = const_1554_to_fp16)[name = tensor<string, []>("add_211_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_70_cast_fp16 = sqrt(x = add_211_cast_fp16)[name = tensor<string, []>("sqrt_70_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_140_cast_fp16 = real_div(x = slice_141_cast_fp16, y = sqrt_70_cast_fp16)[name = tensor<string, []>("div_140_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_141_cast_fp16 = real_div(x = slice_142_cast_fp16, y = sqrt_70_cast_fp16)[name = tensor<string, []>("div_141_cast_fp16")];
            tensor<fp32, []> log_70_epsilon_0 = const()[name = tensor<string, []>("log_70_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_70_cast_fp16 = log(epsilon = log_70_epsilon_0, x = sqrt_70_cast_fp16)[name = tensor<string, []>("log_70_cast_fp16")];
            tensor<int32, []> const_1555 = const()[name = tensor<string, []>("const_1555"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_70_interleave_0 = const()[name = tensor<string, []>("cat_70_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_70_cast_fp16 = concat(axis = const_1555, interleave = cat_70_interleave_0, values = (div_140_cast_fp16, div_141_cast_fp16, log_70_cast_fp16))[name = tensor<string, []>("cat_70_cast_fp16")];
            tensor<int32, [1]> unsqueeze_141_axes_0 = const()[name = tensor<string, []>("unsqueeze_141_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_141_cast_fp16 = expand_dims(axes = unsqueeze_141_axes_0, x = cat_70_cast_fp16)[name = tensor<string, []>("unsqueeze_141_cast_fp16")];
            tensor<fp16, []> const_1560_promoted_to_fp16 = const()[name = tensor<string, []>("const_1560_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_213_cast_fp16 = pow(x = unsqueeze_141_cast_fp16, y = const_1560_promoted_to_fp16)[name = tensor<string, []>("pow_213_cast_fp16")];
            tensor<int32, [1]> mean_70_axes_0 = const()[name = tensor<string, []>("mean_70_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_70_keep_dims_0 = const()[name = tensor<string, []>("mean_70_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_70_cast_fp16 = reduce_mean(axes = mean_70_axes_0, keep_dims = mean_70_keep_dims_0, x = pow_213_cast_fp16)[name = tensor<string, []>("mean_70_cast_fp16")];
            tensor<fp16, []> const_1563_to_fp16 = const()[name = tensor<string, []>("const_1563_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_212_cast_fp16 = add(x = mean_70_cast_fp16, y = const_1563_to_fp16)[name = tensor<string, []>("add_212_cast_fp16")];
            tensor<fp32, []> rsqrt_70_epsilon_0 = const()[name = tensor<string, []>("rsqrt_70_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_70_cast_fp16 = rsqrt(epsilon = rsqrt_70_epsilon_0, x = add_212_cast_fp16)[name = tensor<string, []>("rsqrt_70_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_140_cast_fp16 = mul(x = unsqueeze_141_cast_fp16, y = rsqrt_70_cast_fp16)[name = tensor<string, []>("mul_140_cast_fp16")];
            tensor<int32, [1]> squeeze_70_axes_0 = const()[name = tensor<string, []>("squeeze_70_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_70_cast_fp16 = squeeze(axes = squeeze_70_axes_0, x = mul_140_cast_fp16)[name = tensor<string, []>("squeeze_70_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_70_to_fp16 = const()[name = tensor<string, []>("view_70_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448064)))];
            tensor<fp16, [1, 11, 801]> mul_141_cast_fp16 = mul(x = squeeze_70_cast_fp16, y = view_70_to_fp16)[name = tensor<string, []>("mul_141_cast_fp16")];
            tensor<string, []> conv1d_70_pad_type_0 = const()[name = tensor<string, []>("conv1d_70_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_70_strides_0 = const()[name = tensor<string, []>("conv1d_70_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_70_pad_0 = const()[name = tensor<string, []>("conv1d_70_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_70_dilations_0 = const()[name = tensor<string, []>("conv1d_70_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_70_groups_0 = const()[name = tensor<string, []>("conv1d_70_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_70_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_70_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448192)))];
            tensor<fp16, [256]> p_bn_70_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_70_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(453888)))];
            tensor<fp16, [1, 256, 801]> conv1d_70_cast_fp16 = conv(bias = p_bn_70_1_bias_to_fp16, dilations = conv1d_70_dilations_0, groups = conv1d_70_groups_0, pad = conv1d_70_pad_0, pad_type = conv1d_70_pad_type_0, strides = conv1d_70_strides_0, weight = p_bn_70_1_weight_to_fp16, x = mul_141_cast_fp16)[name = tensor<string, []>("conv1d_70_cast_fp16")];
            tensor<int32, [3]> slice_143_begin_0 = const()[name = tensor<string, []>("slice_143_begin_0"), val = tensor<int32, [3]>([0, 355, 0])];
            tensor<int32, [3]> slice_143_end_0 = const()[name = tensor<string, []>("slice_143_end_0"), val = tensor<int32, [3]>([1, 360, 801])];
            tensor<bool, [3]> slice_143_end_mask_0 = const()[name = tensor<string, []>("slice_143_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_143_cast_fp16 = slice_by_index(begin = slice_143_begin_0, end = slice_143_end_0, end_mask = slice_143_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_143_cast_fp16")];
            tensor<int32, [3]> slice_144_begin_0 = const()[name = tensor<string, []>("slice_144_begin_0"), val = tensor<int32, [3]>([0, 355, 0])];
            tensor<int32, [3]> slice_144_end_0 = const()[name = tensor<string, []>("slice_144_end_0"), val = tensor<int32, [3]>([1, 360, 801])];
            tensor<bool, [3]> slice_144_end_mask_0 = const()[name = tensor<string, []>("slice_144_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_144_cast_fp16 = slice_by_index(begin = slice_144_begin_0, end = slice_144_end_0, end_mask = slice_144_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_144_cast_fp16")];
            tensor<fp16, []> const_1572_promoted_to_fp16 = const()[name = tensor<string, []>("const_1572_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_214_cast_fp16 = pow(x = slice_143_cast_fp16, y = const_1572_promoted_to_fp16)[name = tensor<string, []>("pow_214_cast_fp16")];
            tensor<fp16, []> const_1573_promoted_to_fp16 = const()[name = tensor<string, []>("const_1573_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_215_cast_fp16 = pow(x = slice_144_cast_fp16, y = const_1573_promoted_to_fp16)[name = tensor<string, []>("pow_215_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_213_cast_fp16 = add(x = pow_214_cast_fp16, y = pow_215_cast_fp16)[name = tensor<string, []>("add_213_cast_fp16")];
            tensor<int32, [1]> sum_72_axes_0 = const()[name = tensor<string, []>("sum_72_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_72_keep_dims_0 = const()[name = tensor<string, []>("sum_72_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_72_cast_fp16 = reduce_sum(axes = sum_72_axes_0, keep_dims = sum_72_keep_dims_0, x = add_213_cast_fp16)[name = tensor<string, []>("sum_72_cast_fp16")];
            tensor<fp16, []> const_1576_to_fp16 = const()[name = tensor<string, []>("const_1576_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_214_cast_fp16 = add(x = sum_72_cast_fp16, y = const_1576_to_fp16)[name = tensor<string, []>("add_214_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_71_cast_fp16 = sqrt(x = add_214_cast_fp16)[name = tensor<string, []>("sqrt_71_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_142_cast_fp16 = real_div(x = slice_143_cast_fp16, y = sqrt_71_cast_fp16)[name = tensor<string, []>("div_142_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_143_cast_fp16 = real_div(x = slice_144_cast_fp16, y = sqrt_71_cast_fp16)[name = tensor<string, []>("div_143_cast_fp16")];
            tensor<fp32, []> log_71_epsilon_0 = const()[name = tensor<string, []>("log_71_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_71_cast_fp16 = log(epsilon = log_71_epsilon_0, x = sqrt_71_cast_fp16)[name = tensor<string, []>("log_71_cast_fp16")];
            tensor<int32, []> const_1577 = const()[name = tensor<string, []>("const_1577"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_71_interleave_0 = const()[name = tensor<string, []>("cat_71_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_71_cast_fp16 = concat(axis = const_1577, interleave = cat_71_interleave_0, values = (div_142_cast_fp16, div_143_cast_fp16, log_71_cast_fp16))[name = tensor<string, []>("cat_71_cast_fp16")];
            tensor<int32, [1]> unsqueeze_143_axes_0 = const()[name = tensor<string, []>("unsqueeze_143_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_143_cast_fp16 = expand_dims(axes = unsqueeze_143_axes_0, x = cat_71_cast_fp16)[name = tensor<string, []>("unsqueeze_143_cast_fp16")];
            tensor<fp16, []> const_1582_promoted_to_fp16 = const()[name = tensor<string, []>("const_1582_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_216_cast_fp16 = pow(x = unsqueeze_143_cast_fp16, y = const_1582_promoted_to_fp16)[name = tensor<string, []>("pow_216_cast_fp16")];
            tensor<int32, [1]> mean_71_axes_0 = const()[name = tensor<string, []>("mean_71_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_71_keep_dims_0 = const()[name = tensor<string, []>("mean_71_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_71_cast_fp16 = reduce_mean(axes = mean_71_axes_0, keep_dims = mean_71_keep_dims_0, x = pow_216_cast_fp16)[name = tensor<string, []>("mean_71_cast_fp16")];
            tensor<fp16, []> const_1585_to_fp16 = const()[name = tensor<string, []>("const_1585_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_215_cast_fp16 = add(x = mean_71_cast_fp16, y = const_1585_to_fp16)[name = tensor<string, []>("add_215_cast_fp16")];
            tensor<fp32, []> rsqrt_71_epsilon_0 = const()[name = tensor<string, []>("rsqrt_71_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_71_cast_fp16 = rsqrt(epsilon = rsqrt_71_epsilon_0, x = add_215_cast_fp16)[name = tensor<string, []>("rsqrt_71_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_142_cast_fp16 = mul(x = unsqueeze_143_cast_fp16, y = rsqrt_71_cast_fp16)[name = tensor<string, []>("mul_142_cast_fp16")];
            tensor<int32, [1]> squeeze_71_axes_0 = const()[name = tensor<string, []>("squeeze_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_71_cast_fp16 = squeeze(axes = squeeze_71_axes_0, x = mul_142_cast_fp16)[name = tensor<string, []>("squeeze_71_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_71_to_fp16 = const()[name = tensor<string, []>("view_71_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454464)))];
            tensor<fp16, [1, 11, 801]> mul_143_cast_fp16 = mul(x = squeeze_71_cast_fp16, y = view_71_to_fp16)[name = tensor<string, []>("mul_143_cast_fp16")];
            tensor<string, []> conv1d_71_pad_type_0 = const()[name = tensor<string, []>("conv1d_71_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_71_strides_0 = const()[name = tensor<string, []>("conv1d_71_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_71_pad_0 = const()[name = tensor<string, []>("conv1d_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_71_dilations_0 = const()[name = tensor<string, []>("conv1d_71_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_71_groups_0 = const()[name = tensor<string, []>("conv1d_71_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_71_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_71_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454592)))];
            tensor<fp16, [256]> p_bn_71_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_71_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460288)))];
            tensor<fp16, [1, 256, 801]> conv1d_71_cast_fp16 = conv(bias = p_bn_71_1_bias_to_fp16, dilations = conv1d_71_dilations_0, groups = conv1d_71_groups_0, pad = conv1d_71_pad_0, pad_type = conv1d_71_pad_type_0, strides = conv1d_71_strides_0, weight = p_bn_71_1_weight_to_fp16, x = mul_143_cast_fp16)[name = tensor<string, []>("conv1d_71_cast_fp16")];
            tensor<int32, [3]> slice_145_begin_0 = const()[name = tensor<string, []>("slice_145_begin_0"), val = tensor<int32, [3]>([0, 360, 0])];
            tensor<int32, [3]> slice_145_end_0 = const()[name = tensor<string, []>("slice_145_end_0"), val = tensor<int32, [3]>([1, 365, 801])];
            tensor<bool, [3]> slice_145_end_mask_0 = const()[name = tensor<string, []>("slice_145_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_145_cast_fp16 = slice_by_index(begin = slice_145_begin_0, end = slice_145_end_0, end_mask = slice_145_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_145_cast_fp16")];
            tensor<int32, [3]> slice_146_begin_0 = const()[name = tensor<string, []>("slice_146_begin_0"), val = tensor<int32, [3]>([0, 360, 0])];
            tensor<int32, [3]> slice_146_end_0 = const()[name = tensor<string, []>("slice_146_end_0"), val = tensor<int32, [3]>([1, 365, 801])];
            tensor<bool, [3]> slice_146_end_mask_0 = const()[name = tensor<string, []>("slice_146_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_146_cast_fp16 = slice_by_index(begin = slice_146_begin_0, end = slice_146_end_0, end_mask = slice_146_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_146_cast_fp16")];
            tensor<fp16, []> const_1594_promoted_to_fp16 = const()[name = tensor<string, []>("const_1594_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_217_cast_fp16 = pow(x = slice_145_cast_fp16, y = const_1594_promoted_to_fp16)[name = tensor<string, []>("pow_217_cast_fp16")];
            tensor<fp16, []> const_1595_promoted_to_fp16 = const()[name = tensor<string, []>("const_1595_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_218_cast_fp16 = pow(x = slice_146_cast_fp16, y = const_1595_promoted_to_fp16)[name = tensor<string, []>("pow_218_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_216_cast_fp16 = add(x = pow_217_cast_fp16, y = pow_218_cast_fp16)[name = tensor<string, []>("add_216_cast_fp16")];
            tensor<int32, [1]> sum_73_axes_0 = const()[name = tensor<string, []>("sum_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_73_keep_dims_0 = const()[name = tensor<string, []>("sum_73_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_73_cast_fp16 = reduce_sum(axes = sum_73_axes_0, keep_dims = sum_73_keep_dims_0, x = add_216_cast_fp16)[name = tensor<string, []>("sum_73_cast_fp16")];
            tensor<fp16, []> const_1598_to_fp16 = const()[name = tensor<string, []>("const_1598_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_217_cast_fp16 = add(x = sum_73_cast_fp16, y = const_1598_to_fp16)[name = tensor<string, []>("add_217_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_72_cast_fp16 = sqrt(x = add_217_cast_fp16)[name = tensor<string, []>("sqrt_72_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_144_cast_fp16 = real_div(x = slice_145_cast_fp16, y = sqrt_72_cast_fp16)[name = tensor<string, []>("div_144_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_145_cast_fp16 = real_div(x = slice_146_cast_fp16, y = sqrt_72_cast_fp16)[name = tensor<string, []>("div_145_cast_fp16")];
            tensor<fp32, []> log_72_epsilon_0 = const()[name = tensor<string, []>("log_72_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_72_cast_fp16 = log(epsilon = log_72_epsilon_0, x = sqrt_72_cast_fp16)[name = tensor<string, []>("log_72_cast_fp16")];
            tensor<int32, []> const_1599 = const()[name = tensor<string, []>("const_1599"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_72_interleave_0 = const()[name = tensor<string, []>("cat_72_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_72_cast_fp16 = concat(axis = const_1599, interleave = cat_72_interleave_0, values = (div_144_cast_fp16, div_145_cast_fp16, log_72_cast_fp16))[name = tensor<string, []>("cat_72_cast_fp16")];
            tensor<int32, [1]> unsqueeze_145_axes_0 = const()[name = tensor<string, []>("unsqueeze_145_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_145_cast_fp16 = expand_dims(axes = unsqueeze_145_axes_0, x = cat_72_cast_fp16)[name = tensor<string, []>("unsqueeze_145_cast_fp16")];
            tensor<fp16, []> const_1604_promoted_to_fp16 = const()[name = tensor<string, []>("const_1604_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_219_cast_fp16 = pow(x = unsqueeze_145_cast_fp16, y = const_1604_promoted_to_fp16)[name = tensor<string, []>("pow_219_cast_fp16")];
            tensor<int32, [1]> mean_72_axes_0 = const()[name = tensor<string, []>("mean_72_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_72_keep_dims_0 = const()[name = tensor<string, []>("mean_72_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_72_cast_fp16 = reduce_mean(axes = mean_72_axes_0, keep_dims = mean_72_keep_dims_0, x = pow_219_cast_fp16)[name = tensor<string, []>("mean_72_cast_fp16")];
            tensor<fp16, []> const_1607_to_fp16 = const()[name = tensor<string, []>("const_1607_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_218_cast_fp16 = add(x = mean_72_cast_fp16, y = const_1607_to_fp16)[name = tensor<string, []>("add_218_cast_fp16")];
            tensor<fp32, []> rsqrt_72_epsilon_0 = const()[name = tensor<string, []>("rsqrt_72_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_72_cast_fp16 = rsqrt(epsilon = rsqrt_72_epsilon_0, x = add_218_cast_fp16)[name = tensor<string, []>("rsqrt_72_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_144_cast_fp16 = mul(x = unsqueeze_145_cast_fp16, y = rsqrt_72_cast_fp16)[name = tensor<string, []>("mul_144_cast_fp16")];
            tensor<int32, [1]> squeeze_72_axes_0 = const()[name = tensor<string, []>("squeeze_72_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_72_cast_fp16 = squeeze(axes = squeeze_72_axes_0, x = mul_144_cast_fp16)[name = tensor<string, []>("squeeze_72_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_72_to_fp16 = const()[name = tensor<string, []>("view_72_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460864)))];
            tensor<fp16, [1, 11, 801]> mul_145_cast_fp16 = mul(x = squeeze_72_cast_fp16, y = view_72_to_fp16)[name = tensor<string, []>("mul_145_cast_fp16")];
            tensor<string, []> conv1d_72_pad_type_0 = const()[name = tensor<string, []>("conv1d_72_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_72_strides_0 = const()[name = tensor<string, []>("conv1d_72_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_72_pad_0 = const()[name = tensor<string, []>("conv1d_72_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_72_dilations_0 = const()[name = tensor<string, []>("conv1d_72_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_72_groups_0 = const()[name = tensor<string, []>("conv1d_72_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_72_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_72_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460992)))];
            tensor<fp16, [256]> p_bn_72_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_72_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466688)))];
            tensor<fp16, [1, 256, 801]> conv1d_72_cast_fp16 = conv(bias = p_bn_72_1_bias_to_fp16, dilations = conv1d_72_dilations_0, groups = conv1d_72_groups_0, pad = conv1d_72_pad_0, pad_type = conv1d_72_pad_type_0, strides = conv1d_72_strides_0, weight = p_bn_72_1_weight_to_fp16, x = mul_145_cast_fp16)[name = tensor<string, []>("conv1d_72_cast_fp16")];
            tensor<int32, [3]> slice_147_begin_0 = const()[name = tensor<string, []>("slice_147_begin_0"), val = tensor<int32, [3]>([0, 365, 0])];
            tensor<int32, [3]> slice_147_end_0 = const()[name = tensor<string, []>("slice_147_end_0"), val = tensor<int32, [3]>([1, 370, 801])];
            tensor<bool, [3]> slice_147_end_mask_0 = const()[name = tensor<string, []>("slice_147_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_147_cast_fp16 = slice_by_index(begin = slice_147_begin_0, end = slice_147_end_0, end_mask = slice_147_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_147_cast_fp16")];
            tensor<int32, [3]> slice_148_begin_0 = const()[name = tensor<string, []>("slice_148_begin_0"), val = tensor<int32, [3]>([0, 365, 0])];
            tensor<int32, [3]> slice_148_end_0 = const()[name = tensor<string, []>("slice_148_end_0"), val = tensor<int32, [3]>([1, 370, 801])];
            tensor<bool, [3]> slice_148_end_mask_0 = const()[name = tensor<string, []>("slice_148_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_148_cast_fp16 = slice_by_index(begin = slice_148_begin_0, end = slice_148_end_0, end_mask = slice_148_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_148_cast_fp16")];
            tensor<fp16, []> const_1616_promoted_to_fp16 = const()[name = tensor<string, []>("const_1616_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_220_cast_fp16 = pow(x = slice_147_cast_fp16, y = const_1616_promoted_to_fp16)[name = tensor<string, []>("pow_220_cast_fp16")];
            tensor<fp16, []> const_1617_promoted_to_fp16 = const()[name = tensor<string, []>("const_1617_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_221_cast_fp16 = pow(x = slice_148_cast_fp16, y = const_1617_promoted_to_fp16)[name = tensor<string, []>("pow_221_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_219_cast_fp16 = add(x = pow_220_cast_fp16, y = pow_221_cast_fp16)[name = tensor<string, []>("add_219_cast_fp16")];
            tensor<int32, [1]> sum_74_axes_0 = const()[name = tensor<string, []>("sum_74_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_74_keep_dims_0 = const()[name = tensor<string, []>("sum_74_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_74_cast_fp16 = reduce_sum(axes = sum_74_axes_0, keep_dims = sum_74_keep_dims_0, x = add_219_cast_fp16)[name = tensor<string, []>("sum_74_cast_fp16")];
            tensor<fp16, []> const_1620_to_fp16 = const()[name = tensor<string, []>("const_1620_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_220_cast_fp16 = add(x = sum_74_cast_fp16, y = const_1620_to_fp16)[name = tensor<string, []>("add_220_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_73_cast_fp16 = sqrt(x = add_220_cast_fp16)[name = tensor<string, []>("sqrt_73_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_146_cast_fp16 = real_div(x = slice_147_cast_fp16, y = sqrt_73_cast_fp16)[name = tensor<string, []>("div_146_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_147_cast_fp16 = real_div(x = slice_148_cast_fp16, y = sqrt_73_cast_fp16)[name = tensor<string, []>("div_147_cast_fp16")];
            tensor<fp32, []> log_73_epsilon_0 = const()[name = tensor<string, []>("log_73_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_73_cast_fp16 = log(epsilon = log_73_epsilon_0, x = sqrt_73_cast_fp16)[name = tensor<string, []>("log_73_cast_fp16")];
            tensor<int32, []> const_1621 = const()[name = tensor<string, []>("const_1621"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_73_interleave_0 = const()[name = tensor<string, []>("cat_73_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_73_cast_fp16 = concat(axis = const_1621, interleave = cat_73_interleave_0, values = (div_146_cast_fp16, div_147_cast_fp16, log_73_cast_fp16))[name = tensor<string, []>("cat_73_cast_fp16")];
            tensor<int32, [1]> unsqueeze_147_axes_0 = const()[name = tensor<string, []>("unsqueeze_147_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_147_cast_fp16 = expand_dims(axes = unsqueeze_147_axes_0, x = cat_73_cast_fp16)[name = tensor<string, []>("unsqueeze_147_cast_fp16")];
            tensor<fp16, []> const_1626_promoted_to_fp16 = const()[name = tensor<string, []>("const_1626_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_222_cast_fp16 = pow(x = unsqueeze_147_cast_fp16, y = const_1626_promoted_to_fp16)[name = tensor<string, []>("pow_222_cast_fp16")];
            tensor<int32, [1]> mean_73_axes_0 = const()[name = tensor<string, []>("mean_73_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_73_keep_dims_0 = const()[name = tensor<string, []>("mean_73_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_73_cast_fp16 = reduce_mean(axes = mean_73_axes_0, keep_dims = mean_73_keep_dims_0, x = pow_222_cast_fp16)[name = tensor<string, []>("mean_73_cast_fp16")];
            tensor<fp16, []> const_1629_to_fp16 = const()[name = tensor<string, []>("const_1629_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_221_cast_fp16 = add(x = mean_73_cast_fp16, y = const_1629_to_fp16)[name = tensor<string, []>("add_221_cast_fp16")];
            tensor<fp32, []> rsqrt_73_epsilon_0 = const()[name = tensor<string, []>("rsqrt_73_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_73_cast_fp16 = rsqrt(epsilon = rsqrt_73_epsilon_0, x = add_221_cast_fp16)[name = tensor<string, []>("rsqrt_73_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_146_cast_fp16 = mul(x = unsqueeze_147_cast_fp16, y = rsqrt_73_cast_fp16)[name = tensor<string, []>("mul_146_cast_fp16")];
            tensor<int32, [1]> squeeze_73_axes_0 = const()[name = tensor<string, []>("squeeze_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_73_cast_fp16 = squeeze(axes = squeeze_73_axes_0, x = mul_146_cast_fp16)[name = tensor<string, []>("squeeze_73_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_73_to_fp16 = const()[name = tensor<string, []>("view_73_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467264)))];
            tensor<fp16, [1, 11, 801]> mul_147_cast_fp16 = mul(x = squeeze_73_cast_fp16, y = view_73_to_fp16)[name = tensor<string, []>("mul_147_cast_fp16")];
            tensor<string, []> conv1d_73_pad_type_0 = const()[name = tensor<string, []>("conv1d_73_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_73_strides_0 = const()[name = tensor<string, []>("conv1d_73_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_73_pad_0 = const()[name = tensor<string, []>("conv1d_73_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_73_dilations_0 = const()[name = tensor<string, []>("conv1d_73_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_73_groups_0 = const()[name = tensor<string, []>("conv1d_73_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_73_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_73_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467392)))];
            tensor<fp16, [256]> p_bn_73_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_73_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473088)))];
            tensor<fp16, [1, 256, 801]> conv1d_73_cast_fp16 = conv(bias = p_bn_73_1_bias_to_fp16, dilations = conv1d_73_dilations_0, groups = conv1d_73_groups_0, pad = conv1d_73_pad_0, pad_type = conv1d_73_pad_type_0, strides = conv1d_73_strides_0, weight = p_bn_73_1_weight_to_fp16, x = mul_147_cast_fp16)[name = tensor<string, []>("conv1d_73_cast_fp16")];
            tensor<int32, [3]> slice_149_begin_0 = const()[name = tensor<string, []>("slice_149_begin_0"), val = tensor<int32, [3]>([0, 370, 0])];
            tensor<int32, [3]> slice_149_end_0 = const()[name = tensor<string, []>("slice_149_end_0"), val = tensor<int32, [3]>([1, 375, 801])];
            tensor<bool, [3]> slice_149_end_mask_0 = const()[name = tensor<string, []>("slice_149_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_149_cast_fp16 = slice_by_index(begin = slice_149_begin_0, end = slice_149_end_0, end_mask = slice_149_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_149_cast_fp16")];
            tensor<int32, [3]> slice_150_begin_0 = const()[name = tensor<string, []>("slice_150_begin_0"), val = tensor<int32, [3]>([0, 370, 0])];
            tensor<int32, [3]> slice_150_end_0 = const()[name = tensor<string, []>("slice_150_end_0"), val = tensor<int32, [3]>([1, 375, 801])];
            tensor<bool, [3]> slice_150_end_mask_0 = const()[name = tensor<string, []>("slice_150_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_150_cast_fp16 = slice_by_index(begin = slice_150_begin_0, end = slice_150_end_0, end_mask = slice_150_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_150_cast_fp16")];
            tensor<fp16, []> const_1638_promoted_to_fp16 = const()[name = tensor<string, []>("const_1638_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_223_cast_fp16 = pow(x = slice_149_cast_fp16, y = const_1638_promoted_to_fp16)[name = tensor<string, []>("pow_223_cast_fp16")];
            tensor<fp16, []> const_1639_promoted_to_fp16 = const()[name = tensor<string, []>("const_1639_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_224_cast_fp16 = pow(x = slice_150_cast_fp16, y = const_1639_promoted_to_fp16)[name = tensor<string, []>("pow_224_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_222_cast_fp16 = add(x = pow_223_cast_fp16, y = pow_224_cast_fp16)[name = tensor<string, []>("add_222_cast_fp16")];
            tensor<int32, [1]> sum_75_axes_0 = const()[name = tensor<string, []>("sum_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_75_keep_dims_0 = const()[name = tensor<string, []>("sum_75_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_75_cast_fp16 = reduce_sum(axes = sum_75_axes_0, keep_dims = sum_75_keep_dims_0, x = add_222_cast_fp16)[name = tensor<string, []>("sum_75_cast_fp16")];
            tensor<fp16, []> const_1642_to_fp16 = const()[name = tensor<string, []>("const_1642_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_223_cast_fp16 = add(x = sum_75_cast_fp16, y = const_1642_to_fp16)[name = tensor<string, []>("add_223_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_74_cast_fp16 = sqrt(x = add_223_cast_fp16)[name = tensor<string, []>("sqrt_74_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_148_cast_fp16 = real_div(x = slice_149_cast_fp16, y = sqrt_74_cast_fp16)[name = tensor<string, []>("div_148_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_149_cast_fp16 = real_div(x = slice_150_cast_fp16, y = sqrt_74_cast_fp16)[name = tensor<string, []>("div_149_cast_fp16")];
            tensor<fp32, []> log_74_epsilon_0 = const()[name = tensor<string, []>("log_74_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_74_cast_fp16 = log(epsilon = log_74_epsilon_0, x = sqrt_74_cast_fp16)[name = tensor<string, []>("log_74_cast_fp16")];
            tensor<int32, []> const_1643 = const()[name = tensor<string, []>("const_1643"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_74_interleave_0 = const()[name = tensor<string, []>("cat_74_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_74_cast_fp16 = concat(axis = const_1643, interleave = cat_74_interleave_0, values = (div_148_cast_fp16, div_149_cast_fp16, log_74_cast_fp16))[name = tensor<string, []>("cat_74_cast_fp16")];
            tensor<int32, [1]> unsqueeze_149_axes_0 = const()[name = tensor<string, []>("unsqueeze_149_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_149_cast_fp16 = expand_dims(axes = unsqueeze_149_axes_0, x = cat_74_cast_fp16)[name = tensor<string, []>("unsqueeze_149_cast_fp16")];
            tensor<fp16, []> const_1648_promoted_to_fp16 = const()[name = tensor<string, []>("const_1648_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_225_cast_fp16 = pow(x = unsqueeze_149_cast_fp16, y = const_1648_promoted_to_fp16)[name = tensor<string, []>("pow_225_cast_fp16")];
            tensor<int32, [1]> mean_74_axes_0 = const()[name = tensor<string, []>("mean_74_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_74_keep_dims_0 = const()[name = tensor<string, []>("mean_74_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_74_cast_fp16 = reduce_mean(axes = mean_74_axes_0, keep_dims = mean_74_keep_dims_0, x = pow_225_cast_fp16)[name = tensor<string, []>("mean_74_cast_fp16")];
            tensor<fp16, []> const_1651_to_fp16 = const()[name = tensor<string, []>("const_1651_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_224_cast_fp16 = add(x = mean_74_cast_fp16, y = const_1651_to_fp16)[name = tensor<string, []>("add_224_cast_fp16")];
            tensor<fp32, []> rsqrt_74_epsilon_0 = const()[name = tensor<string, []>("rsqrt_74_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_74_cast_fp16 = rsqrt(epsilon = rsqrt_74_epsilon_0, x = add_224_cast_fp16)[name = tensor<string, []>("rsqrt_74_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_148_cast_fp16 = mul(x = unsqueeze_149_cast_fp16, y = rsqrt_74_cast_fp16)[name = tensor<string, []>("mul_148_cast_fp16")];
            tensor<int32, [1]> squeeze_74_axes_0 = const()[name = tensor<string, []>("squeeze_74_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_74_cast_fp16 = squeeze(axes = squeeze_74_axes_0, x = mul_148_cast_fp16)[name = tensor<string, []>("squeeze_74_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_74_to_fp16 = const()[name = tensor<string, []>("view_74_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473664)))];
            tensor<fp16, [1, 11, 801]> mul_149_cast_fp16 = mul(x = squeeze_74_cast_fp16, y = view_74_to_fp16)[name = tensor<string, []>("mul_149_cast_fp16")];
            tensor<string, []> conv1d_74_pad_type_0 = const()[name = tensor<string, []>("conv1d_74_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_74_strides_0 = const()[name = tensor<string, []>("conv1d_74_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_74_pad_0 = const()[name = tensor<string, []>("conv1d_74_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_74_dilations_0 = const()[name = tensor<string, []>("conv1d_74_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_74_groups_0 = const()[name = tensor<string, []>("conv1d_74_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_74_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_74_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(473792)))];
            tensor<fp16, [256]> p_bn_74_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_74_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479488)))];
            tensor<fp16, [1, 256, 801]> conv1d_74_cast_fp16 = conv(bias = p_bn_74_1_bias_to_fp16, dilations = conv1d_74_dilations_0, groups = conv1d_74_groups_0, pad = conv1d_74_pad_0, pad_type = conv1d_74_pad_type_0, strides = conv1d_74_strides_0, weight = p_bn_74_1_weight_to_fp16, x = mul_149_cast_fp16)[name = tensor<string, []>("conv1d_74_cast_fp16")];
            tensor<int32, [3]> slice_151_begin_0 = const()[name = tensor<string, []>("slice_151_begin_0"), val = tensor<int32, [3]>([0, 375, 0])];
            tensor<int32, [3]> slice_151_end_0 = const()[name = tensor<string, []>("slice_151_end_0"), val = tensor<int32, [3]>([1, 380, 801])];
            tensor<bool, [3]> slice_151_end_mask_0 = const()[name = tensor<string, []>("slice_151_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_151_cast_fp16 = slice_by_index(begin = slice_151_begin_0, end = slice_151_end_0, end_mask = slice_151_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_151_cast_fp16")];
            tensor<int32, [3]> slice_152_begin_0 = const()[name = tensor<string, []>("slice_152_begin_0"), val = tensor<int32, [3]>([0, 375, 0])];
            tensor<int32, [3]> slice_152_end_0 = const()[name = tensor<string, []>("slice_152_end_0"), val = tensor<int32, [3]>([1, 380, 801])];
            tensor<bool, [3]> slice_152_end_mask_0 = const()[name = tensor<string, []>("slice_152_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_152_cast_fp16 = slice_by_index(begin = slice_152_begin_0, end = slice_152_end_0, end_mask = slice_152_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_152_cast_fp16")];
            tensor<fp16, []> const_1660_promoted_to_fp16 = const()[name = tensor<string, []>("const_1660_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_226_cast_fp16 = pow(x = slice_151_cast_fp16, y = const_1660_promoted_to_fp16)[name = tensor<string, []>("pow_226_cast_fp16")];
            tensor<fp16, []> const_1661_promoted_to_fp16 = const()[name = tensor<string, []>("const_1661_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_227_cast_fp16 = pow(x = slice_152_cast_fp16, y = const_1661_promoted_to_fp16)[name = tensor<string, []>("pow_227_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_225_cast_fp16 = add(x = pow_226_cast_fp16, y = pow_227_cast_fp16)[name = tensor<string, []>("add_225_cast_fp16")];
            tensor<int32, [1]> sum_76_axes_0 = const()[name = tensor<string, []>("sum_76_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_76_keep_dims_0 = const()[name = tensor<string, []>("sum_76_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_76_cast_fp16 = reduce_sum(axes = sum_76_axes_0, keep_dims = sum_76_keep_dims_0, x = add_225_cast_fp16)[name = tensor<string, []>("sum_76_cast_fp16")];
            tensor<fp16, []> const_1664_to_fp16 = const()[name = tensor<string, []>("const_1664_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_226_cast_fp16 = add(x = sum_76_cast_fp16, y = const_1664_to_fp16)[name = tensor<string, []>("add_226_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_75_cast_fp16 = sqrt(x = add_226_cast_fp16)[name = tensor<string, []>("sqrt_75_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_150_cast_fp16 = real_div(x = slice_151_cast_fp16, y = sqrt_75_cast_fp16)[name = tensor<string, []>("div_150_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_151_cast_fp16 = real_div(x = slice_152_cast_fp16, y = sqrt_75_cast_fp16)[name = tensor<string, []>("div_151_cast_fp16")];
            tensor<fp32, []> log_75_epsilon_0 = const()[name = tensor<string, []>("log_75_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_75_cast_fp16 = log(epsilon = log_75_epsilon_0, x = sqrt_75_cast_fp16)[name = tensor<string, []>("log_75_cast_fp16")];
            tensor<int32, []> const_1665 = const()[name = tensor<string, []>("const_1665"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_75_interleave_0 = const()[name = tensor<string, []>("cat_75_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_75_cast_fp16 = concat(axis = const_1665, interleave = cat_75_interleave_0, values = (div_150_cast_fp16, div_151_cast_fp16, log_75_cast_fp16))[name = tensor<string, []>("cat_75_cast_fp16")];
            tensor<int32, [1]> unsqueeze_151_axes_0 = const()[name = tensor<string, []>("unsqueeze_151_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_151_cast_fp16 = expand_dims(axes = unsqueeze_151_axes_0, x = cat_75_cast_fp16)[name = tensor<string, []>("unsqueeze_151_cast_fp16")];
            tensor<fp16, []> const_1670_promoted_to_fp16 = const()[name = tensor<string, []>("const_1670_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_228_cast_fp16 = pow(x = unsqueeze_151_cast_fp16, y = const_1670_promoted_to_fp16)[name = tensor<string, []>("pow_228_cast_fp16")];
            tensor<int32, [1]> mean_75_axes_0 = const()[name = tensor<string, []>("mean_75_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_75_keep_dims_0 = const()[name = tensor<string, []>("mean_75_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_75_cast_fp16 = reduce_mean(axes = mean_75_axes_0, keep_dims = mean_75_keep_dims_0, x = pow_228_cast_fp16)[name = tensor<string, []>("mean_75_cast_fp16")];
            tensor<fp16, []> const_1673_to_fp16 = const()[name = tensor<string, []>("const_1673_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_227_cast_fp16 = add(x = mean_75_cast_fp16, y = const_1673_to_fp16)[name = tensor<string, []>("add_227_cast_fp16")];
            tensor<fp32, []> rsqrt_75_epsilon_0 = const()[name = tensor<string, []>("rsqrt_75_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_75_cast_fp16 = rsqrt(epsilon = rsqrt_75_epsilon_0, x = add_227_cast_fp16)[name = tensor<string, []>("rsqrt_75_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_150_cast_fp16 = mul(x = unsqueeze_151_cast_fp16, y = rsqrt_75_cast_fp16)[name = tensor<string, []>("mul_150_cast_fp16")];
            tensor<int32, [1]> squeeze_75_axes_0 = const()[name = tensor<string, []>("squeeze_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_75_cast_fp16 = squeeze(axes = squeeze_75_axes_0, x = mul_150_cast_fp16)[name = tensor<string, []>("squeeze_75_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_75_to_fp16 = const()[name = tensor<string, []>("view_75_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480064)))];
            tensor<fp16, [1, 11, 801]> mul_151_cast_fp16 = mul(x = squeeze_75_cast_fp16, y = view_75_to_fp16)[name = tensor<string, []>("mul_151_cast_fp16")];
            tensor<string, []> conv1d_75_pad_type_0 = const()[name = tensor<string, []>("conv1d_75_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_75_strides_0 = const()[name = tensor<string, []>("conv1d_75_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_75_pad_0 = const()[name = tensor<string, []>("conv1d_75_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_75_dilations_0 = const()[name = tensor<string, []>("conv1d_75_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_75_groups_0 = const()[name = tensor<string, []>("conv1d_75_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_75_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_75_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480192)))];
            tensor<fp16, [256]> p_bn_75_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_75_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(485888)))];
            tensor<fp16, [1, 256, 801]> conv1d_75_cast_fp16 = conv(bias = p_bn_75_1_bias_to_fp16, dilations = conv1d_75_dilations_0, groups = conv1d_75_groups_0, pad = conv1d_75_pad_0, pad_type = conv1d_75_pad_type_0, strides = conv1d_75_strides_0, weight = p_bn_75_1_weight_to_fp16, x = mul_151_cast_fp16)[name = tensor<string, []>("conv1d_75_cast_fp16")];
            tensor<int32, [3]> slice_153_begin_0 = const()[name = tensor<string, []>("slice_153_begin_0"), val = tensor<int32, [3]>([0, 380, 0])];
            tensor<int32, [3]> slice_153_end_0 = const()[name = tensor<string, []>("slice_153_end_0"), val = tensor<int32, [3]>([1, 385, 801])];
            tensor<bool, [3]> slice_153_end_mask_0 = const()[name = tensor<string, []>("slice_153_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_153_cast_fp16 = slice_by_index(begin = slice_153_begin_0, end = slice_153_end_0, end_mask = slice_153_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_153_cast_fp16")];
            tensor<int32, [3]> slice_154_begin_0 = const()[name = tensor<string, []>("slice_154_begin_0"), val = tensor<int32, [3]>([0, 380, 0])];
            tensor<int32, [3]> slice_154_end_0 = const()[name = tensor<string, []>("slice_154_end_0"), val = tensor<int32, [3]>([1, 385, 801])];
            tensor<bool, [3]> slice_154_end_mask_0 = const()[name = tensor<string, []>("slice_154_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_154_cast_fp16 = slice_by_index(begin = slice_154_begin_0, end = slice_154_end_0, end_mask = slice_154_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_154_cast_fp16")];
            tensor<fp16, []> const_1682_promoted_to_fp16 = const()[name = tensor<string, []>("const_1682_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_229_cast_fp16 = pow(x = slice_153_cast_fp16, y = const_1682_promoted_to_fp16)[name = tensor<string, []>("pow_229_cast_fp16")];
            tensor<fp16, []> const_1683_promoted_to_fp16 = const()[name = tensor<string, []>("const_1683_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_230_cast_fp16 = pow(x = slice_154_cast_fp16, y = const_1683_promoted_to_fp16)[name = tensor<string, []>("pow_230_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_228_cast_fp16 = add(x = pow_229_cast_fp16, y = pow_230_cast_fp16)[name = tensor<string, []>("add_228_cast_fp16")];
            tensor<int32, [1]> sum_77_axes_0 = const()[name = tensor<string, []>("sum_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_77_keep_dims_0 = const()[name = tensor<string, []>("sum_77_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_77_cast_fp16 = reduce_sum(axes = sum_77_axes_0, keep_dims = sum_77_keep_dims_0, x = add_228_cast_fp16)[name = tensor<string, []>("sum_77_cast_fp16")];
            tensor<fp16, []> const_1686_to_fp16 = const()[name = tensor<string, []>("const_1686_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_229_cast_fp16 = add(x = sum_77_cast_fp16, y = const_1686_to_fp16)[name = tensor<string, []>("add_229_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_76_cast_fp16 = sqrt(x = add_229_cast_fp16)[name = tensor<string, []>("sqrt_76_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_152_cast_fp16 = real_div(x = slice_153_cast_fp16, y = sqrt_76_cast_fp16)[name = tensor<string, []>("div_152_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_153_cast_fp16 = real_div(x = slice_154_cast_fp16, y = sqrt_76_cast_fp16)[name = tensor<string, []>("div_153_cast_fp16")];
            tensor<fp32, []> log_76_epsilon_0 = const()[name = tensor<string, []>("log_76_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_76_cast_fp16 = log(epsilon = log_76_epsilon_0, x = sqrt_76_cast_fp16)[name = tensor<string, []>("log_76_cast_fp16")];
            tensor<int32, []> const_1687 = const()[name = tensor<string, []>("const_1687"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_76_interleave_0 = const()[name = tensor<string, []>("cat_76_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_76_cast_fp16 = concat(axis = const_1687, interleave = cat_76_interleave_0, values = (div_152_cast_fp16, div_153_cast_fp16, log_76_cast_fp16))[name = tensor<string, []>("cat_76_cast_fp16")];
            tensor<int32, [1]> unsqueeze_153_axes_0 = const()[name = tensor<string, []>("unsqueeze_153_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_153_cast_fp16 = expand_dims(axes = unsqueeze_153_axes_0, x = cat_76_cast_fp16)[name = tensor<string, []>("unsqueeze_153_cast_fp16")];
            tensor<fp16, []> const_1692_promoted_to_fp16 = const()[name = tensor<string, []>("const_1692_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_231_cast_fp16 = pow(x = unsqueeze_153_cast_fp16, y = const_1692_promoted_to_fp16)[name = tensor<string, []>("pow_231_cast_fp16")];
            tensor<int32, [1]> mean_76_axes_0 = const()[name = tensor<string, []>("mean_76_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_76_keep_dims_0 = const()[name = tensor<string, []>("mean_76_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_76_cast_fp16 = reduce_mean(axes = mean_76_axes_0, keep_dims = mean_76_keep_dims_0, x = pow_231_cast_fp16)[name = tensor<string, []>("mean_76_cast_fp16")];
            tensor<fp16, []> const_1695_to_fp16 = const()[name = tensor<string, []>("const_1695_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_230_cast_fp16 = add(x = mean_76_cast_fp16, y = const_1695_to_fp16)[name = tensor<string, []>("add_230_cast_fp16")];
            tensor<fp32, []> rsqrt_76_epsilon_0 = const()[name = tensor<string, []>("rsqrt_76_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_76_cast_fp16 = rsqrt(epsilon = rsqrt_76_epsilon_0, x = add_230_cast_fp16)[name = tensor<string, []>("rsqrt_76_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_152_cast_fp16 = mul(x = unsqueeze_153_cast_fp16, y = rsqrt_76_cast_fp16)[name = tensor<string, []>("mul_152_cast_fp16")];
            tensor<int32, [1]> squeeze_76_axes_0 = const()[name = tensor<string, []>("squeeze_76_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_76_cast_fp16 = squeeze(axes = squeeze_76_axes_0, x = mul_152_cast_fp16)[name = tensor<string, []>("squeeze_76_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_76_to_fp16 = const()[name = tensor<string, []>("view_76_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486464)))];
            tensor<fp16, [1, 11, 801]> mul_153_cast_fp16 = mul(x = squeeze_76_cast_fp16, y = view_76_to_fp16)[name = tensor<string, []>("mul_153_cast_fp16")];
            tensor<string, []> conv1d_76_pad_type_0 = const()[name = tensor<string, []>("conv1d_76_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_76_strides_0 = const()[name = tensor<string, []>("conv1d_76_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_76_pad_0 = const()[name = tensor<string, []>("conv1d_76_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_76_dilations_0 = const()[name = tensor<string, []>("conv1d_76_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_76_groups_0 = const()[name = tensor<string, []>("conv1d_76_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_76_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_76_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486592)))];
            tensor<fp16, [256]> p_bn_76_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_76_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(492288)))];
            tensor<fp16, [1, 256, 801]> conv1d_76_cast_fp16 = conv(bias = p_bn_76_1_bias_to_fp16, dilations = conv1d_76_dilations_0, groups = conv1d_76_groups_0, pad = conv1d_76_pad_0, pad_type = conv1d_76_pad_type_0, strides = conv1d_76_strides_0, weight = p_bn_76_1_weight_to_fp16, x = mul_153_cast_fp16)[name = tensor<string, []>("conv1d_76_cast_fp16")];
            tensor<int32, [3]> slice_155_begin_0 = const()[name = tensor<string, []>("slice_155_begin_0"), val = tensor<int32, [3]>([0, 385, 0])];
            tensor<int32, [3]> slice_155_end_0 = const()[name = tensor<string, []>("slice_155_end_0"), val = tensor<int32, [3]>([1, 390, 801])];
            tensor<bool, [3]> slice_155_end_mask_0 = const()[name = tensor<string, []>("slice_155_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_155_cast_fp16 = slice_by_index(begin = slice_155_begin_0, end = slice_155_end_0, end_mask = slice_155_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_155_cast_fp16")];
            tensor<int32, [3]> slice_156_begin_0 = const()[name = tensor<string, []>("slice_156_begin_0"), val = tensor<int32, [3]>([0, 385, 0])];
            tensor<int32, [3]> slice_156_end_0 = const()[name = tensor<string, []>("slice_156_end_0"), val = tensor<int32, [3]>([1, 390, 801])];
            tensor<bool, [3]> slice_156_end_mask_0 = const()[name = tensor<string, []>("slice_156_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_156_cast_fp16 = slice_by_index(begin = slice_156_begin_0, end = slice_156_end_0, end_mask = slice_156_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_156_cast_fp16")];
            tensor<fp16, []> const_1704_promoted_to_fp16 = const()[name = tensor<string, []>("const_1704_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_232_cast_fp16 = pow(x = slice_155_cast_fp16, y = const_1704_promoted_to_fp16)[name = tensor<string, []>("pow_232_cast_fp16")];
            tensor<fp16, []> const_1705_promoted_to_fp16 = const()[name = tensor<string, []>("const_1705_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_233_cast_fp16 = pow(x = slice_156_cast_fp16, y = const_1705_promoted_to_fp16)[name = tensor<string, []>("pow_233_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_231_cast_fp16 = add(x = pow_232_cast_fp16, y = pow_233_cast_fp16)[name = tensor<string, []>("add_231_cast_fp16")];
            tensor<int32, [1]> sum_78_axes_0 = const()[name = tensor<string, []>("sum_78_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_78_keep_dims_0 = const()[name = tensor<string, []>("sum_78_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_78_cast_fp16 = reduce_sum(axes = sum_78_axes_0, keep_dims = sum_78_keep_dims_0, x = add_231_cast_fp16)[name = tensor<string, []>("sum_78_cast_fp16")];
            tensor<fp16, []> const_1708_to_fp16 = const()[name = tensor<string, []>("const_1708_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_232_cast_fp16 = add(x = sum_78_cast_fp16, y = const_1708_to_fp16)[name = tensor<string, []>("add_232_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_77_cast_fp16 = sqrt(x = add_232_cast_fp16)[name = tensor<string, []>("sqrt_77_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_154_cast_fp16 = real_div(x = slice_155_cast_fp16, y = sqrt_77_cast_fp16)[name = tensor<string, []>("div_154_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_155_cast_fp16 = real_div(x = slice_156_cast_fp16, y = sqrt_77_cast_fp16)[name = tensor<string, []>("div_155_cast_fp16")];
            tensor<fp32, []> log_77_epsilon_0 = const()[name = tensor<string, []>("log_77_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_77_cast_fp16 = log(epsilon = log_77_epsilon_0, x = sqrt_77_cast_fp16)[name = tensor<string, []>("log_77_cast_fp16")];
            tensor<int32, []> const_1709 = const()[name = tensor<string, []>("const_1709"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_77_interleave_0 = const()[name = tensor<string, []>("cat_77_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_77_cast_fp16 = concat(axis = const_1709, interleave = cat_77_interleave_0, values = (div_154_cast_fp16, div_155_cast_fp16, log_77_cast_fp16))[name = tensor<string, []>("cat_77_cast_fp16")];
            tensor<int32, [1]> unsqueeze_155_axes_0 = const()[name = tensor<string, []>("unsqueeze_155_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_155_cast_fp16 = expand_dims(axes = unsqueeze_155_axes_0, x = cat_77_cast_fp16)[name = tensor<string, []>("unsqueeze_155_cast_fp16")];
            tensor<fp16, []> const_1714_promoted_to_fp16 = const()[name = tensor<string, []>("const_1714_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_234_cast_fp16 = pow(x = unsqueeze_155_cast_fp16, y = const_1714_promoted_to_fp16)[name = tensor<string, []>("pow_234_cast_fp16")];
            tensor<int32, [1]> mean_77_axes_0 = const()[name = tensor<string, []>("mean_77_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_77_keep_dims_0 = const()[name = tensor<string, []>("mean_77_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_77_cast_fp16 = reduce_mean(axes = mean_77_axes_0, keep_dims = mean_77_keep_dims_0, x = pow_234_cast_fp16)[name = tensor<string, []>("mean_77_cast_fp16")];
            tensor<fp16, []> const_1717_to_fp16 = const()[name = tensor<string, []>("const_1717_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_233_cast_fp16 = add(x = mean_77_cast_fp16, y = const_1717_to_fp16)[name = tensor<string, []>("add_233_cast_fp16")];
            tensor<fp32, []> rsqrt_77_epsilon_0 = const()[name = tensor<string, []>("rsqrt_77_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_77_cast_fp16 = rsqrt(epsilon = rsqrt_77_epsilon_0, x = add_233_cast_fp16)[name = tensor<string, []>("rsqrt_77_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_154_cast_fp16 = mul(x = unsqueeze_155_cast_fp16, y = rsqrt_77_cast_fp16)[name = tensor<string, []>("mul_154_cast_fp16")];
            tensor<int32, [1]> squeeze_77_axes_0 = const()[name = tensor<string, []>("squeeze_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_77_cast_fp16 = squeeze(axes = squeeze_77_axes_0, x = mul_154_cast_fp16)[name = tensor<string, []>("squeeze_77_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_77_to_fp16 = const()[name = tensor<string, []>("view_77_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(492864)))];
            tensor<fp16, [1, 11, 801]> mul_155_cast_fp16 = mul(x = squeeze_77_cast_fp16, y = view_77_to_fp16)[name = tensor<string, []>("mul_155_cast_fp16")];
            tensor<string, []> conv1d_77_pad_type_0 = const()[name = tensor<string, []>("conv1d_77_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_77_strides_0 = const()[name = tensor<string, []>("conv1d_77_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_77_pad_0 = const()[name = tensor<string, []>("conv1d_77_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_77_dilations_0 = const()[name = tensor<string, []>("conv1d_77_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_77_groups_0 = const()[name = tensor<string, []>("conv1d_77_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_77_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_77_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(492992)))];
            tensor<fp16, [256]> p_bn_77_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_77_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498688)))];
            tensor<fp16, [1, 256, 801]> conv1d_77_cast_fp16 = conv(bias = p_bn_77_1_bias_to_fp16, dilations = conv1d_77_dilations_0, groups = conv1d_77_groups_0, pad = conv1d_77_pad_0, pad_type = conv1d_77_pad_type_0, strides = conv1d_77_strides_0, weight = p_bn_77_1_weight_to_fp16, x = mul_155_cast_fp16)[name = tensor<string, []>("conv1d_77_cast_fp16")];
            tensor<int32, [3]> slice_157_begin_0 = const()[name = tensor<string, []>("slice_157_begin_0"), val = tensor<int32, [3]>([0, 390, 0])];
            tensor<int32, [3]> slice_157_end_0 = const()[name = tensor<string, []>("slice_157_end_0"), val = tensor<int32, [3]>([1, 395, 801])];
            tensor<bool, [3]> slice_157_end_mask_0 = const()[name = tensor<string, []>("slice_157_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_157_cast_fp16 = slice_by_index(begin = slice_157_begin_0, end = slice_157_end_0, end_mask = slice_157_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_157_cast_fp16")];
            tensor<int32, [3]> slice_158_begin_0 = const()[name = tensor<string, []>("slice_158_begin_0"), val = tensor<int32, [3]>([0, 390, 0])];
            tensor<int32, [3]> slice_158_end_0 = const()[name = tensor<string, []>("slice_158_end_0"), val = tensor<int32, [3]>([1, 395, 801])];
            tensor<bool, [3]> slice_158_end_mask_0 = const()[name = tensor<string, []>("slice_158_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp16, [1, 5, 801]> slice_158_cast_fp16 = slice_by_index(begin = slice_158_begin_0, end = slice_158_end_0, end_mask = slice_158_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_158_cast_fp16")];
            tensor<fp16, []> const_1726_promoted_to_fp16 = const()[name = tensor<string, []>("const_1726_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_235_cast_fp16 = pow(x = slice_157_cast_fp16, y = const_1726_promoted_to_fp16)[name = tensor<string, []>("pow_235_cast_fp16")];
            tensor<fp16, []> const_1727_promoted_to_fp16 = const()[name = tensor<string, []>("const_1727_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 5, 801]> pow_236_cast_fp16 = pow(x = slice_158_cast_fp16, y = const_1727_promoted_to_fp16)[name = tensor<string, []>("pow_236_cast_fp16")];
            tensor<fp16, [1, 5, 801]> add_234_cast_fp16 = add(x = pow_235_cast_fp16, y = pow_236_cast_fp16)[name = tensor<string, []>("add_234_cast_fp16")];
            tensor<int32, [1]> sum_79_axes_0 = const()[name = tensor<string, []>("sum_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_79_keep_dims_0 = const()[name = tensor<string, []>("sum_79_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_79_cast_fp16 = reduce_sum(axes = sum_79_axes_0, keep_dims = sum_79_keep_dims_0, x = add_234_cast_fp16)[name = tensor<string, []>("sum_79_cast_fp16")];
            tensor<fp16, []> const_1730_to_fp16 = const()[name = tensor<string, []>("const_1730_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_235_cast_fp16 = add(x = sum_79_cast_fp16, y = const_1730_to_fp16)[name = tensor<string, []>("add_235_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_78_cast_fp16 = sqrt(x = add_235_cast_fp16)[name = tensor<string, []>("sqrt_78_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_156_cast_fp16 = real_div(x = slice_157_cast_fp16, y = sqrt_78_cast_fp16)[name = tensor<string, []>("div_156_cast_fp16")];
            tensor<fp16, [1, 5, 801]> div_157_cast_fp16 = real_div(x = slice_158_cast_fp16, y = sqrt_78_cast_fp16)[name = tensor<string, []>("div_157_cast_fp16")];
            tensor<fp32, []> log_78_epsilon_0 = const()[name = tensor<string, []>("log_78_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_78_cast_fp16 = log(epsilon = log_78_epsilon_0, x = sqrt_78_cast_fp16)[name = tensor<string, []>("log_78_cast_fp16")];
            tensor<int32, []> const_1731 = const()[name = tensor<string, []>("const_1731"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_78_interleave_0 = const()[name = tensor<string, []>("cat_78_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 11, 801]> cat_78_cast_fp16 = concat(axis = const_1731, interleave = cat_78_interleave_0, values = (div_156_cast_fp16, div_157_cast_fp16, log_78_cast_fp16))[name = tensor<string, []>("cat_78_cast_fp16")];
            tensor<int32, [1]> unsqueeze_157_axes_0 = const()[name = tensor<string, []>("unsqueeze_157_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 11, 801]> unsqueeze_157_cast_fp16 = expand_dims(axes = unsqueeze_157_axes_0, x = cat_78_cast_fp16)[name = tensor<string, []>("unsqueeze_157_cast_fp16")];
            tensor<fp16, []> const_1736_promoted_to_fp16 = const()[name = tensor<string, []>("const_1736_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 11, 801]> pow_237_cast_fp16 = pow(x = unsqueeze_157_cast_fp16, y = const_1736_promoted_to_fp16)[name = tensor<string, []>("pow_237_cast_fp16")];
            tensor<int32, [1]> mean_78_axes_0 = const()[name = tensor<string, []>("mean_78_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_78_keep_dims_0 = const()[name = tensor<string, []>("mean_78_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_78_cast_fp16 = reduce_mean(axes = mean_78_axes_0, keep_dims = mean_78_keep_dims_0, x = pow_237_cast_fp16)[name = tensor<string, []>("mean_78_cast_fp16")];
            tensor<fp16, []> const_1739_to_fp16 = const()[name = tensor<string, []>("const_1739_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_236_cast_fp16 = add(x = mean_78_cast_fp16, y = const_1739_to_fp16)[name = tensor<string, []>("add_236_cast_fp16")];
            tensor<fp32, []> rsqrt_78_epsilon_0 = const()[name = tensor<string, []>("rsqrt_78_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_78_cast_fp16 = rsqrt(epsilon = rsqrt_78_epsilon_0, x = add_236_cast_fp16)[name = tensor<string, []>("rsqrt_78_cast_fp16")];
            tensor<fp16, [1, 1, 11, 801]> mul_156_cast_fp16 = mul(x = unsqueeze_157_cast_fp16, y = rsqrt_78_cast_fp16)[name = tensor<string, []>("mul_156_cast_fp16")];
            tensor<int32, [1]> squeeze_78_axes_0 = const()[name = tensor<string, []>("squeeze_78_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 11, 801]> squeeze_78_cast_fp16 = squeeze(axes = squeeze_78_axes_0, x = mul_156_cast_fp16)[name = tensor<string, []>("squeeze_78_cast_fp16")];
            tensor<fp16, [1, 11, 1]> view_78_to_fp16 = const()[name = tensor<string, []>("view_78_to_fp16"), val = tensor<fp16, [1, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499264)))];
            tensor<fp16, [1, 11, 801]> mul_157_cast_fp16 = mul(x = squeeze_78_cast_fp16, y = view_78_to_fp16)[name = tensor<string, []>("mul_157_cast_fp16")];
            tensor<string, []> conv1d_78_pad_type_0 = const()[name = tensor<string, []>("conv1d_78_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_78_strides_0 = const()[name = tensor<string, []>("conv1d_78_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_78_pad_0 = const()[name = tensor<string, []>("conv1d_78_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_78_dilations_0 = const()[name = tensor<string, []>("conv1d_78_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_78_groups_0 = const()[name = tensor<string, []>("conv1d_78_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 11, 1]> p_bn_78_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_78_1_weight_to_fp16"), val = tensor<fp16, [256, 11, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499392)))];
            tensor<fp16, [256]> p_bn_78_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_78_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505088)))];
            tensor<fp16, [1, 256, 801]> conv1d_78_cast_fp16 = conv(bias = p_bn_78_1_bias_to_fp16, dilations = conv1d_78_dilations_0, groups = conv1d_78_groups_0, pad = conv1d_78_pad_0, pad_type = conv1d_78_pad_type_0, strides = conv1d_78_strides_0, weight = p_bn_78_1_weight_to_fp16, x = mul_157_cast_fp16)[name = tensor<string, []>("conv1d_78_cast_fp16")];
            tensor<int32, [3]> slice_159_begin_0 = const()[name = tensor<string, []>("slice_159_begin_0"), val = tensor<int32, [3]>([0, 395, 0])];
            tensor<int32, [3]> slice_159_end_0 = const()[name = tensor<string, []>("slice_159_end_0"), val = tensor<int32, [3]>([1, 1, 801])];
            tensor<bool, [3]> slice_159_end_mask_0 = const()[name = tensor<string, []>("slice_159_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 47, 801]> slice_159_cast_fp16 = slice_by_index(begin = slice_159_begin_0, end = slice_159_end_0, end_mask = slice_159_end_mask_0, x = select_cast_fp16)[name = tensor<string, []>("slice_159_cast_fp16")];
            tensor<int32, [3]> slice_160_begin_0 = const()[name = tensor<string, []>("slice_160_begin_0"), val = tensor<int32, [3]>([0, 395, 0])];
            tensor<int32, [3]> slice_160_end_0 = const()[name = tensor<string, []>("slice_160_end_0"), val = tensor<int32, [3]>([1, 1, 801])];
            tensor<bool, [3]> slice_160_end_mask_0 = const()[name = tensor<string, []>("slice_160_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 47, 801]> slice_160_cast_fp16 = slice_by_index(begin = slice_160_begin_0, end = slice_160_end_0, end_mask = slice_160_end_mask_0, x = select_1_cast_fp16)[name = tensor<string, []>("slice_160_cast_fp16")];
            tensor<fp16, []> const_1748_promoted_to_fp16 = const()[name = tensor<string, []>("const_1748_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 47, 801]> pow_238_cast_fp16 = pow(x = slice_159_cast_fp16, y = const_1748_promoted_to_fp16)[name = tensor<string, []>("pow_238_cast_fp16")];
            tensor<fp16, []> const_1749_promoted_to_fp16 = const()[name = tensor<string, []>("const_1749_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 47, 801]> pow_239_cast_fp16 = pow(x = slice_160_cast_fp16, y = const_1749_promoted_to_fp16)[name = tensor<string, []>("pow_239_cast_fp16")];
            tensor<fp16, [1, 47, 801]> add_237_cast_fp16 = add(x = pow_238_cast_fp16, y = pow_239_cast_fp16)[name = tensor<string, []>("add_237_cast_fp16")];
            tensor<int32, [1]> sum_80_axes_0 = const()[name = tensor<string, []>("sum_80_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> sum_80_keep_dims_0 = const()[name = tensor<string, []>("sum_80_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 801]> sum_80_cast_fp16 = reduce_sum(axes = sum_80_axes_0, keep_dims = sum_80_keep_dims_0, x = add_237_cast_fp16)[name = tensor<string, []>("sum_80_cast_fp16")];
            tensor<fp16, []> const_1752_to_fp16 = const()[name = tensor<string, []>("const_1752_to_fp16"), val = tensor<fp16, []>(0x1p-23)];
            tensor<fp16, [1, 1, 801]> add_238_cast_fp16 = add(x = sum_80_cast_fp16, y = const_1752_to_fp16)[name = tensor<string, []>("add_238_cast_fp16")];
            tensor<fp16, [1, 1, 801]> sqrt_79_cast_fp16 = sqrt(x = add_238_cast_fp16)[name = tensor<string, []>("sqrt_79_cast_fp16")];
            tensor<fp16, [1, 47, 801]> div_158_cast_fp16 = real_div(x = slice_159_cast_fp16, y = sqrt_79_cast_fp16)[name = tensor<string, []>("div_158_cast_fp16")];
            tensor<fp16, [1, 47, 801]> div_159_cast_fp16 = real_div(x = slice_160_cast_fp16, y = sqrt_79_cast_fp16)[name = tensor<string, []>("div_159_cast_fp16")];
            tensor<fp32, []> log_79_epsilon_0 = const()[name = tensor<string, []>("log_79_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 1, 801]> log_79_cast_fp16 = log(epsilon = log_79_epsilon_0, x = sqrt_79_cast_fp16)[name = tensor<string, []>("log_79_cast_fp16")];
            tensor<int32, []> const_1753 = const()[name = tensor<string, []>("const_1753"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_79_interleave_0 = const()[name = tensor<string, []>("cat_79_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 95, 801]> cat_79_cast_fp16 = concat(axis = const_1753, interleave = cat_79_interleave_0, values = (div_158_cast_fp16, div_159_cast_fp16, log_79_cast_fp16))[name = tensor<string, []>("cat_79_cast_fp16")];
            tensor<int32, [1]> unsqueeze_159_axes_0 = const()[name = tensor<string, []>("unsqueeze_159_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 95, 801]> unsqueeze_159_cast_fp16 = expand_dims(axes = unsqueeze_159_axes_0, x = cat_79_cast_fp16)[name = tensor<string, []>("unsqueeze_159_cast_fp16")];
            tensor<fp16, []> const_1758_promoted_to_fp16 = const()[name = tensor<string, []>("const_1758_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 95, 801]> pow_240_cast_fp16 = pow(x = unsqueeze_159_cast_fp16, y = const_1758_promoted_to_fp16)[name = tensor<string, []>("pow_240_cast_fp16")];
            tensor<int32, [1]> mean_79_axes_0 = const()[name = tensor<string, []>("mean_79_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_79_keep_dims_0 = const()[name = tensor<string, []>("mean_79_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_79_cast_fp16 = reduce_mean(axes = mean_79_axes_0, keep_dims = mean_79_keep_dims_0, x = pow_240_cast_fp16)[name = tensor<string, []>("mean_79_cast_fp16")];
            tensor<fp16, []> const_1761_to_fp16 = const()[name = tensor<string, []>("const_1761_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_239_cast_fp16 = add(x = mean_79_cast_fp16, y = const_1761_to_fp16)[name = tensor<string, []>("add_239_cast_fp16")];
            tensor<fp32, []> rsqrt_79_epsilon_0 = const()[name = tensor<string, []>("rsqrt_79_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_79_cast_fp16 = rsqrt(epsilon = rsqrt_79_epsilon_0, x = add_239_cast_fp16)[name = tensor<string, []>("rsqrt_79_cast_fp16")];
            tensor<fp16, [1, 1, 95, 801]> mul_158_cast_fp16 = mul(x = unsqueeze_159_cast_fp16, y = rsqrt_79_cast_fp16)[name = tensor<string, []>("mul_158_cast_fp16")];
            tensor<int32, [1]> squeeze_79_axes_0 = const()[name = tensor<string, []>("squeeze_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 95, 801]> squeeze_79_cast_fp16 = squeeze(axes = squeeze_79_axes_0, x = mul_158_cast_fp16)[name = tensor<string, []>("squeeze_79_cast_fp16")];
            tensor<fp16, [1, 95, 1]> view_79_to_fp16 = const()[name = tensor<string, []>("view_79_to_fp16"), val = tensor<fp16, [1, 95, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505664)))];
            tensor<fp16, [1, 95, 801]> mul_159_cast_fp16 = mul(x = squeeze_79_cast_fp16, y = view_79_to_fp16)[name = tensor<string, []>("mul_159_cast_fp16")];
            tensor<string, []> conv1d_79_pad_type_0 = const()[name = tensor<string, []>("conv1d_79_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_79_strides_0 = const()[name = tensor<string, []>("conv1d_79_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_79_pad_0 = const()[name = tensor<string, []>("conv1d_79_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_79_dilations_0 = const()[name = tensor<string, []>("conv1d_79_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_79_groups_0 = const()[name = tensor<string, []>("conv1d_79_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 95, 1]> p_bn_79_1_weight_to_fp16 = const()[name = tensor<string, []>("p_bn_79_1_weight_to_fp16"), val = tensor<fp16, [256, 95, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505920)))];
            tensor<fp16, [256]> p_bn_79_1_bias_to_fp16 = const()[name = tensor<string, []>("p_bn_79_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554624)))];
            tensor<fp16, [1, 256, 801]> conv1d_79_cast_fp16 = conv(bias = p_bn_79_1_bias_to_fp16, dilations = conv1d_79_dilations_0, groups = conv1d_79_groups_0, pad = conv1d_79_pad_0, pad_type = conv1d_79_pad_type_0, strides = conv1d_79_strides_0, weight = p_bn_79_1_weight_to_fp16, x = mul_159_cast_fp16)[name = tensor<string, []>("conv1d_79_cast_fp16")];
            tensor<int32, []> stack_axis_0 = const()[name = tensor<string, []>("stack_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 80, 256, 801]> stack_cast_fp16 = stack(axis = stack_axis_0, values = (conv1d_cast_fp16, conv1d_1_cast_fp16, conv1d_2_cast_fp16, conv1d_3_cast_fp16, conv1d_4_cast_fp16, conv1d_5_cast_fp16, conv1d_6_cast_fp16, conv1d_7_cast_fp16, conv1d_8_cast_fp16, conv1d_9_cast_fp16, conv1d_10_cast_fp16, conv1d_11_cast_fp16, conv1d_12_cast_fp16, conv1d_13_cast_fp16, conv1d_14_cast_fp16, conv1d_15_cast_fp16, conv1d_16_cast_fp16, conv1d_17_cast_fp16, conv1d_18_cast_fp16, conv1d_19_cast_fp16, conv1d_20_cast_fp16, conv1d_21_cast_fp16, conv1d_22_cast_fp16, conv1d_23_cast_fp16, conv1d_24_cast_fp16, conv1d_25_cast_fp16, conv1d_26_cast_fp16, conv1d_27_cast_fp16, conv1d_28_cast_fp16, conv1d_29_cast_fp16, conv1d_30_cast_fp16, conv1d_31_cast_fp16, conv1d_32_cast_fp16, conv1d_33_cast_fp16, conv1d_34_cast_fp16, conv1d_35_cast_fp16, conv1d_36_cast_fp16, conv1d_37_cast_fp16, conv1d_38_cast_fp16, conv1d_39_cast_fp16, conv1d_40_cast_fp16, conv1d_41_cast_fp16, conv1d_42_cast_fp16, conv1d_43_cast_fp16, conv1d_44_cast_fp16, conv1d_45_cast_fp16, conv1d_46_cast_fp16, conv1d_47_cast_fp16, conv1d_48_cast_fp16, conv1d_49_cast_fp16, conv1d_50_cast_fp16, conv1d_51_cast_fp16, conv1d_52_cast_fp16, conv1d_53_cast_fp16, conv1d_54_cast_fp16, conv1d_55_cast_fp16, conv1d_56_cast_fp16, conv1d_57_cast_fp16, conv1d_58_cast_fp16, conv1d_59_cast_fp16, conv1d_60_cast_fp16, conv1d_61_cast_fp16, conv1d_62_cast_fp16, conv1d_63_cast_fp16, conv1d_64_cast_fp16, conv1d_65_cast_fp16, conv1d_66_cast_fp16, conv1d_67_cast_fp16, conv1d_68_cast_fp16, conv1d_69_cast_fp16, conv1d_70_cast_fp16, conv1d_71_cast_fp16, conv1d_72_cast_fp16, conv1d_73_cast_fp16, conv1d_74_cast_fp16, conv1d_75_cast_fp16, conv1d_76_cast_fp16, conv1d_77_cast_fp16, conv1d_78_cast_fp16, conv1d_79_cast_fp16))[name = tensor<string, []>("stack_cast_fp16")];
            tensor<int32, [1]> squeeze_80_axes_0 = const()[name = tensor<string, []>("squeeze_80_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [80, 256, 801]> squeeze_80_cast_fp16 = squeeze(axes = squeeze_80_axes_0, x = stack_cast_fp16)[name = tensor<string, []>("squeeze_80_cast_fp16")];
            tensor<int32, [3]> const_1766 = const()[name = tensor<string, []>("const_1766"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<int32, [1]> unsqueeze_161_axes_0 = const()[name = tensor<string, []>("unsqueeze_161_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> permute_cast_fp16 = transpose(perm = const_1766, x = squeeze_80_cast_fp16)[name = tensor<string, []>("transpose_23")];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_161_cast_fp16 = expand_dims(axes = unsqueeze_161_axes_0, x = permute_cast_fp16)[name = tensor<string, []>("unsqueeze_161_cast_fp16")];
            tensor<fp16, []> const_1771_promoted_to_fp16 = const()[name = tensor<string, []>("const_1771_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_241_cast_fp16 = pow(x = unsqueeze_161_cast_fp16, y = const_1771_promoted_to_fp16)[name = tensor<string, []>("pow_241_cast_fp16")];
            tensor<int32, [1]> mean_80_axes_0 = const()[name = tensor<string, []>("mean_80_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_80_keep_dims_0 = const()[name = tensor<string, []>("mean_80_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_80_cast_fp16 = reduce_mean(axes = mean_80_axes_0, keep_dims = mean_80_keep_dims_0, x = pow_241_cast_fp16)[name = tensor<string, []>("mean_80_cast_fp16")];
            tensor<fp16, []> const_1774_to_fp16 = const()[name = tensor<string, []>("const_1774_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_240_cast_fp16 = add(x = mean_80_cast_fp16, y = const_1774_to_fp16)[name = tensor<string, []>("add_240_cast_fp16")];
            tensor<fp32, []> rsqrt_80_epsilon_0 = const()[name = tensor<string, []>("rsqrt_80_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_80_cast_fp16 = rsqrt(epsilon = rsqrt_80_epsilon_0, x = add_240_cast_fp16)[name = tensor<string, []>("rsqrt_80_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_160_cast_fp16 = mul(x = unsqueeze_161_cast_fp16, y = rsqrt_80_cast_fp16)[name = tensor<string, []>("mul_160_cast_fp16")];
            tensor<int32, [1]> squeeze_81_axes_0 = const()[name = tensor<string, []>("squeeze_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_81_cast_fp16 = squeeze(axes = squeeze_81_axes_0, x = mul_160_cast_fp16)[name = tensor<string, []>("squeeze_81_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_80_to_fp16 = const()[name = tensor<string, []>("view_80_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555200)))];
            tensor<fp16, [801, 256, 80]> mul_161_cast_fp16 = mul(x = squeeze_81_cast_fp16, y = view_80_to_fp16)[name = tensor<string, []>("mul_161_cast_fp16")];
            tensor<string, []> conv1d_80_pad_type_0 = const()[name = tensor<string, []>("conv1d_80_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_80_strides_0 = const()[name = tensor<string, []>("conv1d_80_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_80_pad_0 = const()[name = tensor<string, []>("conv1d_80_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_80_dilations_0 = const()[name = tensor<string, []>("conv1d_80_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_80_groups_0 = const()[name = tensor<string, []>("conv1d_80_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [768, 256, 1]> p_net_0_band_net_weight_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_band_net_weight_weight_to_fp16"), val = tensor<fp16, [768, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555776)))];
            tensor<fp16, [801, 768, 80]> conv1d_80_cast_fp16 = conv(dilations = conv1d_80_dilations_0, groups = conv1d_80_groups_0, pad = conv1d_80_pad_0, pad_type = conv1d_80_pad_type_0, strides = conv1d_80_strides_0, weight = p_net_0_band_net_weight_weight_to_fp16, x = mul_161_cast_fp16)[name = tensor<string, []>("conv1d_80_cast_fp16")];
            tensor<int32, [4]> const_1777 = const()[name = tensor<string, []>("const_1777"), val = tensor<int32, [4]>([-1, 8, 96, 80])];
            tensor<fp16, [801, 8, 96, 80]> view_81_cast_fp16 = reshape(shape = const_1777, x = conv1d_80_cast_fp16)[name = tensor<string, []>("view_81_cast_fp16")];
            tensor<int32, [4]> const_1778 = const()[name = tensor<string, []>("const_1778"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> tile_0 = const()[name = tensor<string, []>("tile_0"), val = tensor<int32, [3]>([32, 32, 32])];
            tensor<int32, []> split_axis_0 = const()[name = tensor<string, []>("split_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 96]> permute_1_cast_fp16 = transpose(perm = const_1778, x = view_81_cast_fp16)[name = tensor<string, []>("transpose_22")];
            tensor<fp16, [801, 8, 80, 32]> split_cast_fp16_0, tensor<fp16, [801, 8, 80, 32]> split_cast_fp16_1, tensor<fp16, [801, 8, 80, 32]> split_cast_fp16_2 = split(axis = split_axis_0, split_sizes = tile_0, x = permute_1_cast_fp16)[name = tensor<string, []>("split_cast_fp16")];
            tensor<int32, [3]> const_1786 = const()[name = tensor<string, []>("const_1786"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_82_cast_fp16 = reshape(shape = const_1786, x = split_cast_fp16_0)[name = tensor<string, []>("view_82_cast_fp16")];
            tensor<int32, [3]> const_1793 = const()[name = tensor<string, []>("const_1793"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_cast_fp16 = reshape(shape = const_1793, x = view_82_cast_fp16)[name = tensor<string, []>("_unsafe_view_cast_fp16")];
            tensor<int32, [1]> const_1794 = const()[name = tensor<string, []>("const_1794"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_cast_fp16 = reverse(axes = const_1794, x = _unsafe_view_cast_fp16)[name = tensor<string, []>("flip_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_83_to_fp16 = const()[name = tensor<string, []>("view_83_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_162_cast_fp16 = mul(x = flip_cast_fp16, y = view_83_to_fp16)[name = tensor<string, []>("mul_162_cast_fp16")];
            tensor<int32, [3]> const_1796 = const()[name = tensor<string, []>("const_1796"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_84_cast_fp16 = reshape(shape = const_1796, x = mul_162_cast_fp16)[name = tensor<string, []>("view_84_cast_fp16")];
            tensor<fp16, [1, 80, 32]> unsqueeze_162_to_fp16 = const()[name = tensor<string, []>("unsqueeze_162_to_fp16"), val = tensor<fp16, [1, 80, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(949056)))];
            tensor<fp16, [6408, 80, 32]> mul_163_cast_fp16 = mul(x = view_82_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_163_cast_fp16")];
            tensor<fp16, [1, 80, 32]> unsqueeze_163_to_fp16 = const()[name = tensor<string, []>("unsqueeze_163_to_fp16"), val = tensor<fp16, [1, 80, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(954240)))];
            tensor<fp16, [6408, 80, 32]> mul_164_cast_fp16 = mul(x = view_84_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_164_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_241_cast_fp16 = add(x = mul_163_cast_fp16, y = mul_164_cast_fp16)[name = tensor<string, []>("add_241_cast_fp16")];
            tensor<int32, [4]> const_1799 = const()[name = tensor<string, []>("const_1799"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_85_cast_fp16 = reshape(shape = const_1799, x = add_241_cast_fp16)[name = tensor<string, []>("view_85_cast_fp16")];
            tensor<int32, [3]> const_1800 = const()[name = tensor<string, []>("const_1800"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_86_cast_fp16 = reshape(shape = const_1800, x = split_cast_fp16_1)[name = tensor<string, []>("view_86_cast_fp16")];
            tensor<int32, [3]> const_1807 = const()[name = tensor<string, []>("const_1807"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_1_cast_fp16 = reshape(shape = const_1807, x = view_86_cast_fp16)[name = tensor<string, []>("_unsafe_view_1_cast_fp16")];
            tensor<int32, [1]> const_1808 = const()[name = tensor<string, []>("const_1808"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_1_cast_fp16 = reverse(axes = const_1808, x = _unsafe_view_1_cast_fp16)[name = tensor<string, []>("flip_1_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_87_to_fp16 = const()[name = tensor<string, []>("view_87_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_165_cast_fp16 = mul(x = flip_1_cast_fp16, y = view_87_to_fp16)[name = tensor<string, []>("mul_165_cast_fp16")];
            tensor<int32, [3]> const_1810 = const()[name = tensor<string, []>("const_1810"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_88_cast_fp16 = reshape(shape = const_1810, x = mul_165_cast_fp16)[name = tensor<string, []>("view_88_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_166_cast_fp16 = mul(x = view_86_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_166_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_167_cast_fp16 = mul(x = view_88_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_167_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_242_cast_fp16 = add(x = mul_166_cast_fp16, y = mul_167_cast_fp16)[name = tensor<string, []>("add_242_cast_fp16")];
            tensor<int32, [4]> const_1813 = const()[name = tensor<string, []>("const_1813"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_89_cast_fp16 = reshape(shape = const_1813, x = add_242_cast_fp16)[name = tensor<string, []>("view_89_cast_fp16")];
            tensor<bool, []> matmul_transpose_x_1 = const()[name = tensor<string, []>("matmul_transpose_x_1"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_transpose_y_1 = const()[name = tensor<string, []>("matmul_transpose_y_1"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 8, 80, 80]> matmul_cast_fp16 = matmul(transpose_x = matmul_transpose_x_1, transpose_y = matmul_transpose_y_1, x = view_85_cast_fp16, y = view_89_cast_fp16)[name = tensor<string, []>("matmul_cast_fp16")];
            tensor<fp16, []> const_1816_to_fp16 = const()[name = tensor<string, []>("const_1816_to_fp16"), val = tensor<fp16, []>(0x1.6ap-3)];
            tensor<fp16, [801, 8, 80, 80]> mul_168_cast_fp16 = mul(x = matmul_cast_fp16, y = const_1816_to_fp16)[name = tensor<string, []>("mul_168_cast_fp16")];
            tensor<int32, []> const_1817 = const()[name = tensor<string, []>("const_1817"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 80]> softmax_cast_fp16 = softmax(axis = const_1817, x = mul_168_cast_fp16)[name = tensor<string, []>("softmax_cast_fp16")];
            tensor<bool, []> matmul_1_transpose_x_0 = const()[name = tensor<string, []>("matmul_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_1_transpose_y_0 = const()[name = tensor<string, []>("matmul_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [801, 8, 80, 32]> matmul_1_cast_fp16 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = softmax_cast_fp16, y = split_cast_fp16_2)[name = tensor<string, []>("matmul_1_cast_fp16")];
            tensor<int32, [4]> const_1818 = const()[name = tensor<string, []>("const_1818"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> const_1819 = const()[name = tensor<string, []>("const_1819"), val = tensor<int32, [3]>([801, 256, 80])];
            tensor<fp16, [801, 8, 32, 80]> permute_2_cast_fp16 = transpose(perm = const_1818, x = matmul_1_cast_fp16)[name = tensor<string, []>("transpose_21")];
            tensor<fp16, [801, 256, 80]> _unsafe_view_2_cast_fp16 = reshape(shape = const_1819, x = permute_2_cast_fp16)[name = tensor<string, []>("_unsafe_view_2_cast_fp16")];
            tensor<string, []> conv1d_81_pad_type_0 = const()[name = tensor<string, []>("conv1d_81_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_81_strides_0 = const()[name = tensor<string, []>("conv1d_81_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_81_pad_0 = const()[name = tensor<string, []>("conv1d_81_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_81_dilations_0 = const()[name = tensor<string, []>("conv1d_81_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_81_groups_0 = const()[name = tensor<string, []>("conv1d_81_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1]> p_net_0_band_net_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_band_net_output_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(959424)))];
            tensor<fp16, [801, 256, 80]> conv1d_81_cast_fp16 = conv(dilations = conv1d_81_dilations_0, groups = conv1d_81_groups_0, pad = conv1d_81_pad_0, pad_type = conv1d_81_pad_type_0, strides = conv1d_81_strides_0, weight = p_net_0_band_net_output_weight_to_fp16, x = _unsafe_view_2_cast_fp16)[name = tensor<string, []>("conv1d_81_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_243_cast_fp16 = add(x = conv1d_81_cast_fp16, y = permute_cast_fp16)[name = tensor<string, []>("add_243_cast_fp16")];
            tensor<int32, [1]> unsqueeze_167_axes_0 = const()[name = tensor<string, []>("unsqueeze_167_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_167_cast_fp16 = expand_dims(axes = unsqueeze_167_axes_0, x = add_243_cast_fp16)[name = tensor<string, []>("unsqueeze_167_cast_fp16")];
            tensor<fp16, []> const_1824_promoted_to_fp16 = const()[name = tensor<string, []>("const_1824_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_242_cast_fp16 = pow(x = unsqueeze_167_cast_fp16, y = const_1824_promoted_to_fp16)[name = tensor<string, []>("pow_242_cast_fp16")];
            tensor<int32, [1]> mean_81_axes_0 = const()[name = tensor<string, []>("mean_81_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_81_keep_dims_0 = const()[name = tensor<string, []>("mean_81_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_81_cast_fp16 = reduce_mean(axes = mean_81_axes_0, keep_dims = mean_81_keep_dims_0, x = pow_242_cast_fp16)[name = tensor<string, []>("mean_81_cast_fp16")];
            tensor<fp16, []> const_1827_to_fp16 = const()[name = tensor<string, []>("const_1827_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_244_cast_fp16 = add(x = mean_81_cast_fp16, y = const_1827_to_fp16)[name = tensor<string, []>("add_244_cast_fp16")];
            tensor<fp32, []> rsqrt_81_epsilon_0 = const()[name = tensor<string, []>("rsqrt_81_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_81_cast_fp16 = rsqrt(epsilon = rsqrt_81_epsilon_0, x = add_244_cast_fp16)[name = tensor<string, []>("rsqrt_81_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_169_cast_fp16 = mul(x = unsqueeze_167_cast_fp16, y = rsqrt_81_cast_fp16)[name = tensor<string, []>("mul_169_cast_fp16")];
            tensor<int32, [1]> squeeze_82_axes_0 = const()[name = tensor<string, []>("squeeze_82_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_82_cast_fp16 = squeeze(axes = squeeze_82_axes_0, x = mul_169_cast_fp16)[name = tensor<string, []>("squeeze_82_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_90_to_fp16 = const()[name = tensor<string, []>("view_90_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1090560)))];
            tensor<fp16, [801, 256, 80]> mul_170_cast_fp16 = mul(x = squeeze_82_cast_fp16, y = view_90_to_fp16)[name = tensor<string, []>("mul_170_cast_fp16")];
            tensor<string, []> conv1d_82_pad_type_0 = const()[name = tensor<string, []>("conv1d_82_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_82_strides_0 = const()[name = tensor<string, []>("conv1d_82_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_82_pad_0 = const()[name = tensor<string, []>("conv1d_82_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_82_dilations_0 = const()[name = tensor<string, []>("conv1d_82_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_82_groups_0 = const()[name = tensor<string, []>("conv1d_82_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 256, 1]> p_net_0_band_net_mlp_1_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_band_net_mlp_1_weight_to_fp16"), val = tensor<fp16, [2048, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091136)))];
            tensor<fp16, [801, 2048, 80]> conv1d_82_cast_fp16 = conv(dilations = conv1d_82_dilations_0, groups = conv1d_82_groups_0, pad = conv1d_82_pad_0, pad_type = conv1d_82_pad_type_0, strides = conv1d_82_strides_0, weight = p_net_0_band_net_mlp_1_weight_to_fp16, x = mul_170_cast_fp16)[name = tensor<string, []>("conv1d_82_cast_fp16")];
            tensor<fp16, [801, 2048, 80]> silu_cast_fp16 = silu(x = conv1d_82_cast_fp16)[name = tensor<string, []>("silu_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = tensor<string, []>("tile_1"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> split_1_axis_0 = const()[name = tensor<string, []>("split_1_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [801, 1024, 80]> split_1_cast_fp16_0, tensor<fp16, [801, 1024, 80]> split_1_cast_fp16_1 = split(axis = split_1_axis_0, split_sizes = tile_1, x = silu_cast_fp16)[name = tensor<string, []>("split_1_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> silu_1_cast_fp16 = silu(x = split_1_cast_fp16_0)[name = tensor<string, []>("silu_1_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> mul_171_cast_fp16 = mul(x = silu_1_cast_fp16, y = split_1_cast_fp16_1)[name = tensor<string, []>("mul_171_cast_fp16")];
            tensor<string, []> conv1d_83_pad_type_0 = const()[name = tensor<string, []>("conv1d_83_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_83_strides_0 = const()[name = tensor<string, []>("conv1d_83_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_83_pad_0 = const()[name = tensor<string, []>("conv1d_83_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_83_dilations_0 = const()[name = tensor<string, []>("conv1d_83_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_83_groups_0 = const()[name = tensor<string, []>("conv1d_83_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_0_band_net_mlp_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_band_net_mlp_output_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2139776)))];
            tensor<fp16, [801, 256, 80]> conv1d_83_cast_fp16 = conv(dilations = conv1d_83_dilations_0, groups = conv1d_83_groups_0, pad = conv1d_83_pad_0, pad_type = conv1d_83_pad_type_0, strides = conv1d_83_strides_0, weight = p_net_0_band_net_mlp_output_weight_to_fp16, x = mul_171_cast_fp16)[name = tensor<string, []>("conv1d_83_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_245_cast_fp16 = add(x = add_243_cast_fp16, y = conv1d_83_cast_fp16)[name = tensor<string, []>("add_245_cast_fp16")];
            tensor<int32, [3]> const_1836 = const()[name = tensor<string, []>("const_1836"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<string, []> conv1d_84_pad_type_0 = const()[name = tensor<string, []>("conv1d_84_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_84_pad_0 = const()[name = tensor<string, []>("conv1d_84_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_84_groups_0 = const()[name = tensor<string, []>("conv1d_84_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_84_strides_0 = const()[name = tensor<string, []>("conv1d_84_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_84_dilations_0 = const()[name = tensor<string, []>("conv1d_84_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_0_seq_net_blocks_0_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_0_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2664128)))];
            tensor<fp16, [256]> p_net_0_seq_net_blocks_0_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_0_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2667776)))];
            tensor<fp16, [80, 256, 801]> permute_3_cast_fp16 = transpose(perm = const_1836, x = add_245_cast_fp16)[name = tensor<string, []>("transpose_20")];
            tensor<fp16, [80, 256, 801]> conv1d_84_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_0_conv_0_bias_to_fp16, dilations = conv1d_84_dilations_0, groups = conv1d_84_groups_0, pad = conv1d_84_pad_0, pad_type = conv1d_84_pad_type_0, strides = conv1d_84_strides_0, weight = p_net_0_seq_net_blocks_0_conv_0_weight_to_fp16, x = permute_3_cast_fp16)[name = tensor<string, []>("conv1d_84_cast_fp16")];
            tensor<int32, [1]> unsqueeze_169_axes_0 = const()[name = tensor<string, []>("unsqueeze_169_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_169_cast_fp16 = expand_dims(axes = unsqueeze_169_axes_0, x = conv1d_84_cast_fp16)[name = tensor<string, []>("unsqueeze_169_cast_fp16")];
            tensor<fp16, []> const_1845_promoted_to_fp16 = const()[name = tensor<string, []>("const_1845_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_243_cast_fp16 = pow(x = unsqueeze_169_cast_fp16, y = const_1845_promoted_to_fp16)[name = tensor<string, []>("pow_243_cast_fp16")];
            tensor<int32, [1]> mean_82_axes_0 = const()[name = tensor<string, []>("mean_82_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_82_keep_dims_0 = const()[name = tensor<string, []>("mean_82_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_82_cast_fp16 = reduce_mean(axes = mean_82_axes_0, keep_dims = mean_82_keep_dims_0, x = pow_243_cast_fp16)[name = tensor<string, []>("mean_82_cast_fp16")];
            tensor<fp16, []> const_1848_to_fp16 = const()[name = tensor<string, []>("const_1848_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_246_cast_fp16 = add(x = mean_82_cast_fp16, y = const_1848_to_fp16)[name = tensor<string, []>("add_246_cast_fp16")];
            tensor<fp32, []> rsqrt_82_epsilon_0 = const()[name = tensor<string, []>("rsqrt_82_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_82_cast_fp16 = rsqrt(epsilon = rsqrt_82_epsilon_0, x = add_246_cast_fp16)[name = tensor<string, []>("rsqrt_82_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_172_cast_fp16 = mul(x = unsqueeze_169_cast_fp16, y = rsqrt_82_cast_fp16)[name = tensor<string, []>("mul_172_cast_fp16")];
            tensor<int32, [1]> squeeze_83_axes_0 = const()[name = tensor<string, []>("squeeze_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_83_cast_fp16 = squeeze(axes = squeeze_83_axes_0, x = mul_172_cast_fp16)[name = tensor<string, []>("squeeze_83_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_91_to_fp16 = const()[name = tensor<string, []>("view_91_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2668352)))];
            tensor<fp16, [80, 256, 801]> mul_173_cast_fp16 = mul(x = squeeze_83_cast_fp16, y = view_91_to_fp16)[name = tensor<string, []>("mul_173_cast_fp16")];
            tensor<string, []> conv1d_85_pad_type_0 = const()[name = tensor<string, []>("conv1d_85_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_85_strides_0 = const()[name = tensor<string, []>("conv1d_85_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_85_pad_0 = const()[name = tensor<string, []>("conv1d_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_85_dilations_0 = const()[name = tensor<string, []>("conv1d_85_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_85_groups_0 = const()[name = tensor<string, []>("conv1d_85_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_0_seq_net_blocks_0_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_0_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2668928)))];
            tensor<fp16, [1024]> p_net_0_seq_net_blocks_0_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_0_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3193280)))];
            tensor<fp16, [80, 1024, 801]> conv1d_85_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_0_conv_2_bias_to_fp16, dilations = conv1d_85_dilations_0, groups = conv1d_85_groups_0, pad = conv1d_85_pad_0, pad_type = conv1d_85_pad_type_0, strides = conv1d_85_strides_0, weight = p_net_0_seq_net_blocks_0_conv_2_weight_to_fp16, x = mul_173_cast_fp16)[name = tensor<string, []>("conv1d_85_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_2_cast_fp16 = silu(x = conv1d_85_cast_fp16)[name = tensor<string, []>("silu_2_cast_fp16")];
            tensor<string, []> conv1d_86_pad_type_0 = const()[name = tensor<string, []>("conv1d_86_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_86_strides_0 = const()[name = tensor<string, []>("conv1d_86_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_86_pad_0 = const()[name = tensor<string, []>("conv1d_86_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_86_dilations_0 = const()[name = tensor<string, []>("conv1d_86_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_86_groups_0 = const()[name = tensor<string, []>("conv1d_86_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_0_seq_net_blocks_0_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_0_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3195392)))];
            tensor<fp16, [256]> p_net_0_seq_net_blocks_0_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_0_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3719744)))];
            tensor<fp16, [80, 256, 801]> conv1d_86_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_0_conv_4_bias_to_fp16, dilations = conv1d_86_dilations_0, groups = conv1d_86_groups_0, pad = conv1d_86_pad_0, pad_type = conv1d_86_pad_type_0, strides = conv1d_86_strides_0, weight = p_net_0_seq_net_blocks_0_conv_4_weight_to_fp16, x = silu_2_cast_fp16)[name = tensor<string, []>("conv1d_86_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_247_cast_fp16 = add(x = permute_3_cast_fp16, y = conv1d_86_cast_fp16)[name = tensor<string, []>("add_247_cast_fp16")];
            tensor<string, []> conv1d_87_pad_type_0 = const()[name = tensor<string, []>("conv1d_87_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_87_pad_0 = const()[name = tensor<string, []>("conv1d_87_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_87_groups_0 = const()[name = tensor<string, []>("conv1d_87_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_87_strides_0 = const()[name = tensor<string, []>("conv1d_87_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_87_dilations_0 = const()[name = tensor<string, []>("conv1d_87_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_0_seq_net_blocks_1_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_1_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3720320)))];
            tensor<fp16, [256]> p_net_0_seq_net_blocks_1_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_1_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3723968)))];
            tensor<fp16, [80, 256, 801]> conv1d_87_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_1_conv_0_bias_to_fp16, dilations = conv1d_87_dilations_0, groups = conv1d_87_groups_0, pad = conv1d_87_pad_0, pad_type = conv1d_87_pad_type_0, strides = conv1d_87_strides_0, weight = p_net_0_seq_net_blocks_1_conv_0_weight_to_fp16, x = add_247_cast_fp16)[name = tensor<string, []>("conv1d_87_cast_fp16")];
            tensor<int32, [1]> unsqueeze_171_axes_0 = const()[name = tensor<string, []>("unsqueeze_171_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_171_cast_fp16 = expand_dims(axes = unsqueeze_171_axes_0, x = conv1d_87_cast_fp16)[name = tensor<string, []>("unsqueeze_171_cast_fp16")];
            tensor<fp16, []> const_1859_promoted_to_fp16 = const()[name = tensor<string, []>("const_1859_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_244_cast_fp16 = pow(x = unsqueeze_171_cast_fp16, y = const_1859_promoted_to_fp16)[name = tensor<string, []>("pow_244_cast_fp16")];
            tensor<int32, [1]> mean_83_axes_0 = const()[name = tensor<string, []>("mean_83_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_83_keep_dims_0 = const()[name = tensor<string, []>("mean_83_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_83_cast_fp16 = reduce_mean(axes = mean_83_axes_0, keep_dims = mean_83_keep_dims_0, x = pow_244_cast_fp16)[name = tensor<string, []>("mean_83_cast_fp16")];
            tensor<fp16, []> const_1862_to_fp16 = const()[name = tensor<string, []>("const_1862_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_248_cast_fp16 = add(x = mean_83_cast_fp16, y = const_1862_to_fp16)[name = tensor<string, []>("add_248_cast_fp16")];
            tensor<fp32, []> rsqrt_83_epsilon_0 = const()[name = tensor<string, []>("rsqrt_83_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_83_cast_fp16 = rsqrt(epsilon = rsqrt_83_epsilon_0, x = add_248_cast_fp16)[name = tensor<string, []>("rsqrt_83_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_174_cast_fp16 = mul(x = unsqueeze_171_cast_fp16, y = rsqrt_83_cast_fp16)[name = tensor<string, []>("mul_174_cast_fp16")];
            tensor<int32, [1]> squeeze_84_axes_0 = const()[name = tensor<string, []>("squeeze_84_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_84_cast_fp16 = squeeze(axes = squeeze_84_axes_0, x = mul_174_cast_fp16)[name = tensor<string, []>("squeeze_84_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_92_to_fp16 = const()[name = tensor<string, []>("view_92_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3724544)))];
            tensor<fp16, [80, 256, 801]> mul_175_cast_fp16 = mul(x = squeeze_84_cast_fp16, y = view_92_to_fp16)[name = tensor<string, []>("mul_175_cast_fp16")];
            tensor<string, []> conv1d_88_pad_type_0 = const()[name = tensor<string, []>("conv1d_88_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_88_strides_0 = const()[name = tensor<string, []>("conv1d_88_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_88_pad_0 = const()[name = tensor<string, []>("conv1d_88_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_88_dilations_0 = const()[name = tensor<string, []>("conv1d_88_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_88_groups_0 = const()[name = tensor<string, []>("conv1d_88_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_0_seq_net_blocks_1_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_1_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3725120)))];
            tensor<fp16, [1024]> p_net_0_seq_net_blocks_1_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_1_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4249472)))];
            tensor<fp16, [80, 1024, 801]> conv1d_88_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_1_conv_2_bias_to_fp16, dilations = conv1d_88_dilations_0, groups = conv1d_88_groups_0, pad = conv1d_88_pad_0, pad_type = conv1d_88_pad_type_0, strides = conv1d_88_strides_0, weight = p_net_0_seq_net_blocks_1_conv_2_weight_to_fp16, x = mul_175_cast_fp16)[name = tensor<string, []>("conv1d_88_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_3_cast_fp16 = silu(x = conv1d_88_cast_fp16)[name = tensor<string, []>("silu_3_cast_fp16")];
            tensor<string, []> conv1d_89_pad_type_0 = const()[name = tensor<string, []>("conv1d_89_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_89_strides_0 = const()[name = tensor<string, []>("conv1d_89_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_89_pad_0 = const()[name = tensor<string, []>("conv1d_89_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_89_dilations_0 = const()[name = tensor<string, []>("conv1d_89_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_89_groups_0 = const()[name = tensor<string, []>("conv1d_89_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_0_seq_net_blocks_1_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_1_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4251584)))];
            tensor<fp16, [256]> p_net_0_seq_net_blocks_1_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_1_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4775936)))];
            tensor<fp16, [80, 256, 801]> conv1d_89_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_1_conv_4_bias_to_fp16, dilations = conv1d_89_dilations_0, groups = conv1d_89_groups_0, pad = conv1d_89_pad_0, pad_type = conv1d_89_pad_type_0, strides = conv1d_89_strides_0, weight = p_net_0_seq_net_blocks_1_conv_4_weight_to_fp16, x = silu_3_cast_fp16)[name = tensor<string, []>("conv1d_89_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_249_cast_fp16 = add(x = add_247_cast_fp16, y = conv1d_89_cast_fp16)[name = tensor<string, []>("add_249_cast_fp16")];
            tensor<string, []> conv1d_90_pad_type_0 = const()[name = tensor<string, []>("conv1d_90_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_90_pad_0 = const()[name = tensor<string, []>("conv1d_90_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_90_groups_0 = const()[name = tensor<string, []>("conv1d_90_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_90_strides_0 = const()[name = tensor<string, []>("conv1d_90_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_90_dilations_0 = const()[name = tensor<string, []>("conv1d_90_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_0_seq_net_blocks_2_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_2_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4776512)))];
            tensor<fp16, [256]> p_net_0_seq_net_blocks_2_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_2_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4780160)))];
            tensor<fp16, [80, 256, 801]> conv1d_90_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_2_conv_0_bias_to_fp16, dilations = conv1d_90_dilations_0, groups = conv1d_90_groups_0, pad = conv1d_90_pad_0, pad_type = conv1d_90_pad_type_0, strides = conv1d_90_strides_0, weight = p_net_0_seq_net_blocks_2_conv_0_weight_to_fp16, x = add_249_cast_fp16)[name = tensor<string, []>("conv1d_90_cast_fp16")];
            tensor<int32, [1]> unsqueeze_173_axes_0 = const()[name = tensor<string, []>("unsqueeze_173_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_173_cast_fp16 = expand_dims(axes = unsqueeze_173_axes_0, x = conv1d_90_cast_fp16)[name = tensor<string, []>("unsqueeze_173_cast_fp16")];
            tensor<fp16, []> const_1873_promoted_to_fp16 = const()[name = tensor<string, []>("const_1873_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_245_cast_fp16 = pow(x = unsqueeze_173_cast_fp16, y = const_1873_promoted_to_fp16)[name = tensor<string, []>("pow_245_cast_fp16")];
            tensor<int32, [1]> mean_84_axes_0 = const()[name = tensor<string, []>("mean_84_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_84_keep_dims_0 = const()[name = tensor<string, []>("mean_84_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_84_cast_fp16 = reduce_mean(axes = mean_84_axes_0, keep_dims = mean_84_keep_dims_0, x = pow_245_cast_fp16)[name = tensor<string, []>("mean_84_cast_fp16")];
            tensor<fp16, []> const_1876_to_fp16 = const()[name = tensor<string, []>("const_1876_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_250_cast_fp16 = add(x = mean_84_cast_fp16, y = const_1876_to_fp16)[name = tensor<string, []>("add_250_cast_fp16")];
            tensor<fp32, []> rsqrt_84_epsilon_0 = const()[name = tensor<string, []>("rsqrt_84_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_84_cast_fp16 = rsqrt(epsilon = rsqrt_84_epsilon_0, x = add_250_cast_fp16)[name = tensor<string, []>("rsqrt_84_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_176_cast_fp16 = mul(x = unsqueeze_173_cast_fp16, y = rsqrt_84_cast_fp16)[name = tensor<string, []>("mul_176_cast_fp16")];
            tensor<int32, [1]> squeeze_85_axes_0 = const()[name = tensor<string, []>("squeeze_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_85_cast_fp16 = squeeze(axes = squeeze_85_axes_0, x = mul_176_cast_fp16)[name = tensor<string, []>("squeeze_85_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_93_to_fp16 = const()[name = tensor<string, []>("view_93_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4780736)))];
            tensor<fp16, [80, 256, 801]> mul_177_cast_fp16 = mul(x = squeeze_85_cast_fp16, y = view_93_to_fp16)[name = tensor<string, []>("mul_177_cast_fp16")];
            tensor<string, []> conv1d_91_pad_type_0 = const()[name = tensor<string, []>("conv1d_91_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_91_strides_0 = const()[name = tensor<string, []>("conv1d_91_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_91_pad_0 = const()[name = tensor<string, []>("conv1d_91_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_91_dilations_0 = const()[name = tensor<string, []>("conv1d_91_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_91_groups_0 = const()[name = tensor<string, []>("conv1d_91_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_0_seq_net_blocks_2_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_2_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4781312)))];
            tensor<fp16, [1024]> p_net_0_seq_net_blocks_2_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_2_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5305664)))];
            tensor<fp16, [80, 1024, 801]> conv1d_91_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_2_conv_2_bias_to_fp16, dilations = conv1d_91_dilations_0, groups = conv1d_91_groups_0, pad = conv1d_91_pad_0, pad_type = conv1d_91_pad_type_0, strides = conv1d_91_strides_0, weight = p_net_0_seq_net_blocks_2_conv_2_weight_to_fp16, x = mul_177_cast_fp16)[name = tensor<string, []>("conv1d_91_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_4_cast_fp16 = silu(x = conv1d_91_cast_fp16)[name = tensor<string, []>("silu_4_cast_fp16")];
            tensor<string, []> conv1d_92_pad_type_0 = const()[name = tensor<string, []>("conv1d_92_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_92_strides_0 = const()[name = tensor<string, []>("conv1d_92_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_92_pad_0 = const()[name = tensor<string, []>("conv1d_92_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_92_dilations_0 = const()[name = tensor<string, []>("conv1d_92_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_92_groups_0 = const()[name = tensor<string, []>("conv1d_92_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_0_seq_net_blocks_2_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_2_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5307776)))];
            tensor<fp16, [256]> p_net_0_seq_net_blocks_2_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_0_seq_net_blocks_2_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5832128)))];
            tensor<fp16, [80, 256, 801]> conv1d_92_cast_fp16 = conv(bias = p_net_0_seq_net_blocks_2_conv_4_bias_to_fp16, dilations = conv1d_92_dilations_0, groups = conv1d_92_groups_0, pad = conv1d_92_pad_0, pad_type = conv1d_92_pad_type_0, strides = conv1d_92_strides_0, weight = p_net_0_seq_net_blocks_2_conv_4_weight_to_fp16, x = silu_4_cast_fp16)[name = tensor<string, []>("conv1d_92_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_251_cast_fp16 = add(x = add_249_cast_fp16, y = conv1d_92_cast_fp16)[name = tensor<string, []>("add_251_cast_fp16")];
            tensor<int32, [1]> unsqueeze_174_axes_0 = const()[name = tensor<string, []>("unsqueeze_174_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 80, 256, 801]> unsqueeze_174_cast_fp16 = expand_dims(axes = unsqueeze_174_axes_0, x = add_251_cast_fp16)[name = tensor<string, []>("unsqueeze_174_cast_fp16")];
            tensor<int32, [1]> squeeze_86_axes_0 = const()[name = tensor<string, []>("squeeze_86_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [80, 256, 801]> squeeze_86_cast_fp16 = squeeze(axes = squeeze_86_axes_0, x = unsqueeze_174_cast_fp16)[name = tensor<string, []>("squeeze_86_cast_fp16")];
            tensor<int32, [3]> const_1881 = const()[name = tensor<string, []>("const_1881"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<int32, [1]> unsqueeze_176_axes_0 = const()[name = tensor<string, []>("unsqueeze_176_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> permute_4_cast_fp16 = transpose(perm = const_1881, x = squeeze_86_cast_fp16)[name = tensor<string, []>("transpose_19")];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_176_cast_fp16 = expand_dims(axes = unsqueeze_176_axes_0, x = permute_4_cast_fp16)[name = tensor<string, []>("unsqueeze_176_cast_fp16")];
            tensor<fp16, []> const_1886_promoted_to_fp16 = const()[name = tensor<string, []>("const_1886_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_246_cast_fp16 = pow(x = unsqueeze_176_cast_fp16, y = const_1886_promoted_to_fp16)[name = tensor<string, []>("pow_246_cast_fp16")];
            tensor<int32, [1]> mean_85_axes_0 = const()[name = tensor<string, []>("mean_85_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_85_keep_dims_0 = const()[name = tensor<string, []>("mean_85_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_85_cast_fp16 = reduce_mean(axes = mean_85_axes_0, keep_dims = mean_85_keep_dims_0, x = pow_246_cast_fp16)[name = tensor<string, []>("mean_85_cast_fp16")];
            tensor<fp16, []> const_1889_to_fp16 = const()[name = tensor<string, []>("const_1889_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_252_cast_fp16 = add(x = mean_85_cast_fp16, y = const_1889_to_fp16)[name = tensor<string, []>("add_252_cast_fp16")];
            tensor<fp32, []> rsqrt_85_epsilon_0 = const()[name = tensor<string, []>("rsqrt_85_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_85_cast_fp16 = rsqrt(epsilon = rsqrt_85_epsilon_0, x = add_252_cast_fp16)[name = tensor<string, []>("rsqrt_85_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_178_cast_fp16 = mul(x = unsqueeze_176_cast_fp16, y = rsqrt_85_cast_fp16)[name = tensor<string, []>("mul_178_cast_fp16")];
            tensor<int32, [1]> squeeze_87_axes_0 = const()[name = tensor<string, []>("squeeze_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_87_cast_fp16 = squeeze(axes = squeeze_87_axes_0, x = mul_178_cast_fp16)[name = tensor<string, []>("squeeze_87_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_94_to_fp16 = const()[name = tensor<string, []>("view_94_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5832704)))];
            tensor<fp16, [801, 256, 80]> mul_179_cast_fp16 = mul(x = squeeze_87_cast_fp16, y = view_94_to_fp16)[name = tensor<string, []>("mul_179_cast_fp16")];
            tensor<string, []> conv1d_93_pad_type_0 = const()[name = tensor<string, []>("conv1d_93_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_93_strides_0 = const()[name = tensor<string, []>("conv1d_93_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_93_pad_0 = const()[name = tensor<string, []>("conv1d_93_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_93_dilations_0 = const()[name = tensor<string, []>("conv1d_93_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_93_groups_0 = const()[name = tensor<string, []>("conv1d_93_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [768, 256, 1]> p_net_1_band_net_weight_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_band_net_weight_weight_to_fp16"), val = tensor<fp16, [768, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5833280)))];
            tensor<fp16, [801, 768, 80]> conv1d_93_cast_fp16 = conv(dilations = conv1d_93_dilations_0, groups = conv1d_93_groups_0, pad = conv1d_93_pad_0, pad_type = conv1d_93_pad_type_0, strides = conv1d_93_strides_0, weight = p_net_1_band_net_weight_weight_to_fp16, x = mul_179_cast_fp16)[name = tensor<string, []>("conv1d_93_cast_fp16")];
            tensor<int32, [4]> const_1892 = const()[name = tensor<string, []>("const_1892"), val = tensor<int32, [4]>([-1, 8, 96, 80])];
            tensor<fp16, [801, 8, 96, 80]> view_95_cast_fp16 = reshape(shape = const_1892, x = conv1d_93_cast_fp16)[name = tensor<string, []>("view_95_cast_fp16")];
            tensor<int32, [4]> const_1893 = const()[name = tensor<string, []>("const_1893"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> tile_2 = const()[name = tensor<string, []>("tile_2"), val = tensor<int32, [3]>([32, 32, 32])];
            tensor<int32, []> split_2_axis_0 = const()[name = tensor<string, []>("split_2_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 96]> permute_5_cast_fp16 = transpose(perm = const_1893, x = view_95_cast_fp16)[name = tensor<string, []>("transpose_18")];
            tensor<fp16, [801, 8, 80, 32]> split_2_cast_fp16_0, tensor<fp16, [801, 8, 80, 32]> split_2_cast_fp16_1, tensor<fp16, [801, 8, 80, 32]> split_2_cast_fp16_2 = split(axis = split_2_axis_0, split_sizes = tile_2, x = permute_5_cast_fp16)[name = tensor<string, []>("split_2_cast_fp16")];
            tensor<int32, [3]> const_1901 = const()[name = tensor<string, []>("const_1901"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_96_cast_fp16 = reshape(shape = const_1901, x = split_2_cast_fp16_0)[name = tensor<string, []>("view_96_cast_fp16")];
            tensor<int32, [3]> const_1908 = const()[name = tensor<string, []>("const_1908"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_3_cast_fp16 = reshape(shape = const_1908, x = view_96_cast_fp16)[name = tensor<string, []>("_unsafe_view_3_cast_fp16")];
            tensor<int32, [1]> const_1909 = const()[name = tensor<string, []>("const_1909"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_2_cast_fp16 = reverse(axes = const_1909, x = _unsafe_view_3_cast_fp16)[name = tensor<string, []>("flip_2_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_97_to_fp16 = const()[name = tensor<string, []>("view_97_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_180_cast_fp16 = mul(x = flip_2_cast_fp16, y = view_97_to_fp16)[name = tensor<string, []>("mul_180_cast_fp16")];
            tensor<int32, [3]> const_1911 = const()[name = tensor<string, []>("const_1911"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_98_cast_fp16 = reshape(shape = const_1911, x = mul_180_cast_fp16)[name = tensor<string, []>("view_98_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_181_cast_fp16 = mul(x = view_96_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_181_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_182_cast_fp16 = mul(x = view_98_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_182_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_253_cast_fp16 = add(x = mul_181_cast_fp16, y = mul_182_cast_fp16)[name = tensor<string, []>("add_253_cast_fp16")];
            tensor<int32, [4]> const_1914 = const()[name = tensor<string, []>("const_1914"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_99_cast_fp16 = reshape(shape = const_1914, x = add_253_cast_fp16)[name = tensor<string, []>("view_99_cast_fp16")];
            tensor<int32, [3]> const_1915 = const()[name = tensor<string, []>("const_1915"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_100_cast_fp16 = reshape(shape = const_1915, x = split_2_cast_fp16_1)[name = tensor<string, []>("view_100_cast_fp16")];
            tensor<int32, [3]> const_1922 = const()[name = tensor<string, []>("const_1922"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_4_cast_fp16 = reshape(shape = const_1922, x = view_100_cast_fp16)[name = tensor<string, []>("_unsafe_view_4_cast_fp16")];
            tensor<int32, [1]> const_1923 = const()[name = tensor<string, []>("const_1923"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_3_cast_fp16 = reverse(axes = const_1923, x = _unsafe_view_4_cast_fp16)[name = tensor<string, []>("flip_3_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_101_to_fp16 = const()[name = tensor<string, []>("view_101_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_183_cast_fp16 = mul(x = flip_3_cast_fp16, y = view_101_to_fp16)[name = tensor<string, []>("mul_183_cast_fp16")];
            tensor<int32, [3]> const_1925 = const()[name = tensor<string, []>("const_1925"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_102_cast_fp16 = reshape(shape = const_1925, x = mul_183_cast_fp16)[name = tensor<string, []>("view_102_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_184_cast_fp16 = mul(x = view_100_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_184_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_185_cast_fp16 = mul(x = view_102_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_185_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_254_cast_fp16 = add(x = mul_184_cast_fp16, y = mul_185_cast_fp16)[name = tensor<string, []>("add_254_cast_fp16")];
            tensor<int32, [4]> const_1928 = const()[name = tensor<string, []>("const_1928"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_103_cast_fp16 = reshape(shape = const_1928, x = add_254_cast_fp16)[name = tensor<string, []>("view_103_cast_fp16")];
            tensor<bool, []> matmul_2_transpose_x_1 = const()[name = tensor<string, []>("matmul_2_transpose_x_1"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_2_transpose_y_1 = const()[name = tensor<string, []>("matmul_2_transpose_y_1"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 8, 80, 80]> matmul_2_cast_fp16 = matmul(transpose_x = matmul_2_transpose_x_1, transpose_y = matmul_2_transpose_y_1, x = view_99_cast_fp16, y = view_103_cast_fp16)[name = tensor<string, []>("matmul_2_cast_fp16")];
            tensor<fp16, []> const_1931_to_fp16 = const()[name = tensor<string, []>("const_1931_to_fp16"), val = tensor<fp16, []>(0x1.6ap-3)];
            tensor<fp16, [801, 8, 80, 80]> mul_186_cast_fp16 = mul(x = matmul_2_cast_fp16, y = const_1931_to_fp16)[name = tensor<string, []>("mul_186_cast_fp16")];
            tensor<int32, []> const_1932 = const()[name = tensor<string, []>("const_1932"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 80]> softmax_1_cast_fp16 = softmax(axis = const_1932, x = mul_186_cast_fp16)[name = tensor<string, []>("softmax_1_cast_fp16")];
            tensor<bool, []> matmul_3_transpose_x_0 = const()[name = tensor<string, []>("matmul_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_3_transpose_y_0 = const()[name = tensor<string, []>("matmul_3_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [801, 8, 80, 32]> matmul_3_cast_fp16 = matmul(transpose_x = matmul_3_transpose_x_0, transpose_y = matmul_3_transpose_y_0, x = softmax_1_cast_fp16, y = split_2_cast_fp16_2)[name = tensor<string, []>("matmul_3_cast_fp16")];
            tensor<int32, [4]> const_1933 = const()[name = tensor<string, []>("const_1933"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> const_1934 = const()[name = tensor<string, []>("const_1934"), val = tensor<int32, [3]>([801, 256, 80])];
            tensor<fp16, [801, 8, 32, 80]> permute_6_cast_fp16 = transpose(perm = const_1933, x = matmul_3_cast_fp16)[name = tensor<string, []>("transpose_17")];
            tensor<fp16, [801, 256, 80]> _unsafe_view_5_cast_fp16 = reshape(shape = const_1934, x = permute_6_cast_fp16)[name = tensor<string, []>("_unsafe_view_5_cast_fp16")];
            tensor<string, []> conv1d_94_pad_type_0 = const()[name = tensor<string, []>("conv1d_94_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_94_strides_0 = const()[name = tensor<string, []>("conv1d_94_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_94_pad_0 = const()[name = tensor<string, []>("conv1d_94_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_94_dilations_0 = const()[name = tensor<string, []>("conv1d_94_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_94_groups_0 = const()[name = tensor<string, []>("conv1d_94_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1]> p_net_1_band_net_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_band_net_output_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6226560)))];
            tensor<fp16, [801, 256, 80]> conv1d_94_cast_fp16 = conv(dilations = conv1d_94_dilations_0, groups = conv1d_94_groups_0, pad = conv1d_94_pad_0, pad_type = conv1d_94_pad_type_0, strides = conv1d_94_strides_0, weight = p_net_1_band_net_output_weight_to_fp16, x = _unsafe_view_5_cast_fp16)[name = tensor<string, []>("conv1d_94_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_255_cast_fp16 = add(x = conv1d_94_cast_fp16, y = permute_4_cast_fp16)[name = tensor<string, []>("add_255_cast_fp16")];
            tensor<int32, [1]> unsqueeze_182_axes_0 = const()[name = tensor<string, []>("unsqueeze_182_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_182_cast_fp16 = expand_dims(axes = unsqueeze_182_axes_0, x = add_255_cast_fp16)[name = tensor<string, []>("unsqueeze_182_cast_fp16")];
            tensor<fp16, []> const_1939_promoted_to_fp16 = const()[name = tensor<string, []>("const_1939_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_247_cast_fp16 = pow(x = unsqueeze_182_cast_fp16, y = const_1939_promoted_to_fp16)[name = tensor<string, []>("pow_247_cast_fp16")];
            tensor<int32, [1]> mean_86_axes_0 = const()[name = tensor<string, []>("mean_86_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_86_keep_dims_0 = const()[name = tensor<string, []>("mean_86_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_86_cast_fp16 = reduce_mean(axes = mean_86_axes_0, keep_dims = mean_86_keep_dims_0, x = pow_247_cast_fp16)[name = tensor<string, []>("mean_86_cast_fp16")];
            tensor<fp16, []> const_1942_to_fp16 = const()[name = tensor<string, []>("const_1942_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_256_cast_fp16 = add(x = mean_86_cast_fp16, y = const_1942_to_fp16)[name = tensor<string, []>("add_256_cast_fp16")];
            tensor<fp32, []> rsqrt_86_epsilon_0 = const()[name = tensor<string, []>("rsqrt_86_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_86_cast_fp16 = rsqrt(epsilon = rsqrt_86_epsilon_0, x = add_256_cast_fp16)[name = tensor<string, []>("rsqrt_86_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_187_cast_fp16 = mul(x = unsqueeze_182_cast_fp16, y = rsqrt_86_cast_fp16)[name = tensor<string, []>("mul_187_cast_fp16")];
            tensor<int32, [1]> squeeze_88_axes_0 = const()[name = tensor<string, []>("squeeze_88_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_88_cast_fp16 = squeeze(axes = squeeze_88_axes_0, x = mul_187_cast_fp16)[name = tensor<string, []>("squeeze_88_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_104_to_fp16 = const()[name = tensor<string, []>("view_104_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6357696)))];
            tensor<fp16, [801, 256, 80]> mul_188_cast_fp16 = mul(x = squeeze_88_cast_fp16, y = view_104_to_fp16)[name = tensor<string, []>("mul_188_cast_fp16")];
            tensor<string, []> conv1d_95_pad_type_0 = const()[name = tensor<string, []>("conv1d_95_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_95_strides_0 = const()[name = tensor<string, []>("conv1d_95_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_95_pad_0 = const()[name = tensor<string, []>("conv1d_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_95_dilations_0 = const()[name = tensor<string, []>("conv1d_95_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_95_groups_0 = const()[name = tensor<string, []>("conv1d_95_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 256, 1]> p_net_1_band_net_mlp_1_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_band_net_mlp_1_weight_to_fp16"), val = tensor<fp16, [2048, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6358272)))];
            tensor<fp16, [801, 2048, 80]> conv1d_95_cast_fp16 = conv(dilations = conv1d_95_dilations_0, groups = conv1d_95_groups_0, pad = conv1d_95_pad_0, pad_type = conv1d_95_pad_type_0, strides = conv1d_95_strides_0, weight = p_net_1_band_net_mlp_1_weight_to_fp16, x = mul_188_cast_fp16)[name = tensor<string, []>("conv1d_95_cast_fp16")];
            tensor<fp16, [801, 2048, 80]> silu_5_cast_fp16 = silu(x = conv1d_95_cast_fp16)[name = tensor<string, []>("silu_5_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = tensor<string, []>("tile_3"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> split_3_axis_0 = const()[name = tensor<string, []>("split_3_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [801, 1024, 80]> split_3_cast_fp16_0, tensor<fp16, [801, 1024, 80]> split_3_cast_fp16_1 = split(axis = split_3_axis_0, split_sizes = tile_3, x = silu_5_cast_fp16)[name = tensor<string, []>("split_3_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> silu_6_cast_fp16 = silu(x = split_3_cast_fp16_0)[name = tensor<string, []>("silu_6_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> mul_189_cast_fp16 = mul(x = silu_6_cast_fp16, y = split_3_cast_fp16_1)[name = tensor<string, []>("mul_189_cast_fp16")];
            tensor<string, []> conv1d_96_pad_type_0 = const()[name = tensor<string, []>("conv1d_96_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_96_strides_0 = const()[name = tensor<string, []>("conv1d_96_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_96_pad_0 = const()[name = tensor<string, []>("conv1d_96_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_96_dilations_0 = const()[name = tensor<string, []>("conv1d_96_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_96_groups_0 = const()[name = tensor<string, []>("conv1d_96_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_1_band_net_mlp_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_band_net_mlp_output_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7406912)))];
            tensor<fp16, [801, 256, 80]> conv1d_96_cast_fp16 = conv(dilations = conv1d_96_dilations_0, groups = conv1d_96_groups_0, pad = conv1d_96_pad_0, pad_type = conv1d_96_pad_type_0, strides = conv1d_96_strides_0, weight = p_net_1_band_net_mlp_output_weight_to_fp16, x = mul_189_cast_fp16)[name = tensor<string, []>("conv1d_96_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_257_cast_fp16 = add(x = add_255_cast_fp16, y = conv1d_96_cast_fp16)[name = tensor<string, []>("add_257_cast_fp16")];
            tensor<int32, [3]> const_1951 = const()[name = tensor<string, []>("const_1951"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<string, []> conv1d_97_pad_type_0 = const()[name = tensor<string, []>("conv1d_97_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_97_pad_0 = const()[name = tensor<string, []>("conv1d_97_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_97_groups_0 = const()[name = tensor<string, []>("conv1d_97_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_97_strides_0 = const()[name = tensor<string, []>("conv1d_97_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_97_dilations_0 = const()[name = tensor<string, []>("conv1d_97_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_1_seq_net_blocks_0_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_0_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7931264)))];
            tensor<fp16, [256]> p_net_1_seq_net_blocks_0_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_0_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7934912)))];
            tensor<fp16, [80, 256, 801]> permute_7_cast_fp16 = transpose(perm = const_1951, x = add_257_cast_fp16)[name = tensor<string, []>("transpose_16")];
            tensor<fp16, [80, 256, 801]> conv1d_97_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_0_conv_0_bias_to_fp16, dilations = conv1d_97_dilations_0, groups = conv1d_97_groups_0, pad = conv1d_97_pad_0, pad_type = conv1d_97_pad_type_0, strides = conv1d_97_strides_0, weight = p_net_1_seq_net_blocks_0_conv_0_weight_to_fp16, x = permute_7_cast_fp16)[name = tensor<string, []>("conv1d_97_cast_fp16")];
            tensor<int32, [1]> unsqueeze_184_axes_0 = const()[name = tensor<string, []>("unsqueeze_184_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_184_cast_fp16 = expand_dims(axes = unsqueeze_184_axes_0, x = conv1d_97_cast_fp16)[name = tensor<string, []>("unsqueeze_184_cast_fp16")];
            tensor<fp16, []> const_1960_promoted_to_fp16 = const()[name = tensor<string, []>("const_1960_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_248_cast_fp16 = pow(x = unsqueeze_184_cast_fp16, y = const_1960_promoted_to_fp16)[name = tensor<string, []>("pow_248_cast_fp16")];
            tensor<int32, [1]> mean_87_axes_0 = const()[name = tensor<string, []>("mean_87_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_87_keep_dims_0 = const()[name = tensor<string, []>("mean_87_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_87_cast_fp16 = reduce_mean(axes = mean_87_axes_0, keep_dims = mean_87_keep_dims_0, x = pow_248_cast_fp16)[name = tensor<string, []>("mean_87_cast_fp16")];
            tensor<fp16, []> const_1963_to_fp16 = const()[name = tensor<string, []>("const_1963_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_258_cast_fp16 = add(x = mean_87_cast_fp16, y = const_1963_to_fp16)[name = tensor<string, []>("add_258_cast_fp16")];
            tensor<fp32, []> rsqrt_87_epsilon_0 = const()[name = tensor<string, []>("rsqrt_87_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_87_cast_fp16 = rsqrt(epsilon = rsqrt_87_epsilon_0, x = add_258_cast_fp16)[name = tensor<string, []>("rsqrt_87_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_190_cast_fp16 = mul(x = unsqueeze_184_cast_fp16, y = rsqrt_87_cast_fp16)[name = tensor<string, []>("mul_190_cast_fp16")];
            tensor<int32, [1]> squeeze_89_axes_0 = const()[name = tensor<string, []>("squeeze_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_89_cast_fp16 = squeeze(axes = squeeze_89_axes_0, x = mul_190_cast_fp16)[name = tensor<string, []>("squeeze_89_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_105_to_fp16 = const()[name = tensor<string, []>("view_105_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7935488)))];
            tensor<fp16, [80, 256, 801]> mul_191_cast_fp16 = mul(x = squeeze_89_cast_fp16, y = view_105_to_fp16)[name = tensor<string, []>("mul_191_cast_fp16")];
            tensor<string, []> conv1d_98_pad_type_0 = const()[name = tensor<string, []>("conv1d_98_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_98_strides_0 = const()[name = tensor<string, []>("conv1d_98_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_98_pad_0 = const()[name = tensor<string, []>("conv1d_98_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_98_dilations_0 = const()[name = tensor<string, []>("conv1d_98_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_98_groups_0 = const()[name = tensor<string, []>("conv1d_98_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_1_seq_net_blocks_0_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_0_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7936064)))];
            tensor<fp16, [1024]> p_net_1_seq_net_blocks_0_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_0_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8460416)))];
            tensor<fp16, [80, 1024, 801]> conv1d_98_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_0_conv_2_bias_to_fp16, dilations = conv1d_98_dilations_0, groups = conv1d_98_groups_0, pad = conv1d_98_pad_0, pad_type = conv1d_98_pad_type_0, strides = conv1d_98_strides_0, weight = p_net_1_seq_net_blocks_0_conv_2_weight_to_fp16, x = mul_191_cast_fp16)[name = tensor<string, []>("conv1d_98_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_7_cast_fp16 = silu(x = conv1d_98_cast_fp16)[name = tensor<string, []>("silu_7_cast_fp16")];
            tensor<string, []> conv1d_99_pad_type_0 = const()[name = tensor<string, []>("conv1d_99_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_99_strides_0 = const()[name = tensor<string, []>("conv1d_99_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_99_pad_0 = const()[name = tensor<string, []>("conv1d_99_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_99_dilations_0 = const()[name = tensor<string, []>("conv1d_99_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_99_groups_0 = const()[name = tensor<string, []>("conv1d_99_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_1_seq_net_blocks_0_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_0_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8462528)))];
            tensor<fp16, [256]> p_net_1_seq_net_blocks_0_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_0_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8986880)))];
            tensor<fp16, [80, 256, 801]> conv1d_99_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_0_conv_4_bias_to_fp16, dilations = conv1d_99_dilations_0, groups = conv1d_99_groups_0, pad = conv1d_99_pad_0, pad_type = conv1d_99_pad_type_0, strides = conv1d_99_strides_0, weight = p_net_1_seq_net_blocks_0_conv_4_weight_to_fp16, x = silu_7_cast_fp16)[name = tensor<string, []>("conv1d_99_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_259_cast_fp16 = add(x = permute_7_cast_fp16, y = conv1d_99_cast_fp16)[name = tensor<string, []>("add_259_cast_fp16")];
            tensor<string, []> conv1d_100_pad_type_0 = const()[name = tensor<string, []>("conv1d_100_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_100_pad_0 = const()[name = tensor<string, []>("conv1d_100_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_100_groups_0 = const()[name = tensor<string, []>("conv1d_100_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_100_strides_0 = const()[name = tensor<string, []>("conv1d_100_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_100_dilations_0 = const()[name = tensor<string, []>("conv1d_100_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_1_seq_net_blocks_1_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_1_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8987456)))];
            tensor<fp16, [256]> p_net_1_seq_net_blocks_1_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_1_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8991104)))];
            tensor<fp16, [80, 256, 801]> conv1d_100_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_1_conv_0_bias_to_fp16, dilations = conv1d_100_dilations_0, groups = conv1d_100_groups_0, pad = conv1d_100_pad_0, pad_type = conv1d_100_pad_type_0, strides = conv1d_100_strides_0, weight = p_net_1_seq_net_blocks_1_conv_0_weight_to_fp16, x = add_259_cast_fp16)[name = tensor<string, []>("conv1d_100_cast_fp16")];
            tensor<int32, [1]> unsqueeze_186_axes_0 = const()[name = tensor<string, []>("unsqueeze_186_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_186_cast_fp16 = expand_dims(axes = unsqueeze_186_axes_0, x = conv1d_100_cast_fp16)[name = tensor<string, []>("unsqueeze_186_cast_fp16")];
            tensor<fp16, []> const_1974_promoted_to_fp16 = const()[name = tensor<string, []>("const_1974_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_249_cast_fp16 = pow(x = unsqueeze_186_cast_fp16, y = const_1974_promoted_to_fp16)[name = tensor<string, []>("pow_249_cast_fp16")];
            tensor<int32, [1]> mean_88_axes_0 = const()[name = tensor<string, []>("mean_88_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_88_keep_dims_0 = const()[name = tensor<string, []>("mean_88_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_88_cast_fp16 = reduce_mean(axes = mean_88_axes_0, keep_dims = mean_88_keep_dims_0, x = pow_249_cast_fp16)[name = tensor<string, []>("mean_88_cast_fp16")];
            tensor<fp16, []> const_1977_to_fp16 = const()[name = tensor<string, []>("const_1977_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_260_cast_fp16 = add(x = mean_88_cast_fp16, y = const_1977_to_fp16)[name = tensor<string, []>("add_260_cast_fp16")];
            tensor<fp32, []> rsqrt_88_epsilon_0 = const()[name = tensor<string, []>("rsqrt_88_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_88_cast_fp16 = rsqrt(epsilon = rsqrt_88_epsilon_0, x = add_260_cast_fp16)[name = tensor<string, []>("rsqrt_88_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_192_cast_fp16 = mul(x = unsqueeze_186_cast_fp16, y = rsqrt_88_cast_fp16)[name = tensor<string, []>("mul_192_cast_fp16")];
            tensor<int32, [1]> squeeze_90_axes_0 = const()[name = tensor<string, []>("squeeze_90_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_90_cast_fp16 = squeeze(axes = squeeze_90_axes_0, x = mul_192_cast_fp16)[name = tensor<string, []>("squeeze_90_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_106_to_fp16 = const()[name = tensor<string, []>("view_106_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8991680)))];
            tensor<fp16, [80, 256, 801]> mul_193_cast_fp16 = mul(x = squeeze_90_cast_fp16, y = view_106_to_fp16)[name = tensor<string, []>("mul_193_cast_fp16")];
            tensor<string, []> conv1d_101_pad_type_0 = const()[name = tensor<string, []>("conv1d_101_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_101_strides_0 = const()[name = tensor<string, []>("conv1d_101_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_101_pad_0 = const()[name = tensor<string, []>("conv1d_101_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_101_dilations_0 = const()[name = tensor<string, []>("conv1d_101_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_101_groups_0 = const()[name = tensor<string, []>("conv1d_101_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_1_seq_net_blocks_1_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_1_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8992256)))];
            tensor<fp16, [1024]> p_net_1_seq_net_blocks_1_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_1_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9516608)))];
            tensor<fp16, [80, 1024, 801]> conv1d_101_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_1_conv_2_bias_to_fp16, dilations = conv1d_101_dilations_0, groups = conv1d_101_groups_0, pad = conv1d_101_pad_0, pad_type = conv1d_101_pad_type_0, strides = conv1d_101_strides_0, weight = p_net_1_seq_net_blocks_1_conv_2_weight_to_fp16, x = mul_193_cast_fp16)[name = tensor<string, []>("conv1d_101_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_8_cast_fp16 = silu(x = conv1d_101_cast_fp16)[name = tensor<string, []>("silu_8_cast_fp16")];
            tensor<string, []> conv1d_102_pad_type_0 = const()[name = tensor<string, []>("conv1d_102_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_102_strides_0 = const()[name = tensor<string, []>("conv1d_102_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_102_pad_0 = const()[name = tensor<string, []>("conv1d_102_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_102_dilations_0 = const()[name = tensor<string, []>("conv1d_102_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_102_groups_0 = const()[name = tensor<string, []>("conv1d_102_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_1_seq_net_blocks_1_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_1_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9518720)))];
            tensor<fp16, [256]> p_net_1_seq_net_blocks_1_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_1_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10043072)))];
            tensor<fp16, [80, 256, 801]> conv1d_102_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_1_conv_4_bias_to_fp16, dilations = conv1d_102_dilations_0, groups = conv1d_102_groups_0, pad = conv1d_102_pad_0, pad_type = conv1d_102_pad_type_0, strides = conv1d_102_strides_0, weight = p_net_1_seq_net_blocks_1_conv_4_weight_to_fp16, x = silu_8_cast_fp16)[name = tensor<string, []>("conv1d_102_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_261_cast_fp16 = add(x = add_259_cast_fp16, y = conv1d_102_cast_fp16)[name = tensor<string, []>("add_261_cast_fp16")];
            tensor<string, []> conv1d_103_pad_type_0 = const()[name = tensor<string, []>("conv1d_103_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_103_pad_0 = const()[name = tensor<string, []>("conv1d_103_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_103_groups_0 = const()[name = tensor<string, []>("conv1d_103_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_103_strides_0 = const()[name = tensor<string, []>("conv1d_103_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_103_dilations_0 = const()[name = tensor<string, []>("conv1d_103_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_1_seq_net_blocks_2_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_2_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10043648)))];
            tensor<fp16, [256]> p_net_1_seq_net_blocks_2_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_2_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10047296)))];
            tensor<fp16, [80, 256, 801]> conv1d_103_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_2_conv_0_bias_to_fp16, dilations = conv1d_103_dilations_0, groups = conv1d_103_groups_0, pad = conv1d_103_pad_0, pad_type = conv1d_103_pad_type_0, strides = conv1d_103_strides_0, weight = p_net_1_seq_net_blocks_2_conv_0_weight_to_fp16, x = add_261_cast_fp16)[name = tensor<string, []>("conv1d_103_cast_fp16")];
            tensor<int32, [1]> unsqueeze_188_axes_0 = const()[name = tensor<string, []>("unsqueeze_188_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_188_cast_fp16 = expand_dims(axes = unsqueeze_188_axes_0, x = conv1d_103_cast_fp16)[name = tensor<string, []>("unsqueeze_188_cast_fp16")];
            tensor<fp16, []> const_1988_promoted_to_fp16 = const()[name = tensor<string, []>("const_1988_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_250_cast_fp16 = pow(x = unsqueeze_188_cast_fp16, y = const_1988_promoted_to_fp16)[name = tensor<string, []>("pow_250_cast_fp16")];
            tensor<int32, [1]> mean_89_axes_0 = const()[name = tensor<string, []>("mean_89_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_89_keep_dims_0 = const()[name = tensor<string, []>("mean_89_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_89_cast_fp16 = reduce_mean(axes = mean_89_axes_0, keep_dims = mean_89_keep_dims_0, x = pow_250_cast_fp16)[name = tensor<string, []>("mean_89_cast_fp16")];
            tensor<fp16, []> const_1991_to_fp16 = const()[name = tensor<string, []>("const_1991_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_262_cast_fp16 = add(x = mean_89_cast_fp16, y = const_1991_to_fp16)[name = tensor<string, []>("add_262_cast_fp16")];
            tensor<fp32, []> rsqrt_89_epsilon_0 = const()[name = tensor<string, []>("rsqrt_89_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_89_cast_fp16 = rsqrt(epsilon = rsqrt_89_epsilon_0, x = add_262_cast_fp16)[name = tensor<string, []>("rsqrt_89_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_194_cast_fp16 = mul(x = unsqueeze_188_cast_fp16, y = rsqrt_89_cast_fp16)[name = tensor<string, []>("mul_194_cast_fp16")];
            tensor<int32, [1]> squeeze_91_axes_0 = const()[name = tensor<string, []>("squeeze_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_91_cast_fp16 = squeeze(axes = squeeze_91_axes_0, x = mul_194_cast_fp16)[name = tensor<string, []>("squeeze_91_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_107_to_fp16 = const()[name = tensor<string, []>("view_107_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10047872)))];
            tensor<fp16, [80, 256, 801]> mul_195_cast_fp16 = mul(x = squeeze_91_cast_fp16, y = view_107_to_fp16)[name = tensor<string, []>("mul_195_cast_fp16")];
            tensor<string, []> conv1d_104_pad_type_0 = const()[name = tensor<string, []>("conv1d_104_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_104_strides_0 = const()[name = tensor<string, []>("conv1d_104_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_104_pad_0 = const()[name = tensor<string, []>("conv1d_104_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_104_dilations_0 = const()[name = tensor<string, []>("conv1d_104_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_104_groups_0 = const()[name = tensor<string, []>("conv1d_104_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_1_seq_net_blocks_2_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_2_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10048448)))];
            tensor<fp16, [1024]> p_net_1_seq_net_blocks_2_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_2_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10572800)))];
            tensor<fp16, [80, 1024, 801]> conv1d_104_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_2_conv_2_bias_to_fp16, dilations = conv1d_104_dilations_0, groups = conv1d_104_groups_0, pad = conv1d_104_pad_0, pad_type = conv1d_104_pad_type_0, strides = conv1d_104_strides_0, weight = p_net_1_seq_net_blocks_2_conv_2_weight_to_fp16, x = mul_195_cast_fp16)[name = tensor<string, []>("conv1d_104_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_9_cast_fp16 = silu(x = conv1d_104_cast_fp16)[name = tensor<string, []>("silu_9_cast_fp16")];
            tensor<string, []> conv1d_105_pad_type_0 = const()[name = tensor<string, []>("conv1d_105_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_105_strides_0 = const()[name = tensor<string, []>("conv1d_105_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_105_pad_0 = const()[name = tensor<string, []>("conv1d_105_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_105_dilations_0 = const()[name = tensor<string, []>("conv1d_105_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_105_groups_0 = const()[name = tensor<string, []>("conv1d_105_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_1_seq_net_blocks_2_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_2_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10574912)))];
            tensor<fp16, [256]> p_net_1_seq_net_blocks_2_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_1_seq_net_blocks_2_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11099264)))];
            tensor<fp16, [80, 256, 801]> conv1d_105_cast_fp16 = conv(bias = p_net_1_seq_net_blocks_2_conv_4_bias_to_fp16, dilations = conv1d_105_dilations_0, groups = conv1d_105_groups_0, pad = conv1d_105_pad_0, pad_type = conv1d_105_pad_type_0, strides = conv1d_105_strides_0, weight = p_net_1_seq_net_blocks_2_conv_4_weight_to_fp16, x = silu_9_cast_fp16)[name = tensor<string, []>("conv1d_105_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_263_cast_fp16 = add(x = add_261_cast_fp16, y = conv1d_105_cast_fp16)[name = tensor<string, []>("add_263_cast_fp16")];
            tensor<int32, [1]> unsqueeze_189_axes_0 = const()[name = tensor<string, []>("unsqueeze_189_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 80, 256, 801]> unsqueeze_189_cast_fp16 = expand_dims(axes = unsqueeze_189_axes_0, x = add_263_cast_fp16)[name = tensor<string, []>("unsqueeze_189_cast_fp16")];
            tensor<int32, [1]> squeeze_92_axes_0 = const()[name = tensor<string, []>("squeeze_92_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [80, 256, 801]> squeeze_92_cast_fp16 = squeeze(axes = squeeze_92_axes_0, x = unsqueeze_189_cast_fp16)[name = tensor<string, []>("squeeze_92_cast_fp16")];
            tensor<int32, [3]> const_1996 = const()[name = tensor<string, []>("const_1996"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<int32, [1]> unsqueeze_191_axes_0 = const()[name = tensor<string, []>("unsqueeze_191_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> permute_8_cast_fp16 = transpose(perm = const_1996, x = squeeze_92_cast_fp16)[name = tensor<string, []>("transpose_15")];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_191_cast_fp16 = expand_dims(axes = unsqueeze_191_axes_0, x = permute_8_cast_fp16)[name = tensor<string, []>("unsqueeze_191_cast_fp16")];
            tensor<fp16, []> const_2001_promoted_to_fp16 = const()[name = tensor<string, []>("const_2001_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_251_cast_fp16 = pow(x = unsqueeze_191_cast_fp16, y = const_2001_promoted_to_fp16)[name = tensor<string, []>("pow_251_cast_fp16")];
            tensor<int32, [1]> mean_90_axes_0 = const()[name = tensor<string, []>("mean_90_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_90_keep_dims_0 = const()[name = tensor<string, []>("mean_90_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_90_cast_fp16 = reduce_mean(axes = mean_90_axes_0, keep_dims = mean_90_keep_dims_0, x = pow_251_cast_fp16)[name = tensor<string, []>("mean_90_cast_fp16")];
            tensor<fp16, []> const_2004_to_fp16 = const()[name = tensor<string, []>("const_2004_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_264_cast_fp16 = add(x = mean_90_cast_fp16, y = const_2004_to_fp16)[name = tensor<string, []>("add_264_cast_fp16")];
            tensor<fp32, []> rsqrt_90_epsilon_0 = const()[name = tensor<string, []>("rsqrt_90_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_90_cast_fp16 = rsqrt(epsilon = rsqrt_90_epsilon_0, x = add_264_cast_fp16)[name = tensor<string, []>("rsqrt_90_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_196_cast_fp16 = mul(x = unsqueeze_191_cast_fp16, y = rsqrt_90_cast_fp16)[name = tensor<string, []>("mul_196_cast_fp16")];
            tensor<int32, [1]> squeeze_93_axes_0 = const()[name = tensor<string, []>("squeeze_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_93_cast_fp16 = squeeze(axes = squeeze_93_axes_0, x = mul_196_cast_fp16)[name = tensor<string, []>("squeeze_93_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_108_to_fp16 = const()[name = tensor<string, []>("view_108_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11099840)))];
            tensor<fp16, [801, 256, 80]> mul_197_cast_fp16 = mul(x = squeeze_93_cast_fp16, y = view_108_to_fp16)[name = tensor<string, []>("mul_197_cast_fp16")];
            tensor<string, []> conv1d_106_pad_type_0 = const()[name = tensor<string, []>("conv1d_106_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_106_strides_0 = const()[name = tensor<string, []>("conv1d_106_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_106_pad_0 = const()[name = tensor<string, []>("conv1d_106_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_106_dilations_0 = const()[name = tensor<string, []>("conv1d_106_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_106_groups_0 = const()[name = tensor<string, []>("conv1d_106_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [768, 256, 1]> p_net_2_band_net_weight_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_band_net_weight_weight_to_fp16"), val = tensor<fp16, [768, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11100416)))];
            tensor<fp16, [801, 768, 80]> conv1d_106_cast_fp16 = conv(dilations = conv1d_106_dilations_0, groups = conv1d_106_groups_0, pad = conv1d_106_pad_0, pad_type = conv1d_106_pad_type_0, strides = conv1d_106_strides_0, weight = p_net_2_band_net_weight_weight_to_fp16, x = mul_197_cast_fp16)[name = tensor<string, []>("conv1d_106_cast_fp16")];
            tensor<int32, [4]> const_2007 = const()[name = tensor<string, []>("const_2007"), val = tensor<int32, [4]>([-1, 8, 96, 80])];
            tensor<fp16, [801, 8, 96, 80]> view_109_cast_fp16 = reshape(shape = const_2007, x = conv1d_106_cast_fp16)[name = tensor<string, []>("view_109_cast_fp16")];
            tensor<int32, [4]> const_2008 = const()[name = tensor<string, []>("const_2008"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> tile_4 = const()[name = tensor<string, []>("tile_4"), val = tensor<int32, [3]>([32, 32, 32])];
            tensor<int32, []> split_4_axis_0 = const()[name = tensor<string, []>("split_4_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 96]> permute_9_cast_fp16 = transpose(perm = const_2008, x = view_109_cast_fp16)[name = tensor<string, []>("transpose_14")];
            tensor<fp16, [801, 8, 80, 32]> split_4_cast_fp16_0, tensor<fp16, [801, 8, 80, 32]> split_4_cast_fp16_1, tensor<fp16, [801, 8, 80, 32]> split_4_cast_fp16_2 = split(axis = split_4_axis_0, split_sizes = tile_4, x = permute_9_cast_fp16)[name = tensor<string, []>("split_4_cast_fp16")];
            tensor<int32, [3]> const_2016 = const()[name = tensor<string, []>("const_2016"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_110_cast_fp16 = reshape(shape = const_2016, x = split_4_cast_fp16_0)[name = tensor<string, []>("view_110_cast_fp16")];
            tensor<int32, [3]> const_2023 = const()[name = tensor<string, []>("const_2023"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_6_cast_fp16 = reshape(shape = const_2023, x = view_110_cast_fp16)[name = tensor<string, []>("_unsafe_view_6_cast_fp16")];
            tensor<int32, [1]> const_2024 = const()[name = tensor<string, []>("const_2024"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_4_cast_fp16 = reverse(axes = const_2024, x = _unsafe_view_6_cast_fp16)[name = tensor<string, []>("flip_4_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_111_to_fp16 = const()[name = tensor<string, []>("view_111_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_198_cast_fp16 = mul(x = flip_4_cast_fp16, y = view_111_to_fp16)[name = tensor<string, []>("mul_198_cast_fp16")];
            tensor<int32, [3]> const_2026 = const()[name = tensor<string, []>("const_2026"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_112_cast_fp16 = reshape(shape = const_2026, x = mul_198_cast_fp16)[name = tensor<string, []>("view_112_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_199_cast_fp16 = mul(x = view_110_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_199_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_200_cast_fp16 = mul(x = view_112_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_200_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_265_cast_fp16 = add(x = mul_199_cast_fp16, y = mul_200_cast_fp16)[name = tensor<string, []>("add_265_cast_fp16")];
            tensor<int32, [4]> const_2029 = const()[name = tensor<string, []>("const_2029"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_113_cast_fp16 = reshape(shape = const_2029, x = add_265_cast_fp16)[name = tensor<string, []>("view_113_cast_fp16")];
            tensor<int32, [3]> const_2030 = const()[name = tensor<string, []>("const_2030"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_114_cast_fp16 = reshape(shape = const_2030, x = split_4_cast_fp16_1)[name = tensor<string, []>("view_114_cast_fp16")];
            tensor<int32, [3]> const_2037 = const()[name = tensor<string, []>("const_2037"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_7_cast_fp16 = reshape(shape = const_2037, x = view_114_cast_fp16)[name = tensor<string, []>("_unsafe_view_7_cast_fp16")];
            tensor<int32, [1]> const_2038 = const()[name = tensor<string, []>("const_2038"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_5_cast_fp16 = reverse(axes = const_2038, x = _unsafe_view_7_cast_fp16)[name = tensor<string, []>("flip_5_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_115_to_fp16 = const()[name = tensor<string, []>("view_115_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_201_cast_fp16 = mul(x = flip_5_cast_fp16, y = view_115_to_fp16)[name = tensor<string, []>("mul_201_cast_fp16")];
            tensor<int32, [3]> const_2040 = const()[name = tensor<string, []>("const_2040"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_116_cast_fp16 = reshape(shape = const_2040, x = mul_201_cast_fp16)[name = tensor<string, []>("view_116_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_202_cast_fp16 = mul(x = view_114_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_202_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_203_cast_fp16 = mul(x = view_116_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_203_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_266_cast_fp16 = add(x = mul_202_cast_fp16, y = mul_203_cast_fp16)[name = tensor<string, []>("add_266_cast_fp16")];
            tensor<int32, [4]> const_2043 = const()[name = tensor<string, []>("const_2043"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_117_cast_fp16 = reshape(shape = const_2043, x = add_266_cast_fp16)[name = tensor<string, []>("view_117_cast_fp16")];
            tensor<bool, []> matmul_4_transpose_x_1 = const()[name = tensor<string, []>("matmul_4_transpose_x_1"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_4_transpose_y_1 = const()[name = tensor<string, []>("matmul_4_transpose_y_1"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 8, 80, 80]> matmul_4_cast_fp16 = matmul(transpose_x = matmul_4_transpose_x_1, transpose_y = matmul_4_transpose_y_1, x = view_113_cast_fp16, y = view_117_cast_fp16)[name = tensor<string, []>("matmul_4_cast_fp16")];
            tensor<fp16, []> const_2046_to_fp16 = const()[name = tensor<string, []>("const_2046_to_fp16"), val = tensor<fp16, []>(0x1.6ap-3)];
            tensor<fp16, [801, 8, 80, 80]> mul_204_cast_fp16 = mul(x = matmul_4_cast_fp16, y = const_2046_to_fp16)[name = tensor<string, []>("mul_204_cast_fp16")];
            tensor<int32, []> const_2047 = const()[name = tensor<string, []>("const_2047"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 80]> softmax_2_cast_fp16 = softmax(axis = const_2047, x = mul_204_cast_fp16)[name = tensor<string, []>("softmax_2_cast_fp16")];
            tensor<bool, []> matmul_5_transpose_x_0 = const()[name = tensor<string, []>("matmul_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_5_transpose_y_0 = const()[name = tensor<string, []>("matmul_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [801, 8, 80, 32]> matmul_5_cast_fp16 = matmul(transpose_x = matmul_5_transpose_x_0, transpose_y = matmul_5_transpose_y_0, x = softmax_2_cast_fp16, y = split_4_cast_fp16_2)[name = tensor<string, []>("matmul_5_cast_fp16")];
            tensor<int32, [4]> const_2048 = const()[name = tensor<string, []>("const_2048"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> const_2049 = const()[name = tensor<string, []>("const_2049"), val = tensor<int32, [3]>([801, 256, 80])];
            tensor<fp16, [801, 8, 32, 80]> permute_10_cast_fp16 = transpose(perm = const_2048, x = matmul_5_cast_fp16)[name = tensor<string, []>("transpose_13")];
            tensor<fp16, [801, 256, 80]> _unsafe_view_8_cast_fp16 = reshape(shape = const_2049, x = permute_10_cast_fp16)[name = tensor<string, []>("_unsafe_view_8_cast_fp16")];
            tensor<string, []> conv1d_107_pad_type_0 = const()[name = tensor<string, []>("conv1d_107_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_107_strides_0 = const()[name = tensor<string, []>("conv1d_107_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_107_pad_0 = const()[name = tensor<string, []>("conv1d_107_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_107_dilations_0 = const()[name = tensor<string, []>("conv1d_107_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_107_groups_0 = const()[name = tensor<string, []>("conv1d_107_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1]> p_net_2_band_net_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_band_net_output_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11493696)))];
            tensor<fp16, [801, 256, 80]> conv1d_107_cast_fp16 = conv(dilations = conv1d_107_dilations_0, groups = conv1d_107_groups_0, pad = conv1d_107_pad_0, pad_type = conv1d_107_pad_type_0, strides = conv1d_107_strides_0, weight = p_net_2_band_net_output_weight_to_fp16, x = _unsafe_view_8_cast_fp16)[name = tensor<string, []>("conv1d_107_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_267_cast_fp16 = add(x = conv1d_107_cast_fp16, y = permute_8_cast_fp16)[name = tensor<string, []>("add_267_cast_fp16")];
            tensor<int32, [1]> unsqueeze_197_axes_0 = const()[name = tensor<string, []>("unsqueeze_197_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_197_cast_fp16 = expand_dims(axes = unsqueeze_197_axes_0, x = add_267_cast_fp16)[name = tensor<string, []>("unsqueeze_197_cast_fp16")];
            tensor<fp16, []> const_2054_promoted_to_fp16 = const()[name = tensor<string, []>("const_2054_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_252_cast_fp16 = pow(x = unsqueeze_197_cast_fp16, y = const_2054_promoted_to_fp16)[name = tensor<string, []>("pow_252_cast_fp16")];
            tensor<int32, [1]> mean_91_axes_0 = const()[name = tensor<string, []>("mean_91_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_91_keep_dims_0 = const()[name = tensor<string, []>("mean_91_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_91_cast_fp16 = reduce_mean(axes = mean_91_axes_0, keep_dims = mean_91_keep_dims_0, x = pow_252_cast_fp16)[name = tensor<string, []>("mean_91_cast_fp16")];
            tensor<fp16, []> const_2057_to_fp16 = const()[name = tensor<string, []>("const_2057_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_268_cast_fp16 = add(x = mean_91_cast_fp16, y = const_2057_to_fp16)[name = tensor<string, []>("add_268_cast_fp16")];
            tensor<fp32, []> rsqrt_91_epsilon_0 = const()[name = tensor<string, []>("rsqrt_91_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_91_cast_fp16 = rsqrt(epsilon = rsqrt_91_epsilon_0, x = add_268_cast_fp16)[name = tensor<string, []>("rsqrt_91_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_205_cast_fp16 = mul(x = unsqueeze_197_cast_fp16, y = rsqrt_91_cast_fp16)[name = tensor<string, []>("mul_205_cast_fp16")];
            tensor<int32, [1]> squeeze_94_axes_0 = const()[name = tensor<string, []>("squeeze_94_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_94_cast_fp16 = squeeze(axes = squeeze_94_axes_0, x = mul_205_cast_fp16)[name = tensor<string, []>("squeeze_94_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_118_to_fp16 = const()[name = tensor<string, []>("view_118_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11624832)))];
            tensor<fp16, [801, 256, 80]> mul_206_cast_fp16 = mul(x = squeeze_94_cast_fp16, y = view_118_to_fp16)[name = tensor<string, []>("mul_206_cast_fp16")];
            tensor<string, []> conv1d_108_pad_type_0 = const()[name = tensor<string, []>("conv1d_108_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_108_strides_0 = const()[name = tensor<string, []>("conv1d_108_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_108_pad_0 = const()[name = tensor<string, []>("conv1d_108_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_108_dilations_0 = const()[name = tensor<string, []>("conv1d_108_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_108_groups_0 = const()[name = tensor<string, []>("conv1d_108_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 256, 1]> p_net_2_band_net_mlp_1_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_band_net_mlp_1_weight_to_fp16"), val = tensor<fp16, [2048, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11625408)))];
            tensor<fp16, [801, 2048, 80]> conv1d_108_cast_fp16 = conv(dilations = conv1d_108_dilations_0, groups = conv1d_108_groups_0, pad = conv1d_108_pad_0, pad_type = conv1d_108_pad_type_0, strides = conv1d_108_strides_0, weight = p_net_2_band_net_mlp_1_weight_to_fp16, x = mul_206_cast_fp16)[name = tensor<string, []>("conv1d_108_cast_fp16")];
            tensor<fp16, [801, 2048, 80]> silu_10_cast_fp16 = silu(x = conv1d_108_cast_fp16)[name = tensor<string, []>("silu_10_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = tensor<string, []>("tile_5"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> split_5_axis_0 = const()[name = tensor<string, []>("split_5_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [801, 1024, 80]> split_5_cast_fp16_0, tensor<fp16, [801, 1024, 80]> split_5_cast_fp16_1 = split(axis = split_5_axis_0, split_sizes = tile_5, x = silu_10_cast_fp16)[name = tensor<string, []>("split_5_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> silu_11_cast_fp16 = silu(x = split_5_cast_fp16_0)[name = tensor<string, []>("silu_11_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> mul_207_cast_fp16 = mul(x = silu_11_cast_fp16, y = split_5_cast_fp16_1)[name = tensor<string, []>("mul_207_cast_fp16")];
            tensor<string, []> conv1d_109_pad_type_0 = const()[name = tensor<string, []>("conv1d_109_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_109_strides_0 = const()[name = tensor<string, []>("conv1d_109_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_109_pad_0 = const()[name = tensor<string, []>("conv1d_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_109_dilations_0 = const()[name = tensor<string, []>("conv1d_109_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_109_groups_0 = const()[name = tensor<string, []>("conv1d_109_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_2_band_net_mlp_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_band_net_mlp_output_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12674048)))];
            tensor<fp16, [801, 256, 80]> conv1d_109_cast_fp16 = conv(dilations = conv1d_109_dilations_0, groups = conv1d_109_groups_0, pad = conv1d_109_pad_0, pad_type = conv1d_109_pad_type_0, strides = conv1d_109_strides_0, weight = p_net_2_band_net_mlp_output_weight_to_fp16, x = mul_207_cast_fp16)[name = tensor<string, []>("conv1d_109_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_269_cast_fp16 = add(x = add_267_cast_fp16, y = conv1d_109_cast_fp16)[name = tensor<string, []>("add_269_cast_fp16")];
            tensor<int32, [3]> const_2066 = const()[name = tensor<string, []>("const_2066"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<string, []> conv1d_110_pad_type_0 = const()[name = tensor<string, []>("conv1d_110_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_110_pad_0 = const()[name = tensor<string, []>("conv1d_110_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_110_groups_0 = const()[name = tensor<string, []>("conv1d_110_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_110_strides_0 = const()[name = tensor<string, []>("conv1d_110_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_110_dilations_0 = const()[name = tensor<string, []>("conv1d_110_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_2_seq_net_blocks_0_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_0_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13198400)))];
            tensor<fp16, [256]> p_net_2_seq_net_blocks_0_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_0_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13202048)))];
            tensor<fp16, [80, 256, 801]> permute_11_cast_fp16 = transpose(perm = const_2066, x = add_269_cast_fp16)[name = tensor<string, []>("transpose_12")];
            tensor<fp16, [80, 256, 801]> conv1d_110_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_0_conv_0_bias_to_fp16, dilations = conv1d_110_dilations_0, groups = conv1d_110_groups_0, pad = conv1d_110_pad_0, pad_type = conv1d_110_pad_type_0, strides = conv1d_110_strides_0, weight = p_net_2_seq_net_blocks_0_conv_0_weight_to_fp16, x = permute_11_cast_fp16)[name = tensor<string, []>("conv1d_110_cast_fp16")];
            tensor<int32, [1]> unsqueeze_199_axes_0 = const()[name = tensor<string, []>("unsqueeze_199_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_199_cast_fp16 = expand_dims(axes = unsqueeze_199_axes_0, x = conv1d_110_cast_fp16)[name = tensor<string, []>("unsqueeze_199_cast_fp16")];
            tensor<fp16, []> const_2075_promoted_to_fp16 = const()[name = tensor<string, []>("const_2075_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_253_cast_fp16 = pow(x = unsqueeze_199_cast_fp16, y = const_2075_promoted_to_fp16)[name = tensor<string, []>("pow_253_cast_fp16")];
            tensor<int32, [1]> mean_92_axes_0 = const()[name = tensor<string, []>("mean_92_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_92_keep_dims_0 = const()[name = tensor<string, []>("mean_92_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_92_cast_fp16 = reduce_mean(axes = mean_92_axes_0, keep_dims = mean_92_keep_dims_0, x = pow_253_cast_fp16)[name = tensor<string, []>("mean_92_cast_fp16")];
            tensor<fp16, []> const_2078_to_fp16 = const()[name = tensor<string, []>("const_2078_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_270_cast_fp16 = add(x = mean_92_cast_fp16, y = const_2078_to_fp16)[name = tensor<string, []>("add_270_cast_fp16")];
            tensor<fp32, []> rsqrt_92_epsilon_0 = const()[name = tensor<string, []>("rsqrt_92_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_92_cast_fp16 = rsqrt(epsilon = rsqrt_92_epsilon_0, x = add_270_cast_fp16)[name = tensor<string, []>("rsqrt_92_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_208_cast_fp16 = mul(x = unsqueeze_199_cast_fp16, y = rsqrt_92_cast_fp16)[name = tensor<string, []>("mul_208_cast_fp16")];
            tensor<int32, [1]> squeeze_95_axes_0 = const()[name = tensor<string, []>("squeeze_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_95_cast_fp16 = squeeze(axes = squeeze_95_axes_0, x = mul_208_cast_fp16)[name = tensor<string, []>("squeeze_95_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_119_to_fp16 = const()[name = tensor<string, []>("view_119_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13202624)))];
            tensor<fp16, [80, 256, 801]> mul_209_cast_fp16 = mul(x = squeeze_95_cast_fp16, y = view_119_to_fp16)[name = tensor<string, []>("mul_209_cast_fp16")];
            tensor<string, []> conv1d_111_pad_type_0 = const()[name = tensor<string, []>("conv1d_111_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_111_strides_0 = const()[name = tensor<string, []>("conv1d_111_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_111_pad_0 = const()[name = tensor<string, []>("conv1d_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_111_dilations_0 = const()[name = tensor<string, []>("conv1d_111_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_111_groups_0 = const()[name = tensor<string, []>("conv1d_111_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_2_seq_net_blocks_0_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_0_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13203200)))];
            tensor<fp16, [1024]> p_net_2_seq_net_blocks_0_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_0_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13727552)))];
            tensor<fp16, [80, 1024, 801]> conv1d_111_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_0_conv_2_bias_to_fp16, dilations = conv1d_111_dilations_0, groups = conv1d_111_groups_0, pad = conv1d_111_pad_0, pad_type = conv1d_111_pad_type_0, strides = conv1d_111_strides_0, weight = p_net_2_seq_net_blocks_0_conv_2_weight_to_fp16, x = mul_209_cast_fp16)[name = tensor<string, []>("conv1d_111_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_12_cast_fp16 = silu(x = conv1d_111_cast_fp16)[name = tensor<string, []>("silu_12_cast_fp16")];
            tensor<string, []> conv1d_112_pad_type_0 = const()[name = tensor<string, []>("conv1d_112_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_112_strides_0 = const()[name = tensor<string, []>("conv1d_112_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_112_pad_0 = const()[name = tensor<string, []>("conv1d_112_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_112_dilations_0 = const()[name = tensor<string, []>("conv1d_112_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_112_groups_0 = const()[name = tensor<string, []>("conv1d_112_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_2_seq_net_blocks_0_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_0_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13729664)))];
            tensor<fp16, [256]> p_net_2_seq_net_blocks_0_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_0_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14254016)))];
            tensor<fp16, [80, 256, 801]> conv1d_112_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_0_conv_4_bias_to_fp16, dilations = conv1d_112_dilations_0, groups = conv1d_112_groups_0, pad = conv1d_112_pad_0, pad_type = conv1d_112_pad_type_0, strides = conv1d_112_strides_0, weight = p_net_2_seq_net_blocks_0_conv_4_weight_to_fp16, x = silu_12_cast_fp16)[name = tensor<string, []>("conv1d_112_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_271_cast_fp16 = add(x = permute_11_cast_fp16, y = conv1d_112_cast_fp16)[name = tensor<string, []>("add_271_cast_fp16")];
            tensor<string, []> conv1d_113_pad_type_0 = const()[name = tensor<string, []>("conv1d_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_113_pad_0 = const()[name = tensor<string, []>("conv1d_113_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_113_groups_0 = const()[name = tensor<string, []>("conv1d_113_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_113_strides_0 = const()[name = tensor<string, []>("conv1d_113_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_113_dilations_0 = const()[name = tensor<string, []>("conv1d_113_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_2_seq_net_blocks_1_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_1_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14254592)))];
            tensor<fp16, [256]> p_net_2_seq_net_blocks_1_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_1_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14258240)))];
            tensor<fp16, [80, 256, 801]> conv1d_113_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_1_conv_0_bias_to_fp16, dilations = conv1d_113_dilations_0, groups = conv1d_113_groups_0, pad = conv1d_113_pad_0, pad_type = conv1d_113_pad_type_0, strides = conv1d_113_strides_0, weight = p_net_2_seq_net_blocks_1_conv_0_weight_to_fp16, x = add_271_cast_fp16)[name = tensor<string, []>("conv1d_113_cast_fp16")];
            tensor<int32, [1]> unsqueeze_201_axes_0 = const()[name = tensor<string, []>("unsqueeze_201_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_201_cast_fp16 = expand_dims(axes = unsqueeze_201_axes_0, x = conv1d_113_cast_fp16)[name = tensor<string, []>("unsqueeze_201_cast_fp16")];
            tensor<fp16, []> const_2089_promoted_to_fp16 = const()[name = tensor<string, []>("const_2089_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_254_cast_fp16 = pow(x = unsqueeze_201_cast_fp16, y = const_2089_promoted_to_fp16)[name = tensor<string, []>("pow_254_cast_fp16")];
            tensor<int32, [1]> mean_93_axes_0 = const()[name = tensor<string, []>("mean_93_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_93_keep_dims_0 = const()[name = tensor<string, []>("mean_93_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_93_cast_fp16 = reduce_mean(axes = mean_93_axes_0, keep_dims = mean_93_keep_dims_0, x = pow_254_cast_fp16)[name = tensor<string, []>("mean_93_cast_fp16")];
            tensor<fp16, []> const_2092_to_fp16 = const()[name = tensor<string, []>("const_2092_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_272_cast_fp16 = add(x = mean_93_cast_fp16, y = const_2092_to_fp16)[name = tensor<string, []>("add_272_cast_fp16")];
            tensor<fp32, []> rsqrt_93_epsilon_0 = const()[name = tensor<string, []>("rsqrt_93_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_93_cast_fp16 = rsqrt(epsilon = rsqrt_93_epsilon_0, x = add_272_cast_fp16)[name = tensor<string, []>("rsqrt_93_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_210_cast_fp16 = mul(x = unsqueeze_201_cast_fp16, y = rsqrt_93_cast_fp16)[name = tensor<string, []>("mul_210_cast_fp16")];
            tensor<int32, [1]> squeeze_96_axes_0 = const()[name = tensor<string, []>("squeeze_96_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_96_cast_fp16 = squeeze(axes = squeeze_96_axes_0, x = mul_210_cast_fp16)[name = tensor<string, []>("squeeze_96_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_120_to_fp16 = const()[name = tensor<string, []>("view_120_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14258816)))];
            tensor<fp16, [80, 256, 801]> mul_211_cast_fp16 = mul(x = squeeze_96_cast_fp16, y = view_120_to_fp16)[name = tensor<string, []>("mul_211_cast_fp16")];
            tensor<string, []> conv1d_114_pad_type_0 = const()[name = tensor<string, []>("conv1d_114_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_114_strides_0 = const()[name = tensor<string, []>("conv1d_114_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_114_pad_0 = const()[name = tensor<string, []>("conv1d_114_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_114_dilations_0 = const()[name = tensor<string, []>("conv1d_114_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_114_groups_0 = const()[name = tensor<string, []>("conv1d_114_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_2_seq_net_blocks_1_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_1_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14259392)))];
            tensor<fp16, [1024]> p_net_2_seq_net_blocks_1_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_1_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14783744)))];
            tensor<fp16, [80, 1024, 801]> conv1d_114_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_1_conv_2_bias_to_fp16, dilations = conv1d_114_dilations_0, groups = conv1d_114_groups_0, pad = conv1d_114_pad_0, pad_type = conv1d_114_pad_type_0, strides = conv1d_114_strides_0, weight = p_net_2_seq_net_blocks_1_conv_2_weight_to_fp16, x = mul_211_cast_fp16)[name = tensor<string, []>("conv1d_114_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_13_cast_fp16 = silu(x = conv1d_114_cast_fp16)[name = tensor<string, []>("silu_13_cast_fp16")];
            tensor<string, []> conv1d_115_pad_type_0 = const()[name = tensor<string, []>("conv1d_115_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_115_strides_0 = const()[name = tensor<string, []>("conv1d_115_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_115_pad_0 = const()[name = tensor<string, []>("conv1d_115_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_115_dilations_0 = const()[name = tensor<string, []>("conv1d_115_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_115_groups_0 = const()[name = tensor<string, []>("conv1d_115_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_2_seq_net_blocks_1_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_1_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14785856)))];
            tensor<fp16, [256]> p_net_2_seq_net_blocks_1_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_1_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15310208)))];
            tensor<fp16, [80, 256, 801]> conv1d_115_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_1_conv_4_bias_to_fp16, dilations = conv1d_115_dilations_0, groups = conv1d_115_groups_0, pad = conv1d_115_pad_0, pad_type = conv1d_115_pad_type_0, strides = conv1d_115_strides_0, weight = p_net_2_seq_net_blocks_1_conv_4_weight_to_fp16, x = silu_13_cast_fp16)[name = tensor<string, []>("conv1d_115_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_273_cast_fp16 = add(x = add_271_cast_fp16, y = conv1d_115_cast_fp16)[name = tensor<string, []>("add_273_cast_fp16")];
            tensor<string, []> conv1d_116_pad_type_0 = const()[name = tensor<string, []>("conv1d_116_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_116_pad_0 = const()[name = tensor<string, []>("conv1d_116_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_116_groups_0 = const()[name = tensor<string, []>("conv1d_116_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_116_strides_0 = const()[name = tensor<string, []>("conv1d_116_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_116_dilations_0 = const()[name = tensor<string, []>("conv1d_116_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_2_seq_net_blocks_2_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_2_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15310784)))];
            tensor<fp16, [256]> p_net_2_seq_net_blocks_2_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_2_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15314432)))];
            tensor<fp16, [80, 256, 801]> conv1d_116_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_2_conv_0_bias_to_fp16, dilations = conv1d_116_dilations_0, groups = conv1d_116_groups_0, pad = conv1d_116_pad_0, pad_type = conv1d_116_pad_type_0, strides = conv1d_116_strides_0, weight = p_net_2_seq_net_blocks_2_conv_0_weight_to_fp16, x = add_273_cast_fp16)[name = tensor<string, []>("conv1d_116_cast_fp16")];
            tensor<int32, [1]> unsqueeze_203_axes_0 = const()[name = tensor<string, []>("unsqueeze_203_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_203_cast_fp16 = expand_dims(axes = unsqueeze_203_axes_0, x = conv1d_116_cast_fp16)[name = tensor<string, []>("unsqueeze_203_cast_fp16")];
            tensor<fp16, []> const_2103_promoted_to_fp16 = const()[name = tensor<string, []>("const_2103_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_255_cast_fp16 = pow(x = unsqueeze_203_cast_fp16, y = const_2103_promoted_to_fp16)[name = tensor<string, []>("pow_255_cast_fp16")];
            tensor<int32, [1]> mean_94_axes_0 = const()[name = tensor<string, []>("mean_94_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_94_keep_dims_0 = const()[name = tensor<string, []>("mean_94_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_94_cast_fp16 = reduce_mean(axes = mean_94_axes_0, keep_dims = mean_94_keep_dims_0, x = pow_255_cast_fp16)[name = tensor<string, []>("mean_94_cast_fp16")];
            tensor<fp16, []> const_2106_to_fp16 = const()[name = tensor<string, []>("const_2106_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_274_cast_fp16 = add(x = mean_94_cast_fp16, y = const_2106_to_fp16)[name = tensor<string, []>("add_274_cast_fp16")];
            tensor<fp32, []> rsqrt_94_epsilon_0 = const()[name = tensor<string, []>("rsqrt_94_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_94_cast_fp16 = rsqrt(epsilon = rsqrt_94_epsilon_0, x = add_274_cast_fp16)[name = tensor<string, []>("rsqrt_94_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_212_cast_fp16 = mul(x = unsqueeze_203_cast_fp16, y = rsqrt_94_cast_fp16)[name = tensor<string, []>("mul_212_cast_fp16")];
            tensor<int32, [1]> squeeze_97_axes_0 = const()[name = tensor<string, []>("squeeze_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_97_cast_fp16 = squeeze(axes = squeeze_97_axes_0, x = mul_212_cast_fp16)[name = tensor<string, []>("squeeze_97_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_121_to_fp16 = const()[name = tensor<string, []>("view_121_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15315008)))];
            tensor<fp16, [80, 256, 801]> mul_213_cast_fp16 = mul(x = squeeze_97_cast_fp16, y = view_121_to_fp16)[name = tensor<string, []>("mul_213_cast_fp16")];
            tensor<string, []> conv1d_117_pad_type_0 = const()[name = tensor<string, []>("conv1d_117_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_117_strides_0 = const()[name = tensor<string, []>("conv1d_117_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_117_pad_0 = const()[name = tensor<string, []>("conv1d_117_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_117_dilations_0 = const()[name = tensor<string, []>("conv1d_117_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_117_groups_0 = const()[name = tensor<string, []>("conv1d_117_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_2_seq_net_blocks_2_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_2_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15315584)))];
            tensor<fp16, [1024]> p_net_2_seq_net_blocks_2_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_2_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15839936)))];
            tensor<fp16, [80, 1024, 801]> conv1d_117_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_2_conv_2_bias_to_fp16, dilations = conv1d_117_dilations_0, groups = conv1d_117_groups_0, pad = conv1d_117_pad_0, pad_type = conv1d_117_pad_type_0, strides = conv1d_117_strides_0, weight = p_net_2_seq_net_blocks_2_conv_2_weight_to_fp16, x = mul_213_cast_fp16)[name = tensor<string, []>("conv1d_117_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_14_cast_fp16 = silu(x = conv1d_117_cast_fp16)[name = tensor<string, []>("silu_14_cast_fp16")];
            tensor<string, []> conv1d_118_pad_type_0 = const()[name = tensor<string, []>("conv1d_118_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_118_strides_0 = const()[name = tensor<string, []>("conv1d_118_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_118_pad_0 = const()[name = tensor<string, []>("conv1d_118_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_118_dilations_0 = const()[name = tensor<string, []>("conv1d_118_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_118_groups_0 = const()[name = tensor<string, []>("conv1d_118_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_2_seq_net_blocks_2_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_2_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15842048)))];
            tensor<fp16, [256]> p_net_2_seq_net_blocks_2_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_2_seq_net_blocks_2_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16366400)))];
            tensor<fp16, [80, 256, 801]> conv1d_118_cast_fp16 = conv(bias = p_net_2_seq_net_blocks_2_conv_4_bias_to_fp16, dilations = conv1d_118_dilations_0, groups = conv1d_118_groups_0, pad = conv1d_118_pad_0, pad_type = conv1d_118_pad_type_0, strides = conv1d_118_strides_0, weight = p_net_2_seq_net_blocks_2_conv_4_weight_to_fp16, x = silu_14_cast_fp16)[name = tensor<string, []>("conv1d_118_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_275_cast_fp16 = add(x = add_273_cast_fp16, y = conv1d_118_cast_fp16)[name = tensor<string, []>("add_275_cast_fp16")];
            tensor<int32, [1]> unsqueeze_204_axes_0 = const()[name = tensor<string, []>("unsqueeze_204_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 80, 256, 801]> unsqueeze_204_cast_fp16 = expand_dims(axes = unsqueeze_204_axes_0, x = add_275_cast_fp16)[name = tensor<string, []>("unsqueeze_204_cast_fp16")];
            tensor<int32, [1]> squeeze_98_axes_0 = const()[name = tensor<string, []>("squeeze_98_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [80, 256, 801]> squeeze_98_cast_fp16 = squeeze(axes = squeeze_98_axes_0, x = unsqueeze_204_cast_fp16)[name = tensor<string, []>("squeeze_98_cast_fp16")];
            tensor<int32, [3]> const_2111 = const()[name = tensor<string, []>("const_2111"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<int32, [1]> unsqueeze_206_axes_0 = const()[name = tensor<string, []>("unsqueeze_206_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> permute_12_cast_fp16 = transpose(perm = const_2111, x = squeeze_98_cast_fp16)[name = tensor<string, []>("transpose_11")];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_206_cast_fp16 = expand_dims(axes = unsqueeze_206_axes_0, x = permute_12_cast_fp16)[name = tensor<string, []>("unsqueeze_206_cast_fp16")];
            tensor<fp16, []> const_2116_promoted_to_fp16 = const()[name = tensor<string, []>("const_2116_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_256_cast_fp16 = pow(x = unsqueeze_206_cast_fp16, y = const_2116_promoted_to_fp16)[name = tensor<string, []>("pow_256_cast_fp16")];
            tensor<int32, [1]> mean_95_axes_0 = const()[name = tensor<string, []>("mean_95_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_95_keep_dims_0 = const()[name = tensor<string, []>("mean_95_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_95_cast_fp16 = reduce_mean(axes = mean_95_axes_0, keep_dims = mean_95_keep_dims_0, x = pow_256_cast_fp16)[name = tensor<string, []>("mean_95_cast_fp16")];
            tensor<fp16, []> const_2119_to_fp16 = const()[name = tensor<string, []>("const_2119_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_276_cast_fp16 = add(x = mean_95_cast_fp16, y = const_2119_to_fp16)[name = tensor<string, []>("add_276_cast_fp16")];
            tensor<fp32, []> rsqrt_95_epsilon_0 = const()[name = tensor<string, []>("rsqrt_95_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_95_cast_fp16 = rsqrt(epsilon = rsqrt_95_epsilon_0, x = add_276_cast_fp16)[name = tensor<string, []>("rsqrt_95_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_214_cast_fp16 = mul(x = unsqueeze_206_cast_fp16, y = rsqrt_95_cast_fp16)[name = tensor<string, []>("mul_214_cast_fp16")];
            tensor<int32, [1]> squeeze_99_axes_0 = const()[name = tensor<string, []>("squeeze_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_99_cast_fp16 = squeeze(axes = squeeze_99_axes_0, x = mul_214_cast_fp16)[name = tensor<string, []>("squeeze_99_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_122_to_fp16 = const()[name = tensor<string, []>("view_122_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16366976)))];
            tensor<fp16, [801, 256, 80]> mul_215_cast_fp16 = mul(x = squeeze_99_cast_fp16, y = view_122_to_fp16)[name = tensor<string, []>("mul_215_cast_fp16")];
            tensor<string, []> conv1d_119_pad_type_0 = const()[name = tensor<string, []>("conv1d_119_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_119_strides_0 = const()[name = tensor<string, []>("conv1d_119_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_119_pad_0 = const()[name = tensor<string, []>("conv1d_119_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_119_dilations_0 = const()[name = tensor<string, []>("conv1d_119_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_119_groups_0 = const()[name = tensor<string, []>("conv1d_119_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [768, 256, 1]> p_net_3_band_net_weight_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_band_net_weight_weight_to_fp16"), val = tensor<fp16, [768, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16367552)))];
            tensor<fp16, [801, 768, 80]> conv1d_119_cast_fp16 = conv(dilations = conv1d_119_dilations_0, groups = conv1d_119_groups_0, pad = conv1d_119_pad_0, pad_type = conv1d_119_pad_type_0, strides = conv1d_119_strides_0, weight = p_net_3_band_net_weight_weight_to_fp16, x = mul_215_cast_fp16)[name = tensor<string, []>("conv1d_119_cast_fp16")];
            tensor<int32, [4]> const_2122 = const()[name = tensor<string, []>("const_2122"), val = tensor<int32, [4]>([-1, 8, 96, 80])];
            tensor<fp16, [801, 8, 96, 80]> view_123_cast_fp16 = reshape(shape = const_2122, x = conv1d_119_cast_fp16)[name = tensor<string, []>("view_123_cast_fp16")];
            tensor<int32, [4]> const_2123 = const()[name = tensor<string, []>("const_2123"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> tile_6 = const()[name = tensor<string, []>("tile_6"), val = tensor<int32, [3]>([32, 32, 32])];
            tensor<int32, []> split_6_axis_0 = const()[name = tensor<string, []>("split_6_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 96]> permute_13_cast_fp16 = transpose(perm = const_2123, x = view_123_cast_fp16)[name = tensor<string, []>("transpose_10")];
            tensor<fp16, [801, 8, 80, 32]> split_6_cast_fp16_0, tensor<fp16, [801, 8, 80, 32]> split_6_cast_fp16_1, tensor<fp16, [801, 8, 80, 32]> split_6_cast_fp16_2 = split(axis = split_6_axis_0, split_sizes = tile_6, x = permute_13_cast_fp16)[name = tensor<string, []>("split_6_cast_fp16")];
            tensor<int32, [3]> const_2131 = const()[name = tensor<string, []>("const_2131"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_124_cast_fp16 = reshape(shape = const_2131, x = split_6_cast_fp16_0)[name = tensor<string, []>("view_124_cast_fp16")];
            tensor<int32, [3]> const_2138 = const()[name = tensor<string, []>("const_2138"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_9_cast_fp16 = reshape(shape = const_2138, x = view_124_cast_fp16)[name = tensor<string, []>("_unsafe_view_9_cast_fp16")];
            tensor<int32, [1]> const_2139 = const()[name = tensor<string, []>("const_2139"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_6_cast_fp16 = reverse(axes = const_2139, x = _unsafe_view_9_cast_fp16)[name = tensor<string, []>("flip_6_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_125_to_fp16 = const()[name = tensor<string, []>("view_125_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_216_cast_fp16 = mul(x = flip_6_cast_fp16, y = view_125_to_fp16)[name = tensor<string, []>("mul_216_cast_fp16")];
            tensor<int32, [3]> const_2141 = const()[name = tensor<string, []>("const_2141"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_126_cast_fp16 = reshape(shape = const_2141, x = mul_216_cast_fp16)[name = tensor<string, []>("view_126_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_217_cast_fp16 = mul(x = view_124_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_217_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_218_cast_fp16 = mul(x = view_126_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_218_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_277_cast_fp16 = add(x = mul_217_cast_fp16, y = mul_218_cast_fp16)[name = tensor<string, []>("add_277_cast_fp16")];
            tensor<int32, [4]> const_2144 = const()[name = tensor<string, []>("const_2144"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_127_cast_fp16 = reshape(shape = const_2144, x = add_277_cast_fp16)[name = tensor<string, []>("view_127_cast_fp16")];
            tensor<int32, [3]> const_2145 = const()[name = tensor<string, []>("const_2145"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_128_cast_fp16 = reshape(shape = const_2145, x = split_6_cast_fp16_1)[name = tensor<string, []>("view_128_cast_fp16")];
            tensor<int32, [3]> const_2152 = const()[name = tensor<string, []>("const_2152"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_10_cast_fp16 = reshape(shape = const_2152, x = view_128_cast_fp16)[name = tensor<string, []>("_unsafe_view_10_cast_fp16")];
            tensor<int32, [1]> const_2153 = const()[name = tensor<string, []>("const_2153"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_7_cast_fp16 = reverse(axes = const_2153, x = _unsafe_view_10_cast_fp16)[name = tensor<string, []>("flip_7_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_129_to_fp16 = const()[name = tensor<string, []>("view_129_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_219_cast_fp16 = mul(x = flip_7_cast_fp16, y = view_129_to_fp16)[name = tensor<string, []>("mul_219_cast_fp16")];
            tensor<int32, [3]> const_2155 = const()[name = tensor<string, []>("const_2155"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_130_cast_fp16 = reshape(shape = const_2155, x = mul_219_cast_fp16)[name = tensor<string, []>("view_130_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_220_cast_fp16 = mul(x = view_128_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_220_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_221_cast_fp16 = mul(x = view_130_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_221_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_278_cast_fp16 = add(x = mul_220_cast_fp16, y = mul_221_cast_fp16)[name = tensor<string, []>("add_278_cast_fp16")];
            tensor<int32, [4]> const_2158 = const()[name = tensor<string, []>("const_2158"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_131_cast_fp16 = reshape(shape = const_2158, x = add_278_cast_fp16)[name = tensor<string, []>("view_131_cast_fp16")];
            tensor<bool, []> matmul_6_transpose_x_1 = const()[name = tensor<string, []>("matmul_6_transpose_x_1"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_6_transpose_y_1 = const()[name = tensor<string, []>("matmul_6_transpose_y_1"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 8, 80, 80]> matmul_6_cast_fp16 = matmul(transpose_x = matmul_6_transpose_x_1, transpose_y = matmul_6_transpose_y_1, x = view_127_cast_fp16, y = view_131_cast_fp16)[name = tensor<string, []>("matmul_6_cast_fp16")];
            tensor<fp16, []> const_2161_to_fp16 = const()[name = tensor<string, []>("const_2161_to_fp16"), val = tensor<fp16, []>(0x1.6ap-3)];
            tensor<fp16, [801, 8, 80, 80]> mul_222_cast_fp16 = mul(x = matmul_6_cast_fp16, y = const_2161_to_fp16)[name = tensor<string, []>("mul_222_cast_fp16")];
            tensor<int32, []> const_2162 = const()[name = tensor<string, []>("const_2162"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 80]> softmax_3_cast_fp16 = softmax(axis = const_2162, x = mul_222_cast_fp16)[name = tensor<string, []>("softmax_3_cast_fp16")];
            tensor<bool, []> matmul_7_transpose_x_0 = const()[name = tensor<string, []>("matmul_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_7_transpose_y_0 = const()[name = tensor<string, []>("matmul_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [801, 8, 80, 32]> matmul_7_cast_fp16 = matmul(transpose_x = matmul_7_transpose_x_0, transpose_y = matmul_7_transpose_y_0, x = softmax_3_cast_fp16, y = split_6_cast_fp16_2)[name = tensor<string, []>("matmul_7_cast_fp16")];
            tensor<int32, [4]> const_2163 = const()[name = tensor<string, []>("const_2163"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> const_2164 = const()[name = tensor<string, []>("const_2164"), val = tensor<int32, [3]>([801, 256, 80])];
            tensor<fp16, [801, 8, 32, 80]> permute_14_cast_fp16 = transpose(perm = const_2163, x = matmul_7_cast_fp16)[name = tensor<string, []>("transpose_9")];
            tensor<fp16, [801, 256, 80]> _unsafe_view_11_cast_fp16 = reshape(shape = const_2164, x = permute_14_cast_fp16)[name = tensor<string, []>("_unsafe_view_11_cast_fp16")];
            tensor<string, []> conv1d_120_pad_type_0 = const()[name = tensor<string, []>("conv1d_120_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_120_strides_0 = const()[name = tensor<string, []>("conv1d_120_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_120_pad_0 = const()[name = tensor<string, []>("conv1d_120_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_120_dilations_0 = const()[name = tensor<string, []>("conv1d_120_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_120_groups_0 = const()[name = tensor<string, []>("conv1d_120_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1]> p_net_3_band_net_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_band_net_output_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16760832)))];
            tensor<fp16, [801, 256, 80]> conv1d_120_cast_fp16 = conv(dilations = conv1d_120_dilations_0, groups = conv1d_120_groups_0, pad = conv1d_120_pad_0, pad_type = conv1d_120_pad_type_0, strides = conv1d_120_strides_0, weight = p_net_3_band_net_output_weight_to_fp16, x = _unsafe_view_11_cast_fp16)[name = tensor<string, []>("conv1d_120_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_279_cast_fp16 = add(x = conv1d_120_cast_fp16, y = permute_12_cast_fp16)[name = tensor<string, []>("add_279_cast_fp16")];
            tensor<int32, [1]> unsqueeze_212_axes_0 = const()[name = tensor<string, []>("unsqueeze_212_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_212_cast_fp16 = expand_dims(axes = unsqueeze_212_axes_0, x = add_279_cast_fp16)[name = tensor<string, []>("unsqueeze_212_cast_fp16")];
            tensor<fp16, []> const_2169_promoted_to_fp16 = const()[name = tensor<string, []>("const_2169_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_257_cast_fp16 = pow(x = unsqueeze_212_cast_fp16, y = const_2169_promoted_to_fp16)[name = tensor<string, []>("pow_257_cast_fp16")];
            tensor<int32, [1]> mean_96_axes_0 = const()[name = tensor<string, []>("mean_96_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_96_keep_dims_0 = const()[name = tensor<string, []>("mean_96_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_96_cast_fp16 = reduce_mean(axes = mean_96_axes_0, keep_dims = mean_96_keep_dims_0, x = pow_257_cast_fp16)[name = tensor<string, []>("mean_96_cast_fp16")];
            tensor<fp16, []> const_2172_to_fp16 = const()[name = tensor<string, []>("const_2172_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_280_cast_fp16 = add(x = mean_96_cast_fp16, y = const_2172_to_fp16)[name = tensor<string, []>("add_280_cast_fp16")];
            tensor<fp32, []> rsqrt_96_epsilon_0 = const()[name = tensor<string, []>("rsqrt_96_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_96_cast_fp16 = rsqrt(epsilon = rsqrt_96_epsilon_0, x = add_280_cast_fp16)[name = tensor<string, []>("rsqrt_96_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_223_cast_fp16 = mul(x = unsqueeze_212_cast_fp16, y = rsqrt_96_cast_fp16)[name = tensor<string, []>("mul_223_cast_fp16")];
            tensor<int32, [1]> squeeze_100_axes_0 = const()[name = tensor<string, []>("squeeze_100_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_100_cast_fp16 = squeeze(axes = squeeze_100_axes_0, x = mul_223_cast_fp16)[name = tensor<string, []>("squeeze_100_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_132_to_fp16 = const()[name = tensor<string, []>("view_132_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16891968)))];
            tensor<fp16, [801, 256, 80]> mul_224_cast_fp16 = mul(x = squeeze_100_cast_fp16, y = view_132_to_fp16)[name = tensor<string, []>("mul_224_cast_fp16")];
            tensor<string, []> conv1d_121_pad_type_0 = const()[name = tensor<string, []>("conv1d_121_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_121_strides_0 = const()[name = tensor<string, []>("conv1d_121_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_121_pad_0 = const()[name = tensor<string, []>("conv1d_121_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_121_dilations_0 = const()[name = tensor<string, []>("conv1d_121_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_121_groups_0 = const()[name = tensor<string, []>("conv1d_121_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 256, 1]> p_net_3_band_net_mlp_1_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_band_net_mlp_1_weight_to_fp16"), val = tensor<fp16, [2048, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16892544)))];
            tensor<fp16, [801, 2048, 80]> conv1d_121_cast_fp16 = conv(dilations = conv1d_121_dilations_0, groups = conv1d_121_groups_0, pad = conv1d_121_pad_0, pad_type = conv1d_121_pad_type_0, strides = conv1d_121_strides_0, weight = p_net_3_band_net_mlp_1_weight_to_fp16, x = mul_224_cast_fp16)[name = tensor<string, []>("conv1d_121_cast_fp16")];
            tensor<fp16, [801, 2048, 80]> silu_15_cast_fp16 = silu(x = conv1d_121_cast_fp16)[name = tensor<string, []>("silu_15_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = tensor<string, []>("tile_7"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> split_7_axis_0 = const()[name = tensor<string, []>("split_7_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [801, 1024, 80]> split_7_cast_fp16_0, tensor<fp16, [801, 1024, 80]> split_7_cast_fp16_1 = split(axis = split_7_axis_0, split_sizes = tile_7, x = silu_15_cast_fp16)[name = tensor<string, []>("split_7_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> silu_16_cast_fp16 = silu(x = split_7_cast_fp16_0)[name = tensor<string, []>("silu_16_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> mul_225_cast_fp16 = mul(x = silu_16_cast_fp16, y = split_7_cast_fp16_1)[name = tensor<string, []>("mul_225_cast_fp16")];
            tensor<string, []> conv1d_122_pad_type_0 = const()[name = tensor<string, []>("conv1d_122_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_122_strides_0 = const()[name = tensor<string, []>("conv1d_122_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_122_pad_0 = const()[name = tensor<string, []>("conv1d_122_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_122_dilations_0 = const()[name = tensor<string, []>("conv1d_122_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_122_groups_0 = const()[name = tensor<string, []>("conv1d_122_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_3_band_net_mlp_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_band_net_mlp_output_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17941184)))];
            tensor<fp16, [801, 256, 80]> conv1d_122_cast_fp16 = conv(dilations = conv1d_122_dilations_0, groups = conv1d_122_groups_0, pad = conv1d_122_pad_0, pad_type = conv1d_122_pad_type_0, strides = conv1d_122_strides_0, weight = p_net_3_band_net_mlp_output_weight_to_fp16, x = mul_225_cast_fp16)[name = tensor<string, []>("conv1d_122_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_281_cast_fp16 = add(x = add_279_cast_fp16, y = conv1d_122_cast_fp16)[name = tensor<string, []>("add_281_cast_fp16")];
            tensor<int32, [3]> const_2181 = const()[name = tensor<string, []>("const_2181"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<string, []> conv1d_123_pad_type_0 = const()[name = tensor<string, []>("conv1d_123_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_123_pad_0 = const()[name = tensor<string, []>("conv1d_123_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_123_groups_0 = const()[name = tensor<string, []>("conv1d_123_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_123_strides_0 = const()[name = tensor<string, []>("conv1d_123_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_123_dilations_0 = const()[name = tensor<string, []>("conv1d_123_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_3_seq_net_blocks_0_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_0_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18465536)))];
            tensor<fp16, [256]> p_net_3_seq_net_blocks_0_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_0_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18469184)))];
            tensor<fp16, [80, 256, 801]> permute_15_cast_fp16 = transpose(perm = const_2181, x = add_281_cast_fp16)[name = tensor<string, []>("transpose_8")];
            tensor<fp16, [80, 256, 801]> conv1d_123_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_0_conv_0_bias_to_fp16, dilations = conv1d_123_dilations_0, groups = conv1d_123_groups_0, pad = conv1d_123_pad_0, pad_type = conv1d_123_pad_type_0, strides = conv1d_123_strides_0, weight = p_net_3_seq_net_blocks_0_conv_0_weight_to_fp16, x = permute_15_cast_fp16)[name = tensor<string, []>("conv1d_123_cast_fp16")];
            tensor<int32, [1]> unsqueeze_214_axes_0 = const()[name = tensor<string, []>("unsqueeze_214_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_214_cast_fp16 = expand_dims(axes = unsqueeze_214_axes_0, x = conv1d_123_cast_fp16)[name = tensor<string, []>("unsqueeze_214_cast_fp16")];
            tensor<fp16, []> const_2190_promoted_to_fp16 = const()[name = tensor<string, []>("const_2190_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_258_cast_fp16 = pow(x = unsqueeze_214_cast_fp16, y = const_2190_promoted_to_fp16)[name = tensor<string, []>("pow_258_cast_fp16")];
            tensor<int32, [1]> mean_97_axes_0 = const()[name = tensor<string, []>("mean_97_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_97_keep_dims_0 = const()[name = tensor<string, []>("mean_97_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_97_cast_fp16 = reduce_mean(axes = mean_97_axes_0, keep_dims = mean_97_keep_dims_0, x = pow_258_cast_fp16)[name = tensor<string, []>("mean_97_cast_fp16")];
            tensor<fp16, []> const_2193_to_fp16 = const()[name = tensor<string, []>("const_2193_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_282_cast_fp16 = add(x = mean_97_cast_fp16, y = const_2193_to_fp16)[name = tensor<string, []>("add_282_cast_fp16")];
            tensor<fp32, []> rsqrt_97_epsilon_0 = const()[name = tensor<string, []>("rsqrt_97_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_97_cast_fp16 = rsqrt(epsilon = rsqrt_97_epsilon_0, x = add_282_cast_fp16)[name = tensor<string, []>("rsqrt_97_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_226_cast_fp16 = mul(x = unsqueeze_214_cast_fp16, y = rsqrt_97_cast_fp16)[name = tensor<string, []>("mul_226_cast_fp16")];
            tensor<int32, [1]> squeeze_101_axes_0 = const()[name = tensor<string, []>("squeeze_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_101_cast_fp16 = squeeze(axes = squeeze_101_axes_0, x = mul_226_cast_fp16)[name = tensor<string, []>("squeeze_101_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_133_to_fp16 = const()[name = tensor<string, []>("view_133_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18469760)))];
            tensor<fp16, [80, 256, 801]> mul_227_cast_fp16 = mul(x = squeeze_101_cast_fp16, y = view_133_to_fp16)[name = tensor<string, []>("mul_227_cast_fp16")];
            tensor<string, []> conv1d_124_pad_type_0 = const()[name = tensor<string, []>("conv1d_124_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_124_strides_0 = const()[name = tensor<string, []>("conv1d_124_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_124_pad_0 = const()[name = tensor<string, []>("conv1d_124_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_124_dilations_0 = const()[name = tensor<string, []>("conv1d_124_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_124_groups_0 = const()[name = tensor<string, []>("conv1d_124_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_3_seq_net_blocks_0_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_0_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18470336)))];
            tensor<fp16, [1024]> p_net_3_seq_net_blocks_0_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_0_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18994688)))];
            tensor<fp16, [80, 1024, 801]> conv1d_124_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_0_conv_2_bias_to_fp16, dilations = conv1d_124_dilations_0, groups = conv1d_124_groups_0, pad = conv1d_124_pad_0, pad_type = conv1d_124_pad_type_0, strides = conv1d_124_strides_0, weight = p_net_3_seq_net_blocks_0_conv_2_weight_to_fp16, x = mul_227_cast_fp16)[name = tensor<string, []>("conv1d_124_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_17_cast_fp16 = silu(x = conv1d_124_cast_fp16)[name = tensor<string, []>("silu_17_cast_fp16")];
            tensor<string, []> conv1d_125_pad_type_0 = const()[name = tensor<string, []>("conv1d_125_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_125_strides_0 = const()[name = tensor<string, []>("conv1d_125_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_125_pad_0 = const()[name = tensor<string, []>("conv1d_125_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_125_dilations_0 = const()[name = tensor<string, []>("conv1d_125_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_125_groups_0 = const()[name = tensor<string, []>("conv1d_125_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_3_seq_net_blocks_0_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_0_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18996800)))];
            tensor<fp16, [256]> p_net_3_seq_net_blocks_0_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_0_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19521152)))];
            tensor<fp16, [80, 256, 801]> conv1d_125_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_0_conv_4_bias_to_fp16, dilations = conv1d_125_dilations_0, groups = conv1d_125_groups_0, pad = conv1d_125_pad_0, pad_type = conv1d_125_pad_type_0, strides = conv1d_125_strides_0, weight = p_net_3_seq_net_blocks_0_conv_4_weight_to_fp16, x = silu_17_cast_fp16)[name = tensor<string, []>("conv1d_125_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_283_cast_fp16 = add(x = permute_15_cast_fp16, y = conv1d_125_cast_fp16)[name = tensor<string, []>("add_283_cast_fp16")];
            tensor<string, []> conv1d_126_pad_type_0 = const()[name = tensor<string, []>("conv1d_126_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_126_pad_0 = const()[name = tensor<string, []>("conv1d_126_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_126_groups_0 = const()[name = tensor<string, []>("conv1d_126_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_126_strides_0 = const()[name = tensor<string, []>("conv1d_126_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_126_dilations_0 = const()[name = tensor<string, []>("conv1d_126_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_3_seq_net_blocks_1_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_1_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19521728)))];
            tensor<fp16, [256]> p_net_3_seq_net_blocks_1_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_1_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19525376)))];
            tensor<fp16, [80, 256, 801]> conv1d_126_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_1_conv_0_bias_to_fp16, dilations = conv1d_126_dilations_0, groups = conv1d_126_groups_0, pad = conv1d_126_pad_0, pad_type = conv1d_126_pad_type_0, strides = conv1d_126_strides_0, weight = p_net_3_seq_net_blocks_1_conv_0_weight_to_fp16, x = add_283_cast_fp16)[name = tensor<string, []>("conv1d_126_cast_fp16")];
            tensor<int32, [1]> unsqueeze_216_axes_0 = const()[name = tensor<string, []>("unsqueeze_216_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_216_cast_fp16 = expand_dims(axes = unsqueeze_216_axes_0, x = conv1d_126_cast_fp16)[name = tensor<string, []>("unsqueeze_216_cast_fp16")];
            tensor<fp16, []> const_2204_promoted_to_fp16 = const()[name = tensor<string, []>("const_2204_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_259_cast_fp16 = pow(x = unsqueeze_216_cast_fp16, y = const_2204_promoted_to_fp16)[name = tensor<string, []>("pow_259_cast_fp16")];
            tensor<int32, [1]> mean_98_axes_0 = const()[name = tensor<string, []>("mean_98_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_98_keep_dims_0 = const()[name = tensor<string, []>("mean_98_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_98_cast_fp16 = reduce_mean(axes = mean_98_axes_0, keep_dims = mean_98_keep_dims_0, x = pow_259_cast_fp16)[name = tensor<string, []>("mean_98_cast_fp16")];
            tensor<fp16, []> const_2207_to_fp16 = const()[name = tensor<string, []>("const_2207_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_284_cast_fp16 = add(x = mean_98_cast_fp16, y = const_2207_to_fp16)[name = tensor<string, []>("add_284_cast_fp16")];
            tensor<fp32, []> rsqrt_98_epsilon_0 = const()[name = tensor<string, []>("rsqrt_98_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_98_cast_fp16 = rsqrt(epsilon = rsqrt_98_epsilon_0, x = add_284_cast_fp16)[name = tensor<string, []>("rsqrt_98_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_228_cast_fp16 = mul(x = unsqueeze_216_cast_fp16, y = rsqrt_98_cast_fp16)[name = tensor<string, []>("mul_228_cast_fp16")];
            tensor<int32, [1]> squeeze_102_axes_0 = const()[name = tensor<string, []>("squeeze_102_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_102_cast_fp16 = squeeze(axes = squeeze_102_axes_0, x = mul_228_cast_fp16)[name = tensor<string, []>("squeeze_102_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_134_to_fp16 = const()[name = tensor<string, []>("view_134_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19525952)))];
            tensor<fp16, [80, 256, 801]> mul_229_cast_fp16 = mul(x = squeeze_102_cast_fp16, y = view_134_to_fp16)[name = tensor<string, []>("mul_229_cast_fp16")];
            tensor<string, []> conv1d_127_pad_type_0 = const()[name = tensor<string, []>("conv1d_127_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_127_strides_0 = const()[name = tensor<string, []>("conv1d_127_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_127_pad_0 = const()[name = tensor<string, []>("conv1d_127_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_127_dilations_0 = const()[name = tensor<string, []>("conv1d_127_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_127_groups_0 = const()[name = tensor<string, []>("conv1d_127_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_3_seq_net_blocks_1_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_1_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19526528)))];
            tensor<fp16, [1024]> p_net_3_seq_net_blocks_1_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_1_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20050880)))];
            tensor<fp16, [80, 1024, 801]> conv1d_127_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_1_conv_2_bias_to_fp16, dilations = conv1d_127_dilations_0, groups = conv1d_127_groups_0, pad = conv1d_127_pad_0, pad_type = conv1d_127_pad_type_0, strides = conv1d_127_strides_0, weight = p_net_3_seq_net_blocks_1_conv_2_weight_to_fp16, x = mul_229_cast_fp16)[name = tensor<string, []>("conv1d_127_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_18_cast_fp16 = silu(x = conv1d_127_cast_fp16)[name = tensor<string, []>("silu_18_cast_fp16")];
            tensor<string, []> conv1d_128_pad_type_0 = const()[name = tensor<string, []>("conv1d_128_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_128_strides_0 = const()[name = tensor<string, []>("conv1d_128_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_128_pad_0 = const()[name = tensor<string, []>("conv1d_128_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_128_dilations_0 = const()[name = tensor<string, []>("conv1d_128_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_128_groups_0 = const()[name = tensor<string, []>("conv1d_128_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_3_seq_net_blocks_1_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_1_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20052992)))];
            tensor<fp16, [256]> p_net_3_seq_net_blocks_1_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_1_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20577344)))];
            tensor<fp16, [80, 256, 801]> conv1d_128_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_1_conv_4_bias_to_fp16, dilations = conv1d_128_dilations_0, groups = conv1d_128_groups_0, pad = conv1d_128_pad_0, pad_type = conv1d_128_pad_type_0, strides = conv1d_128_strides_0, weight = p_net_3_seq_net_blocks_1_conv_4_weight_to_fp16, x = silu_18_cast_fp16)[name = tensor<string, []>("conv1d_128_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_285_cast_fp16 = add(x = add_283_cast_fp16, y = conv1d_128_cast_fp16)[name = tensor<string, []>("add_285_cast_fp16")];
            tensor<string, []> conv1d_129_pad_type_0 = const()[name = tensor<string, []>("conv1d_129_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_129_pad_0 = const()[name = tensor<string, []>("conv1d_129_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_129_groups_0 = const()[name = tensor<string, []>("conv1d_129_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_129_strides_0 = const()[name = tensor<string, []>("conv1d_129_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_129_dilations_0 = const()[name = tensor<string, []>("conv1d_129_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_3_seq_net_blocks_2_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_2_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20577920)))];
            tensor<fp16, [256]> p_net_3_seq_net_blocks_2_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_2_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20581568)))];
            tensor<fp16, [80, 256, 801]> conv1d_129_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_2_conv_0_bias_to_fp16, dilations = conv1d_129_dilations_0, groups = conv1d_129_groups_0, pad = conv1d_129_pad_0, pad_type = conv1d_129_pad_type_0, strides = conv1d_129_strides_0, weight = p_net_3_seq_net_blocks_2_conv_0_weight_to_fp16, x = add_285_cast_fp16)[name = tensor<string, []>("conv1d_129_cast_fp16")];
            tensor<int32, [1]> unsqueeze_218_axes_0 = const()[name = tensor<string, []>("unsqueeze_218_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_218_cast_fp16 = expand_dims(axes = unsqueeze_218_axes_0, x = conv1d_129_cast_fp16)[name = tensor<string, []>("unsqueeze_218_cast_fp16")];
            tensor<fp16, []> const_2218_promoted_to_fp16 = const()[name = tensor<string, []>("const_2218_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_260_cast_fp16 = pow(x = unsqueeze_218_cast_fp16, y = const_2218_promoted_to_fp16)[name = tensor<string, []>("pow_260_cast_fp16")];
            tensor<int32, [1]> mean_99_axes_0 = const()[name = tensor<string, []>("mean_99_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_99_keep_dims_0 = const()[name = tensor<string, []>("mean_99_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_99_cast_fp16 = reduce_mean(axes = mean_99_axes_0, keep_dims = mean_99_keep_dims_0, x = pow_260_cast_fp16)[name = tensor<string, []>("mean_99_cast_fp16")];
            tensor<fp16, []> const_2221_to_fp16 = const()[name = tensor<string, []>("const_2221_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_286_cast_fp16 = add(x = mean_99_cast_fp16, y = const_2221_to_fp16)[name = tensor<string, []>("add_286_cast_fp16")];
            tensor<fp32, []> rsqrt_99_epsilon_0 = const()[name = tensor<string, []>("rsqrt_99_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_99_cast_fp16 = rsqrt(epsilon = rsqrt_99_epsilon_0, x = add_286_cast_fp16)[name = tensor<string, []>("rsqrt_99_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_230_cast_fp16 = mul(x = unsqueeze_218_cast_fp16, y = rsqrt_99_cast_fp16)[name = tensor<string, []>("mul_230_cast_fp16")];
            tensor<int32, [1]> squeeze_103_axes_0 = const()[name = tensor<string, []>("squeeze_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_103_cast_fp16 = squeeze(axes = squeeze_103_axes_0, x = mul_230_cast_fp16)[name = tensor<string, []>("squeeze_103_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_135_to_fp16 = const()[name = tensor<string, []>("view_135_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20582144)))];
            tensor<fp16, [80, 256, 801]> mul_231_cast_fp16 = mul(x = squeeze_103_cast_fp16, y = view_135_to_fp16)[name = tensor<string, []>("mul_231_cast_fp16")];
            tensor<string, []> conv1d_130_pad_type_0 = const()[name = tensor<string, []>("conv1d_130_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_130_strides_0 = const()[name = tensor<string, []>("conv1d_130_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_130_pad_0 = const()[name = tensor<string, []>("conv1d_130_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_130_dilations_0 = const()[name = tensor<string, []>("conv1d_130_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_130_groups_0 = const()[name = tensor<string, []>("conv1d_130_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_3_seq_net_blocks_2_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_2_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20582720)))];
            tensor<fp16, [1024]> p_net_3_seq_net_blocks_2_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_2_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21107072)))];
            tensor<fp16, [80, 1024, 801]> conv1d_130_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_2_conv_2_bias_to_fp16, dilations = conv1d_130_dilations_0, groups = conv1d_130_groups_0, pad = conv1d_130_pad_0, pad_type = conv1d_130_pad_type_0, strides = conv1d_130_strides_0, weight = p_net_3_seq_net_blocks_2_conv_2_weight_to_fp16, x = mul_231_cast_fp16)[name = tensor<string, []>("conv1d_130_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_19_cast_fp16 = silu(x = conv1d_130_cast_fp16)[name = tensor<string, []>("silu_19_cast_fp16")];
            tensor<string, []> conv1d_131_pad_type_0 = const()[name = tensor<string, []>("conv1d_131_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_131_strides_0 = const()[name = tensor<string, []>("conv1d_131_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_131_pad_0 = const()[name = tensor<string, []>("conv1d_131_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_131_dilations_0 = const()[name = tensor<string, []>("conv1d_131_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_131_groups_0 = const()[name = tensor<string, []>("conv1d_131_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_3_seq_net_blocks_2_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_2_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21109184)))];
            tensor<fp16, [256]> p_net_3_seq_net_blocks_2_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_3_seq_net_blocks_2_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21633536)))];
            tensor<fp16, [80, 256, 801]> conv1d_131_cast_fp16 = conv(bias = p_net_3_seq_net_blocks_2_conv_4_bias_to_fp16, dilations = conv1d_131_dilations_0, groups = conv1d_131_groups_0, pad = conv1d_131_pad_0, pad_type = conv1d_131_pad_type_0, strides = conv1d_131_strides_0, weight = p_net_3_seq_net_blocks_2_conv_4_weight_to_fp16, x = silu_19_cast_fp16)[name = tensor<string, []>("conv1d_131_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_287_cast_fp16 = add(x = add_285_cast_fp16, y = conv1d_131_cast_fp16)[name = tensor<string, []>("add_287_cast_fp16")];
            tensor<int32, [1]> unsqueeze_219_axes_0 = const()[name = tensor<string, []>("unsqueeze_219_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 80, 256, 801]> unsqueeze_219_cast_fp16 = expand_dims(axes = unsqueeze_219_axes_0, x = add_287_cast_fp16)[name = tensor<string, []>("unsqueeze_219_cast_fp16")];
            tensor<int32, [1]> squeeze_104_axes_0 = const()[name = tensor<string, []>("squeeze_104_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [80, 256, 801]> squeeze_104_cast_fp16 = squeeze(axes = squeeze_104_axes_0, x = unsqueeze_219_cast_fp16)[name = tensor<string, []>("squeeze_104_cast_fp16")];
            tensor<int32, [3]> const_2226 = const()[name = tensor<string, []>("const_2226"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<int32, [1]> unsqueeze_221_axes_0 = const()[name = tensor<string, []>("unsqueeze_221_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> permute_16_cast_fp16 = transpose(perm = const_2226, x = squeeze_104_cast_fp16)[name = tensor<string, []>("transpose_7")];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_221_cast_fp16 = expand_dims(axes = unsqueeze_221_axes_0, x = permute_16_cast_fp16)[name = tensor<string, []>("unsqueeze_221_cast_fp16")];
            tensor<fp16, []> const_2231_promoted_to_fp16 = const()[name = tensor<string, []>("const_2231_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_261_cast_fp16 = pow(x = unsqueeze_221_cast_fp16, y = const_2231_promoted_to_fp16)[name = tensor<string, []>("pow_261_cast_fp16")];
            tensor<int32, [1]> mean_100_axes_0 = const()[name = tensor<string, []>("mean_100_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_100_keep_dims_0 = const()[name = tensor<string, []>("mean_100_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_100_cast_fp16 = reduce_mean(axes = mean_100_axes_0, keep_dims = mean_100_keep_dims_0, x = pow_261_cast_fp16)[name = tensor<string, []>("mean_100_cast_fp16")];
            tensor<fp16, []> const_2234_to_fp16 = const()[name = tensor<string, []>("const_2234_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_288_cast_fp16 = add(x = mean_100_cast_fp16, y = const_2234_to_fp16)[name = tensor<string, []>("add_288_cast_fp16")];
            tensor<fp32, []> rsqrt_100_epsilon_0 = const()[name = tensor<string, []>("rsqrt_100_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_100_cast_fp16 = rsqrt(epsilon = rsqrt_100_epsilon_0, x = add_288_cast_fp16)[name = tensor<string, []>("rsqrt_100_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_232_cast_fp16 = mul(x = unsqueeze_221_cast_fp16, y = rsqrt_100_cast_fp16)[name = tensor<string, []>("mul_232_cast_fp16")];
            tensor<int32, [1]> squeeze_105_axes_0 = const()[name = tensor<string, []>("squeeze_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_105_cast_fp16 = squeeze(axes = squeeze_105_axes_0, x = mul_232_cast_fp16)[name = tensor<string, []>("squeeze_105_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_136_to_fp16 = const()[name = tensor<string, []>("view_136_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21634112)))];
            tensor<fp16, [801, 256, 80]> mul_233_cast_fp16 = mul(x = squeeze_105_cast_fp16, y = view_136_to_fp16)[name = tensor<string, []>("mul_233_cast_fp16")];
            tensor<string, []> conv1d_132_pad_type_0 = const()[name = tensor<string, []>("conv1d_132_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_132_strides_0 = const()[name = tensor<string, []>("conv1d_132_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_132_pad_0 = const()[name = tensor<string, []>("conv1d_132_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_132_dilations_0 = const()[name = tensor<string, []>("conv1d_132_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_132_groups_0 = const()[name = tensor<string, []>("conv1d_132_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [768, 256, 1]> p_net_4_band_net_weight_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_band_net_weight_weight_to_fp16"), val = tensor<fp16, [768, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21634688)))];
            tensor<fp16, [801, 768, 80]> conv1d_132_cast_fp16 = conv(dilations = conv1d_132_dilations_0, groups = conv1d_132_groups_0, pad = conv1d_132_pad_0, pad_type = conv1d_132_pad_type_0, strides = conv1d_132_strides_0, weight = p_net_4_band_net_weight_weight_to_fp16, x = mul_233_cast_fp16)[name = tensor<string, []>("conv1d_132_cast_fp16")];
            tensor<int32, [4]> const_2237 = const()[name = tensor<string, []>("const_2237"), val = tensor<int32, [4]>([-1, 8, 96, 80])];
            tensor<fp16, [801, 8, 96, 80]> view_137_cast_fp16 = reshape(shape = const_2237, x = conv1d_132_cast_fp16)[name = tensor<string, []>("view_137_cast_fp16")];
            tensor<int32, [4]> const_2238 = const()[name = tensor<string, []>("const_2238"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> tile_8 = const()[name = tensor<string, []>("tile_8"), val = tensor<int32, [3]>([32, 32, 32])];
            tensor<int32, []> split_8_axis_0 = const()[name = tensor<string, []>("split_8_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 96]> permute_17_cast_fp16 = transpose(perm = const_2238, x = view_137_cast_fp16)[name = tensor<string, []>("transpose_6")];
            tensor<fp16, [801, 8, 80, 32]> split_8_cast_fp16_0, tensor<fp16, [801, 8, 80, 32]> split_8_cast_fp16_1, tensor<fp16, [801, 8, 80, 32]> split_8_cast_fp16_2 = split(axis = split_8_axis_0, split_sizes = tile_8, x = permute_17_cast_fp16)[name = tensor<string, []>("split_8_cast_fp16")];
            tensor<int32, [3]> const_2246 = const()[name = tensor<string, []>("const_2246"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_138_cast_fp16 = reshape(shape = const_2246, x = split_8_cast_fp16_0)[name = tensor<string, []>("view_138_cast_fp16")];
            tensor<int32, [3]> const_2253 = const()[name = tensor<string, []>("const_2253"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_12_cast_fp16 = reshape(shape = const_2253, x = view_138_cast_fp16)[name = tensor<string, []>("_unsafe_view_12_cast_fp16")];
            tensor<int32, [1]> const_2254 = const()[name = tensor<string, []>("const_2254"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_8_cast_fp16 = reverse(axes = const_2254, x = _unsafe_view_12_cast_fp16)[name = tensor<string, []>("flip_8_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_139_to_fp16 = const()[name = tensor<string, []>("view_139_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_234_cast_fp16 = mul(x = flip_8_cast_fp16, y = view_139_to_fp16)[name = tensor<string, []>("mul_234_cast_fp16")];
            tensor<int32, [3]> const_2256 = const()[name = tensor<string, []>("const_2256"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_140_cast_fp16 = reshape(shape = const_2256, x = mul_234_cast_fp16)[name = tensor<string, []>("view_140_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_235_cast_fp16 = mul(x = view_138_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_235_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_236_cast_fp16 = mul(x = view_140_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_236_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_289_cast_fp16 = add(x = mul_235_cast_fp16, y = mul_236_cast_fp16)[name = tensor<string, []>("add_289_cast_fp16")];
            tensor<int32, [4]> const_2259 = const()[name = tensor<string, []>("const_2259"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_141_cast_fp16 = reshape(shape = const_2259, x = add_289_cast_fp16)[name = tensor<string, []>("view_141_cast_fp16")];
            tensor<int32, [3]> const_2260 = const()[name = tensor<string, []>("const_2260"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_142_cast_fp16 = reshape(shape = const_2260, x = split_8_cast_fp16_1)[name = tensor<string, []>("view_142_cast_fp16")];
            tensor<int32, [3]> const_2267 = const()[name = tensor<string, []>("const_2267"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_13_cast_fp16 = reshape(shape = const_2267, x = view_142_cast_fp16)[name = tensor<string, []>("_unsafe_view_13_cast_fp16")];
            tensor<int32, [1]> const_2268 = const()[name = tensor<string, []>("const_2268"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_9_cast_fp16 = reverse(axes = const_2268, x = _unsafe_view_13_cast_fp16)[name = tensor<string, []>("flip_9_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_143_to_fp16 = const()[name = tensor<string, []>("view_143_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_237_cast_fp16 = mul(x = flip_9_cast_fp16, y = view_143_to_fp16)[name = tensor<string, []>("mul_237_cast_fp16")];
            tensor<int32, [3]> const_2270 = const()[name = tensor<string, []>("const_2270"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_144_cast_fp16 = reshape(shape = const_2270, x = mul_237_cast_fp16)[name = tensor<string, []>("view_144_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_238_cast_fp16 = mul(x = view_142_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_238_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_239_cast_fp16 = mul(x = view_144_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_239_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_290_cast_fp16 = add(x = mul_238_cast_fp16, y = mul_239_cast_fp16)[name = tensor<string, []>("add_290_cast_fp16")];
            tensor<int32, [4]> const_2273 = const()[name = tensor<string, []>("const_2273"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_145_cast_fp16 = reshape(shape = const_2273, x = add_290_cast_fp16)[name = tensor<string, []>("view_145_cast_fp16")];
            tensor<bool, []> matmul_8_transpose_x_1 = const()[name = tensor<string, []>("matmul_8_transpose_x_1"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_8_transpose_y_1 = const()[name = tensor<string, []>("matmul_8_transpose_y_1"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 8, 80, 80]> matmul_8_cast_fp16 = matmul(transpose_x = matmul_8_transpose_x_1, transpose_y = matmul_8_transpose_y_1, x = view_141_cast_fp16, y = view_145_cast_fp16)[name = tensor<string, []>("matmul_8_cast_fp16")];
            tensor<fp16, []> const_2276_to_fp16 = const()[name = tensor<string, []>("const_2276_to_fp16"), val = tensor<fp16, []>(0x1.6ap-3)];
            tensor<fp16, [801, 8, 80, 80]> mul_240_cast_fp16 = mul(x = matmul_8_cast_fp16, y = const_2276_to_fp16)[name = tensor<string, []>("mul_240_cast_fp16")];
            tensor<int32, []> const_2277 = const()[name = tensor<string, []>("const_2277"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 80]> softmax_4_cast_fp16 = softmax(axis = const_2277, x = mul_240_cast_fp16)[name = tensor<string, []>("softmax_4_cast_fp16")];
            tensor<bool, []> matmul_9_transpose_x_0 = const()[name = tensor<string, []>("matmul_9_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_9_transpose_y_0 = const()[name = tensor<string, []>("matmul_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [801, 8, 80, 32]> matmul_9_cast_fp16 = matmul(transpose_x = matmul_9_transpose_x_0, transpose_y = matmul_9_transpose_y_0, x = softmax_4_cast_fp16, y = split_8_cast_fp16_2)[name = tensor<string, []>("matmul_9_cast_fp16")];
            tensor<int32, [4]> const_2278 = const()[name = tensor<string, []>("const_2278"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> const_2279 = const()[name = tensor<string, []>("const_2279"), val = tensor<int32, [3]>([801, 256, 80])];
            tensor<fp16, [801, 8, 32, 80]> permute_18_cast_fp16 = transpose(perm = const_2278, x = matmul_9_cast_fp16)[name = tensor<string, []>("transpose_5")];
            tensor<fp16, [801, 256, 80]> _unsafe_view_14_cast_fp16 = reshape(shape = const_2279, x = permute_18_cast_fp16)[name = tensor<string, []>("_unsafe_view_14_cast_fp16")];
            tensor<string, []> conv1d_133_pad_type_0 = const()[name = tensor<string, []>("conv1d_133_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_133_strides_0 = const()[name = tensor<string, []>("conv1d_133_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_133_pad_0 = const()[name = tensor<string, []>("conv1d_133_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_133_dilations_0 = const()[name = tensor<string, []>("conv1d_133_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_133_groups_0 = const()[name = tensor<string, []>("conv1d_133_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1]> p_net_4_band_net_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_band_net_output_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22027968)))];
            tensor<fp16, [801, 256, 80]> conv1d_133_cast_fp16 = conv(dilations = conv1d_133_dilations_0, groups = conv1d_133_groups_0, pad = conv1d_133_pad_0, pad_type = conv1d_133_pad_type_0, strides = conv1d_133_strides_0, weight = p_net_4_band_net_output_weight_to_fp16, x = _unsafe_view_14_cast_fp16)[name = tensor<string, []>("conv1d_133_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_291_cast_fp16 = add(x = conv1d_133_cast_fp16, y = permute_16_cast_fp16)[name = tensor<string, []>("add_291_cast_fp16")];
            tensor<int32, [1]> unsqueeze_227_axes_0 = const()[name = tensor<string, []>("unsqueeze_227_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_227_cast_fp16 = expand_dims(axes = unsqueeze_227_axes_0, x = add_291_cast_fp16)[name = tensor<string, []>("unsqueeze_227_cast_fp16")];
            tensor<fp16, []> const_2284_promoted_to_fp16 = const()[name = tensor<string, []>("const_2284_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_262_cast_fp16 = pow(x = unsqueeze_227_cast_fp16, y = const_2284_promoted_to_fp16)[name = tensor<string, []>("pow_262_cast_fp16")];
            tensor<int32, [1]> mean_101_axes_0 = const()[name = tensor<string, []>("mean_101_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_101_keep_dims_0 = const()[name = tensor<string, []>("mean_101_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_101_cast_fp16 = reduce_mean(axes = mean_101_axes_0, keep_dims = mean_101_keep_dims_0, x = pow_262_cast_fp16)[name = tensor<string, []>("mean_101_cast_fp16")];
            tensor<fp16, []> const_2287_to_fp16 = const()[name = tensor<string, []>("const_2287_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_292_cast_fp16 = add(x = mean_101_cast_fp16, y = const_2287_to_fp16)[name = tensor<string, []>("add_292_cast_fp16")];
            tensor<fp32, []> rsqrt_101_epsilon_0 = const()[name = tensor<string, []>("rsqrt_101_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_101_cast_fp16 = rsqrt(epsilon = rsqrt_101_epsilon_0, x = add_292_cast_fp16)[name = tensor<string, []>("rsqrt_101_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_241_cast_fp16 = mul(x = unsqueeze_227_cast_fp16, y = rsqrt_101_cast_fp16)[name = tensor<string, []>("mul_241_cast_fp16")];
            tensor<int32, [1]> squeeze_106_axes_0 = const()[name = tensor<string, []>("squeeze_106_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_106_cast_fp16 = squeeze(axes = squeeze_106_axes_0, x = mul_241_cast_fp16)[name = tensor<string, []>("squeeze_106_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_146_to_fp16 = const()[name = tensor<string, []>("view_146_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22159104)))];
            tensor<fp16, [801, 256, 80]> mul_242_cast_fp16 = mul(x = squeeze_106_cast_fp16, y = view_146_to_fp16)[name = tensor<string, []>("mul_242_cast_fp16")];
            tensor<string, []> conv1d_134_pad_type_0 = const()[name = tensor<string, []>("conv1d_134_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_134_strides_0 = const()[name = tensor<string, []>("conv1d_134_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_134_pad_0 = const()[name = tensor<string, []>("conv1d_134_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_134_dilations_0 = const()[name = tensor<string, []>("conv1d_134_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_134_groups_0 = const()[name = tensor<string, []>("conv1d_134_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 256, 1]> p_net_4_band_net_mlp_1_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_band_net_mlp_1_weight_to_fp16"), val = tensor<fp16, [2048, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22159680)))];
            tensor<fp16, [801, 2048, 80]> conv1d_134_cast_fp16 = conv(dilations = conv1d_134_dilations_0, groups = conv1d_134_groups_0, pad = conv1d_134_pad_0, pad_type = conv1d_134_pad_type_0, strides = conv1d_134_strides_0, weight = p_net_4_band_net_mlp_1_weight_to_fp16, x = mul_242_cast_fp16)[name = tensor<string, []>("conv1d_134_cast_fp16")];
            tensor<fp16, [801, 2048, 80]> silu_20_cast_fp16 = silu(x = conv1d_134_cast_fp16)[name = tensor<string, []>("silu_20_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = tensor<string, []>("tile_9"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> split_9_axis_0 = const()[name = tensor<string, []>("split_9_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [801, 1024, 80]> split_9_cast_fp16_0, tensor<fp16, [801, 1024, 80]> split_9_cast_fp16_1 = split(axis = split_9_axis_0, split_sizes = tile_9, x = silu_20_cast_fp16)[name = tensor<string, []>("split_9_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> silu_21_cast_fp16 = silu(x = split_9_cast_fp16_0)[name = tensor<string, []>("silu_21_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> mul_243_cast_fp16 = mul(x = silu_21_cast_fp16, y = split_9_cast_fp16_1)[name = tensor<string, []>("mul_243_cast_fp16")];
            tensor<string, []> conv1d_135_pad_type_0 = const()[name = tensor<string, []>("conv1d_135_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_135_strides_0 = const()[name = tensor<string, []>("conv1d_135_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_135_pad_0 = const()[name = tensor<string, []>("conv1d_135_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_135_dilations_0 = const()[name = tensor<string, []>("conv1d_135_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_135_groups_0 = const()[name = tensor<string, []>("conv1d_135_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_4_band_net_mlp_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_band_net_mlp_output_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23208320)))];
            tensor<fp16, [801, 256, 80]> conv1d_135_cast_fp16 = conv(dilations = conv1d_135_dilations_0, groups = conv1d_135_groups_0, pad = conv1d_135_pad_0, pad_type = conv1d_135_pad_type_0, strides = conv1d_135_strides_0, weight = p_net_4_band_net_mlp_output_weight_to_fp16, x = mul_243_cast_fp16)[name = tensor<string, []>("conv1d_135_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_293_cast_fp16 = add(x = add_291_cast_fp16, y = conv1d_135_cast_fp16)[name = tensor<string, []>("add_293_cast_fp16")];
            tensor<int32, [3]> const_2296 = const()[name = tensor<string, []>("const_2296"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<string, []> conv1d_136_pad_type_0 = const()[name = tensor<string, []>("conv1d_136_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_136_pad_0 = const()[name = tensor<string, []>("conv1d_136_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_136_groups_0 = const()[name = tensor<string, []>("conv1d_136_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_136_strides_0 = const()[name = tensor<string, []>("conv1d_136_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_136_dilations_0 = const()[name = tensor<string, []>("conv1d_136_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_4_seq_net_blocks_0_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_0_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23732672)))];
            tensor<fp16, [256]> p_net_4_seq_net_blocks_0_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_0_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23736320)))];
            tensor<fp16, [80, 256, 801]> permute_19_cast_fp16 = transpose(perm = const_2296, x = add_293_cast_fp16)[name = tensor<string, []>("transpose_4")];
            tensor<fp16, [80, 256, 801]> conv1d_136_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_0_conv_0_bias_to_fp16, dilations = conv1d_136_dilations_0, groups = conv1d_136_groups_0, pad = conv1d_136_pad_0, pad_type = conv1d_136_pad_type_0, strides = conv1d_136_strides_0, weight = p_net_4_seq_net_blocks_0_conv_0_weight_to_fp16, x = permute_19_cast_fp16)[name = tensor<string, []>("conv1d_136_cast_fp16")];
            tensor<int32, [1]> unsqueeze_229_axes_0 = const()[name = tensor<string, []>("unsqueeze_229_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_229_cast_fp16 = expand_dims(axes = unsqueeze_229_axes_0, x = conv1d_136_cast_fp16)[name = tensor<string, []>("unsqueeze_229_cast_fp16")];
            tensor<fp16, []> const_2305_promoted_to_fp16 = const()[name = tensor<string, []>("const_2305_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_263_cast_fp16 = pow(x = unsqueeze_229_cast_fp16, y = const_2305_promoted_to_fp16)[name = tensor<string, []>("pow_263_cast_fp16")];
            tensor<int32, [1]> mean_102_axes_0 = const()[name = tensor<string, []>("mean_102_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_102_keep_dims_0 = const()[name = tensor<string, []>("mean_102_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_102_cast_fp16 = reduce_mean(axes = mean_102_axes_0, keep_dims = mean_102_keep_dims_0, x = pow_263_cast_fp16)[name = tensor<string, []>("mean_102_cast_fp16")];
            tensor<fp16, []> const_2308_to_fp16 = const()[name = tensor<string, []>("const_2308_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_294_cast_fp16 = add(x = mean_102_cast_fp16, y = const_2308_to_fp16)[name = tensor<string, []>("add_294_cast_fp16")];
            tensor<fp32, []> rsqrt_102_epsilon_0 = const()[name = tensor<string, []>("rsqrt_102_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_102_cast_fp16 = rsqrt(epsilon = rsqrt_102_epsilon_0, x = add_294_cast_fp16)[name = tensor<string, []>("rsqrt_102_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_244_cast_fp16 = mul(x = unsqueeze_229_cast_fp16, y = rsqrt_102_cast_fp16)[name = tensor<string, []>("mul_244_cast_fp16")];
            tensor<int32, [1]> squeeze_107_axes_0 = const()[name = tensor<string, []>("squeeze_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_107_cast_fp16 = squeeze(axes = squeeze_107_axes_0, x = mul_244_cast_fp16)[name = tensor<string, []>("squeeze_107_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_147_to_fp16 = const()[name = tensor<string, []>("view_147_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23736896)))];
            tensor<fp16, [80, 256, 801]> mul_245_cast_fp16 = mul(x = squeeze_107_cast_fp16, y = view_147_to_fp16)[name = tensor<string, []>("mul_245_cast_fp16")];
            tensor<string, []> conv1d_137_pad_type_0 = const()[name = tensor<string, []>("conv1d_137_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_137_strides_0 = const()[name = tensor<string, []>("conv1d_137_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_137_pad_0 = const()[name = tensor<string, []>("conv1d_137_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_137_dilations_0 = const()[name = tensor<string, []>("conv1d_137_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_137_groups_0 = const()[name = tensor<string, []>("conv1d_137_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_4_seq_net_blocks_0_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_0_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23737472)))];
            tensor<fp16, [1024]> p_net_4_seq_net_blocks_0_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_0_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24261824)))];
            tensor<fp16, [80, 1024, 801]> conv1d_137_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_0_conv_2_bias_to_fp16, dilations = conv1d_137_dilations_0, groups = conv1d_137_groups_0, pad = conv1d_137_pad_0, pad_type = conv1d_137_pad_type_0, strides = conv1d_137_strides_0, weight = p_net_4_seq_net_blocks_0_conv_2_weight_to_fp16, x = mul_245_cast_fp16)[name = tensor<string, []>("conv1d_137_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_22_cast_fp16 = silu(x = conv1d_137_cast_fp16)[name = tensor<string, []>("silu_22_cast_fp16")];
            tensor<string, []> conv1d_138_pad_type_0 = const()[name = tensor<string, []>("conv1d_138_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_138_strides_0 = const()[name = tensor<string, []>("conv1d_138_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_138_pad_0 = const()[name = tensor<string, []>("conv1d_138_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_138_dilations_0 = const()[name = tensor<string, []>("conv1d_138_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_138_groups_0 = const()[name = tensor<string, []>("conv1d_138_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_4_seq_net_blocks_0_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_0_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24263936)))];
            tensor<fp16, [256]> p_net_4_seq_net_blocks_0_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_0_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24788288)))];
            tensor<fp16, [80, 256, 801]> conv1d_138_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_0_conv_4_bias_to_fp16, dilations = conv1d_138_dilations_0, groups = conv1d_138_groups_0, pad = conv1d_138_pad_0, pad_type = conv1d_138_pad_type_0, strides = conv1d_138_strides_0, weight = p_net_4_seq_net_blocks_0_conv_4_weight_to_fp16, x = silu_22_cast_fp16)[name = tensor<string, []>("conv1d_138_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_295_cast_fp16 = add(x = permute_19_cast_fp16, y = conv1d_138_cast_fp16)[name = tensor<string, []>("add_295_cast_fp16")];
            tensor<string, []> conv1d_139_pad_type_0 = const()[name = tensor<string, []>("conv1d_139_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_139_pad_0 = const()[name = tensor<string, []>("conv1d_139_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_139_groups_0 = const()[name = tensor<string, []>("conv1d_139_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_139_strides_0 = const()[name = tensor<string, []>("conv1d_139_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_139_dilations_0 = const()[name = tensor<string, []>("conv1d_139_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_4_seq_net_blocks_1_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_1_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24788864)))];
            tensor<fp16, [256]> p_net_4_seq_net_blocks_1_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_1_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24792512)))];
            tensor<fp16, [80, 256, 801]> conv1d_139_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_1_conv_0_bias_to_fp16, dilations = conv1d_139_dilations_0, groups = conv1d_139_groups_0, pad = conv1d_139_pad_0, pad_type = conv1d_139_pad_type_0, strides = conv1d_139_strides_0, weight = p_net_4_seq_net_blocks_1_conv_0_weight_to_fp16, x = add_295_cast_fp16)[name = tensor<string, []>("conv1d_139_cast_fp16")];
            tensor<int32, [1]> unsqueeze_231_axes_0 = const()[name = tensor<string, []>("unsqueeze_231_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_231_cast_fp16 = expand_dims(axes = unsqueeze_231_axes_0, x = conv1d_139_cast_fp16)[name = tensor<string, []>("unsqueeze_231_cast_fp16")];
            tensor<fp16, []> const_2319_promoted_to_fp16 = const()[name = tensor<string, []>("const_2319_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_264_cast_fp16 = pow(x = unsqueeze_231_cast_fp16, y = const_2319_promoted_to_fp16)[name = tensor<string, []>("pow_264_cast_fp16")];
            tensor<int32, [1]> mean_103_axes_0 = const()[name = tensor<string, []>("mean_103_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_103_keep_dims_0 = const()[name = tensor<string, []>("mean_103_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_103_cast_fp16 = reduce_mean(axes = mean_103_axes_0, keep_dims = mean_103_keep_dims_0, x = pow_264_cast_fp16)[name = tensor<string, []>("mean_103_cast_fp16")];
            tensor<fp16, []> const_2322_to_fp16 = const()[name = tensor<string, []>("const_2322_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_296_cast_fp16 = add(x = mean_103_cast_fp16, y = const_2322_to_fp16)[name = tensor<string, []>("add_296_cast_fp16")];
            tensor<fp32, []> rsqrt_103_epsilon_0 = const()[name = tensor<string, []>("rsqrt_103_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_103_cast_fp16 = rsqrt(epsilon = rsqrt_103_epsilon_0, x = add_296_cast_fp16)[name = tensor<string, []>("rsqrt_103_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_246_cast_fp16 = mul(x = unsqueeze_231_cast_fp16, y = rsqrt_103_cast_fp16)[name = tensor<string, []>("mul_246_cast_fp16")];
            tensor<int32, [1]> squeeze_108_axes_0 = const()[name = tensor<string, []>("squeeze_108_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_108_cast_fp16 = squeeze(axes = squeeze_108_axes_0, x = mul_246_cast_fp16)[name = tensor<string, []>("squeeze_108_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_148_to_fp16 = const()[name = tensor<string, []>("view_148_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24793088)))];
            tensor<fp16, [80, 256, 801]> mul_247_cast_fp16 = mul(x = squeeze_108_cast_fp16, y = view_148_to_fp16)[name = tensor<string, []>("mul_247_cast_fp16")];
            tensor<string, []> conv1d_140_pad_type_0 = const()[name = tensor<string, []>("conv1d_140_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_140_strides_0 = const()[name = tensor<string, []>("conv1d_140_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_140_pad_0 = const()[name = tensor<string, []>("conv1d_140_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_140_dilations_0 = const()[name = tensor<string, []>("conv1d_140_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_140_groups_0 = const()[name = tensor<string, []>("conv1d_140_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_4_seq_net_blocks_1_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_1_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24793664)))];
            tensor<fp16, [1024]> p_net_4_seq_net_blocks_1_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_1_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25318016)))];
            tensor<fp16, [80, 1024, 801]> conv1d_140_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_1_conv_2_bias_to_fp16, dilations = conv1d_140_dilations_0, groups = conv1d_140_groups_0, pad = conv1d_140_pad_0, pad_type = conv1d_140_pad_type_0, strides = conv1d_140_strides_0, weight = p_net_4_seq_net_blocks_1_conv_2_weight_to_fp16, x = mul_247_cast_fp16)[name = tensor<string, []>("conv1d_140_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_23_cast_fp16 = silu(x = conv1d_140_cast_fp16)[name = tensor<string, []>("silu_23_cast_fp16")];
            tensor<string, []> conv1d_141_pad_type_0 = const()[name = tensor<string, []>("conv1d_141_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_141_strides_0 = const()[name = tensor<string, []>("conv1d_141_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_141_pad_0 = const()[name = tensor<string, []>("conv1d_141_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_141_dilations_0 = const()[name = tensor<string, []>("conv1d_141_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_141_groups_0 = const()[name = tensor<string, []>("conv1d_141_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_4_seq_net_blocks_1_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_1_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25320128)))];
            tensor<fp16, [256]> p_net_4_seq_net_blocks_1_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_1_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25844480)))];
            tensor<fp16, [80, 256, 801]> conv1d_141_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_1_conv_4_bias_to_fp16, dilations = conv1d_141_dilations_0, groups = conv1d_141_groups_0, pad = conv1d_141_pad_0, pad_type = conv1d_141_pad_type_0, strides = conv1d_141_strides_0, weight = p_net_4_seq_net_blocks_1_conv_4_weight_to_fp16, x = silu_23_cast_fp16)[name = tensor<string, []>("conv1d_141_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_297_cast_fp16 = add(x = add_295_cast_fp16, y = conv1d_141_cast_fp16)[name = tensor<string, []>("add_297_cast_fp16")];
            tensor<string, []> conv1d_142_pad_type_0 = const()[name = tensor<string, []>("conv1d_142_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_142_pad_0 = const()[name = tensor<string, []>("conv1d_142_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_142_groups_0 = const()[name = tensor<string, []>("conv1d_142_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_142_strides_0 = const()[name = tensor<string, []>("conv1d_142_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_142_dilations_0 = const()[name = tensor<string, []>("conv1d_142_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_4_seq_net_blocks_2_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_2_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25845056)))];
            tensor<fp16, [256]> p_net_4_seq_net_blocks_2_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_2_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25848704)))];
            tensor<fp16, [80, 256, 801]> conv1d_142_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_2_conv_0_bias_to_fp16, dilations = conv1d_142_dilations_0, groups = conv1d_142_groups_0, pad = conv1d_142_pad_0, pad_type = conv1d_142_pad_type_0, strides = conv1d_142_strides_0, weight = p_net_4_seq_net_blocks_2_conv_0_weight_to_fp16, x = add_297_cast_fp16)[name = tensor<string, []>("conv1d_142_cast_fp16")];
            tensor<int32, [1]> unsqueeze_233_axes_0 = const()[name = tensor<string, []>("unsqueeze_233_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_233_cast_fp16 = expand_dims(axes = unsqueeze_233_axes_0, x = conv1d_142_cast_fp16)[name = tensor<string, []>("unsqueeze_233_cast_fp16")];
            tensor<fp16, []> const_2333_promoted_to_fp16 = const()[name = tensor<string, []>("const_2333_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_265_cast_fp16 = pow(x = unsqueeze_233_cast_fp16, y = const_2333_promoted_to_fp16)[name = tensor<string, []>("pow_265_cast_fp16")];
            tensor<int32, [1]> mean_104_axes_0 = const()[name = tensor<string, []>("mean_104_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_104_keep_dims_0 = const()[name = tensor<string, []>("mean_104_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_104_cast_fp16 = reduce_mean(axes = mean_104_axes_0, keep_dims = mean_104_keep_dims_0, x = pow_265_cast_fp16)[name = tensor<string, []>("mean_104_cast_fp16")];
            tensor<fp16, []> const_2336_to_fp16 = const()[name = tensor<string, []>("const_2336_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_298_cast_fp16 = add(x = mean_104_cast_fp16, y = const_2336_to_fp16)[name = tensor<string, []>("add_298_cast_fp16")];
            tensor<fp32, []> rsqrt_104_epsilon_0 = const()[name = tensor<string, []>("rsqrt_104_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_104_cast_fp16 = rsqrt(epsilon = rsqrt_104_epsilon_0, x = add_298_cast_fp16)[name = tensor<string, []>("rsqrt_104_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_248_cast_fp16 = mul(x = unsqueeze_233_cast_fp16, y = rsqrt_104_cast_fp16)[name = tensor<string, []>("mul_248_cast_fp16")];
            tensor<int32, [1]> squeeze_109_axes_0 = const()[name = tensor<string, []>("squeeze_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_109_cast_fp16 = squeeze(axes = squeeze_109_axes_0, x = mul_248_cast_fp16)[name = tensor<string, []>("squeeze_109_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_149_to_fp16 = const()[name = tensor<string, []>("view_149_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25849280)))];
            tensor<fp16, [80, 256, 801]> mul_249_cast_fp16 = mul(x = squeeze_109_cast_fp16, y = view_149_to_fp16)[name = tensor<string, []>("mul_249_cast_fp16")];
            tensor<string, []> conv1d_143_pad_type_0 = const()[name = tensor<string, []>("conv1d_143_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_143_strides_0 = const()[name = tensor<string, []>("conv1d_143_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_143_pad_0 = const()[name = tensor<string, []>("conv1d_143_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_143_dilations_0 = const()[name = tensor<string, []>("conv1d_143_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_143_groups_0 = const()[name = tensor<string, []>("conv1d_143_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_4_seq_net_blocks_2_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_2_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25849856)))];
            tensor<fp16, [1024]> p_net_4_seq_net_blocks_2_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_2_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26374208)))];
            tensor<fp16, [80, 1024, 801]> conv1d_143_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_2_conv_2_bias_to_fp16, dilations = conv1d_143_dilations_0, groups = conv1d_143_groups_0, pad = conv1d_143_pad_0, pad_type = conv1d_143_pad_type_0, strides = conv1d_143_strides_0, weight = p_net_4_seq_net_blocks_2_conv_2_weight_to_fp16, x = mul_249_cast_fp16)[name = tensor<string, []>("conv1d_143_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_24_cast_fp16 = silu(x = conv1d_143_cast_fp16)[name = tensor<string, []>("silu_24_cast_fp16")];
            tensor<string, []> conv1d_144_pad_type_0 = const()[name = tensor<string, []>("conv1d_144_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_144_strides_0 = const()[name = tensor<string, []>("conv1d_144_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_144_pad_0 = const()[name = tensor<string, []>("conv1d_144_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_144_dilations_0 = const()[name = tensor<string, []>("conv1d_144_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_144_groups_0 = const()[name = tensor<string, []>("conv1d_144_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_4_seq_net_blocks_2_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_2_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26376320)))];
            tensor<fp16, [256]> p_net_4_seq_net_blocks_2_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_4_seq_net_blocks_2_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26900672)))];
            tensor<fp16, [80, 256, 801]> conv1d_144_cast_fp16 = conv(bias = p_net_4_seq_net_blocks_2_conv_4_bias_to_fp16, dilations = conv1d_144_dilations_0, groups = conv1d_144_groups_0, pad = conv1d_144_pad_0, pad_type = conv1d_144_pad_type_0, strides = conv1d_144_strides_0, weight = p_net_4_seq_net_blocks_2_conv_4_weight_to_fp16, x = silu_24_cast_fp16)[name = tensor<string, []>("conv1d_144_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_299_cast_fp16 = add(x = add_297_cast_fp16, y = conv1d_144_cast_fp16)[name = tensor<string, []>("add_299_cast_fp16")];
            tensor<int32, [1]> unsqueeze_234_axes_0 = const()[name = tensor<string, []>("unsqueeze_234_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 80, 256, 801]> unsqueeze_234_cast_fp16 = expand_dims(axes = unsqueeze_234_axes_0, x = add_299_cast_fp16)[name = tensor<string, []>("unsqueeze_234_cast_fp16")];
            tensor<int32, [1]> squeeze_110_axes_0 = const()[name = tensor<string, []>("squeeze_110_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [80, 256, 801]> squeeze_110_cast_fp16 = squeeze(axes = squeeze_110_axes_0, x = unsqueeze_234_cast_fp16)[name = tensor<string, []>("squeeze_110_cast_fp16")];
            tensor<int32, [3]> const_2341 = const()[name = tensor<string, []>("const_2341"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<int32, [1]> unsqueeze_236_axes_0 = const()[name = tensor<string, []>("unsqueeze_236_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> permute_20_cast_fp16 = transpose(perm = const_2341, x = squeeze_110_cast_fp16)[name = tensor<string, []>("transpose_3")];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_236_cast_fp16 = expand_dims(axes = unsqueeze_236_axes_0, x = permute_20_cast_fp16)[name = tensor<string, []>("unsqueeze_236_cast_fp16")];
            tensor<fp16, []> const_2346_promoted_to_fp16 = const()[name = tensor<string, []>("const_2346_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_266_cast_fp16 = pow(x = unsqueeze_236_cast_fp16, y = const_2346_promoted_to_fp16)[name = tensor<string, []>("pow_266_cast_fp16")];
            tensor<int32, [1]> mean_105_axes_0 = const()[name = tensor<string, []>("mean_105_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_105_keep_dims_0 = const()[name = tensor<string, []>("mean_105_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_105_cast_fp16 = reduce_mean(axes = mean_105_axes_0, keep_dims = mean_105_keep_dims_0, x = pow_266_cast_fp16)[name = tensor<string, []>("mean_105_cast_fp16")];
            tensor<fp16, []> const_2349_to_fp16 = const()[name = tensor<string, []>("const_2349_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_300_cast_fp16 = add(x = mean_105_cast_fp16, y = const_2349_to_fp16)[name = tensor<string, []>("add_300_cast_fp16")];
            tensor<fp32, []> rsqrt_105_epsilon_0 = const()[name = tensor<string, []>("rsqrt_105_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_105_cast_fp16 = rsqrt(epsilon = rsqrt_105_epsilon_0, x = add_300_cast_fp16)[name = tensor<string, []>("rsqrt_105_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_250_cast_fp16 = mul(x = unsqueeze_236_cast_fp16, y = rsqrt_105_cast_fp16)[name = tensor<string, []>("mul_250_cast_fp16")];
            tensor<int32, [1]> squeeze_111_axes_0 = const()[name = tensor<string, []>("squeeze_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_111_cast_fp16 = squeeze(axes = squeeze_111_axes_0, x = mul_250_cast_fp16)[name = tensor<string, []>("squeeze_111_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_150_to_fp16 = const()[name = tensor<string, []>("view_150_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26901248)))];
            tensor<fp16, [801, 256, 80]> mul_251_cast_fp16 = mul(x = squeeze_111_cast_fp16, y = view_150_to_fp16)[name = tensor<string, []>("mul_251_cast_fp16")];
            tensor<string, []> conv1d_145_pad_type_0 = const()[name = tensor<string, []>("conv1d_145_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_145_strides_0 = const()[name = tensor<string, []>("conv1d_145_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_145_pad_0 = const()[name = tensor<string, []>("conv1d_145_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_145_dilations_0 = const()[name = tensor<string, []>("conv1d_145_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_145_groups_0 = const()[name = tensor<string, []>("conv1d_145_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [768, 256, 1]> p_net_5_band_net_weight_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_band_net_weight_weight_to_fp16"), val = tensor<fp16, [768, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26901824)))];
            tensor<fp16, [801, 768, 80]> conv1d_145_cast_fp16 = conv(dilations = conv1d_145_dilations_0, groups = conv1d_145_groups_0, pad = conv1d_145_pad_0, pad_type = conv1d_145_pad_type_0, strides = conv1d_145_strides_0, weight = p_net_5_band_net_weight_weight_to_fp16, x = mul_251_cast_fp16)[name = tensor<string, []>("conv1d_145_cast_fp16")];
            tensor<int32, [4]> const_2352 = const()[name = tensor<string, []>("const_2352"), val = tensor<int32, [4]>([-1, 8, 96, 80])];
            tensor<fp16, [801, 8, 96, 80]> view_151_cast_fp16 = reshape(shape = const_2352, x = conv1d_145_cast_fp16)[name = tensor<string, []>("view_151_cast_fp16")];
            tensor<int32, [4]> const_2353 = const()[name = tensor<string, []>("const_2353"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> tile_10 = const()[name = tensor<string, []>("tile_10"), val = tensor<int32, [3]>([32, 32, 32])];
            tensor<int32, []> split_10_axis_0 = const()[name = tensor<string, []>("split_10_axis_0"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 96]> permute_21_cast_fp16 = transpose(perm = const_2353, x = view_151_cast_fp16)[name = tensor<string, []>("transpose_2")];
            tensor<fp16, [801, 8, 80, 32]> split_10_cast_fp16_0, tensor<fp16, [801, 8, 80, 32]> split_10_cast_fp16_1, tensor<fp16, [801, 8, 80, 32]> split_10_cast_fp16_2 = split(axis = split_10_axis_0, split_sizes = tile_10, x = permute_21_cast_fp16)[name = tensor<string, []>("split_10_cast_fp16")];
            tensor<int32, [3]> const_2361 = const()[name = tensor<string, []>("const_2361"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_152_cast_fp16 = reshape(shape = const_2361, x = split_10_cast_fp16_0)[name = tensor<string, []>("view_152_cast_fp16")];
            tensor<int32, [3]> const_2368 = const()[name = tensor<string, []>("const_2368"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_15_cast_fp16 = reshape(shape = const_2368, x = view_152_cast_fp16)[name = tensor<string, []>("_unsafe_view_15_cast_fp16")];
            tensor<int32, [1]> const_2369 = const()[name = tensor<string, []>("const_2369"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_10_cast_fp16 = reverse(axes = const_2369, x = _unsafe_view_15_cast_fp16)[name = tensor<string, []>("flip_10_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_153_to_fp16 = const()[name = tensor<string, []>("view_153_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_252_cast_fp16 = mul(x = flip_10_cast_fp16, y = view_153_to_fp16)[name = tensor<string, []>("mul_252_cast_fp16")];
            tensor<int32, [3]> const_2371 = const()[name = tensor<string, []>("const_2371"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_154_cast_fp16 = reshape(shape = const_2371, x = mul_252_cast_fp16)[name = tensor<string, []>("view_154_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_253_cast_fp16 = mul(x = view_152_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_253_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_254_cast_fp16 = mul(x = view_154_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_254_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_301_cast_fp16 = add(x = mul_253_cast_fp16, y = mul_254_cast_fp16)[name = tensor<string, []>("add_301_cast_fp16")];
            tensor<int32, [4]> const_2374 = const()[name = tensor<string, []>("const_2374"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_155_cast_fp16 = reshape(shape = const_2374, x = add_301_cast_fp16)[name = tensor<string, []>("view_155_cast_fp16")];
            tensor<int32, [3]> const_2375 = const()[name = tensor<string, []>("const_2375"), val = tensor<int32, [3]>([6408, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_156_cast_fp16 = reshape(shape = const_2375, x = split_10_cast_fp16_1)[name = tensor<string, []>("view_156_cast_fp16")];
            tensor<int32, [3]> const_2382 = const()[name = tensor<string, []>("const_2382"), val = tensor<int32, [3]>([512640, 16, 2])];
            tensor<fp16, [512640, 16, 2]> _unsafe_view_16_cast_fp16 = reshape(shape = const_2382, x = view_156_cast_fp16)[name = tensor<string, []>("_unsafe_view_16_cast_fp16")];
            tensor<int32, [1]> const_2383 = const()[name = tensor<string, []>("const_2383"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [512640, 16, 2]> flip_11_cast_fp16 = reverse(axes = const_2383, x = _unsafe_view_16_cast_fp16)[name = tensor<string, []>("flip_11_cast_fp16")];
            tensor<fp16, [1, 1, 2]> view_157_to_fp16 = const()[name = tensor<string, []>("view_157_to_fp16"), val = tensor<fp16, [1, 1, 2]>([[[-0x1p+0, 0x1p+0]]])];
            tensor<fp16, [512640, 16, 2]> mul_255_cast_fp16 = mul(x = flip_11_cast_fp16, y = view_157_to_fp16)[name = tensor<string, []>("mul_255_cast_fp16")];
            tensor<int32, [3]> const_2385 = const()[name = tensor<string, []>("const_2385"), val = tensor<int32, [3]>([-1, 80, 32])];
            tensor<fp16, [6408, 80, 32]> view_158_cast_fp16 = reshape(shape = const_2385, x = mul_255_cast_fp16)[name = tensor<string, []>("view_158_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_256_cast_fp16 = mul(x = view_156_cast_fp16, y = unsqueeze_162_to_fp16)[name = tensor<string, []>("mul_256_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> mul_257_cast_fp16 = mul(x = view_158_cast_fp16, y = unsqueeze_163_to_fp16)[name = tensor<string, []>("mul_257_cast_fp16")];
            tensor<fp16, [6408, 80, 32]> add_302_cast_fp16 = add(x = mul_256_cast_fp16, y = mul_257_cast_fp16)[name = tensor<string, []>("add_302_cast_fp16")];
            tensor<int32, [4]> const_2388 = const()[name = tensor<string, []>("const_2388"), val = tensor<int32, [4]>([801, 8, 80, 32])];
            tensor<fp16, [801, 8, 80, 32]> view_159_cast_fp16 = reshape(shape = const_2388, x = add_302_cast_fp16)[name = tensor<string, []>("view_159_cast_fp16")];
            tensor<bool, []> matmul_10_transpose_x_1 = const()[name = tensor<string, []>("matmul_10_transpose_x_1"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_10_transpose_y_1 = const()[name = tensor<string, []>("matmul_10_transpose_y_1"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 8, 80, 80]> matmul_10_cast_fp16 = matmul(transpose_x = matmul_10_transpose_x_1, transpose_y = matmul_10_transpose_y_1, x = view_155_cast_fp16, y = view_159_cast_fp16)[name = tensor<string, []>("matmul_10_cast_fp16")];
            tensor<fp16, []> const_2391_to_fp16 = const()[name = tensor<string, []>("const_2391_to_fp16"), val = tensor<fp16, []>(0x1.6ap-3)];
            tensor<fp16, [801, 8, 80, 80]> mul_258_cast_fp16 = mul(x = matmul_10_cast_fp16, y = const_2391_to_fp16)[name = tensor<string, []>("mul_258_cast_fp16")];
            tensor<int32, []> const_2392 = const()[name = tensor<string, []>("const_2392"), val = tensor<int32, []>(-1)];
            tensor<fp16, [801, 8, 80, 80]> softmax_5_cast_fp16 = softmax(axis = const_2392, x = mul_258_cast_fp16)[name = tensor<string, []>("softmax_5_cast_fp16")];
            tensor<bool, []> matmul_11_transpose_x_0 = const()[name = tensor<string, []>("matmul_11_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_11_transpose_y_0 = const()[name = tensor<string, []>("matmul_11_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [801, 8, 80, 32]> matmul_11_cast_fp16 = matmul(transpose_x = matmul_11_transpose_x_0, transpose_y = matmul_11_transpose_y_0, x = softmax_5_cast_fp16, y = split_10_cast_fp16_2)[name = tensor<string, []>("matmul_11_cast_fp16")];
            tensor<int32, [4]> const_2393 = const()[name = tensor<string, []>("const_2393"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> const_2394 = const()[name = tensor<string, []>("const_2394"), val = tensor<int32, [3]>([801, 256, 80])];
            tensor<fp16, [801, 8, 32, 80]> permute_22_cast_fp16 = transpose(perm = const_2393, x = matmul_11_cast_fp16)[name = tensor<string, []>("transpose_1")];
            tensor<fp16, [801, 256, 80]> _unsafe_view_17_cast_fp16 = reshape(shape = const_2394, x = permute_22_cast_fp16)[name = tensor<string, []>("_unsafe_view_17_cast_fp16")];
            tensor<string, []> conv1d_146_pad_type_0 = const()[name = tensor<string, []>("conv1d_146_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_146_strides_0 = const()[name = tensor<string, []>("conv1d_146_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_146_pad_0 = const()[name = tensor<string, []>("conv1d_146_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_146_dilations_0 = const()[name = tensor<string, []>("conv1d_146_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_146_groups_0 = const()[name = tensor<string, []>("conv1d_146_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1]> p_net_5_band_net_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_band_net_output_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27295104)))];
            tensor<fp16, [801, 256, 80]> conv1d_146_cast_fp16 = conv(dilations = conv1d_146_dilations_0, groups = conv1d_146_groups_0, pad = conv1d_146_pad_0, pad_type = conv1d_146_pad_type_0, strides = conv1d_146_strides_0, weight = p_net_5_band_net_output_weight_to_fp16, x = _unsafe_view_17_cast_fp16)[name = tensor<string, []>("conv1d_146_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_303_cast_fp16 = add(x = conv1d_146_cast_fp16, y = permute_20_cast_fp16)[name = tensor<string, []>("add_303_cast_fp16")];
            tensor<int32, [1]> unsqueeze_242_axes_0 = const()[name = tensor<string, []>("unsqueeze_242_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 1, 256, 80]> unsqueeze_242_cast_fp16 = expand_dims(axes = unsqueeze_242_axes_0, x = add_303_cast_fp16)[name = tensor<string, []>("unsqueeze_242_cast_fp16")];
            tensor<fp16, []> const_2399_promoted_to_fp16 = const()[name = tensor<string, []>("const_2399_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [801, 1, 256, 80]> pow_267_cast_fp16 = pow(x = unsqueeze_242_cast_fp16, y = const_2399_promoted_to_fp16)[name = tensor<string, []>("pow_267_cast_fp16")];
            tensor<int32, [1]> mean_106_axes_0 = const()[name = tensor<string, []>("mean_106_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_106_keep_dims_0 = const()[name = tensor<string, []>("mean_106_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [801, 1, 1, 80]> mean_106_cast_fp16 = reduce_mean(axes = mean_106_axes_0, keep_dims = mean_106_keep_dims_0, x = pow_267_cast_fp16)[name = tensor<string, []>("mean_106_cast_fp16")];
            tensor<fp16, []> const_2402_to_fp16 = const()[name = tensor<string, []>("const_2402_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [801, 1, 1, 80]> add_304_cast_fp16 = add(x = mean_106_cast_fp16, y = const_2402_to_fp16)[name = tensor<string, []>("add_304_cast_fp16")];
            tensor<fp32, []> rsqrt_106_epsilon_0 = const()[name = tensor<string, []>("rsqrt_106_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [801, 1, 1, 80]> rsqrt_106_cast_fp16 = rsqrt(epsilon = rsqrt_106_epsilon_0, x = add_304_cast_fp16)[name = tensor<string, []>("rsqrt_106_cast_fp16")];
            tensor<fp16, [801, 1, 256, 80]> mul_259_cast_fp16 = mul(x = unsqueeze_242_cast_fp16, y = rsqrt_106_cast_fp16)[name = tensor<string, []>("mul_259_cast_fp16")];
            tensor<int32, [1]> squeeze_112_axes_0 = const()[name = tensor<string, []>("squeeze_112_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [801, 256, 80]> squeeze_112_cast_fp16 = squeeze(axes = squeeze_112_axes_0, x = mul_259_cast_fp16)[name = tensor<string, []>("squeeze_112_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_160_to_fp16 = const()[name = tensor<string, []>("view_160_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27426240)))];
            tensor<fp16, [801, 256, 80]> mul_260_cast_fp16 = mul(x = squeeze_112_cast_fp16, y = view_160_to_fp16)[name = tensor<string, []>("mul_260_cast_fp16")];
            tensor<string, []> conv1d_147_pad_type_0 = const()[name = tensor<string, []>("conv1d_147_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_147_strides_0 = const()[name = tensor<string, []>("conv1d_147_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_147_pad_0 = const()[name = tensor<string, []>("conv1d_147_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_147_dilations_0 = const()[name = tensor<string, []>("conv1d_147_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_147_groups_0 = const()[name = tensor<string, []>("conv1d_147_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 256, 1]> p_net_5_band_net_mlp_1_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_band_net_mlp_1_weight_to_fp16"), val = tensor<fp16, [2048, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27426816)))];
            tensor<fp16, [801, 2048, 80]> conv1d_147_cast_fp16 = conv(dilations = conv1d_147_dilations_0, groups = conv1d_147_groups_0, pad = conv1d_147_pad_0, pad_type = conv1d_147_pad_type_0, strides = conv1d_147_strides_0, weight = p_net_5_band_net_mlp_1_weight_to_fp16, x = mul_260_cast_fp16)[name = tensor<string, []>("conv1d_147_cast_fp16")];
            tensor<fp16, [801, 2048, 80]> silu_25_cast_fp16 = silu(x = conv1d_147_cast_fp16)[name = tensor<string, []>("silu_25_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = tensor<string, []>("tile_11"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> split_11_axis_0 = const()[name = tensor<string, []>("split_11_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [801, 1024, 80]> split_11_cast_fp16_0, tensor<fp16, [801, 1024, 80]> split_11_cast_fp16_1 = split(axis = split_11_axis_0, split_sizes = tile_11, x = silu_25_cast_fp16)[name = tensor<string, []>("split_11_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> silu_26_cast_fp16 = silu(x = split_11_cast_fp16_0)[name = tensor<string, []>("silu_26_cast_fp16")];
            tensor<fp16, [801, 1024, 80]> mul_261_cast_fp16 = mul(x = silu_26_cast_fp16, y = split_11_cast_fp16_1)[name = tensor<string, []>("mul_261_cast_fp16")];
            tensor<string, []> conv1d_148_pad_type_0 = const()[name = tensor<string, []>("conv1d_148_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_148_strides_0 = const()[name = tensor<string, []>("conv1d_148_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_148_pad_0 = const()[name = tensor<string, []>("conv1d_148_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_148_dilations_0 = const()[name = tensor<string, []>("conv1d_148_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_148_groups_0 = const()[name = tensor<string, []>("conv1d_148_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_5_band_net_mlp_output_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_band_net_mlp_output_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28475456)))];
            tensor<fp16, [801, 256, 80]> conv1d_148_cast_fp16 = conv(dilations = conv1d_148_dilations_0, groups = conv1d_148_groups_0, pad = conv1d_148_pad_0, pad_type = conv1d_148_pad_type_0, strides = conv1d_148_strides_0, weight = p_net_5_band_net_mlp_output_weight_to_fp16, x = mul_261_cast_fp16)[name = tensor<string, []>("conv1d_148_cast_fp16")];
            tensor<fp16, [801, 256, 80]> add_305_cast_fp16 = add(x = add_303_cast_fp16, y = conv1d_148_cast_fp16)[name = tensor<string, []>("add_305_cast_fp16")];
            tensor<int32, [3]> const_2411 = const()[name = tensor<string, []>("const_2411"), val = tensor<int32, [3]>([2, 1, 0])];
            tensor<string, []> conv1d_149_pad_type_0 = const()[name = tensor<string, []>("conv1d_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_149_pad_0 = const()[name = tensor<string, []>("conv1d_149_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_149_groups_0 = const()[name = tensor<string, []>("conv1d_149_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_149_strides_0 = const()[name = tensor<string, []>("conv1d_149_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_149_dilations_0 = const()[name = tensor<string, []>("conv1d_149_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_5_seq_net_blocks_0_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_0_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28999808)))];
            tensor<fp16, [256]> p_net_5_seq_net_blocks_0_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_0_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29003456)))];
            tensor<fp16, [80, 256, 801]> permute_23_cast_fp16 = transpose(perm = const_2411, x = add_305_cast_fp16)[name = tensor<string, []>("transpose_0")];
            tensor<fp16, [80, 256, 801]> conv1d_149_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_0_conv_0_bias_to_fp16, dilations = conv1d_149_dilations_0, groups = conv1d_149_groups_0, pad = conv1d_149_pad_0, pad_type = conv1d_149_pad_type_0, strides = conv1d_149_strides_0, weight = p_net_5_seq_net_blocks_0_conv_0_weight_to_fp16, x = permute_23_cast_fp16)[name = tensor<string, []>("conv1d_149_cast_fp16")];
            tensor<int32, [1]> unsqueeze_244_axes_0 = const()[name = tensor<string, []>("unsqueeze_244_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_244_cast_fp16 = expand_dims(axes = unsqueeze_244_axes_0, x = conv1d_149_cast_fp16)[name = tensor<string, []>("unsqueeze_244_cast_fp16")];
            tensor<fp16, []> const_2420_promoted_to_fp16 = const()[name = tensor<string, []>("const_2420_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_268_cast_fp16 = pow(x = unsqueeze_244_cast_fp16, y = const_2420_promoted_to_fp16)[name = tensor<string, []>("pow_268_cast_fp16")];
            tensor<int32, [1]> mean_107_axes_0 = const()[name = tensor<string, []>("mean_107_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_107_keep_dims_0 = const()[name = tensor<string, []>("mean_107_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_107_cast_fp16 = reduce_mean(axes = mean_107_axes_0, keep_dims = mean_107_keep_dims_0, x = pow_268_cast_fp16)[name = tensor<string, []>("mean_107_cast_fp16")];
            tensor<fp16, []> const_2423_to_fp16 = const()[name = tensor<string, []>("const_2423_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_306_cast_fp16 = add(x = mean_107_cast_fp16, y = const_2423_to_fp16)[name = tensor<string, []>("add_306_cast_fp16")];
            tensor<fp32, []> rsqrt_107_epsilon_0 = const()[name = tensor<string, []>("rsqrt_107_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_107_cast_fp16 = rsqrt(epsilon = rsqrt_107_epsilon_0, x = add_306_cast_fp16)[name = tensor<string, []>("rsqrt_107_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_262_cast_fp16 = mul(x = unsqueeze_244_cast_fp16, y = rsqrt_107_cast_fp16)[name = tensor<string, []>("mul_262_cast_fp16")];
            tensor<int32, [1]> squeeze_113_axes_0 = const()[name = tensor<string, []>("squeeze_113_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_113_cast_fp16 = squeeze(axes = squeeze_113_axes_0, x = mul_262_cast_fp16)[name = tensor<string, []>("squeeze_113_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_161_to_fp16 = const()[name = tensor<string, []>("view_161_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29004032)))];
            tensor<fp16, [80, 256, 801]> mul_263_cast_fp16 = mul(x = squeeze_113_cast_fp16, y = view_161_to_fp16)[name = tensor<string, []>("mul_263_cast_fp16")];
            tensor<string, []> conv1d_150_pad_type_0 = const()[name = tensor<string, []>("conv1d_150_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_150_strides_0 = const()[name = tensor<string, []>("conv1d_150_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_150_pad_0 = const()[name = tensor<string, []>("conv1d_150_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_150_dilations_0 = const()[name = tensor<string, []>("conv1d_150_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_150_groups_0 = const()[name = tensor<string, []>("conv1d_150_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_5_seq_net_blocks_0_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_0_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29004608)))];
            tensor<fp16, [1024]> p_net_5_seq_net_blocks_0_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_0_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29528960)))];
            tensor<fp16, [80, 1024, 801]> conv1d_150_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_0_conv_2_bias_to_fp16, dilations = conv1d_150_dilations_0, groups = conv1d_150_groups_0, pad = conv1d_150_pad_0, pad_type = conv1d_150_pad_type_0, strides = conv1d_150_strides_0, weight = p_net_5_seq_net_blocks_0_conv_2_weight_to_fp16, x = mul_263_cast_fp16)[name = tensor<string, []>("conv1d_150_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_27_cast_fp16 = silu(x = conv1d_150_cast_fp16)[name = tensor<string, []>("silu_27_cast_fp16")];
            tensor<string, []> conv1d_151_pad_type_0 = const()[name = tensor<string, []>("conv1d_151_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_151_strides_0 = const()[name = tensor<string, []>("conv1d_151_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_151_pad_0 = const()[name = tensor<string, []>("conv1d_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_151_dilations_0 = const()[name = tensor<string, []>("conv1d_151_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_151_groups_0 = const()[name = tensor<string, []>("conv1d_151_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_5_seq_net_blocks_0_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_0_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29531072)))];
            tensor<fp16, [256]> p_net_5_seq_net_blocks_0_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_0_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30055424)))];
            tensor<fp16, [80, 256, 801]> conv1d_151_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_0_conv_4_bias_to_fp16, dilations = conv1d_151_dilations_0, groups = conv1d_151_groups_0, pad = conv1d_151_pad_0, pad_type = conv1d_151_pad_type_0, strides = conv1d_151_strides_0, weight = p_net_5_seq_net_blocks_0_conv_4_weight_to_fp16, x = silu_27_cast_fp16)[name = tensor<string, []>("conv1d_151_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_307_cast_fp16 = add(x = permute_23_cast_fp16, y = conv1d_151_cast_fp16)[name = tensor<string, []>("add_307_cast_fp16")];
            tensor<string, []> conv1d_152_pad_type_0 = const()[name = tensor<string, []>("conv1d_152_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_152_pad_0 = const()[name = tensor<string, []>("conv1d_152_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_152_groups_0 = const()[name = tensor<string, []>("conv1d_152_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_152_strides_0 = const()[name = tensor<string, []>("conv1d_152_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_152_dilations_0 = const()[name = tensor<string, []>("conv1d_152_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_5_seq_net_blocks_1_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_1_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30056000)))];
            tensor<fp16, [256]> p_net_5_seq_net_blocks_1_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_1_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30059648)))];
            tensor<fp16, [80, 256, 801]> conv1d_152_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_1_conv_0_bias_to_fp16, dilations = conv1d_152_dilations_0, groups = conv1d_152_groups_0, pad = conv1d_152_pad_0, pad_type = conv1d_152_pad_type_0, strides = conv1d_152_strides_0, weight = p_net_5_seq_net_blocks_1_conv_0_weight_to_fp16, x = add_307_cast_fp16)[name = tensor<string, []>("conv1d_152_cast_fp16")];
            tensor<int32, [1]> unsqueeze_246_axes_0 = const()[name = tensor<string, []>("unsqueeze_246_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_246_cast_fp16 = expand_dims(axes = unsqueeze_246_axes_0, x = conv1d_152_cast_fp16)[name = tensor<string, []>("unsqueeze_246_cast_fp16")];
            tensor<fp16, []> const_2434_promoted_to_fp16 = const()[name = tensor<string, []>("const_2434_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_269_cast_fp16 = pow(x = unsqueeze_246_cast_fp16, y = const_2434_promoted_to_fp16)[name = tensor<string, []>("pow_269_cast_fp16")];
            tensor<int32, [1]> mean_108_axes_0 = const()[name = tensor<string, []>("mean_108_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_108_keep_dims_0 = const()[name = tensor<string, []>("mean_108_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_108_cast_fp16 = reduce_mean(axes = mean_108_axes_0, keep_dims = mean_108_keep_dims_0, x = pow_269_cast_fp16)[name = tensor<string, []>("mean_108_cast_fp16")];
            tensor<fp16, []> const_2437_to_fp16 = const()[name = tensor<string, []>("const_2437_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_308_cast_fp16 = add(x = mean_108_cast_fp16, y = const_2437_to_fp16)[name = tensor<string, []>("add_308_cast_fp16")];
            tensor<fp32, []> rsqrt_108_epsilon_0 = const()[name = tensor<string, []>("rsqrt_108_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_108_cast_fp16 = rsqrt(epsilon = rsqrt_108_epsilon_0, x = add_308_cast_fp16)[name = tensor<string, []>("rsqrt_108_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_264_cast_fp16 = mul(x = unsqueeze_246_cast_fp16, y = rsqrt_108_cast_fp16)[name = tensor<string, []>("mul_264_cast_fp16")];
            tensor<int32, [1]> squeeze_114_axes_0 = const()[name = tensor<string, []>("squeeze_114_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_114_cast_fp16 = squeeze(axes = squeeze_114_axes_0, x = mul_264_cast_fp16)[name = tensor<string, []>("squeeze_114_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_162_to_fp16 = const()[name = tensor<string, []>("view_162_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30060224)))];
            tensor<fp16, [80, 256, 801]> mul_265_cast_fp16 = mul(x = squeeze_114_cast_fp16, y = view_162_to_fp16)[name = tensor<string, []>("mul_265_cast_fp16")];
            tensor<string, []> conv1d_153_pad_type_0 = const()[name = tensor<string, []>("conv1d_153_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_153_strides_0 = const()[name = tensor<string, []>("conv1d_153_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_153_pad_0 = const()[name = tensor<string, []>("conv1d_153_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_153_dilations_0 = const()[name = tensor<string, []>("conv1d_153_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_153_groups_0 = const()[name = tensor<string, []>("conv1d_153_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_5_seq_net_blocks_1_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_1_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30060800)))];
            tensor<fp16, [1024]> p_net_5_seq_net_blocks_1_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_1_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30585152)))];
            tensor<fp16, [80, 1024, 801]> conv1d_153_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_1_conv_2_bias_to_fp16, dilations = conv1d_153_dilations_0, groups = conv1d_153_groups_0, pad = conv1d_153_pad_0, pad_type = conv1d_153_pad_type_0, strides = conv1d_153_strides_0, weight = p_net_5_seq_net_blocks_1_conv_2_weight_to_fp16, x = mul_265_cast_fp16)[name = tensor<string, []>("conv1d_153_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_28_cast_fp16 = silu(x = conv1d_153_cast_fp16)[name = tensor<string, []>("silu_28_cast_fp16")];
            tensor<string, []> conv1d_154_pad_type_0 = const()[name = tensor<string, []>("conv1d_154_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_154_strides_0 = const()[name = tensor<string, []>("conv1d_154_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_154_pad_0 = const()[name = tensor<string, []>("conv1d_154_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_154_dilations_0 = const()[name = tensor<string, []>("conv1d_154_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_154_groups_0 = const()[name = tensor<string, []>("conv1d_154_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_5_seq_net_blocks_1_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_1_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30587264)))];
            tensor<fp16, [256]> p_net_5_seq_net_blocks_1_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_1_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31111616)))];
            tensor<fp16, [80, 256, 801]> conv1d_154_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_1_conv_4_bias_to_fp16, dilations = conv1d_154_dilations_0, groups = conv1d_154_groups_0, pad = conv1d_154_pad_0, pad_type = conv1d_154_pad_type_0, strides = conv1d_154_strides_0, weight = p_net_5_seq_net_blocks_1_conv_4_weight_to_fp16, x = silu_28_cast_fp16)[name = tensor<string, []>("conv1d_154_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_309_cast_fp16 = add(x = add_307_cast_fp16, y = conv1d_154_cast_fp16)[name = tensor<string, []>("add_309_cast_fp16")];
            tensor<string, []> conv1d_155_pad_type_0 = const()[name = tensor<string, []>("conv1d_155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv1d_155_pad_0 = const()[name = tensor<string, []>("conv1d_155_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, []> conv1d_155_groups_0 = const()[name = tensor<string, []>("conv1d_155_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [1]> conv1d_155_strides_0 = const()[name = tensor<string, []>("conv1d_155_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> conv1d_155_dilations_0 = const()[name = tensor<string, []>("conv1d_155_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> p_net_5_seq_net_blocks_2_conv_0_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_2_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31112192)))];
            tensor<fp16, [256]> p_net_5_seq_net_blocks_2_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_2_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31115840)))];
            tensor<fp16, [80, 256, 801]> conv1d_155_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_2_conv_0_bias_to_fp16, dilations = conv1d_155_dilations_0, groups = conv1d_155_groups_0, pad = conv1d_155_pad_0, pad_type = conv1d_155_pad_type_0, strides = conv1d_155_strides_0, weight = p_net_5_seq_net_blocks_2_conv_0_weight_to_fp16, x = add_309_cast_fp16)[name = tensor<string, []>("conv1d_155_cast_fp16")];
            tensor<int32, [1]> unsqueeze_248_axes_0 = const()[name = tensor<string, []>("unsqueeze_248_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 1, 256, 801]> unsqueeze_248_cast_fp16 = expand_dims(axes = unsqueeze_248_axes_0, x = conv1d_155_cast_fp16)[name = tensor<string, []>("unsqueeze_248_cast_fp16")];
            tensor<fp16, []> const_2448_promoted_to_fp16 = const()[name = tensor<string, []>("const_2448_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [80, 1, 256, 801]> pow_270_cast_fp16 = pow(x = unsqueeze_248_cast_fp16, y = const_2448_promoted_to_fp16)[name = tensor<string, []>("pow_270_cast_fp16")];
            tensor<int32, [1]> mean_109_axes_0 = const()[name = tensor<string, []>("mean_109_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_109_keep_dims_0 = const()[name = tensor<string, []>("mean_109_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [80, 1, 1, 801]> mean_109_cast_fp16 = reduce_mean(axes = mean_109_axes_0, keep_dims = mean_109_keep_dims_0, x = pow_270_cast_fp16)[name = tensor<string, []>("mean_109_cast_fp16")];
            tensor<fp16, []> const_2451_to_fp16 = const()[name = tensor<string, []>("const_2451_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [80, 1, 1, 801]> add_310_cast_fp16 = add(x = mean_109_cast_fp16, y = const_2451_to_fp16)[name = tensor<string, []>("add_310_cast_fp16")];
            tensor<fp32, []> rsqrt_109_epsilon_0 = const()[name = tensor<string, []>("rsqrt_109_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [80, 1, 1, 801]> rsqrt_109_cast_fp16 = rsqrt(epsilon = rsqrt_109_epsilon_0, x = add_310_cast_fp16)[name = tensor<string, []>("rsqrt_109_cast_fp16")];
            tensor<fp16, [80, 1, 256, 801]> mul_266_cast_fp16 = mul(x = unsqueeze_248_cast_fp16, y = rsqrt_109_cast_fp16)[name = tensor<string, []>("mul_266_cast_fp16")];
            tensor<int32, [1]> squeeze_115_axes_0 = const()[name = tensor<string, []>("squeeze_115_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [80, 256, 801]> squeeze_115_cast_fp16 = squeeze(axes = squeeze_115_axes_0, x = mul_266_cast_fp16)[name = tensor<string, []>("squeeze_115_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_163_to_fp16 = const()[name = tensor<string, []>("view_163_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31116416)))];
            tensor<fp16, [80, 256, 801]> mul_267_cast_fp16 = mul(x = squeeze_115_cast_fp16, y = view_163_to_fp16)[name = tensor<string, []>("mul_267_cast_fp16")];
            tensor<string, []> conv1d_156_pad_type_0 = const()[name = tensor<string, []>("conv1d_156_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_156_strides_0 = const()[name = tensor<string, []>("conv1d_156_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_156_pad_0 = const()[name = tensor<string, []>("conv1d_156_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_156_dilations_0 = const()[name = tensor<string, []>("conv1d_156_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_156_groups_0 = const()[name = tensor<string, []>("conv1d_156_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 256, 1]> p_net_5_seq_net_blocks_2_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_2_conv_2_weight_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31116992)))];
            tensor<fp16, [1024]> p_net_5_seq_net_blocks_2_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_2_conv_2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31641344)))];
            tensor<fp16, [80, 1024, 801]> conv1d_156_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_2_conv_2_bias_to_fp16, dilations = conv1d_156_dilations_0, groups = conv1d_156_groups_0, pad = conv1d_156_pad_0, pad_type = conv1d_156_pad_type_0, strides = conv1d_156_strides_0, weight = p_net_5_seq_net_blocks_2_conv_2_weight_to_fp16, x = mul_267_cast_fp16)[name = tensor<string, []>("conv1d_156_cast_fp16")];
            tensor<fp16, [80, 1024, 801]> silu_29_cast_fp16 = silu(x = conv1d_156_cast_fp16)[name = tensor<string, []>("silu_29_cast_fp16")];
            tensor<string, []> conv1d_157_pad_type_0 = const()[name = tensor<string, []>("conv1d_157_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_157_strides_0 = const()[name = tensor<string, []>("conv1d_157_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_157_pad_0 = const()[name = tensor<string, []>("conv1d_157_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_157_dilations_0 = const()[name = tensor<string, []>("conv1d_157_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_157_groups_0 = const()[name = tensor<string, []>("conv1d_157_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1024, 1]> p_net_5_seq_net_blocks_2_conv_4_weight_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_2_conv_4_weight_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31643456)))];
            tensor<fp16, [256]> p_net_5_seq_net_blocks_2_conv_4_bias_to_fp16 = const()[name = tensor<string, []>("p_net_5_seq_net_blocks_2_conv_4_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32167808)))];
            tensor<fp16, [80, 256, 801]> conv1d_157_cast_fp16 = conv(bias = p_net_5_seq_net_blocks_2_conv_4_bias_to_fp16, dilations = conv1d_157_dilations_0, groups = conv1d_157_groups_0, pad = conv1d_157_pad_0, pad_type = conv1d_157_pad_type_0, strides = conv1d_157_strides_0, weight = p_net_5_seq_net_blocks_2_conv_4_weight_to_fp16, x = silu_29_cast_fp16)[name = tensor<string, []>("conv1d_157_cast_fp16")];
            tensor<fp16, [80, 256, 801]> add_311_cast_fp16 = add(x = add_309_cast_fp16, y = conv1d_157_cast_fp16)[name = tensor<string, []>("add_311_cast_fp16")];
            tensor<int32, [1]> unsqueeze_249_axes_0 = const()[name = tensor<string, []>("unsqueeze_249_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 80, 256, 801]> unsqueeze_249_cast_fp16 = expand_dims(axes = unsqueeze_249_axes_0, x = add_311_cast_fp16)[name = tensor<string, []>("unsqueeze_249_cast_fp16")];
            tensor<int32, [4]> select_2_begin_0 = const()[name = tensor<string, []>("select_2_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_2_end_0 = const()[name = tensor<string, []>("select_2_end_0"), val = tensor<int32, [4]>([1, 1, 256, 801])];
            tensor<bool, [4]> select_2_end_mask_0 = const()[name = tensor<string, []>("select_2_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_2_squeeze_mask_0 = const()[name = tensor<string, []>("select_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_2_cast_fp16 = slice_by_index(begin = select_2_begin_0, end = select_2_end_0, end_mask = select_2_end_mask_0, squeeze_mask = select_2_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_2_cast_fp16")];
            tensor<int32, [1]> unsqueeze_251_axes_0 = const()[name = tensor<string, []>("unsqueeze_251_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_251_cast_fp16 = expand_dims(axes = unsqueeze_251_axes_0, x = select_2_cast_fp16)[name = tensor<string, []>("unsqueeze_251_cast_fp16")];
            tensor<fp16, []> const_2461_promoted_to_fp16 = const()[name = tensor<string, []>("const_2461_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_271_cast_fp16 = pow(x = unsqueeze_251_cast_fp16, y = const_2461_promoted_to_fp16)[name = tensor<string, []>("pow_271_cast_fp16")];
            tensor<int32, [1]> mean_110_axes_0 = const()[name = tensor<string, []>("mean_110_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_110_keep_dims_0 = const()[name = tensor<string, []>("mean_110_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_110_cast_fp16 = reduce_mean(axes = mean_110_axes_0, keep_dims = mean_110_keep_dims_0, x = pow_271_cast_fp16)[name = tensor<string, []>("mean_110_cast_fp16")];
            tensor<fp16, []> const_2464_to_fp16 = const()[name = tensor<string, []>("const_2464_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_312_cast_fp16 = add(x = mean_110_cast_fp16, y = const_2464_to_fp16)[name = tensor<string, []>("add_312_cast_fp16")];
            tensor<fp32, []> rsqrt_110_epsilon_0 = const()[name = tensor<string, []>("rsqrt_110_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_110_cast_fp16 = rsqrt(epsilon = rsqrt_110_epsilon_0, x = add_312_cast_fp16)[name = tensor<string, []>("rsqrt_110_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_268_cast_fp16 = mul(x = unsqueeze_251_cast_fp16, y = rsqrt_110_cast_fp16)[name = tensor<string, []>("mul_268_cast_fp16")];
            tensor<int32, [1]> squeeze_116_axes_0 = const()[name = tensor<string, []>("squeeze_116_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_116_cast_fp16 = squeeze(axes = squeeze_116_axes_0, x = mul_268_cast_fp16)[name = tensor<string, []>("squeeze_116_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_164_to_fp16 = const()[name = tensor<string, []>("view_164_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32168384)))];
            tensor<fp16, [1, 256, 801]> mul_269_cast_fp16 = mul(x = squeeze_116_cast_fp16, y = view_164_to_fp16)[name = tensor<string, []>("mul_269_cast_fp16")];
            tensor<string, []> conv1d_158_pad_type_0 = const()[name = tensor<string, []>("conv1d_158_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_158_strides_0 = const()[name = tensor<string, []>("conv1d_158_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_158_pad_0 = const()[name = tensor<string, []>("conv1d_158_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_158_dilations_0 = const()[name = tensor<string, []>("conv1d_158_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_158_groups_0 = const()[name = tensor<string, []>("conv1d_158_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_0_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_0_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32168960)))];
            tensor<fp16, [20]> p_output_0_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_0_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32179264)))];
            tensor<fp16, [1, 20, 801]> conv1d_158_cast_fp16 = conv(bias = p_output_0_1_bias_to_fp16, dilations = conv1d_158_dilations_0, groups = conv1d_158_groups_0, pad = conv1d_158_pad_0, pad_type = conv1d_158_pad_type_0, strides = conv1d_158_strides_0, weight = p_output_0_1_weight_to_fp16, x = mul_269_cast_fp16)[name = tensor<string, []>("conv1d_158_cast_fp16")];
            tensor<int32, []> glu_split_num_splits_0 = const()[name = tensor<string, []>("glu_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_split_axis_0 = const()[name = tensor<string, []>("glu_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_split_cast_fp16_1 = split(axis = glu_split_axis_0, num_splits = glu_split_num_splits_0, x = conv1d_158_cast_fp16)[name = tensor<string, []>("glu_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_split_cast_fp16_1)[name = tensor<string, []>("glu_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_cast_fp16 = mul(x = glu_split_cast_fp16_0, y = glu_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_cast_fp16")];
            tensor<int32, [4]> const_2468 = const()[name = tensor<string, []>("const_2468"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_165_cast_fp16 = reshape(shape = const_2468, x = glu_cast_fp16)[name = tensor<string, []>("view_165_cast_fp16")];
            tensor<int32, [4]> select_3_begin_0 = const()[name = tensor<string, []>("select_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_3_end_0 = const()[name = tensor<string, []>("select_3_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_3_end_mask_0 = const()[name = tensor<string, []>("select_3_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_3_squeeze_mask_0 = const()[name = tensor<string, []>("select_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_3_cast_fp16 = slice_by_index(begin = select_3_begin_0, end = select_3_end_0, end_mask = select_3_end_mask_0, squeeze_mask = select_3_squeeze_mask_0, x = view_165_cast_fp16)[name = tensor<string, []>("select_3_cast_fp16")];
            tensor<int32, [4]> select_4_begin_0 = const()[name = tensor<string, []>("select_4_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_4_end_0 = const()[name = tensor<string, []>("select_4_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_4_end_mask_0 = const()[name = tensor<string, []>("select_4_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_4_squeeze_mask_0 = const()[name = tensor<string, []>("select_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_4_cast_fp16 = slice_by_index(begin = select_4_begin_0, end = select_4_end_0, end_mask = select_4_end_mask_0, squeeze_mask = select_4_squeeze_mask_0, x = view_165_cast_fp16)[name = tensor<string, []>("select_4_cast_fp16")];
            tensor<int32, [4]> select_5_begin_0 = const()[name = tensor<string, []>("select_5_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_5_end_0 = const()[name = tensor<string, []>("select_5_end_0"), val = tensor<int32, [4]>([1, 2, 256, 801])];
            tensor<bool, [4]> select_5_end_mask_0 = const()[name = tensor<string, []>("select_5_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_5_squeeze_mask_0 = const()[name = tensor<string, []>("select_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_5_cast_fp16 = slice_by_index(begin = select_5_begin_0, end = select_5_end_0, end_mask = select_5_end_mask_0, squeeze_mask = select_5_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_5_cast_fp16")];
            tensor<int32, [1]> unsqueeze_253_axes_0 = const()[name = tensor<string, []>("unsqueeze_253_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_253_cast_fp16 = expand_dims(axes = unsqueeze_253_axes_0, x = select_5_cast_fp16)[name = tensor<string, []>("unsqueeze_253_cast_fp16")];
            tensor<fp16, []> const_2479_promoted_to_fp16 = const()[name = tensor<string, []>("const_2479_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_272_cast_fp16 = pow(x = unsqueeze_253_cast_fp16, y = const_2479_promoted_to_fp16)[name = tensor<string, []>("pow_272_cast_fp16")];
            tensor<int32, [1]> mean_111_axes_0 = const()[name = tensor<string, []>("mean_111_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_111_keep_dims_0 = const()[name = tensor<string, []>("mean_111_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_111_cast_fp16 = reduce_mean(axes = mean_111_axes_0, keep_dims = mean_111_keep_dims_0, x = pow_272_cast_fp16)[name = tensor<string, []>("mean_111_cast_fp16")];
            tensor<fp16, []> const_2482_to_fp16 = const()[name = tensor<string, []>("const_2482_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_313_cast_fp16 = add(x = mean_111_cast_fp16, y = const_2482_to_fp16)[name = tensor<string, []>("add_313_cast_fp16")];
            tensor<fp32, []> rsqrt_111_epsilon_0 = const()[name = tensor<string, []>("rsqrt_111_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_111_cast_fp16 = rsqrt(epsilon = rsqrt_111_epsilon_0, x = add_313_cast_fp16)[name = tensor<string, []>("rsqrt_111_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_270_cast_fp16 = mul(x = unsqueeze_253_cast_fp16, y = rsqrt_111_cast_fp16)[name = tensor<string, []>("mul_270_cast_fp16")];
            tensor<int32, [1]> squeeze_117_axes_0 = const()[name = tensor<string, []>("squeeze_117_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_117_cast_fp16 = squeeze(axes = squeeze_117_axes_0, x = mul_270_cast_fp16)[name = tensor<string, []>("squeeze_117_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_166_to_fp16 = const()[name = tensor<string, []>("view_166_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32179392)))];
            tensor<fp16, [1, 256, 801]> mul_271_cast_fp16 = mul(x = squeeze_117_cast_fp16, y = view_166_to_fp16)[name = tensor<string, []>("mul_271_cast_fp16")];
            tensor<string, []> conv1d_159_pad_type_0 = const()[name = tensor<string, []>("conv1d_159_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_159_strides_0 = const()[name = tensor<string, []>("conv1d_159_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_159_pad_0 = const()[name = tensor<string, []>("conv1d_159_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_159_dilations_0 = const()[name = tensor<string, []>("conv1d_159_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_159_groups_0 = const()[name = tensor<string, []>("conv1d_159_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_1_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_1_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32179968)))];
            tensor<fp16, [20]> p_output_1_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_1_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32190272)))];
            tensor<fp16, [1, 20, 801]> conv1d_159_cast_fp16 = conv(bias = p_output_1_1_bias_to_fp16, dilations = conv1d_159_dilations_0, groups = conv1d_159_groups_0, pad = conv1d_159_pad_0, pad_type = conv1d_159_pad_type_0, strides = conv1d_159_strides_0, weight = p_output_1_1_weight_to_fp16, x = mul_271_cast_fp16)[name = tensor<string, []>("conv1d_159_cast_fp16")];
            tensor<int32, []> glu_1_split_num_splits_0 = const()[name = tensor<string, []>("glu_1_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_1_split_axis_0 = const()[name = tensor<string, []>("glu_1_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_1_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_1_split_cast_fp16_1 = split(axis = glu_1_split_axis_0, num_splits = glu_1_split_num_splits_0, x = conv1d_159_cast_fp16)[name = tensor<string, []>("glu_1_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_1_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_1_split_cast_fp16_1)[name = tensor<string, []>("glu_1_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_1_cast_fp16 = mul(x = glu_1_split_cast_fp16_0, y = glu_1_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_1_cast_fp16")];
            tensor<int32, [4]> const_2486 = const()[name = tensor<string, []>("const_2486"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_167_cast_fp16 = reshape(shape = const_2486, x = glu_1_cast_fp16)[name = tensor<string, []>("view_167_cast_fp16")];
            tensor<int32, [4]> select_6_begin_0 = const()[name = tensor<string, []>("select_6_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_6_end_0 = const()[name = tensor<string, []>("select_6_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_6_end_mask_0 = const()[name = tensor<string, []>("select_6_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_6_squeeze_mask_0 = const()[name = tensor<string, []>("select_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_6_cast_fp16 = slice_by_index(begin = select_6_begin_0, end = select_6_end_0, end_mask = select_6_end_mask_0, squeeze_mask = select_6_squeeze_mask_0, x = view_167_cast_fp16)[name = tensor<string, []>("select_6_cast_fp16")];
            tensor<int32, [4]> select_7_begin_0 = const()[name = tensor<string, []>("select_7_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_7_end_0 = const()[name = tensor<string, []>("select_7_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_7_end_mask_0 = const()[name = tensor<string, []>("select_7_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_7_squeeze_mask_0 = const()[name = tensor<string, []>("select_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_7_cast_fp16 = slice_by_index(begin = select_7_begin_0, end = select_7_end_0, end_mask = select_7_end_mask_0, squeeze_mask = select_7_squeeze_mask_0, x = view_167_cast_fp16)[name = tensor<string, []>("select_7_cast_fp16")];
            tensor<int32, [4]> select_8_begin_0 = const()[name = tensor<string, []>("select_8_begin_0"), val = tensor<int32, [4]>([0, 2, 0, 0])];
            tensor<int32, [4]> select_8_end_0 = const()[name = tensor<string, []>("select_8_end_0"), val = tensor<int32, [4]>([1, 3, 256, 801])];
            tensor<bool, [4]> select_8_end_mask_0 = const()[name = tensor<string, []>("select_8_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_8_squeeze_mask_0 = const()[name = tensor<string, []>("select_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_8_cast_fp16 = slice_by_index(begin = select_8_begin_0, end = select_8_end_0, end_mask = select_8_end_mask_0, squeeze_mask = select_8_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_8_cast_fp16")];
            tensor<int32, [1]> unsqueeze_255_axes_0 = const()[name = tensor<string, []>("unsqueeze_255_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_255_cast_fp16 = expand_dims(axes = unsqueeze_255_axes_0, x = select_8_cast_fp16)[name = tensor<string, []>("unsqueeze_255_cast_fp16")];
            tensor<fp16, []> const_2497_promoted_to_fp16 = const()[name = tensor<string, []>("const_2497_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_273_cast_fp16 = pow(x = unsqueeze_255_cast_fp16, y = const_2497_promoted_to_fp16)[name = tensor<string, []>("pow_273_cast_fp16")];
            tensor<int32, [1]> mean_112_axes_0 = const()[name = tensor<string, []>("mean_112_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_112_keep_dims_0 = const()[name = tensor<string, []>("mean_112_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_112_cast_fp16 = reduce_mean(axes = mean_112_axes_0, keep_dims = mean_112_keep_dims_0, x = pow_273_cast_fp16)[name = tensor<string, []>("mean_112_cast_fp16")];
            tensor<fp16, []> const_2500_to_fp16 = const()[name = tensor<string, []>("const_2500_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_314_cast_fp16 = add(x = mean_112_cast_fp16, y = const_2500_to_fp16)[name = tensor<string, []>("add_314_cast_fp16")];
            tensor<fp32, []> rsqrt_112_epsilon_0 = const()[name = tensor<string, []>("rsqrt_112_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_112_cast_fp16 = rsqrt(epsilon = rsqrt_112_epsilon_0, x = add_314_cast_fp16)[name = tensor<string, []>("rsqrt_112_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_272_cast_fp16 = mul(x = unsqueeze_255_cast_fp16, y = rsqrt_112_cast_fp16)[name = tensor<string, []>("mul_272_cast_fp16")];
            tensor<int32, [1]> squeeze_118_axes_0 = const()[name = tensor<string, []>("squeeze_118_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_118_cast_fp16 = squeeze(axes = squeeze_118_axes_0, x = mul_272_cast_fp16)[name = tensor<string, []>("squeeze_118_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_168_to_fp16 = const()[name = tensor<string, []>("view_168_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32190400)))];
            tensor<fp16, [1, 256, 801]> mul_273_cast_fp16 = mul(x = squeeze_118_cast_fp16, y = view_168_to_fp16)[name = tensor<string, []>("mul_273_cast_fp16")];
            tensor<string, []> conv1d_160_pad_type_0 = const()[name = tensor<string, []>("conv1d_160_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_160_strides_0 = const()[name = tensor<string, []>("conv1d_160_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_160_pad_0 = const()[name = tensor<string, []>("conv1d_160_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_160_dilations_0 = const()[name = tensor<string, []>("conv1d_160_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_160_groups_0 = const()[name = tensor<string, []>("conv1d_160_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_2_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_2_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32190976)))];
            tensor<fp16, [20]> p_output_2_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_2_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32201280)))];
            tensor<fp16, [1, 20, 801]> conv1d_160_cast_fp16 = conv(bias = p_output_2_1_bias_to_fp16, dilations = conv1d_160_dilations_0, groups = conv1d_160_groups_0, pad = conv1d_160_pad_0, pad_type = conv1d_160_pad_type_0, strides = conv1d_160_strides_0, weight = p_output_2_1_weight_to_fp16, x = mul_273_cast_fp16)[name = tensor<string, []>("conv1d_160_cast_fp16")];
            tensor<int32, []> glu_2_split_num_splits_0 = const()[name = tensor<string, []>("glu_2_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_2_split_axis_0 = const()[name = tensor<string, []>("glu_2_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_2_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_2_split_cast_fp16_1 = split(axis = glu_2_split_axis_0, num_splits = glu_2_split_num_splits_0, x = conv1d_160_cast_fp16)[name = tensor<string, []>("glu_2_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_2_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_2_split_cast_fp16_1)[name = tensor<string, []>("glu_2_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_2_cast_fp16 = mul(x = glu_2_split_cast_fp16_0, y = glu_2_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_2_cast_fp16")];
            tensor<int32, [4]> const_2504 = const()[name = tensor<string, []>("const_2504"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_169_cast_fp16 = reshape(shape = const_2504, x = glu_2_cast_fp16)[name = tensor<string, []>("view_169_cast_fp16")];
            tensor<int32, [4]> select_9_begin_0 = const()[name = tensor<string, []>("select_9_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_9_end_0 = const()[name = tensor<string, []>("select_9_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_9_end_mask_0 = const()[name = tensor<string, []>("select_9_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_9_squeeze_mask_0 = const()[name = tensor<string, []>("select_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_9_cast_fp16 = slice_by_index(begin = select_9_begin_0, end = select_9_end_0, end_mask = select_9_end_mask_0, squeeze_mask = select_9_squeeze_mask_0, x = view_169_cast_fp16)[name = tensor<string, []>("select_9_cast_fp16")];
            tensor<int32, [4]> select_10_begin_0 = const()[name = tensor<string, []>("select_10_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_10_end_0 = const()[name = tensor<string, []>("select_10_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_10_end_mask_0 = const()[name = tensor<string, []>("select_10_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_10_squeeze_mask_0 = const()[name = tensor<string, []>("select_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_10_cast_fp16 = slice_by_index(begin = select_10_begin_0, end = select_10_end_0, end_mask = select_10_end_mask_0, squeeze_mask = select_10_squeeze_mask_0, x = view_169_cast_fp16)[name = tensor<string, []>("select_10_cast_fp16")];
            tensor<int32, [4]> select_11_begin_0 = const()[name = tensor<string, []>("select_11_begin_0"), val = tensor<int32, [4]>([0, 3, 0, 0])];
            tensor<int32, [4]> select_11_end_0 = const()[name = tensor<string, []>("select_11_end_0"), val = tensor<int32, [4]>([1, 4, 256, 801])];
            tensor<bool, [4]> select_11_end_mask_0 = const()[name = tensor<string, []>("select_11_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_11_squeeze_mask_0 = const()[name = tensor<string, []>("select_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_11_cast_fp16 = slice_by_index(begin = select_11_begin_0, end = select_11_end_0, end_mask = select_11_end_mask_0, squeeze_mask = select_11_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_11_cast_fp16")];
            tensor<int32, [1]> unsqueeze_257_axes_0 = const()[name = tensor<string, []>("unsqueeze_257_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_257_cast_fp16 = expand_dims(axes = unsqueeze_257_axes_0, x = select_11_cast_fp16)[name = tensor<string, []>("unsqueeze_257_cast_fp16")];
            tensor<fp16, []> const_2515_promoted_to_fp16 = const()[name = tensor<string, []>("const_2515_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_274_cast_fp16 = pow(x = unsqueeze_257_cast_fp16, y = const_2515_promoted_to_fp16)[name = tensor<string, []>("pow_274_cast_fp16")];
            tensor<int32, [1]> mean_113_axes_0 = const()[name = tensor<string, []>("mean_113_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_113_keep_dims_0 = const()[name = tensor<string, []>("mean_113_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_113_cast_fp16 = reduce_mean(axes = mean_113_axes_0, keep_dims = mean_113_keep_dims_0, x = pow_274_cast_fp16)[name = tensor<string, []>("mean_113_cast_fp16")];
            tensor<fp16, []> const_2518_to_fp16 = const()[name = tensor<string, []>("const_2518_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_315_cast_fp16 = add(x = mean_113_cast_fp16, y = const_2518_to_fp16)[name = tensor<string, []>("add_315_cast_fp16")];
            tensor<fp32, []> rsqrt_113_epsilon_0 = const()[name = tensor<string, []>("rsqrt_113_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_113_cast_fp16 = rsqrt(epsilon = rsqrt_113_epsilon_0, x = add_315_cast_fp16)[name = tensor<string, []>("rsqrt_113_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_274_cast_fp16 = mul(x = unsqueeze_257_cast_fp16, y = rsqrt_113_cast_fp16)[name = tensor<string, []>("mul_274_cast_fp16")];
            tensor<int32, [1]> squeeze_119_axes_0 = const()[name = tensor<string, []>("squeeze_119_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_119_cast_fp16 = squeeze(axes = squeeze_119_axes_0, x = mul_274_cast_fp16)[name = tensor<string, []>("squeeze_119_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_170_to_fp16 = const()[name = tensor<string, []>("view_170_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32201408)))];
            tensor<fp16, [1, 256, 801]> mul_275_cast_fp16 = mul(x = squeeze_119_cast_fp16, y = view_170_to_fp16)[name = tensor<string, []>("mul_275_cast_fp16")];
            tensor<string, []> conv1d_161_pad_type_0 = const()[name = tensor<string, []>("conv1d_161_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_161_strides_0 = const()[name = tensor<string, []>("conv1d_161_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_161_pad_0 = const()[name = tensor<string, []>("conv1d_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_161_dilations_0 = const()[name = tensor<string, []>("conv1d_161_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_161_groups_0 = const()[name = tensor<string, []>("conv1d_161_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_3_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_3_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32201984)))];
            tensor<fp16, [20]> p_output_3_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_3_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32212288)))];
            tensor<fp16, [1, 20, 801]> conv1d_161_cast_fp16 = conv(bias = p_output_3_1_bias_to_fp16, dilations = conv1d_161_dilations_0, groups = conv1d_161_groups_0, pad = conv1d_161_pad_0, pad_type = conv1d_161_pad_type_0, strides = conv1d_161_strides_0, weight = p_output_3_1_weight_to_fp16, x = mul_275_cast_fp16)[name = tensor<string, []>("conv1d_161_cast_fp16")];
            tensor<int32, []> glu_3_split_num_splits_0 = const()[name = tensor<string, []>("glu_3_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_3_split_axis_0 = const()[name = tensor<string, []>("glu_3_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_3_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_3_split_cast_fp16_1 = split(axis = glu_3_split_axis_0, num_splits = glu_3_split_num_splits_0, x = conv1d_161_cast_fp16)[name = tensor<string, []>("glu_3_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_3_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_3_split_cast_fp16_1)[name = tensor<string, []>("glu_3_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_3_cast_fp16 = mul(x = glu_3_split_cast_fp16_0, y = glu_3_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_3_cast_fp16")];
            tensor<int32, [4]> const_2522 = const()[name = tensor<string, []>("const_2522"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_171_cast_fp16 = reshape(shape = const_2522, x = glu_3_cast_fp16)[name = tensor<string, []>("view_171_cast_fp16")];
            tensor<int32, [4]> select_12_begin_0 = const()[name = tensor<string, []>("select_12_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_12_end_0 = const()[name = tensor<string, []>("select_12_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_12_end_mask_0 = const()[name = tensor<string, []>("select_12_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_12_squeeze_mask_0 = const()[name = tensor<string, []>("select_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_12_cast_fp16 = slice_by_index(begin = select_12_begin_0, end = select_12_end_0, end_mask = select_12_end_mask_0, squeeze_mask = select_12_squeeze_mask_0, x = view_171_cast_fp16)[name = tensor<string, []>("select_12_cast_fp16")];
            tensor<int32, [4]> select_13_begin_0 = const()[name = tensor<string, []>("select_13_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_13_end_0 = const()[name = tensor<string, []>("select_13_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_13_end_mask_0 = const()[name = tensor<string, []>("select_13_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_13_squeeze_mask_0 = const()[name = tensor<string, []>("select_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_13_cast_fp16 = slice_by_index(begin = select_13_begin_0, end = select_13_end_0, end_mask = select_13_end_mask_0, squeeze_mask = select_13_squeeze_mask_0, x = view_171_cast_fp16)[name = tensor<string, []>("select_13_cast_fp16")];
            tensor<int32, [4]> select_14_begin_0 = const()[name = tensor<string, []>("select_14_begin_0"), val = tensor<int32, [4]>([0, 4, 0, 0])];
            tensor<int32, [4]> select_14_end_0 = const()[name = tensor<string, []>("select_14_end_0"), val = tensor<int32, [4]>([1, 5, 256, 801])];
            tensor<bool, [4]> select_14_end_mask_0 = const()[name = tensor<string, []>("select_14_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_14_squeeze_mask_0 = const()[name = tensor<string, []>("select_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_14_cast_fp16 = slice_by_index(begin = select_14_begin_0, end = select_14_end_0, end_mask = select_14_end_mask_0, squeeze_mask = select_14_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_14_cast_fp16")];
            tensor<int32, [1]> unsqueeze_259_axes_0 = const()[name = tensor<string, []>("unsqueeze_259_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_259_cast_fp16 = expand_dims(axes = unsqueeze_259_axes_0, x = select_14_cast_fp16)[name = tensor<string, []>("unsqueeze_259_cast_fp16")];
            tensor<fp16, []> const_2533_promoted_to_fp16 = const()[name = tensor<string, []>("const_2533_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_275_cast_fp16 = pow(x = unsqueeze_259_cast_fp16, y = const_2533_promoted_to_fp16)[name = tensor<string, []>("pow_275_cast_fp16")];
            tensor<int32, [1]> mean_114_axes_0 = const()[name = tensor<string, []>("mean_114_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_114_keep_dims_0 = const()[name = tensor<string, []>("mean_114_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_114_cast_fp16 = reduce_mean(axes = mean_114_axes_0, keep_dims = mean_114_keep_dims_0, x = pow_275_cast_fp16)[name = tensor<string, []>("mean_114_cast_fp16")];
            tensor<fp16, []> const_2536_to_fp16 = const()[name = tensor<string, []>("const_2536_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_316_cast_fp16 = add(x = mean_114_cast_fp16, y = const_2536_to_fp16)[name = tensor<string, []>("add_316_cast_fp16")];
            tensor<fp32, []> rsqrt_114_epsilon_0 = const()[name = tensor<string, []>("rsqrt_114_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_114_cast_fp16 = rsqrt(epsilon = rsqrt_114_epsilon_0, x = add_316_cast_fp16)[name = tensor<string, []>("rsqrt_114_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_276_cast_fp16 = mul(x = unsqueeze_259_cast_fp16, y = rsqrt_114_cast_fp16)[name = tensor<string, []>("mul_276_cast_fp16")];
            tensor<int32, [1]> squeeze_120_axes_0 = const()[name = tensor<string, []>("squeeze_120_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_120_cast_fp16 = squeeze(axes = squeeze_120_axes_0, x = mul_276_cast_fp16)[name = tensor<string, []>("squeeze_120_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_172_to_fp16 = const()[name = tensor<string, []>("view_172_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32212416)))];
            tensor<fp16, [1, 256, 801]> mul_277_cast_fp16 = mul(x = squeeze_120_cast_fp16, y = view_172_to_fp16)[name = tensor<string, []>("mul_277_cast_fp16")];
            tensor<string, []> conv1d_162_pad_type_0 = const()[name = tensor<string, []>("conv1d_162_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_162_strides_0 = const()[name = tensor<string, []>("conv1d_162_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_162_pad_0 = const()[name = tensor<string, []>("conv1d_162_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_162_dilations_0 = const()[name = tensor<string, []>("conv1d_162_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_162_groups_0 = const()[name = tensor<string, []>("conv1d_162_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_4_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_4_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32212992)))];
            tensor<fp16, [20]> p_output_4_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_4_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32223296)))];
            tensor<fp16, [1, 20, 801]> conv1d_162_cast_fp16 = conv(bias = p_output_4_1_bias_to_fp16, dilations = conv1d_162_dilations_0, groups = conv1d_162_groups_0, pad = conv1d_162_pad_0, pad_type = conv1d_162_pad_type_0, strides = conv1d_162_strides_0, weight = p_output_4_1_weight_to_fp16, x = mul_277_cast_fp16)[name = tensor<string, []>("conv1d_162_cast_fp16")];
            tensor<int32, []> glu_4_split_num_splits_0 = const()[name = tensor<string, []>("glu_4_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_4_split_axis_0 = const()[name = tensor<string, []>("glu_4_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_4_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_4_split_cast_fp16_1 = split(axis = glu_4_split_axis_0, num_splits = glu_4_split_num_splits_0, x = conv1d_162_cast_fp16)[name = tensor<string, []>("glu_4_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_4_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_4_split_cast_fp16_1)[name = tensor<string, []>("glu_4_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_4_cast_fp16 = mul(x = glu_4_split_cast_fp16_0, y = glu_4_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_4_cast_fp16")];
            tensor<int32, [4]> const_2540 = const()[name = tensor<string, []>("const_2540"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_173_cast_fp16 = reshape(shape = const_2540, x = glu_4_cast_fp16)[name = tensor<string, []>("view_173_cast_fp16")];
            tensor<int32, [4]> select_15_begin_0 = const()[name = tensor<string, []>("select_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_15_end_0 = const()[name = tensor<string, []>("select_15_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_15_end_mask_0 = const()[name = tensor<string, []>("select_15_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_15_squeeze_mask_0 = const()[name = tensor<string, []>("select_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_15_cast_fp16 = slice_by_index(begin = select_15_begin_0, end = select_15_end_0, end_mask = select_15_end_mask_0, squeeze_mask = select_15_squeeze_mask_0, x = view_173_cast_fp16)[name = tensor<string, []>("select_15_cast_fp16")];
            tensor<int32, [4]> select_16_begin_0 = const()[name = tensor<string, []>("select_16_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_16_end_0 = const()[name = tensor<string, []>("select_16_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_16_end_mask_0 = const()[name = tensor<string, []>("select_16_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_16_squeeze_mask_0 = const()[name = tensor<string, []>("select_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_16_cast_fp16 = slice_by_index(begin = select_16_begin_0, end = select_16_end_0, end_mask = select_16_end_mask_0, squeeze_mask = select_16_squeeze_mask_0, x = view_173_cast_fp16)[name = tensor<string, []>("select_16_cast_fp16")];
            tensor<int32, [4]> select_17_begin_0 = const()[name = tensor<string, []>("select_17_begin_0"), val = tensor<int32, [4]>([0, 5, 0, 0])];
            tensor<int32, [4]> select_17_end_0 = const()[name = tensor<string, []>("select_17_end_0"), val = tensor<int32, [4]>([1, 6, 256, 801])];
            tensor<bool, [4]> select_17_end_mask_0 = const()[name = tensor<string, []>("select_17_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_17_squeeze_mask_0 = const()[name = tensor<string, []>("select_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_17_cast_fp16 = slice_by_index(begin = select_17_begin_0, end = select_17_end_0, end_mask = select_17_end_mask_0, squeeze_mask = select_17_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_17_cast_fp16")];
            tensor<int32, [1]> unsqueeze_261_axes_0 = const()[name = tensor<string, []>("unsqueeze_261_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_261_cast_fp16 = expand_dims(axes = unsqueeze_261_axes_0, x = select_17_cast_fp16)[name = tensor<string, []>("unsqueeze_261_cast_fp16")];
            tensor<fp16, []> const_2551_promoted_to_fp16 = const()[name = tensor<string, []>("const_2551_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_276_cast_fp16 = pow(x = unsqueeze_261_cast_fp16, y = const_2551_promoted_to_fp16)[name = tensor<string, []>("pow_276_cast_fp16")];
            tensor<int32, [1]> mean_115_axes_0 = const()[name = tensor<string, []>("mean_115_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_115_keep_dims_0 = const()[name = tensor<string, []>("mean_115_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_115_cast_fp16 = reduce_mean(axes = mean_115_axes_0, keep_dims = mean_115_keep_dims_0, x = pow_276_cast_fp16)[name = tensor<string, []>("mean_115_cast_fp16")];
            tensor<fp16, []> const_2554_to_fp16 = const()[name = tensor<string, []>("const_2554_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_317_cast_fp16 = add(x = mean_115_cast_fp16, y = const_2554_to_fp16)[name = tensor<string, []>("add_317_cast_fp16")];
            tensor<fp32, []> rsqrt_115_epsilon_0 = const()[name = tensor<string, []>("rsqrt_115_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_115_cast_fp16 = rsqrt(epsilon = rsqrt_115_epsilon_0, x = add_317_cast_fp16)[name = tensor<string, []>("rsqrt_115_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_278_cast_fp16 = mul(x = unsqueeze_261_cast_fp16, y = rsqrt_115_cast_fp16)[name = tensor<string, []>("mul_278_cast_fp16")];
            tensor<int32, [1]> squeeze_121_axes_0 = const()[name = tensor<string, []>("squeeze_121_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_121_cast_fp16 = squeeze(axes = squeeze_121_axes_0, x = mul_278_cast_fp16)[name = tensor<string, []>("squeeze_121_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_174_to_fp16 = const()[name = tensor<string, []>("view_174_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32223424)))];
            tensor<fp16, [1, 256, 801]> mul_279_cast_fp16 = mul(x = squeeze_121_cast_fp16, y = view_174_to_fp16)[name = tensor<string, []>("mul_279_cast_fp16")];
            tensor<string, []> conv1d_163_pad_type_0 = const()[name = tensor<string, []>("conv1d_163_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_163_strides_0 = const()[name = tensor<string, []>("conv1d_163_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_163_pad_0 = const()[name = tensor<string, []>("conv1d_163_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_163_dilations_0 = const()[name = tensor<string, []>("conv1d_163_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_163_groups_0 = const()[name = tensor<string, []>("conv1d_163_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_5_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_5_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32224000)))];
            tensor<fp16, [20]> p_output_5_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_5_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32234304)))];
            tensor<fp16, [1, 20, 801]> conv1d_163_cast_fp16 = conv(bias = p_output_5_1_bias_to_fp16, dilations = conv1d_163_dilations_0, groups = conv1d_163_groups_0, pad = conv1d_163_pad_0, pad_type = conv1d_163_pad_type_0, strides = conv1d_163_strides_0, weight = p_output_5_1_weight_to_fp16, x = mul_279_cast_fp16)[name = tensor<string, []>("conv1d_163_cast_fp16")];
            tensor<int32, []> glu_5_split_num_splits_0 = const()[name = tensor<string, []>("glu_5_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_5_split_axis_0 = const()[name = tensor<string, []>("glu_5_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_5_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_5_split_cast_fp16_1 = split(axis = glu_5_split_axis_0, num_splits = glu_5_split_num_splits_0, x = conv1d_163_cast_fp16)[name = tensor<string, []>("glu_5_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_5_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_5_split_cast_fp16_1)[name = tensor<string, []>("glu_5_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_5_cast_fp16 = mul(x = glu_5_split_cast_fp16_0, y = glu_5_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_5_cast_fp16")];
            tensor<int32, [4]> const_2558 = const()[name = tensor<string, []>("const_2558"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_175_cast_fp16 = reshape(shape = const_2558, x = glu_5_cast_fp16)[name = tensor<string, []>("view_175_cast_fp16")];
            tensor<int32, [4]> select_18_begin_0 = const()[name = tensor<string, []>("select_18_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_18_end_0 = const()[name = tensor<string, []>("select_18_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_18_end_mask_0 = const()[name = tensor<string, []>("select_18_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_18_squeeze_mask_0 = const()[name = tensor<string, []>("select_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_18_cast_fp16 = slice_by_index(begin = select_18_begin_0, end = select_18_end_0, end_mask = select_18_end_mask_0, squeeze_mask = select_18_squeeze_mask_0, x = view_175_cast_fp16)[name = tensor<string, []>("select_18_cast_fp16")];
            tensor<int32, [4]> select_19_begin_0 = const()[name = tensor<string, []>("select_19_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_19_end_0 = const()[name = tensor<string, []>("select_19_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_19_end_mask_0 = const()[name = tensor<string, []>("select_19_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_19_squeeze_mask_0 = const()[name = tensor<string, []>("select_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_19_cast_fp16 = slice_by_index(begin = select_19_begin_0, end = select_19_end_0, end_mask = select_19_end_mask_0, squeeze_mask = select_19_squeeze_mask_0, x = view_175_cast_fp16)[name = tensor<string, []>("select_19_cast_fp16")];
            tensor<int32, [4]> select_20_begin_0 = const()[name = tensor<string, []>("select_20_begin_0"), val = tensor<int32, [4]>([0, 6, 0, 0])];
            tensor<int32, [4]> select_20_end_0 = const()[name = tensor<string, []>("select_20_end_0"), val = tensor<int32, [4]>([1, 7, 256, 801])];
            tensor<bool, [4]> select_20_end_mask_0 = const()[name = tensor<string, []>("select_20_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_20_squeeze_mask_0 = const()[name = tensor<string, []>("select_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_20_cast_fp16 = slice_by_index(begin = select_20_begin_0, end = select_20_end_0, end_mask = select_20_end_mask_0, squeeze_mask = select_20_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_20_cast_fp16")];
            tensor<int32, [1]> unsqueeze_263_axes_0 = const()[name = tensor<string, []>("unsqueeze_263_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_263_cast_fp16 = expand_dims(axes = unsqueeze_263_axes_0, x = select_20_cast_fp16)[name = tensor<string, []>("unsqueeze_263_cast_fp16")];
            tensor<fp16, []> const_2569_promoted_to_fp16 = const()[name = tensor<string, []>("const_2569_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_277_cast_fp16 = pow(x = unsqueeze_263_cast_fp16, y = const_2569_promoted_to_fp16)[name = tensor<string, []>("pow_277_cast_fp16")];
            tensor<int32, [1]> mean_116_axes_0 = const()[name = tensor<string, []>("mean_116_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_116_keep_dims_0 = const()[name = tensor<string, []>("mean_116_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_116_cast_fp16 = reduce_mean(axes = mean_116_axes_0, keep_dims = mean_116_keep_dims_0, x = pow_277_cast_fp16)[name = tensor<string, []>("mean_116_cast_fp16")];
            tensor<fp16, []> const_2572_to_fp16 = const()[name = tensor<string, []>("const_2572_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_318_cast_fp16 = add(x = mean_116_cast_fp16, y = const_2572_to_fp16)[name = tensor<string, []>("add_318_cast_fp16")];
            tensor<fp32, []> rsqrt_116_epsilon_0 = const()[name = tensor<string, []>("rsqrt_116_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_116_cast_fp16 = rsqrt(epsilon = rsqrt_116_epsilon_0, x = add_318_cast_fp16)[name = tensor<string, []>("rsqrt_116_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_280_cast_fp16 = mul(x = unsqueeze_263_cast_fp16, y = rsqrt_116_cast_fp16)[name = tensor<string, []>("mul_280_cast_fp16")];
            tensor<int32, [1]> squeeze_122_axes_0 = const()[name = tensor<string, []>("squeeze_122_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_122_cast_fp16 = squeeze(axes = squeeze_122_axes_0, x = mul_280_cast_fp16)[name = tensor<string, []>("squeeze_122_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_176_to_fp16 = const()[name = tensor<string, []>("view_176_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32234432)))];
            tensor<fp16, [1, 256, 801]> mul_281_cast_fp16 = mul(x = squeeze_122_cast_fp16, y = view_176_to_fp16)[name = tensor<string, []>("mul_281_cast_fp16")];
            tensor<string, []> conv1d_164_pad_type_0 = const()[name = tensor<string, []>("conv1d_164_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_164_strides_0 = const()[name = tensor<string, []>("conv1d_164_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_164_pad_0 = const()[name = tensor<string, []>("conv1d_164_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_164_dilations_0 = const()[name = tensor<string, []>("conv1d_164_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_164_groups_0 = const()[name = tensor<string, []>("conv1d_164_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_6_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_6_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32235008)))];
            tensor<fp16, [20]> p_output_6_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_6_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32245312)))];
            tensor<fp16, [1, 20, 801]> conv1d_164_cast_fp16 = conv(bias = p_output_6_1_bias_to_fp16, dilations = conv1d_164_dilations_0, groups = conv1d_164_groups_0, pad = conv1d_164_pad_0, pad_type = conv1d_164_pad_type_0, strides = conv1d_164_strides_0, weight = p_output_6_1_weight_to_fp16, x = mul_281_cast_fp16)[name = tensor<string, []>("conv1d_164_cast_fp16")];
            tensor<int32, []> glu_6_split_num_splits_0 = const()[name = tensor<string, []>("glu_6_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_6_split_axis_0 = const()[name = tensor<string, []>("glu_6_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_6_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_6_split_cast_fp16_1 = split(axis = glu_6_split_axis_0, num_splits = glu_6_split_num_splits_0, x = conv1d_164_cast_fp16)[name = tensor<string, []>("glu_6_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_6_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_6_split_cast_fp16_1)[name = tensor<string, []>("glu_6_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_6_cast_fp16 = mul(x = glu_6_split_cast_fp16_0, y = glu_6_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_6_cast_fp16")];
            tensor<int32, [4]> const_2576 = const()[name = tensor<string, []>("const_2576"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_177_cast_fp16 = reshape(shape = const_2576, x = glu_6_cast_fp16)[name = tensor<string, []>("view_177_cast_fp16")];
            tensor<int32, [4]> select_21_begin_0 = const()[name = tensor<string, []>("select_21_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_21_end_0 = const()[name = tensor<string, []>("select_21_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_21_end_mask_0 = const()[name = tensor<string, []>("select_21_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_21_squeeze_mask_0 = const()[name = tensor<string, []>("select_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_21_cast_fp16 = slice_by_index(begin = select_21_begin_0, end = select_21_end_0, end_mask = select_21_end_mask_0, squeeze_mask = select_21_squeeze_mask_0, x = view_177_cast_fp16)[name = tensor<string, []>("select_21_cast_fp16")];
            tensor<int32, [4]> select_22_begin_0 = const()[name = tensor<string, []>("select_22_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_22_end_0 = const()[name = tensor<string, []>("select_22_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_22_end_mask_0 = const()[name = tensor<string, []>("select_22_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_22_squeeze_mask_0 = const()[name = tensor<string, []>("select_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_22_cast_fp16 = slice_by_index(begin = select_22_begin_0, end = select_22_end_0, end_mask = select_22_end_mask_0, squeeze_mask = select_22_squeeze_mask_0, x = view_177_cast_fp16)[name = tensor<string, []>("select_22_cast_fp16")];
            tensor<int32, [4]> select_23_begin_0 = const()[name = tensor<string, []>("select_23_begin_0"), val = tensor<int32, [4]>([0, 7, 0, 0])];
            tensor<int32, [4]> select_23_end_0 = const()[name = tensor<string, []>("select_23_end_0"), val = tensor<int32, [4]>([1, 8, 256, 801])];
            tensor<bool, [4]> select_23_end_mask_0 = const()[name = tensor<string, []>("select_23_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_23_squeeze_mask_0 = const()[name = tensor<string, []>("select_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_23_cast_fp16 = slice_by_index(begin = select_23_begin_0, end = select_23_end_0, end_mask = select_23_end_mask_0, squeeze_mask = select_23_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_23_cast_fp16")];
            tensor<int32, [1]> unsqueeze_265_axes_0 = const()[name = tensor<string, []>("unsqueeze_265_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_265_cast_fp16 = expand_dims(axes = unsqueeze_265_axes_0, x = select_23_cast_fp16)[name = tensor<string, []>("unsqueeze_265_cast_fp16")];
            tensor<fp16, []> const_2587_promoted_to_fp16 = const()[name = tensor<string, []>("const_2587_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_278_cast_fp16 = pow(x = unsqueeze_265_cast_fp16, y = const_2587_promoted_to_fp16)[name = tensor<string, []>("pow_278_cast_fp16")];
            tensor<int32, [1]> mean_117_axes_0 = const()[name = tensor<string, []>("mean_117_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_117_keep_dims_0 = const()[name = tensor<string, []>("mean_117_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_117_cast_fp16 = reduce_mean(axes = mean_117_axes_0, keep_dims = mean_117_keep_dims_0, x = pow_278_cast_fp16)[name = tensor<string, []>("mean_117_cast_fp16")];
            tensor<fp16, []> const_2590_to_fp16 = const()[name = tensor<string, []>("const_2590_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_319_cast_fp16 = add(x = mean_117_cast_fp16, y = const_2590_to_fp16)[name = tensor<string, []>("add_319_cast_fp16")];
            tensor<fp32, []> rsqrt_117_epsilon_0 = const()[name = tensor<string, []>("rsqrt_117_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_117_cast_fp16 = rsqrt(epsilon = rsqrt_117_epsilon_0, x = add_319_cast_fp16)[name = tensor<string, []>("rsqrt_117_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_282_cast_fp16 = mul(x = unsqueeze_265_cast_fp16, y = rsqrt_117_cast_fp16)[name = tensor<string, []>("mul_282_cast_fp16")];
            tensor<int32, [1]> squeeze_123_axes_0 = const()[name = tensor<string, []>("squeeze_123_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_123_cast_fp16 = squeeze(axes = squeeze_123_axes_0, x = mul_282_cast_fp16)[name = tensor<string, []>("squeeze_123_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_178_to_fp16 = const()[name = tensor<string, []>("view_178_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32245440)))];
            tensor<fp16, [1, 256, 801]> mul_283_cast_fp16 = mul(x = squeeze_123_cast_fp16, y = view_178_to_fp16)[name = tensor<string, []>("mul_283_cast_fp16")];
            tensor<string, []> conv1d_165_pad_type_0 = const()[name = tensor<string, []>("conv1d_165_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_165_strides_0 = const()[name = tensor<string, []>("conv1d_165_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_165_pad_0 = const()[name = tensor<string, []>("conv1d_165_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_165_dilations_0 = const()[name = tensor<string, []>("conv1d_165_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_165_groups_0 = const()[name = tensor<string, []>("conv1d_165_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_7_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_7_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32246016)))];
            tensor<fp16, [20]> p_output_7_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_7_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32256320)))];
            tensor<fp16, [1, 20, 801]> conv1d_165_cast_fp16 = conv(bias = p_output_7_1_bias_to_fp16, dilations = conv1d_165_dilations_0, groups = conv1d_165_groups_0, pad = conv1d_165_pad_0, pad_type = conv1d_165_pad_type_0, strides = conv1d_165_strides_0, weight = p_output_7_1_weight_to_fp16, x = mul_283_cast_fp16)[name = tensor<string, []>("conv1d_165_cast_fp16")];
            tensor<int32, []> glu_7_split_num_splits_0 = const()[name = tensor<string, []>("glu_7_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_7_split_axis_0 = const()[name = tensor<string, []>("glu_7_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_7_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_7_split_cast_fp16_1 = split(axis = glu_7_split_axis_0, num_splits = glu_7_split_num_splits_0, x = conv1d_165_cast_fp16)[name = tensor<string, []>("glu_7_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_7_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_7_split_cast_fp16_1)[name = tensor<string, []>("glu_7_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_7_cast_fp16 = mul(x = glu_7_split_cast_fp16_0, y = glu_7_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_7_cast_fp16")];
            tensor<int32, [4]> const_2594 = const()[name = tensor<string, []>("const_2594"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_179_cast_fp16 = reshape(shape = const_2594, x = glu_7_cast_fp16)[name = tensor<string, []>("view_179_cast_fp16")];
            tensor<int32, [4]> select_24_begin_0 = const()[name = tensor<string, []>("select_24_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_24_end_0 = const()[name = tensor<string, []>("select_24_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_24_end_mask_0 = const()[name = tensor<string, []>("select_24_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_24_squeeze_mask_0 = const()[name = tensor<string, []>("select_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_24_cast_fp16 = slice_by_index(begin = select_24_begin_0, end = select_24_end_0, end_mask = select_24_end_mask_0, squeeze_mask = select_24_squeeze_mask_0, x = view_179_cast_fp16)[name = tensor<string, []>("select_24_cast_fp16")];
            tensor<int32, [4]> select_25_begin_0 = const()[name = tensor<string, []>("select_25_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_25_end_0 = const()[name = tensor<string, []>("select_25_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_25_end_mask_0 = const()[name = tensor<string, []>("select_25_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_25_squeeze_mask_0 = const()[name = tensor<string, []>("select_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_25_cast_fp16 = slice_by_index(begin = select_25_begin_0, end = select_25_end_0, end_mask = select_25_end_mask_0, squeeze_mask = select_25_squeeze_mask_0, x = view_179_cast_fp16)[name = tensor<string, []>("select_25_cast_fp16")];
            tensor<int32, [4]> select_26_begin_0 = const()[name = tensor<string, []>("select_26_begin_0"), val = tensor<int32, [4]>([0, 8, 0, 0])];
            tensor<int32, [4]> select_26_end_0 = const()[name = tensor<string, []>("select_26_end_0"), val = tensor<int32, [4]>([1, 9, 256, 801])];
            tensor<bool, [4]> select_26_end_mask_0 = const()[name = tensor<string, []>("select_26_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_26_squeeze_mask_0 = const()[name = tensor<string, []>("select_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_26_cast_fp16 = slice_by_index(begin = select_26_begin_0, end = select_26_end_0, end_mask = select_26_end_mask_0, squeeze_mask = select_26_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_26_cast_fp16")];
            tensor<int32, [1]> unsqueeze_267_axes_0 = const()[name = tensor<string, []>("unsqueeze_267_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_267_cast_fp16 = expand_dims(axes = unsqueeze_267_axes_0, x = select_26_cast_fp16)[name = tensor<string, []>("unsqueeze_267_cast_fp16")];
            tensor<fp16, []> const_2605_promoted_to_fp16 = const()[name = tensor<string, []>("const_2605_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_279_cast_fp16 = pow(x = unsqueeze_267_cast_fp16, y = const_2605_promoted_to_fp16)[name = tensor<string, []>("pow_279_cast_fp16")];
            tensor<int32, [1]> mean_118_axes_0 = const()[name = tensor<string, []>("mean_118_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_118_keep_dims_0 = const()[name = tensor<string, []>("mean_118_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_118_cast_fp16 = reduce_mean(axes = mean_118_axes_0, keep_dims = mean_118_keep_dims_0, x = pow_279_cast_fp16)[name = tensor<string, []>("mean_118_cast_fp16")];
            tensor<fp16, []> const_2608_to_fp16 = const()[name = tensor<string, []>("const_2608_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_320_cast_fp16 = add(x = mean_118_cast_fp16, y = const_2608_to_fp16)[name = tensor<string, []>("add_320_cast_fp16")];
            tensor<fp32, []> rsqrt_118_epsilon_0 = const()[name = tensor<string, []>("rsqrt_118_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_118_cast_fp16 = rsqrt(epsilon = rsqrt_118_epsilon_0, x = add_320_cast_fp16)[name = tensor<string, []>("rsqrt_118_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_284_cast_fp16 = mul(x = unsqueeze_267_cast_fp16, y = rsqrt_118_cast_fp16)[name = tensor<string, []>("mul_284_cast_fp16")];
            tensor<int32, [1]> squeeze_124_axes_0 = const()[name = tensor<string, []>("squeeze_124_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_124_cast_fp16 = squeeze(axes = squeeze_124_axes_0, x = mul_284_cast_fp16)[name = tensor<string, []>("squeeze_124_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_180_to_fp16 = const()[name = tensor<string, []>("view_180_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32256448)))];
            tensor<fp16, [1, 256, 801]> mul_285_cast_fp16 = mul(x = squeeze_124_cast_fp16, y = view_180_to_fp16)[name = tensor<string, []>("mul_285_cast_fp16")];
            tensor<string, []> conv1d_166_pad_type_0 = const()[name = tensor<string, []>("conv1d_166_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_166_strides_0 = const()[name = tensor<string, []>("conv1d_166_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_166_pad_0 = const()[name = tensor<string, []>("conv1d_166_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_166_dilations_0 = const()[name = tensor<string, []>("conv1d_166_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_166_groups_0 = const()[name = tensor<string, []>("conv1d_166_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_8_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_8_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32257024)))];
            tensor<fp16, [20]> p_output_8_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_8_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32267328)))];
            tensor<fp16, [1, 20, 801]> conv1d_166_cast_fp16 = conv(bias = p_output_8_1_bias_to_fp16, dilations = conv1d_166_dilations_0, groups = conv1d_166_groups_0, pad = conv1d_166_pad_0, pad_type = conv1d_166_pad_type_0, strides = conv1d_166_strides_0, weight = p_output_8_1_weight_to_fp16, x = mul_285_cast_fp16)[name = tensor<string, []>("conv1d_166_cast_fp16")];
            tensor<int32, []> glu_8_split_num_splits_0 = const()[name = tensor<string, []>("glu_8_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_8_split_axis_0 = const()[name = tensor<string, []>("glu_8_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_8_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_8_split_cast_fp16_1 = split(axis = glu_8_split_axis_0, num_splits = glu_8_split_num_splits_0, x = conv1d_166_cast_fp16)[name = tensor<string, []>("glu_8_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_8_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_8_split_cast_fp16_1)[name = tensor<string, []>("glu_8_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_8_cast_fp16 = mul(x = glu_8_split_cast_fp16_0, y = glu_8_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_8_cast_fp16")];
            tensor<int32, [4]> const_2612 = const()[name = tensor<string, []>("const_2612"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_181_cast_fp16 = reshape(shape = const_2612, x = glu_8_cast_fp16)[name = tensor<string, []>("view_181_cast_fp16")];
            tensor<int32, [4]> select_27_begin_0 = const()[name = tensor<string, []>("select_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_27_end_0 = const()[name = tensor<string, []>("select_27_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_27_end_mask_0 = const()[name = tensor<string, []>("select_27_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_27_squeeze_mask_0 = const()[name = tensor<string, []>("select_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_27_cast_fp16 = slice_by_index(begin = select_27_begin_0, end = select_27_end_0, end_mask = select_27_end_mask_0, squeeze_mask = select_27_squeeze_mask_0, x = view_181_cast_fp16)[name = tensor<string, []>("select_27_cast_fp16")];
            tensor<int32, [4]> select_28_begin_0 = const()[name = tensor<string, []>("select_28_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_28_end_0 = const()[name = tensor<string, []>("select_28_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_28_end_mask_0 = const()[name = tensor<string, []>("select_28_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_28_squeeze_mask_0 = const()[name = tensor<string, []>("select_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_28_cast_fp16 = slice_by_index(begin = select_28_begin_0, end = select_28_end_0, end_mask = select_28_end_mask_0, squeeze_mask = select_28_squeeze_mask_0, x = view_181_cast_fp16)[name = tensor<string, []>("select_28_cast_fp16")];
            tensor<int32, [4]> select_29_begin_0 = const()[name = tensor<string, []>("select_29_begin_0"), val = tensor<int32, [4]>([0, 9, 0, 0])];
            tensor<int32, [4]> select_29_end_0 = const()[name = tensor<string, []>("select_29_end_0"), val = tensor<int32, [4]>([1, 10, 256, 801])];
            tensor<bool, [4]> select_29_end_mask_0 = const()[name = tensor<string, []>("select_29_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_29_squeeze_mask_0 = const()[name = tensor<string, []>("select_29_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_29_cast_fp16 = slice_by_index(begin = select_29_begin_0, end = select_29_end_0, end_mask = select_29_end_mask_0, squeeze_mask = select_29_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_29_cast_fp16")];
            tensor<int32, [1]> unsqueeze_269_axes_0 = const()[name = tensor<string, []>("unsqueeze_269_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_269_cast_fp16 = expand_dims(axes = unsqueeze_269_axes_0, x = select_29_cast_fp16)[name = tensor<string, []>("unsqueeze_269_cast_fp16")];
            tensor<fp16, []> const_2623_promoted_to_fp16 = const()[name = tensor<string, []>("const_2623_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_280_cast_fp16 = pow(x = unsqueeze_269_cast_fp16, y = const_2623_promoted_to_fp16)[name = tensor<string, []>("pow_280_cast_fp16")];
            tensor<int32, [1]> mean_119_axes_0 = const()[name = tensor<string, []>("mean_119_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_119_keep_dims_0 = const()[name = tensor<string, []>("mean_119_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_119_cast_fp16 = reduce_mean(axes = mean_119_axes_0, keep_dims = mean_119_keep_dims_0, x = pow_280_cast_fp16)[name = tensor<string, []>("mean_119_cast_fp16")];
            tensor<fp16, []> const_2626_to_fp16 = const()[name = tensor<string, []>("const_2626_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_321_cast_fp16 = add(x = mean_119_cast_fp16, y = const_2626_to_fp16)[name = tensor<string, []>("add_321_cast_fp16")];
            tensor<fp32, []> rsqrt_119_epsilon_0 = const()[name = tensor<string, []>("rsqrt_119_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_119_cast_fp16 = rsqrt(epsilon = rsqrt_119_epsilon_0, x = add_321_cast_fp16)[name = tensor<string, []>("rsqrt_119_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_286_cast_fp16 = mul(x = unsqueeze_269_cast_fp16, y = rsqrt_119_cast_fp16)[name = tensor<string, []>("mul_286_cast_fp16")];
            tensor<int32, [1]> squeeze_125_axes_0 = const()[name = tensor<string, []>("squeeze_125_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_125_cast_fp16 = squeeze(axes = squeeze_125_axes_0, x = mul_286_cast_fp16)[name = tensor<string, []>("squeeze_125_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_182_to_fp16 = const()[name = tensor<string, []>("view_182_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32267456)))];
            tensor<fp16, [1, 256, 801]> mul_287_cast_fp16 = mul(x = squeeze_125_cast_fp16, y = view_182_to_fp16)[name = tensor<string, []>("mul_287_cast_fp16")];
            tensor<string, []> conv1d_167_pad_type_0 = const()[name = tensor<string, []>("conv1d_167_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_167_strides_0 = const()[name = tensor<string, []>("conv1d_167_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_167_pad_0 = const()[name = tensor<string, []>("conv1d_167_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_167_dilations_0 = const()[name = tensor<string, []>("conv1d_167_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_167_groups_0 = const()[name = tensor<string, []>("conv1d_167_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_9_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_9_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32268032)))];
            tensor<fp16, [20]> p_output_9_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_9_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32278336)))];
            tensor<fp16, [1, 20, 801]> conv1d_167_cast_fp16 = conv(bias = p_output_9_1_bias_to_fp16, dilations = conv1d_167_dilations_0, groups = conv1d_167_groups_0, pad = conv1d_167_pad_0, pad_type = conv1d_167_pad_type_0, strides = conv1d_167_strides_0, weight = p_output_9_1_weight_to_fp16, x = mul_287_cast_fp16)[name = tensor<string, []>("conv1d_167_cast_fp16")];
            tensor<int32, []> glu_9_split_num_splits_0 = const()[name = tensor<string, []>("glu_9_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_9_split_axis_0 = const()[name = tensor<string, []>("glu_9_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_9_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_9_split_cast_fp16_1 = split(axis = glu_9_split_axis_0, num_splits = glu_9_split_num_splits_0, x = conv1d_167_cast_fp16)[name = tensor<string, []>("glu_9_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_9_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_9_split_cast_fp16_1)[name = tensor<string, []>("glu_9_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_9_cast_fp16 = mul(x = glu_9_split_cast_fp16_0, y = glu_9_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_9_cast_fp16")];
            tensor<int32, [4]> const_2630 = const()[name = tensor<string, []>("const_2630"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_183_cast_fp16 = reshape(shape = const_2630, x = glu_9_cast_fp16)[name = tensor<string, []>("view_183_cast_fp16")];
            tensor<int32, [4]> select_30_begin_0 = const()[name = tensor<string, []>("select_30_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_30_end_0 = const()[name = tensor<string, []>("select_30_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_30_end_mask_0 = const()[name = tensor<string, []>("select_30_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_30_squeeze_mask_0 = const()[name = tensor<string, []>("select_30_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_30_cast_fp16 = slice_by_index(begin = select_30_begin_0, end = select_30_end_0, end_mask = select_30_end_mask_0, squeeze_mask = select_30_squeeze_mask_0, x = view_183_cast_fp16)[name = tensor<string, []>("select_30_cast_fp16")];
            tensor<int32, [4]> select_31_begin_0 = const()[name = tensor<string, []>("select_31_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_31_end_0 = const()[name = tensor<string, []>("select_31_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_31_end_mask_0 = const()[name = tensor<string, []>("select_31_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_31_squeeze_mask_0 = const()[name = tensor<string, []>("select_31_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_31_cast_fp16 = slice_by_index(begin = select_31_begin_0, end = select_31_end_0, end_mask = select_31_end_mask_0, squeeze_mask = select_31_squeeze_mask_0, x = view_183_cast_fp16)[name = tensor<string, []>("select_31_cast_fp16")];
            tensor<int32, [4]> select_32_begin_0 = const()[name = tensor<string, []>("select_32_begin_0"), val = tensor<int32, [4]>([0, 10, 0, 0])];
            tensor<int32, [4]> select_32_end_0 = const()[name = tensor<string, []>("select_32_end_0"), val = tensor<int32, [4]>([1, 11, 256, 801])];
            tensor<bool, [4]> select_32_end_mask_0 = const()[name = tensor<string, []>("select_32_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_32_squeeze_mask_0 = const()[name = tensor<string, []>("select_32_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_32_cast_fp16 = slice_by_index(begin = select_32_begin_0, end = select_32_end_0, end_mask = select_32_end_mask_0, squeeze_mask = select_32_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_32_cast_fp16")];
            tensor<int32, [1]> unsqueeze_271_axes_0 = const()[name = tensor<string, []>("unsqueeze_271_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_271_cast_fp16 = expand_dims(axes = unsqueeze_271_axes_0, x = select_32_cast_fp16)[name = tensor<string, []>("unsqueeze_271_cast_fp16")];
            tensor<fp16, []> const_2641_promoted_to_fp16 = const()[name = tensor<string, []>("const_2641_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_281_cast_fp16 = pow(x = unsqueeze_271_cast_fp16, y = const_2641_promoted_to_fp16)[name = tensor<string, []>("pow_281_cast_fp16")];
            tensor<int32, [1]> mean_120_axes_0 = const()[name = tensor<string, []>("mean_120_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_120_keep_dims_0 = const()[name = tensor<string, []>("mean_120_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_120_cast_fp16 = reduce_mean(axes = mean_120_axes_0, keep_dims = mean_120_keep_dims_0, x = pow_281_cast_fp16)[name = tensor<string, []>("mean_120_cast_fp16")];
            tensor<fp16, []> const_2644_to_fp16 = const()[name = tensor<string, []>("const_2644_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_322_cast_fp16 = add(x = mean_120_cast_fp16, y = const_2644_to_fp16)[name = tensor<string, []>("add_322_cast_fp16")];
            tensor<fp32, []> rsqrt_120_epsilon_0 = const()[name = tensor<string, []>("rsqrt_120_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_120_cast_fp16 = rsqrt(epsilon = rsqrt_120_epsilon_0, x = add_322_cast_fp16)[name = tensor<string, []>("rsqrt_120_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_288_cast_fp16 = mul(x = unsqueeze_271_cast_fp16, y = rsqrt_120_cast_fp16)[name = tensor<string, []>("mul_288_cast_fp16")];
            tensor<int32, [1]> squeeze_126_axes_0 = const()[name = tensor<string, []>("squeeze_126_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_126_cast_fp16 = squeeze(axes = squeeze_126_axes_0, x = mul_288_cast_fp16)[name = tensor<string, []>("squeeze_126_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_184_to_fp16 = const()[name = tensor<string, []>("view_184_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32278464)))];
            tensor<fp16, [1, 256, 801]> mul_289_cast_fp16 = mul(x = squeeze_126_cast_fp16, y = view_184_to_fp16)[name = tensor<string, []>("mul_289_cast_fp16")];
            tensor<string, []> conv1d_168_pad_type_0 = const()[name = tensor<string, []>("conv1d_168_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_168_strides_0 = const()[name = tensor<string, []>("conv1d_168_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_168_pad_0 = const()[name = tensor<string, []>("conv1d_168_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_168_dilations_0 = const()[name = tensor<string, []>("conv1d_168_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_168_groups_0 = const()[name = tensor<string, []>("conv1d_168_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_10_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_10_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32279040)))];
            tensor<fp16, [20]> p_output_10_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_10_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32289344)))];
            tensor<fp16, [1, 20, 801]> conv1d_168_cast_fp16 = conv(bias = p_output_10_1_bias_to_fp16, dilations = conv1d_168_dilations_0, groups = conv1d_168_groups_0, pad = conv1d_168_pad_0, pad_type = conv1d_168_pad_type_0, strides = conv1d_168_strides_0, weight = p_output_10_1_weight_to_fp16, x = mul_289_cast_fp16)[name = tensor<string, []>("conv1d_168_cast_fp16")];
            tensor<int32, []> glu_10_split_num_splits_0 = const()[name = tensor<string, []>("glu_10_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_10_split_axis_0 = const()[name = tensor<string, []>("glu_10_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_10_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_10_split_cast_fp16_1 = split(axis = glu_10_split_axis_0, num_splits = glu_10_split_num_splits_0, x = conv1d_168_cast_fp16)[name = tensor<string, []>("glu_10_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_10_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_10_split_cast_fp16_1)[name = tensor<string, []>("glu_10_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_10_cast_fp16 = mul(x = glu_10_split_cast_fp16_0, y = glu_10_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_10_cast_fp16")];
            tensor<int32, [4]> const_2648 = const()[name = tensor<string, []>("const_2648"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_185_cast_fp16 = reshape(shape = const_2648, x = glu_10_cast_fp16)[name = tensor<string, []>("view_185_cast_fp16")];
            tensor<int32, [4]> select_33_begin_0 = const()[name = tensor<string, []>("select_33_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_33_end_0 = const()[name = tensor<string, []>("select_33_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_33_end_mask_0 = const()[name = tensor<string, []>("select_33_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_33_squeeze_mask_0 = const()[name = tensor<string, []>("select_33_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_33_cast_fp16 = slice_by_index(begin = select_33_begin_0, end = select_33_end_0, end_mask = select_33_end_mask_0, squeeze_mask = select_33_squeeze_mask_0, x = view_185_cast_fp16)[name = tensor<string, []>("select_33_cast_fp16")];
            tensor<int32, [4]> select_34_begin_0 = const()[name = tensor<string, []>("select_34_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_34_end_0 = const()[name = tensor<string, []>("select_34_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_34_end_mask_0 = const()[name = tensor<string, []>("select_34_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_34_squeeze_mask_0 = const()[name = tensor<string, []>("select_34_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_34_cast_fp16 = slice_by_index(begin = select_34_begin_0, end = select_34_end_0, end_mask = select_34_end_mask_0, squeeze_mask = select_34_squeeze_mask_0, x = view_185_cast_fp16)[name = tensor<string, []>("select_34_cast_fp16")];
            tensor<int32, [4]> select_35_begin_0 = const()[name = tensor<string, []>("select_35_begin_0"), val = tensor<int32, [4]>([0, 11, 0, 0])];
            tensor<int32, [4]> select_35_end_0 = const()[name = tensor<string, []>("select_35_end_0"), val = tensor<int32, [4]>([1, 12, 256, 801])];
            tensor<bool, [4]> select_35_end_mask_0 = const()[name = tensor<string, []>("select_35_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_35_squeeze_mask_0 = const()[name = tensor<string, []>("select_35_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_35_cast_fp16 = slice_by_index(begin = select_35_begin_0, end = select_35_end_0, end_mask = select_35_end_mask_0, squeeze_mask = select_35_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_35_cast_fp16")];
            tensor<int32, [1]> unsqueeze_273_axes_0 = const()[name = tensor<string, []>("unsqueeze_273_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_273_cast_fp16 = expand_dims(axes = unsqueeze_273_axes_0, x = select_35_cast_fp16)[name = tensor<string, []>("unsqueeze_273_cast_fp16")];
            tensor<fp16, []> const_2659_promoted_to_fp16 = const()[name = tensor<string, []>("const_2659_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_282_cast_fp16 = pow(x = unsqueeze_273_cast_fp16, y = const_2659_promoted_to_fp16)[name = tensor<string, []>("pow_282_cast_fp16")];
            tensor<int32, [1]> mean_121_axes_0 = const()[name = tensor<string, []>("mean_121_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_121_keep_dims_0 = const()[name = tensor<string, []>("mean_121_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_121_cast_fp16 = reduce_mean(axes = mean_121_axes_0, keep_dims = mean_121_keep_dims_0, x = pow_282_cast_fp16)[name = tensor<string, []>("mean_121_cast_fp16")];
            tensor<fp16, []> const_2662_to_fp16 = const()[name = tensor<string, []>("const_2662_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_323_cast_fp16 = add(x = mean_121_cast_fp16, y = const_2662_to_fp16)[name = tensor<string, []>("add_323_cast_fp16")];
            tensor<fp32, []> rsqrt_121_epsilon_0 = const()[name = tensor<string, []>("rsqrt_121_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_121_cast_fp16 = rsqrt(epsilon = rsqrt_121_epsilon_0, x = add_323_cast_fp16)[name = tensor<string, []>("rsqrt_121_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_290_cast_fp16 = mul(x = unsqueeze_273_cast_fp16, y = rsqrt_121_cast_fp16)[name = tensor<string, []>("mul_290_cast_fp16")];
            tensor<int32, [1]> squeeze_127_axes_0 = const()[name = tensor<string, []>("squeeze_127_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_127_cast_fp16 = squeeze(axes = squeeze_127_axes_0, x = mul_290_cast_fp16)[name = tensor<string, []>("squeeze_127_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_186_to_fp16 = const()[name = tensor<string, []>("view_186_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32289472)))];
            tensor<fp16, [1, 256, 801]> mul_291_cast_fp16 = mul(x = squeeze_127_cast_fp16, y = view_186_to_fp16)[name = tensor<string, []>("mul_291_cast_fp16")];
            tensor<string, []> conv1d_169_pad_type_0 = const()[name = tensor<string, []>("conv1d_169_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_169_strides_0 = const()[name = tensor<string, []>("conv1d_169_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_169_pad_0 = const()[name = tensor<string, []>("conv1d_169_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_169_dilations_0 = const()[name = tensor<string, []>("conv1d_169_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_169_groups_0 = const()[name = tensor<string, []>("conv1d_169_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_11_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_11_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32290048)))];
            tensor<fp16, [20]> p_output_11_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_11_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32300352)))];
            tensor<fp16, [1, 20, 801]> conv1d_169_cast_fp16 = conv(bias = p_output_11_1_bias_to_fp16, dilations = conv1d_169_dilations_0, groups = conv1d_169_groups_0, pad = conv1d_169_pad_0, pad_type = conv1d_169_pad_type_0, strides = conv1d_169_strides_0, weight = p_output_11_1_weight_to_fp16, x = mul_291_cast_fp16)[name = tensor<string, []>("conv1d_169_cast_fp16")];
            tensor<int32, []> glu_11_split_num_splits_0 = const()[name = tensor<string, []>("glu_11_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_11_split_axis_0 = const()[name = tensor<string, []>("glu_11_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_11_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_11_split_cast_fp16_1 = split(axis = glu_11_split_axis_0, num_splits = glu_11_split_num_splits_0, x = conv1d_169_cast_fp16)[name = tensor<string, []>("glu_11_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_11_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_11_split_cast_fp16_1)[name = tensor<string, []>("glu_11_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_11_cast_fp16 = mul(x = glu_11_split_cast_fp16_0, y = glu_11_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_11_cast_fp16")];
            tensor<int32, [4]> const_2666 = const()[name = tensor<string, []>("const_2666"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_187_cast_fp16 = reshape(shape = const_2666, x = glu_11_cast_fp16)[name = tensor<string, []>("view_187_cast_fp16")];
            tensor<int32, [4]> select_36_begin_0 = const()[name = tensor<string, []>("select_36_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_36_end_0 = const()[name = tensor<string, []>("select_36_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_36_end_mask_0 = const()[name = tensor<string, []>("select_36_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_36_squeeze_mask_0 = const()[name = tensor<string, []>("select_36_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_36_cast_fp16 = slice_by_index(begin = select_36_begin_0, end = select_36_end_0, end_mask = select_36_end_mask_0, squeeze_mask = select_36_squeeze_mask_0, x = view_187_cast_fp16)[name = tensor<string, []>("select_36_cast_fp16")];
            tensor<int32, [4]> select_37_begin_0 = const()[name = tensor<string, []>("select_37_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_37_end_0 = const()[name = tensor<string, []>("select_37_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_37_end_mask_0 = const()[name = tensor<string, []>("select_37_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_37_squeeze_mask_0 = const()[name = tensor<string, []>("select_37_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_37_cast_fp16 = slice_by_index(begin = select_37_begin_0, end = select_37_end_0, end_mask = select_37_end_mask_0, squeeze_mask = select_37_squeeze_mask_0, x = view_187_cast_fp16)[name = tensor<string, []>("select_37_cast_fp16")];
            tensor<int32, [4]> select_38_begin_0 = const()[name = tensor<string, []>("select_38_begin_0"), val = tensor<int32, [4]>([0, 12, 0, 0])];
            tensor<int32, [4]> select_38_end_0 = const()[name = tensor<string, []>("select_38_end_0"), val = tensor<int32, [4]>([1, 13, 256, 801])];
            tensor<bool, [4]> select_38_end_mask_0 = const()[name = tensor<string, []>("select_38_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_38_squeeze_mask_0 = const()[name = tensor<string, []>("select_38_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_38_cast_fp16 = slice_by_index(begin = select_38_begin_0, end = select_38_end_0, end_mask = select_38_end_mask_0, squeeze_mask = select_38_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_38_cast_fp16")];
            tensor<int32, [1]> unsqueeze_275_axes_0 = const()[name = tensor<string, []>("unsqueeze_275_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_275_cast_fp16 = expand_dims(axes = unsqueeze_275_axes_0, x = select_38_cast_fp16)[name = tensor<string, []>("unsqueeze_275_cast_fp16")];
            tensor<fp16, []> const_2677_promoted_to_fp16 = const()[name = tensor<string, []>("const_2677_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_283_cast_fp16 = pow(x = unsqueeze_275_cast_fp16, y = const_2677_promoted_to_fp16)[name = tensor<string, []>("pow_283_cast_fp16")];
            tensor<int32, [1]> mean_122_axes_0 = const()[name = tensor<string, []>("mean_122_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_122_keep_dims_0 = const()[name = tensor<string, []>("mean_122_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_122_cast_fp16 = reduce_mean(axes = mean_122_axes_0, keep_dims = mean_122_keep_dims_0, x = pow_283_cast_fp16)[name = tensor<string, []>("mean_122_cast_fp16")];
            tensor<fp16, []> const_2680_to_fp16 = const()[name = tensor<string, []>("const_2680_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_324_cast_fp16 = add(x = mean_122_cast_fp16, y = const_2680_to_fp16)[name = tensor<string, []>("add_324_cast_fp16")];
            tensor<fp32, []> rsqrt_122_epsilon_0 = const()[name = tensor<string, []>("rsqrt_122_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_122_cast_fp16 = rsqrt(epsilon = rsqrt_122_epsilon_0, x = add_324_cast_fp16)[name = tensor<string, []>("rsqrt_122_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_292_cast_fp16 = mul(x = unsqueeze_275_cast_fp16, y = rsqrt_122_cast_fp16)[name = tensor<string, []>("mul_292_cast_fp16")];
            tensor<int32, [1]> squeeze_128_axes_0 = const()[name = tensor<string, []>("squeeze_128_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_128_cast_fp16 = squeeze(axes = squeeze_128_axes_0, x = mul_292_cast_fp16)[name = tensor<string, []>("squeeze_128_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_188_to_fp16 = const()[name = tensor<string, []>("view_188_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32300480)))];
            tensor<fp16, [1, 256, 801]> mul_293_cast_fp16 = mul(x = squeeze_128_cast_fp16, y = view_188_to_fp16)[name = tensor<string, []>("mul_293_cast_fp16")];
            tensor<string, []> conv1d_170_pad_type_0 = const()[name = tensor<string, []>("conv1d_170_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_170_strides_0 = const()[name = tensor<string, []>("conv1d_170_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_170_pad_0 = const()[name = tensor<string, []>("conv1d_170_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_170_dilations_0 = const()[name = tensor<string, []>("conv1d_170_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_170_groups_0 = const()[name = tensor<string, []>("conv1d_170_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_12_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_12_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32301056)))];
            tensor<fp16, [20]> p_output_12_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_12_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32311360)))];
            tensor<fp16, [1, 20, 801]> conv1d_170_cast_fp16 = conv(bias = p_output_12_1_bias_to_fp16, dilations = conv1d_170_dilations_0, groups = conv1d_170_groups_0, pad = conv1d_170_pad_0, pad_type = conv1d_170_pad_type_0, strides = conv1d_170_strides_0, weight = p_output_12_1_weight_to_fp16, x = mul_293_cast_fp16)[name = tensor<string, []>("conv1d_170_cast_fp16")];
            tensor<int32, []> glu_12_split_num_splits_0 = const()[name = tensor<string, []>("glu_12_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_12_split_axis_0 = const()[name = tensor<string, []>("glu_12_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_12_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_12_split_cast_fp16_1 = split(axis = glu_12_split_axis_0, num_splits = glu_12_split_num_splits_0, x = conv1d_170_cast_fp16)[name = tensor<string, []>("glu_12_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_12_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_12_split_cast_fp16_1)[name = tensor<string, []>("glu_12_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_12_cast_fp16 = mul(x = glu_12_split_cast_fp16_0, y = glu_12_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_12_cast_fp16")];
            tensor<int32, [4]> const_2684 = const()[name = tensor<string, []>("const_2684"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_189_cast_fp16 = reshape(shape = const_2684, x = glu_12_cast_fp16)[name = tensor<string, []>("view_189_cast_fp16")];
            tensor<int32, [4]> select_39_begin_0 = const()[name = tensor<string, []>("select_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_39_end_0 = const()[name = tensor<string, []>("select_39_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_39_end_mask_0 = const()[name = tensor<string, []>("select_39_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_39_squeeze_mask_0 = const()[name = tensor<string, []>("select_39_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_39_cast_fp16 = slice_by_index(begin = select_39_begin_0, end = select_39_end_0, end_mask = select_39_end_mask_0, squeeze_mask = select_39_squeeze_mask_0, x = view_189_cast_fp16)[name = tensor<string, []>("select_39_cast_fp16")];
            tensor<int32, [4]> select_40_begin_0 = const()[name = tensor<string, []>("select_40_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_40_end_0 = const()[name = tensor<string, []>("select_40_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_40_end_mask_0 = const()[name = tensor<string, []>("select_40_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_40_squeeze_mask_0 = const()[name = tensor<string, []>("select_40_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_40_cast_fp16 = slice_by_index(begin = select_40_begin_0, end = select_40_end_0, end_mask = select_40_end_mask_0, squeeze_mask = select_40_squeeze_mask_0, x = view_189_cast_fp16)[name = tensor<string, []>("select_40_cast_fp16")];
            tensor<int32, [4]> select_41_begin_0 = const()[name = tensor<string, []>("select_41_begin_0"), val = tensor<int32, [4]>([0, 13, 0, 0])];
            tensor<int32, [4]> select_41_end_0 = const()[name = tensor<string, []>("select_41_end_0"), val = tensor<int32, [4]>([1, 14, 256, 801])];
            tensor<bool, [4]> select_41_end_mask_0 = const()[name = tensor<string, []>("select_41_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_41_squeeze_mask_0 = const()[name = tensor<string, []>("select_41_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_41_cast_fp16 = slice_by_index(begin = select_41_begin_0, end = select_41_end_0, end_mask = select_41_end_mask_0, squeeze_mask = select_41_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_41_cast_fp16")];
            tensor<int32, [1]> unsqueeze_277_axes_0 = const()[name = tensor<string, []>("unsqueeze_277_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_277_cast_fp16 = expand_dims(axes = unsqueeze_277_axes_0, x = select_41_cast_fp16)[name = tensor<string, []>("unsqueeze_277_cast_fp16")];
            tensor<fp16, []> const_2695_promoted_to_fp16 = const()[name = tensor<string, []>("const_2695_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_284_cast_fp16 = pow(x = unsqueeze_277_cast_fp16, y = const_2695_promoted_to_fp16)[name = tensor<string, []>("pow_284_cast_fp16")];
            tensor<int32, [1]> mean_123_axes_0 = const()[name = tensor<string, []>("mean_123_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_123_keep_dims_0 = const()[name = tensor<string, []>("mean_123_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_123_cast_fp16 = reduce_mean(axes = mean_123_axes_0, keep_dims = mean_123_keep_dims_0, x = pow_284_cast_fp16)[name = tensor<string, []>("mean_123_cast_fp16")];
            tensor<fp16, []> const_2698_to_fp16 = const()[name = tensor<string, []>("const_2698_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_325_cast_fp16 = add(x = mean_123_cast_fp16, y = const_2698_to_fp16)[name = tensor<string, []>("add_325_cast_fp16")];
            tensor<fp32, []> rsqrt_123_epsilon_0 = const()[name = tensor<string, []>("rsqrt_123_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_123_cast_fp16 = rsqrt(epsilon = rsqrt_123_epsilon_0, x = add_325_cast_fp16)[name = tensor<string, []>("rsqrt_123_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_294_cast_fp16 = mul(x = unsqueeze_277_cast_fp16, y = rsqrt_123_cast_fp16)[name = tensor<string, []>("mul_294_cast_fp16")];
            tensor<int32, [1]> squeeze_129_axes_0 = const()[name = tensor<string, []>("squeeze_129_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_129_cast_fp16 = squeeze(axes = squeeze_129_axes_0, x = mul_294_cast_fp16)[name = tensor<string, []>("squeeze_129_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_190_to_fp16 = const()[name = tensor<string, []>("view_190_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32311488)))];
            tensor<fp16, [1, 256, 801]> mul_295_cast_fp16 = mul(x = squeeze_129_cast_fp16, y = view_190_to_fp16)[name = tensor<string, []>("mul_295_cast_fp16")];
            tensor<string, []> conv1d_171_pad_type_0 = const()[name = tensor<string, []>("conv1d_171_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_171_strides_0 = const()[name = tensor<string, []>("conv1d_171_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_171_pad_0 = const()[name = tensor<string, []>("conv1d_171_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_171_dilations_0 = const()[name = tensor<string, []>("conv1d_171_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_171_groups_0 = const()[name = tensor<string, []>("conv1d_171_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_13_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_13_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32312064)))];
            tensor<fp16, [20]> p_output_13_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_13_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32322368)))];
            tensor<fp16, [1, 20, 801]> conv1d_171_cast_fp16 = conv(bias = p_output_13_1_bias_to_fp16, dilations = conv1d_171_dilations_0, groups = conv1d_171_groups_0, pad = conv1d_171_pad_0, pad_type = conv1d_171_pad_type_0, strides = conv1d_171_strides_0, weight = p_output_13_1_weight_to_fp16, x = mul_295_cast_fp16)[name = tensor<string, []>("conv1d_171_cast_fp16")];
            tensor<int32, []> glu_13_split_num_splits_0 = const()[name = tensor<string, []>("glu_13_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_13_split_axis_0 = const()[name = tensor<string, []>("glu_13_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_13_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_13_split_cast_fp16_1 = split(axis = glu_13_split_axis_0, num_splits = glu_13_split_num_splits_0, x = conv1d_171_cast_fp16)[name = tensor<string, []>("glu_13_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_13_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_13_split_cast_fp16_1)[name = tensor<string, []>("glu_13_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_13_cast_fp16 = mul(x = glu_13_split_cast_fp16_0, y = glu_13_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_13_cast_fp16")];
            tensor<int32, [4]> const_2702 = const()[name = tensor<string, []>("const_2702"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_191_cast_fp16 = reshape(shape = const_2702, x = glu_13_cast_fp16)[name = tensor<string, []>("view_191_cast_fp16")];
            tensor<int32, [4]> select_42_begin_0 = const()[name = tensor<string, []>("select_42_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_42_end_0 = const()[name = tensor<string, []>("select_42_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_42_end_mask_0 = const()[name = tensor<string, []>("select_42_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_42_squeeze_mask_0 = const()[name = tensor<string, []>("select_42_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_42_cast_fp16 = slice_by_index(begin = select_42_begin_0, end = select_42_end_0, end_mask = select_42_end_mask_0, squeeze_mask = select_42_squeeze_mask_0, x = view_191_cast_fp16)[name = tensor<string, []>("select_42_cast_fp16")];
            tensor<int32, [4]> select_43_begin_0 = const()[name = tensor<string, []>("select_43_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_43_end_0 = const()[name = tensor<string, []>("select_43_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_43_end_mask_0 = const()[name = tensor<string, []>("select_43_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_43_squeeze_mask_0 = const()[name = tensor<string, []>("select_43_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_43_cast_fp16 = slice_by_index(begin = select_43_begin_0, end = select_43_end_0, end_mask = select_43_end_mask_0, squeeze_mask = select_43_squeeze_mask_0, x = view_191_cast_fp16)[name = tensor<string, []>("select_43_cast_fp16")];
            tensor<int32, [4]> select_44_begin_0 = const()[name = tensor<string, []>("select_44_begin_0"), val = tensor<int32, [4]>([0, 14, 0, 0])];
            tensor<int32, [4]> select_44_end_0 = const()[name = tensor<string, []>("select_44_end_0"), val = tensor<int32, [4]>([1, 15, 256, 801])];
            tensor<bool, [4]> select_44_end_mask_0 = const()[name = tensor<string, []>("select_44_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_44_squeeze_mask_0 = const()[name = tensor<string, []>("select_44_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_44_cast_fp16 = slice_by_index(begin = select_44_begin_0, end = select_44_end_0, end_mask = select_44_end_mask_0, squeeze_mask = select_44_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_44_cast_fp16")];
            tensor<int32, [1]> unsqueeze_279_axes_0 = const()[name = tensor<string, []>("unsqueeze_279_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_279_cast_fp16 = expand_dims(axes = unsqueeze_279_axes_0, x = select_44_cast_fp16)[name = tensor<string, []>("unsqueeze_279_cast_fp16")];
            tensor<fp16, []> const_2713_promoted_to_fp16 = const()[name = tensor<string, []>("const_2713_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_285_cast_fp16 = pow(x = unsqueeze_279_cast_fp16, y = const_2713_promoted_to_fp16)[name = tensor<string, []>("pow_285_cast_fp16")];
            tensor<int32, [1]> mean_124_axes_0 = const()[name = tensor<string, []>("mean_124_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_124_keep_dims_0 = const()[name = tensor<string, []>("mean_124_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_124_cast_fp16 = reduce_mean(axes = mean_124_axes_0, keep_dims = mean_124_keep_dims_0, x = pow_285_cast_fp16)[name = tensor<string, []>("mean_124_cast_fp16")];
            tensor<fp16, []> const_2716_to_fp16 = const()[name = tensor<string, []>("const_2716_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_326_cast_fp16 = add(x = mean_124_cast_fp16, y = const_2716_to_fp16)[name = tensor<string, []>("add_326_cast_fp16")];
            tensor<fp32, []> rsqrt_124_epsilon_0 = const()[name = tensor<string, []>("rsqrt_124_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_124_cast_fp16 = rsqrt(epsilon = rsqrt_124_epsilon_0, x = add_326_cast_fp16)[name = tensor<string, []>("rsqrt_124_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_296_cast_fp16 = mul(x = unsqueeze_279_cast_fp16, y = rsqrt_124_cast_fp16)[name = tensor<string, []>("mul_296_cast_fp16")];
            tensor<int32, [1]> squeeze_130_axes_0 = const()[name = tensor<string, []>("squeeze_130_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_130_cast_fp16 = squeeze(axes = squeeze_130_axes_0, x = mul_296_cast_fp16)[name = tensor<string, []>("squeeze_130_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_192_to_fp16 = const()[name = tensor<string, []>("view_192_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32322496)))];
            tensor<fp16, [1, 256, 801]> mul_297_cast_fp16 = mul(x = squeeze_130_cast_fp16, y = view_192_to_fp16)[name = tensor<string, []>("mul_297_cast_fp16")];
            tensor<string, []> conv1d_172_pad_type_0 = const()[name = tensor<string, []>("conv1d_172_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_172_strides_0 = const()[name = tensor<string, []>("conv1d_172_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_172_pad_0 = const()[name = tensor<string, []>("conv1d_172_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_172_dilations_0 = const()[name = tensor<string, []>("conv1d_172_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_172_groups_0 = const()[name = tensor<string, []>("conv1d_172_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_14_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_14_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32323072)))];
            tensor<fp16, [20]> p_output_14_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_14_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32333376)))];
            tensor<fp16, [1, 20, 801]> conv1d_172_cast_fp16 = conv(bias = p_output_14_1_bias_to_fp16, dilations = conv1d_172_dilations_0, groups = conv1d_172_groups_0, pad = conv1d_172_pad_0, pad_type = conv1d_172_pad_type_0, strides = conv1d_172_strides_0, weight = p_output_14_1_weight_to_fp16, x = mul_297_cast_fp16)[name = tensor<string, []>("conv1d_172_cast_fp16")];
            tensor<int32, []> glu_14_split_num_splits_0 = const()[name = tensor<string, []>("glu_14_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_14_split_axis_0 = const()[name = tensor<string, []>("glu_14_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_14_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_14_split_cast_fp16_1 = split(axis = glu_14_split_axis_0, num_splits = glu_14_split_num_splits_0, x = conv1d_172_cast_fp16)[name = tensor<string, []>("glu_14_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_14_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_14_split_cast_fp16_1)[name = tensor<string, []>("glu_14_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_14_cast_fp16 = mul(x = glu_14_split_cast_fp16_0, y = glu_14_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_14_cast_fp16")];
            tensor<int32, [4]> const_2720 = const()[name = tensor<string, []>("const_2720"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_193_cast_fp16 = reshape(shape = const_2720, x = glu_14_cast_fp16)[name = tensor<string, []>("view_193_cast_fp16")];
            tensor<int32, [4]> select_45_begin_0 = const()[name = tensor<string, []>("select_45_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_45_end_0 = const()[name = tensor<string, []>("select_45_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_45_end_mask_0 = const()[name = tensor<string, []>("select_45_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_45_squeeze_mask_0 = const()[name = tensor<string, []>("select_45_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_45_cast_fp16 = slice_by_index(begin = select_45_begin_0, end = select_45_end_0, end_mask = select_45_end_mask_0, squeeze_mask = select_45_squeeze_mask_0, x = view_193_cast_fp16)[name = tensor<string, []>("select_45_cast_fp16")];
            tensor<int32, [4]> select_46_begin_0 = const()[name = tensor<string, []>("select_46_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_46_end_0 = const()[name = tensor<string, []>("select_46_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_46_end_mask_0 = const()[name = tensor<string, []>("select_46_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_46_squeeze_mask_0 = const()[name = tensor<string, []>("select_46_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_46_cast_fp16 = slice_by_index(begin = select_46_begin_0, end = select_46_end_0, end_mask = select_46_end_mask_0, squeeze_mask = select_46_squeeze_mask_0, x = view_193_cast_fp16)[name = tensor<string, []>("select_46_cast_fp16")];
            tensor<int32, [4]> select_47_begin_0 = const()[name = tensor<string, []>("select_47_begin_0"), val = tensor<int32, [4]>([0, 15, 0, 0])];
            tensor<int32, [4]> select_47_end_0 = const()[name = tensor<string, []>("select_47_end_0"), val = tensor<int32, [4]>([1, 16, 256, 801])];
            tensor<bool, [4]> select_47_end_mask_0 = const()[name = tensor<string, []>("select_47_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_47_squeeze_mask_0 = const()[name = tensor<string, []>("select_47_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_47_cast_fp16 = slice_by_index(begin = select_47_begin_0, end = select_47_end_0, end_mask = select_47_end_mask_0, squeeze_mask = select_47_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_47_cast_fp16")];
            tensor<int32, [1]> unsqueeze_281_axes_0 = const()[name = tensor<string, []>("unsqueeze_281_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_281_cast_fp16 = expand_dims(axes = unsqueeze_281_axes_0, x = select_47_cast_fp16)[name = tensor<string, []>("unsqueeze_281_cast_fp16")];
            tensor<fp16, []> const_2731_promoted_to_fp16 = const()[name = tensor<string, []>("const_2731_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_286_cast_fp16 = pow(x = unsqueeze_281_cast_fp16, y = const_2731_promoted_to_fp16)[name = tensor<string, []>("pow_286_cast_fp16")];
            tensor<int32, [1]> mean_125_axes_0 = const()[name = tensor<string, []>("mean_125_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_125_keep_dims_0 = const()[name = tensor<string, []>("mean_125_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_125_cast_fp16 = reduce_mean(axes = mean_125_axes_0, keep_dims = mean_125_keep_dims_0, x = pow_286_cast_fp16)[name = tensor<string, []>("mean_125_cast_fp16")];
            tensor<fp16, []> const_2734_to_fp16 = const()[name = tensor<string, []>("const_2734_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_327_cast_fp16 = add(x = mean_125_cast_fp16, y = const_2734_to_fp16)[name = tensor<string, []>("add_327_cast_fp16")];
            tensor<fp32, []> rsqrt_125_epsilon_0 = const()[name = tensor<string, []>("rsqrt_125_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_125_cast_fp16 = rsqrt(epsilon = rsqrt_125_epsilon_0, x = add_327_cast_fp16)[name = tensor<string, []>("rsqrt_125_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_298_cast_fp16 = mul(x = unsqueeze_281_cast_fp16, y = rsqrt_125_cast_fp16)[name = tensor<string, []>("mul_298_cast_fp16")];
            tensor<int32, [1]> squeeze_131_axes_0 = const()[name = tensor<string, []>("squeeze_131_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_131_cast_fp16 = squeeze(axes = squeeze_131_axes_0, x = mul_298_cast_fp16)[name = tensor<string, []>("squeeze_131_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_194_to_fp16 = const()[name = tensor<string, []>("view_194_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32333504)))];
            tensor<fp16, [1, 256, 801]> mul_299_cast_fp16 = mul(x = squeeze_131_cast_fp16, y = view_194_to_fp16)[name = tensor<string, []>("mul_299_cast_fp16")];
            tensor<string, []> conv1d_173_pad_type_0 = const()[name = tensor<string, []>("conv1d_173_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_173_strides_0 = const()[name = tensor<string, []>("conv1d_173_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_173_pad_0 = const()[name = tensor<string, []>("conv1d_173_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_173_dilations_0 = const()[name = tensor<string, []>("conv1d_173_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_173_groups_0 = const()[name = tensor<string, []>("conv1d_173_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_15_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_15_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32334080)))];
            tensor<fp16, [20]> p_output_15_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_15_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32344384)))];
            tensor<fp16, [1, 20, 801]> conv1d_173_cast_fp16 = conv(bias = p_output_15_1_bias_to_fp16, dilations = conv1d_173_dilations_0, groups = conv1d_173_groups_0, pad = conv1d_173_pad_0, pad_type = conv1d_173_pad_type_0, strides = conv1d_173_strides_0, weight = p_output_15_1_weight_to_fp16, x = mul_299_cast_fp16)[name = tensor<string, []>("conv1d_173_cast_fp16")];
            tensor<int32, []> glu_15_split_num_splits_0 = const()[name = tensor<string, []>("glu_15_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_15_split_axis_0 = const()[name = tensor<string, []>("glu_15_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_15_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_15_split_cast_fp16_1 = split(axis = glu_15_split_axis_0, num_splits = glu_15_split_num_splits_0, x = conv1d_173_cast_fp16)[name = tensor<string, []>("glu_15_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_15_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_15_split_cast_fp16_1)[name = tensor<string, []>("glu_15_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_15_cast_fp16 = mul(x = glu_15_split_cast_fp16_0, y = glu_15_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_15_cast_fp16")];
            tensor<int32, [4]> const_2738 = const()[name = tensor<string, []>("const_2738"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_195_cast_fp16 = reshape(shape = const_2738, x = glu_15_cast_fp16)[name = tensor<string, []>("view_195_cast_fp16")];
            tensor<int32, [4]> select_48_begin_0 = const()[name = tensor<string, []>("select_48_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_48_end_0 = const()[name = tensor<string, []>("select_48_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_48_end_mask_0 = const()[name = tensor<string, []>("select_48_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_48_squeeze_mask_0 = const()[name = tensor<string, []>("select_48_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_48_cast_fp16 = slice_by_index(begin = select_48_begin_0, end = select_48_end_0, end_mask = select_48_end_mask_0, squeeze_mask = select_48_squeeze_mask_0, x = view_195_cast_fp16)[name = tensor<string, []>("select_48_cast_fp16")];
            tensor<int32, [4]> select_49_begin_0 = const()[name = tensor<string, []>("select_49_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_49_end_0 = const()[name = tensor<string, []>("select_49_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_49_end_mask_0 = const()[name = tensor<string, []>("select_49_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_49_squeeze_mask_0 = const()[name = tensor<string, []>("select_49_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_49_cast_fp16 = slice_by_index(begin = select_49_begin_0, end = select_49_end_0, end_mask = select_49_end_mask_0, squeeze_mask = select_49_squeeze_mask_0, x = view_195_cast_fp16)[name = tensor<string, []>("select_49_cast_fp16")];
            tensor<int32, [4]> select_50_begin_0 = const()[name = tensor<string, []>("select_50_begin_0"), val = tensor<int32, [4]>([0, 16, 0, 0])];
            tensor<int32, [4]> select_50_end_0 = const()[name = tensor<string, []>("select_50_end_0"), val = tensor<int32, [4]>([1, 17, 256, 801])];
            tensor<bool, [4]> select_50_end_mask_0 = const()[name = tensor<string, []>("select_50_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_50_squeeze_mask_0 = const()[name = tensor<string, []>("select_50_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_50_cast_fp16 = slice_by_index(begin = select_50_begin_0, end = select_50_end_0, end_mask = select_50_end_mask_0, squeeze_mask = select_50_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_50_cast_fp16")];
            tensor<int32, [1]> unsqueeze_283_axes_0 = const()[name = tensor<string, []>("unsqueeze_283_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_283_cast_fp16 = expand_dims(axes = unsqueeze_283_axes_0, x = select_50_cast_fp16)[name = tensor<string, []>("unsqueeze_283_cast_fp16")];
            tensor<fp16, []> const_2749_promoted_to_fp16 = const()[name = tensor<string, []>("const_2749_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_287_cast_fp16 = pow(x = unsqueeze_283_cast_fp16, y = const_2749_promoted_to_fp16)[name = tensor<string, []>("pow_287_cast_fp16")];
            tensor<int32, [1]> mean_126_axes_0 = const()[name = tensor<string, []>("mean_126_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_126_keep_dims_0 = const()[name = tensor<string, []>("mean_126_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_126_cast_fp16 = reduce_mean(axes = mean_126_axes_0, keep_dims = mean_126_keep_dims_0, x = pow_287_cast_fp16)[name = tensor<string, []>("mean_126_cast_fp16")];
            tensor<fp16, []> const_2752_to_fp16 = const()[name = tensor<string, []>("const_2752_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_328_cast_fp16 = add(x = mean_126_cast_fp16, y = const_2752_to_fp16)[name = tensor<string, []>("add_328_cast_fp16")];
            tensor<fp32, []> rsqrt_126_epsilon_0 = const()[name = tensor<string, []>("rsqrt_126_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_126_cast_fp16 = rsqrt(epsilon = rsqrt_126_epsilon_0, x = add_328_cast_fp16)[name = tensor<string, []>("rsqrt_126_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_300_cast_fp16 = mul(x = unsqueeze_283_cast_fp16, y = rsqrt_126_cast_fp16)[name = tensor<string, []>("mul_300_cast_fp16")];
            tensor<int32, [1]> squeeze_132_axes_0 = const()[name = tensor<string, []>("squeeze_132_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_132_cast_fp16 = squeeze(axes = squeeze_132_axes_0, x = mul_300_cast_fp16)[name = tensor<string, []>("squeeze_132_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_196_to_fp16 = const()[name = tensor<string, []>("view_196_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32344512)))];
            tensor<fp16, [1, 256, 801]> mul_301_cast_fp16 = mul(x = squeeze_132_cast_fp16, y = view_196_to_fp16)[name = tensor<string, []>("mul_301_cast_fp16")];
            tensor<string, []> conv1d_174_pad_type_0 = const()[name = tensor<string, []>("conv1d_174_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_174_strides_0 = const()[name = tensor<string, []>("conv1d_174_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_174_pad_0 = const()[name = tensor<string, []>("conv1d_174_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_174_dilations_0 = const()[name = tensor<string, []>("conv1d_174_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_174_groups_0 = const()[name = tensor<string, []>("conv1d_174_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_16_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_16_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32345088)))];
            tensor<fp16, [20]> p_output_16_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_16_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32355392)))];
            tensor<fp16, [1, 20, 801]> conv1d_174_cast_fp16 = conv(bias = p_output_16_1_bias_to_fp16, dilations = conv1d_174_dilations_0, groups = conv1d_174_groups_0, pad = conv1d_174_pad_0, pad_type = conv1d_174_pad_type_0, strides = conv1d_174_strides_0, weight = p_output_16_1_weight_to_fp16, x = mul_301_cast_fp16)[name = tensor<string, []>("conv1d_174_cast_fp16")];
            tensor<int32, []> glu_16_split_num_splits_0 = const()[name = tensor<string, []>("glu_16_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_16_split_axis_0 = const()[name = tensor<string, []>("glu_16_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_16_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_16_split_cast_fp16_1 = split(axis = glu_16_split_axis_0, num_splits = glu_16_split_num_splits_0, x = conv1d_174_cast_fp16)[name = tensor<string, []>("glu_16_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_16_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_16_split_cast_fp16_1)[name = tensor<string, []>("glu_16_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_16_cast_fp16 = mul(x = glu_16_split_cast_fp16_0, y = glu_16_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_16_cast_fp16")];
            tensor<int32, [4]> const_2756 = const()[name = tensor<string, []>("const_2756"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_197_cast_fp16 = reshape(shape = const_2756, x = glu_16_cast_fp16)[name = tensor<string, []>("view_197_cast_fp16")];
            tensor<int32, [4]> select_51_begin_0 = const()[name = tensor<string, []>("select_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_51_end_0 = const()[name = tensor<string, []>("select_51_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_51_end_mask_0 = const()[name = tensor<string, []>("select_51_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_51_squeeze_mask_0 = const()[name = tensor<string, []>("select_51_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_51_cast_fp16 = slice_by_index(begin = select_51_begin_0, end = select_51_end_0, end_mask = select_51_end_mask_0, squeeze_mask = select_51_squeeze_mask_0, x = view_197_cast_fp16)[name = tensor<string, []>("select_51_cast_fp16")];
            tensor<int32, [4]> select_52_begin_0 = const()[name = tensor<string, []>("select_52_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_52_end_0 = const()[name = tensor<string, []>("select_52_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_52_end_mask_0 = const()[name = tensor<string, []>("select_52_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_52_squeeze_mask_0 = const()[name = tensor<string, []>("select_52_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_52_cast_fp16 = slice_by_index(begin = select_52_begin_0, end = select_52_end_0, end_mask = select_52_end_mask_0, squeeze_mask = select_52_squeeze_mask_0, x = view_197_cast_fp16)[name = tensor<string, []>("select_52_cast_fp16")];
            tensor<int32, [4]> select_53_begin_0 = const()[name = tensor<string, []>("select_53_begin_0"), val = tensor<int32, [4]>([0, 17, 0, 0])];
            tensor<int32, [4]> select_53_end_0 = const()[name = tensor<string, []>("select_53_end_0"), val = tensor<int32, [4]>([1, 18, 256, 801])];
            tensor<bool, [4]> select_53_end_mask_0 = const()[name = tensor<string, []>("select_53_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_53_squeeze_mask_0 = const()[name = tensor<string, []>("select_53_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_53_cast_fp16 = slice_by_index(begin = select_53_begin_0, end = select_53_end_0, end_mask = select_53_end_mask_0, squeeze_mask = select_53_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_53_cast_fp16")];
            tensor<int32, [1]> unsqueeze_285_axes_0 = const()[name = tensor<string, []>("unsqueeze_285_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_285_cast_fp16 = expand_dims(axes = unsqueeze_285_axes_0, x = select_53_cast_fp16)[name = tensor<string, []>("unsqueeze_285_cast_fp16")];
            tensor<fp16, []> const_2767_promoted_to_fp16 = const()[name = tensor<string, []>("const_2767_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_288_cast_fp16 = pow(x = unsqueeze_285_cast_fp16, y = const_2767_promoted_to_fp16)[name = tensor<string, []>("pow_288_cast_fp16")];
            tensor<int32, [1]> mean_127_axes_0 = const()[name = tensor<string, []>("mean_127_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_127_keep_dims_0 = const()[name = tensor<string, []>("mean_127_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_127_cast_fp16 = reduce_mean(axes = mean_127_axes_0, keep_dims = mean_127_keep_dims_0, x = pow_288_cast_fp16)[name = tensor<string, []>("mean_127_cast_fp16")];
            tensor<fp16, []> const_2770_to_fp16 = const()[name = tensor<string, []>("const_2770_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_329_cast_fp16 = add(x = mean_127_cast_fp16, y = const_2770_to_fp16)[name = tensor<string, []>("add_329_cast_fp16")];
            tensor<fp32, []> rsqrt_127_epsilon_0 = const()[name = tensor<string, []>("rsqrt_127_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_127_cast_fp16 = rsqrt(epsilon = rsqrt_127_epsilon_0, x = add_329_cast_fp16)[name = tensor<string, []>("rsqrt_127_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_302_cast_fp16 = mul(x = unsqueeze_285_cast_fp16, y = rsqrt_127_cast_fp16)[name = tensor<string, []>("mul_302_cast_fp16")];
            tensor<int32, [1]> squeeze_133_axes_0 = const()[name = tensor<string, []>("squeeze_133_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_133_cast_fp16 = squeeze(axes = squeeze_133_axes_0, x = mul_302_cast_fp16)[name = tensor<string, []>("squeeze_133_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_198_to_fp16 = const()[name = tensor<string, []>("view_198_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32355520)))];
            tensor<fp16, [1, 256, 801]> mul_303_cast_fp16 = mul(x = squeeze_133_cast_fp16, y = view_198_to_fp16)[name = tensor<string, []>("mul_303_cast_fp16")];
            tensor<string, []> conv1d_175_pad_type_0 = const()[name = tensor<string, []>("conv1d_175_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_175_strides_0 = const()[name = tensor<string, []>("conv1d_175_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_175_pad_0 = const()[name = tensor<string, []>("conv1d_175_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_175_dilations_0 = const()[name = tensor<string, []>("conv1d_175_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_175_groups_0 = const()[name = tensor<string, []>("conv1d_175_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_17_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_17_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32356096)))];
            tensor<fp16, [20]> p_output_17_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_17_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32366400)))];
            tensor<fp16, [1, 20, 801]> conv1d_175_cast_fp16 = conv(bias = p_output_17_1_bias_to_fp16, dilations = conv1d_175_dilations_0, groups = conv1d_175_groups_0, pad = conv1d_175_pad_0, pad_type = conv1d_175_pad_type_0, strides = conv1d_175_strides_0, weight = p_output_17_1_weight_to_fp16, x = mul_303_cast_fp16)[name = tensor<string, []>("conv1d_175_cast_fp16")];
            tensor<int32, []> glu_17_split_num_splits_0 = const()[name = tensor<string, []>("glu_17_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_17_split_axis_0 = const()[name = tensor<string, []>("glu_17_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_17_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_17_split_cast_fp16_1 = split(axis = glu_17_split_axis_0, num_splits = glu_17_split_num_splits_0, x = conv1d_175_cast_fp16)[name = tensor<string, []>("glu_17_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_17_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_17_split_cast_fp16_1)[name = tensor<string, []>("glu_17_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_17_cast_fp16 = mul(x = glu_17_split_cast_fp16_0, y = glu_17_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_17_cast_fp16")];
            tensor<int32, [4]> const_2774 = const()[name = tensor<string, []>("const_2774"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_199_cast_fp16 = reshape(shape = const_2774, x = glu_17_cast_fp16)[name = tensor<string, []>("view_199_cast_fp16")];
            tensor<int32, [4]> select_54_begin_0 = const()[name = tensor<string, []>("select_54_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_54_end_0 = const()[name = tensor<string, []>("select_54_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_54_end_mask_0 = const()[name = tensor<string, []>("select_54_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_54_squeeze_mask_0 = const()[name = tensor<string, []>("select_54_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_54_cast_fp16 = slice_by_index(begin = select_54_begin_0, end = select_54_end_0, end_mask = select_54_end_mask_0, squeeze_mask = select_54_squeeze_mask_0, x = view_199_cast_fp16)[name = tensor<string, []>("select_54_cast_fp16")];
            tensor<int32, [4]> select_55_begin_0 = const()[name = tensor<string, []>("select_55_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_55_end_0 = const()[name = tensor<string, []>("select_55_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_55_end_mask_0 = const()[name = tensor<string, []>("select_55_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_55_squeeze_mask_0 = const()[name = tensor<string, []>("select_55_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_55_cast_fp16 = slice_by_index(begin = select_55_begin_0, end = select_55_end_0, end_mask = select_55_end_mask_0, squeeze_mask = select_55_squeeze_mask_0, x = view_199_cast_fp16)[name = tensor<string, []>("select_55_cast_fp16")];
            tensor<int32, [4]> select_56_begin_0 = const()[name = tensor<string, []>("select_56_begin_0"), val = tensor<int32, [4]>([0, 18, 0, 0])];
            tensor<int32, [4]> select_56_end_0 = const()[name = tensor<string, []>("select_56_end_0"), val = tensor<int32, [4]>([1, 19, 256, 801])];
            tensor<bool, [4]> select_56_end_mask_0 = const()[name = tensor<string, []>("select_56_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_56_squeeze_mask_0 = const()[name = tensor<string, []>("select_56_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_56_cast_fp16 = slice_by_index(begin = select_56_begin_0, end = select_56_end_0, end_mask = select_56_end_mask_0, squeeze_mask = select_56_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_56_cast_fp16")];
            tensor<int32, [1]> unsqueeze_287_axes_0 = const()[name = tensor<string, []>("unsqueeze_287_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_287_cast_fp16 = expand_dims(axes = unsqueeze_287_axes_0, x = select_56_cast_fp16)[name = tensor<string, []>("unsqueeze_287_cast_fp16")];
            tensor<fp16, []> const_2785_promoted_to_fp16 = const()[name = tensor<string, []>("const_2785_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_289_cast_fp16 = pow(x = unsqueeze_287_cast_fp16, y = const_2785_promoted_to_fp16)[name = tensor<string, []>("pow_289_cast_fp16")];
            tensor<int32, [1]> mean_128_axes_0 = const()[name = tensor<string, []>("mean_128_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_128_keep_dims_0 = const()[name = tensor<string, []>("mean_128_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_128_cast_fp16 = reduce_mean(axes = mean_128_axes_0, keep_dims = mean_128_keep_dims_0, x = pow_289_cast_fp16)[name = tensor<string, []>("mean_128_cast_fp16")];
            tensor<fp16, []> const_2788_to_fp16 = const()[name = tensor<string, []>("const_2788_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_330_cast_fp16 = add(x = mean_128_cast_fp16, y = const_2788_to_fp16)[name = tensor<string, []>("add_330_cast_fp16")];
            tensor<fp32, []> rsqrt_128_epsilon_0 = const()[name = tensor<string, []>("rsqrt_128_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_128_cast_fp16 = rsqrt(epsilon = rsqrt_128_epsilon_0, x = add_330_cast_fp16)[name = tensor<string, []>("rsqrt_128_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_304_cast_fp16 = mul(x = unsqueeze_287_cast_fp16, y = rsqrt_128_cast_fp16)[name = tensor<string, []>("mul_304_cast_fp16")];
            tensor<int32, [1]> squeeze_134_axes_0 = const()[name = tensor<string, []>("squeeze_134_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_134_cast_fp16 = squeeze(axes = squeeze_134_axes_0, x = mul_304_cast_fp16)[name = tensor<string, []>("squeeze_134_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_200_to_fp16 = const()[name = tensor<string, []>("view_200_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32366528)))];
            tensor<fp16, [1, 256, 801]> mul_305_cast_fp16 = mul(x = squeeze_134_cast_fp16, y = view_200_to_fp16)[name = tensor<string, []>("mul_305_cast_fp16")];
            tensor<string, []> conv1d_176_pad_type_0 = const()[name = tensor<string, []>("conv1d_176_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_176_strides_0 = const()[name = tensor<string, []>("conv1d_176_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_176_pad_0 = const()[name = tensor<string, []>("conv1d_176_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_176_dilations_0 = const()[name = tensor<string, []>("conv1d_176_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_176_groups_0 = const()[name = tensor<string, []>("conv1d_176_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_18_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_18_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32367104)))];
            tensor<fp16, [20]> p_output_18_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_18_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32377408)))];
            tensor<fp16, [1, 20, 801]> conv1d_176_cast_fp16 = conv(bias = p_output_18_1_bias_to_fp16, dilations = conv1d_176_dilations_0, groups = conv1d_176_groups_0, pad = conv1d_176_pad_0, pad_type = conv1d_176_pad_type_0, strides = conv1d_176_strides_0, weight = p_output_18_1_weight_to_fp16, x = mul_305_cast_fp16)[name = tensor<string, []>("conv1d_176_cast_fp16")];
            tensor<int32, []> glu_18_split_num_splits_0 = const()[name = tensor<string, []>("glu_18_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_18_split_axis_0 = const()[name = tensor<string, []>("glu_18_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_18_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_18_split_cast_fp16_1 = split(axis = glu_18_split_axis_0, num_splits = glu_18_split_num_splits_0, x = conv1d_176_cast_fp16)[name = tensor<string, []>("glu_18_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_18_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_18_split_cast_fp16_1)[name = tensor<string, []>("glu_18_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_18_cast_fp16 = mul(x = glu_18_split_cast_fp16_0, y = glu_18_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_18_cast_fp16")];
            tensor<int32, [4]> const_2792 = const()[name = tensor<string, []>("const_2792"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_201_cast_fp16 = reshape(shape = const_2792, x = glu_18_cast_fp16)[name = tensor<string, []>("view_201_cast_fp16")];
            tensor<int32, [4]> select_57_begin_0 = const()[name = tensor<string, []>("select_57_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_57_end_0 = const()[name = tensor<string, []>("select_57_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_57_end_mask_0 = const()[name = tensor<string, []>("select_57_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_57_squeeze_mask_0 = const()[name = tensor<string, []>("select_57_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_57_cast_fp16 = slice_by_index(begin = select_57_begin_0, end = select_57_end_0, end_mask = select_57_end_mask_0, squeeze_mask = select_57_squeeze_mask_0, x = view_201_cast_fp16)[name = tensor<string, []>("select_57_cast_fp16")];
            tensor<int32, [4]> select_58_begin_0 = const()[name = tensor<string, []>("select_58_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_58_end_0 = const()[name = tensor<string, []>("select_58_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_58_end_mask_0 = const()[name = tensor<string, []>("select_58_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_58_squeeze_mask_0 = const()[name = tensor<string, []>("select_58_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_58_cast_fp16 = slice_by_index(begin = select_58_begin_0, end = select_58_end_0, end_mask = select_58_end_mask_0, squeeze_mask = select_58_squeeze_mask_0, x = view_201_cast_fp16)[name = tensor<string, []>("select_58_cast_fp16")];
            tensor<int32, [4]> select_59_begin_0 = const()[name = tensor<string, []>("select_59_begin_0"), val = tensor<int32, [4]>([0, 19, 0, 0])];
            tensor<int32, [4]> select_59_end_0 = const()[name = tensor<string, []>("select_59_end_0"), val = tensor<int32, [4]>([1, 20, 256, 801])];
            tensor<bool, [4]> select_59_end_mask_0 = const()[name = tensor<string, []>("select_59_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_59_squeeze_mask_0 = const()[name = tensor<string, []>("select_59_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_59_cast_fp16 = slice_by_index(begin = select_59_begin_0, end = select_59_end_0, end_mask = select_59_end_mask_0, squeeze_mask = select_59_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_59_cast_fp16")];
            tensor<int32, [1]> unsqueeze_289_axes_0 = const()[name = tensor<string, []>("unsqueeze_289_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_289_cast_fp16 = expand_dims(axes = unsqueeze_289_axes_0, x = select_59_cast_fp16)[name = tensor<string, []>("unsqueeze_289_cast_fp16")];
            tensor<fp16, []> const_2803_promoted_to_fp16 = const()[name = tensor<string, []>("const_2803_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_290_cast_fp16 = pow(x = unsqueeze_289_cast_fp16, y = const_2803_promoted_to_fp16)[name = tensor<string, []>("pow_290_cast_fp16")];
            tensor<int32, [1]> mean_129_axes_0 = const()[name = tensor<string, []>("mean_129_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_129_keep_dims_0 = const()[name = tensor<string, []>("mean_129_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_129_cast_fp16 = reduce_mean(axes = mean_129_axes_0, keep_dims = mean_129_keep_dims_0, x = pow_290_cast_fp16)[name = tensor<string, []>("mean_129_cast_fp16")];
            tensor<fp16, []> const_2806_to_fp16 = const()[name = tensor<string, []>("const_2806_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_331_cast_fp16 = add(x = mean_129_cast_fp16, y = const_2806_to_fp16)[name = tensor<string, []>("add_331_cast_fp16")];
            tensor<fp32, []> rsqrt_129_epsilon_0 = const()[name = tensor<string, []>("rsqrt_129_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_129_cast_fp16 = rsqrt(epsilon = rsqrt_129_epsilon_0, x = add_331_cast_fp16)[name = tensor<string, []>("rsqrt_129_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_306_cast_fp16 = mul(x = unsqueeze_289_cast_fp16, y = rsqrt_129_cast_fp16)[name = tensor<string, []>("mul_306_cast_fp16")];
            tensor<int32, [1]> squeeze_135_axes_0 = const()[name = tensor<string, []>("squeeze_135_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_135_cast_fp16 = squeeze(axes = squeeze_135_axes_0, x = mul_306_cast_fp16)[name = tensor<string, []>("squeeze_135_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_202_to_fp16 = const()[name = tensor<string, []>("view_202_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32377536)))];
            tensor<fp16, [1, 256, 801]> mul_307_cast_fp16 = mul(x = squeeze_135_cast_fp16, y = view_202_to_fp16)[name = tensor<string, []>("mul_307_cast_fp16")];
            tensor<string, []> conv1d_177_pad_type_0 = const()[name = tensor<string, []>("conv1d_177_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_177_strides_0 = const()[name = tensor<string, []>("conv1d_177_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_177_pad_0 = const()[name = tensor<string, []>("conv1d_177_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_177_dilations_0 = const()[name = tensor<string, []>("conv1d_177_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_177_groups_0 = const()[name = tensor<string, []>("conv1d_177_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_19_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_19_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32378112)))];
            tensor<fp16, [20]> p_output_19_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_19_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32388416)))];
            tensor<fp16, [1, 20, 801]> conv1d_177_cast_fp16 = conv(bias = p_output_19_1_bias_to_fp16, dilations = conv1d_177_dilations_0, groups = conv1d_177_groups_0, pad = conv1d_177_pad_0, pad_type = conv1d_177_pad_type_0, strides = conv1d_177_strides_0, weight = p_output_19_1_weight_to_fp16, x = mul_307_cast_fp16)[name = tensor<string, []>("conv1d_177_cast_fp16")];
            tensor<int32, []> glu_19_split_num_splits_0 = const()[name = tensor<string, []>("glu_19_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_19_split_axis_0 = const()[name = tensor<string, []>("glu_19_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_19_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_19_split_cast_fp16_1 = split(axis = glu_19_split_axis_0, num_splits = glu_19_split_num_splits_0, x = conv1d_177_cast_fp16)[name = tensor<string, []>("glu_19_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_19_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_19_split_cast_fp16_1)[name = tensor<string, []>("glu_19_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_19_cast_fp16 = mul(x = glu_19_split_cast_fp16_0, y = glu_19_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_19_cast_fp16")];
            tensor<int32, [4]> const_2810 = const()[name = tensor<string, []>("const_2810"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_203_cast_fp16 = reshape(shape = const_2810, x = glu_19_cast_fp16)[name = tensor<string, []>("view_203_cast_fp16")];
            tensor<int32, [4]> select_60_begin_0 = const()[name = tensor<string, []>("select_60_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_60_end_0 = const()[name = tensor<string, []>("select_60_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_60_end_mask_0 = const()[name = tensor<string, []>("select_60_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_60_squeeze_mask_0 = const()[name = tensor<string, []>("select_60_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_60_cast_fp16 = slice_by_index(begin = select_60_begin_0, end = select_60_end_0, end_mask = select_60_end_mask_0, squeeze_mask = select_60_squeeze_mask_0, x = view_203_cast_fp16)[name = tensor<string, []>("select_60_cast_fp16")];
            tensor<int32, [4]> select_61_begin_0 = const()[name = tensor<string, []>("select_61_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_61_end_0 = const()[name = tensor<string, []>("select_61_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_61_end_mask_0 = const()[name = tensor<string, []>("select_61_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_61_squeeze_mask_0 = const()[name = tensor<string, []>("select_61_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_61_cast_fp16 = slice_by_index(begin = select_61_begin_0, end = select_61_end_0, end_mask = select_61_end_mask_0, squeeze_mask = select_61_squeeze_mask_0, x = view_203_cast_fp16)[name = tensor<string, []>("select_61_cast_fp16")];
            tensor<int32, [4]> select_62_begin_0 = const()[name = tensor<string, []>("select_62_begin_0"), val = tensor<int32, [4]>([0, 20, 0, 0])];
            tensor<int32, [4]> select_62_end_0 = const()[name = tensor<string, []>("select_62_end_0"), val = tensor<int32, [4]>([1, 21, 256, 801])];
            tensor<bool, [4]> select_62_end_mask_0 = const()[name = tensor<string, []>("select_62_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_62_squeeze_mask_0 = const()[name = tensor<string, []>("select_62_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_62_cast_fp16 = slice_by_index(begin = select_62_begin_0, end = select_62_end_0, end_mask = select_62_end_mask_0, squeeze_mask = select_62_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_62_cast_fp16")];
            tensor<int32, [1]> unsqueeze_291_axes_0 = const()[name = tensor<string, []>("unsqueeze_291_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_291_cast_fp16 = expand_dims(axes = unsqueeze_291_axes_0, x = select_62_cast_fp16)[name = tensor<string, []>("unsqueeze_291_cast_fp16")];
            tensor<fp16, []> const_2821_promoted_to_fp16 = const()[name = tensor<string, []>("const_2821_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_291_cast_fp16 = pow(x = unsqueeze_291_cast_fp16, y = const_2821_promoted_to_fp16)[name = tensor<string, []>("pow_291_cast_fp16")];
            tensor<int32, [1]> mean_130_axes_0 = const()[name = tensor<string, []>("mean_130_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_130_keep_dims_0 = const()[name = tensor<string, []>("mean_130_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_130_cast_fp16 = reduce_mean(axes = mean_130_axes_0, keep_dims = mean_130_keep_dims_0, x = pow_291_cast_fp16)[name = tensor<string, []>("mean_130_cast_fp16")];
            tensor<fp16, []> const_2824_to_fp16 = const()[name = tensor<string, []>("const_2824_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_332_cast_fp16 = add(x = mean_130_cast_fp16, y = const_2824_to_fp16)[name = tensor<string, []>("add_332_cast_fp16")];
            tensor<fp32, []> rsqrt_130_epsilon_0 = const()[name = tensor<string, []>("rsqrt_130_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_130_cast_fp16 = rsqrt(epsilon = rsqrt_130_epsilon_0, x = add_332_cast_fp16)[name = tensor<string, []>("rsqrt_130_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_308_cast_fp16 = mul(x = unsqueeze_291_cast_fp16, y = rsqrt_130_cast_fp16)[name = tensor<string, []>("mul_308_cast_fp16")];
            tensor<int32, [1]> squeeze_136_axes_0 = const()[name = tensor<string, []>("squeeze_136_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_136_cast_fp16 = squeeze(axes = squeeze_136_axes_0, x = mul_308_cast_fp16)[name = tensor<string, []>("squeeze_136_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_204_to_fp16 = const()[name = tensor<string, []>("view_204_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32388544)))];
            tensor<fp16, [1, 256, 801]> mul_309_cast_fp16 = mul(x = squeeze_136_cast_fp16, y = view_204_to_fp16)[name = tensor<string, []>("mul_309_cast_fp16")];
            tensor<string, []> conv1d_178_pad_type_0 = const()[name = tensor<string, []>("conv1d_178_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_178_strides_0 = const()[name = tensor<string, []>("conv1d_178_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_178_pad_0 = const()[name = tensor<string, []>("conv1d_178_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_178_dilations_0 = const()[name = tensor<string, []>("conv1d_178_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_178_groups_0 = const()[name = tensor<string, []>("conv1d_178_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_20_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_20_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32389120)))];
            tensor<fp16, [20]> p_output_20_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_20_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32399424)))];
            tensor<fp16, [1, 20, 801]> conv1d_178_cast_fp16 = conv(bias = p_output_20_1_bias_to_fp16, dilations = conv1d_178_dilations_0, groups = conv1d_178_groups_0, pad = conv1d_178_pad_0, pad_type = conv1d_178_pad_type_0, strides = conv1d_178_strides_0, weight = p_output_20_1_weight_to_fp16, x = mul_309_cast_fp16)[name = tensor<string, []>("conv1d_178_cast_fp16")];
            tensor<int32, []> glu_20_split_num_splits_0 = const()[name = tensor<string, []>("glu_20_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_20_split_axis_0 = const()[name = tensor<string, []>("glu_20_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_20_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_20_split_cast_fp16_1 = split(axis = glu_20_split_axis_0, num_splits = glu_20_split_num_splits_0, x = conv1d_178_cast_fp16)[name = tensor<string, []>("glu_20_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_20_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_20_split_cast_fp16_1)[name = tensor<string, []>("glu_20_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_20_cast_fp16 = mul(x = glu_20_split_cast_fp16_0, y = glu_20_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_20_cast_fp16")];
            tensor<int32, [4]> const_2828 = const()[name = tensor<string, []>("const_2828"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_205_cast_fp16 = reshape(shape = const_2828, x = glu_20_cast_fp16)[name = tensor<string, []>("view_205_cast_fp16")];
            tensor<int32, [4]> select_63_begin_0 = const()[name = tensor<string, []>("select_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_63_end_0 = const()[name = tensor<string, []>("select_63_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_63_end_mask_0 = const()[name = tensor<string, []>("select_63_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_63_squeeze_mask_0 = const()[name = tensor<string, []>("select_63_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_63_cast_fp16 = slice_by_index(begin = select_63_begin_0, end = select_63_end_0, end_mask = select_63_end_mask_0, squeeze_mask = select_63_squeeze_mask_0, x = view_205_cast_fp16)[name = tensor<string, []>("select_63_cast_fp16")];
            tensor<int32, [4]> select_64_begin_0 = const()[name = tensor<string, []>("select_64_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_64_end_0 = const()[name = tensor<string, []>("select_64_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_64_end_mask_0 = const()[name = tensor<string, []>("select_64_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_64_squeeze_mask_0 = const()[name = tensor<string, []>("select_64_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_64_cast_fp16 = slice_by_index(begin = select_64_begin_0, end = select_64_end_0, end_mask = select_64_end_mask_0, squeeze_mask = select_64_squeeze_mask_0, x = view_205_cast_fp16)[name = tensor<string, []>("select_64_cast_fp16")];
            tensor<int32, [4]> select_65_begin_0 = const()[name = tensor<string, []>("select_65_begin_0"), val = tensor<int32, [4]>([0, 21, 0, 0])];
            tensor<int32, [4]> select_65_end_0 = const()[name = tensor<string, []>("select_65_end_0"), val = tensor<int32, [4]>([1, 22, 256, 801])];
            tensor<bool, [4]> select_65_end_mask_0 = const()[name = tensor<string, []>("select_65_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_65_squeeze_mask_0 = const()[name = tensor<string, []>("select_65_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_65_cast_fp16 = slice_by_index(begin = select_65_begin_0, end = select_65_end_0, end_mask = select_65_end_mask_0, squeeze_mask = select_65_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_65_cast_fp16")];
            tensor<int32, [1]> unsqueeze_293_axes_0 = const()[name = tensor<string, []>("unsqueeze_293_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_293_cast_fp16 = expand_dims(axes = unsqueeze_293_axes_0, x = select_65_cast_fp16)[name = tensor<string, []>("unsqueeze_293_cast_fp16")];
            tensor<fp16, []> const_2839_promoted_to_fp16 = const()[name = tensor<string, []>("const_2839_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_292_cast_fp16 = pow(x = unsqueeze_293_cast_fp16, y = const_2839_promoted_to_fp16)[name = tensor<string, []>("pow_292_cast_fp16")];
            tensor<int32, [1]> mean_131_axes_0 = const()[name = tensor<string, []>("mean_131_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_131_keep_dims_0 = const()[name = tensor<string, []>("mean_131_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_131_cast_fp16 = reduce_mean(axes = mean_131_axes_0, keep_dims = mean_131_keep_dims_0, x = pow_292_cast_fp16)[name = tensor<string, []>("mean_131_cast_fp16")];
            tensor<fp16, []> const_2842_to_fp16 = const()[name = tensor<string, []>("const_2842_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_333_cast_fp16 = add(x = mean_131_cast_fp16, y = const_2842_to_fp16)[name = tensor<string, []>("add_333_cast_fp16")];
            tensor<fp32, []> rsqrt_131_epsilon_0 = const()[name = tensor<string, []>("rsqrt_131_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_131_cast_fp16 = rsqrt(epsilon = rsqrt_131_epsilon_0, x = add_333_cast_fp16)[name = tensor<string, []>("rsqrt_131_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_310_cast_fp16 = mul(x = unsqueeze_293_cast_fp16, y = rsqrt_131_cast_fp16)[name = tensor<string, []>("mul_310_cast_fp16")];
            tensor<int32, [1]> squeeze_137_axes_0 = const()[name = tensor<string, []>("squeeze_137_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_137_cast_fp16 = squeeze(axes = squeeze_137_axes_0, x = mul_310_cast_fp16)[name = tensor<string, []>("squeeze_137_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_206_to_fp16 = const()[name = tensor<string, []>("view_206_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32399552)))];
            tensor<fp16, [1, 256, 801]> mul_311_cast_fp16 = mul(x = squeeze_137_cast_fp16, y = view_206_to_fp16)[name = tensor<string, []>("mul_311_cast_fp16")];
            tensor<string, []> conv1d_179_pad_type_0 = const()[name = tensor<string, []>("conv1d_179_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_179_strides_0 = const()[name = tensor<string, []>("conv1d_179_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_179_pad_0 = const()[name = tensor<string, []>("conv1d_179_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_179_dilations_0 = const()[name = tensor<string, []>("conv1d_179_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_179_groups_0 = const()[name = tensor<string, []>("conv1d_179_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_21_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_21_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32400128)))];
            tensor<fp16, [20]> p_output_21_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_21_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32410432)))];
            tensor<fp16, [1, 20, 801]> conv1d_179_cast_fp16 = conv(bias = p_output_21_1_bias_to_fp16, dilations = conv1d_179_dilations_0, groups = conv1d_179_groups_0, pad = conv1d_179_pad_0, pad_type = conv1d_179_pad_type_0, strides = conv1d_179_strides_0, weight = p_output_21_1_weight_to_fp16, x = mul_311_cast_fp16)[name = tensor<string, []>("conv1d_179_cast_fp16")];
            tensor<int32, []> glu_21_split_num_splits_0 = const()[name = tensor<string, []>("glu_21_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_21_split_axis_0 = const()[name = tensor<string, []>("glu_21_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_21_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_21_split_cast_fp16_1 = split(axis = glu_21_split_axis_0, num_splits = glu_21_split_num_splits_0, x = conv1d_179_cast_fp16)[name = tensor<string, []>("glu_21_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_21_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_21_split_cast_fp16_1)[name = tensor<string, []>("glu_21_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_21_cast_fp16 = mul(x = glu_21_split_cast_fp16_0, y = glu_21_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_21_cast_fp16")];
            tensor<int32, [4]> const_2846 = const()[name = tensor<string, []>("const_2846"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_207_cast_fp16 = reshape(shape = const_2846, x = glu_21_cast_fp16)[name = tensor<string, []>("view_207_cast_fp16")];
            tensor<int32, [4]> select_66_begin_0 = const()[name = tensor<string, []>("select_66_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_66_end_0 = const()[name = tensor<string, []>("select_66_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_66_end_mask_0 = const()[name = tensor<string, []>("select_66_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_66_squeeze_mask_0 = const()[name = tensor<string, []>("select_66_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_66_cast_fp16 = slice_by_index(begin = select_66_begin_0, end = select_66_end_0, end_mask = select_66_end_mask_0, squeeze_mask = select_66_squeeze_mask_0, x = view_207_cast_fp16)[name = tensor<string, []>("select_66_cast_fp16")];
            tensor<int32, [4]> select_67_begin_0 = const()[name = tensor<string, []>("select_67_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_67_end_0 = const()[name = tensor<string, []>("select_67_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_67_end_mask_0 = const()[name = tensor<string, []>("select_67_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_67_squeeze_mask_0 = const()[name = tensor<string, []>("select_67_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_67_cast_fp16 = slice_by_index(begin = select_67_begin_0, end = select_67_end_0, end_mask = select_67_end_mask_0, squeeze_mask = select_67_squeeze_mask_0, x = view_207_cast_fp16)[name = tensor<string, []>("select_67_cast_fp16")];
            tensor<int32, [4]> select_68_begin_0 = const()[name = tensor<string, []>("select_68_begin_0"), val = tensor<int32, [4]>([0, 22, 0, 0])];
            tensor<int32, [4]> select_68_end_0 = const()[name = tensor<string, []>("select_68_end_0"), val = tensor<int32, [4]>([1, 23, 256, 801])];
            tensor<bool, [4]> select_68_end_mask_0 = const()[name = tensor<string, []>("select_68_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_68_squeeze_mask_0 = const()[name = tensor<string, []>("select_68_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_68_cast_fp16 = slice_by_index(begin = select_68_begin_0, end = select_68_end_0, end_mask = select_68_end_mask_0, squeeze_mask = select_68_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_68_cast_fp16")];
            tensor<int32, [1]> unsqueeze_295_axes_0 = const()[name = tensor<string, []>("unsqueeze_295_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_295_cast_fp16 = expand_dims(axes = unsqueeze_295_axes_0, x = select_68_cast_fp16)[name = tensor<string, []>("unsqueeze_295_cast_fp16")];
            tensor<fp16, []> const_2857_promoted_to_fp16 = const()[name = tensor<string, []>("const_2857_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_293_cast_fp16 = pow(x = unsqueeze_295_cast_fp16, y = const_2857_promoted_to_fp16)[name = tensor<string, []>("pow_293_cast_fp16")];
            tensor<int32, [1]> mean_132_axes_0 = const()[name = tensor<string, []>("mean_132_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_132_keep_dims_0 = const()[name = tensor<string, []>("mean_132_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_132_cast_fp16 = reduce_mean(axes = mean_132_axes_0, keep_dims = mean_132_keep_dims_0, x = pow_293_cast_fp16)[name = tensor<string, []>("mean_132_cast_fp16")];
            tensor<fp16, []> const_2860_to_fp16 = const()[name = tensor<string, []>("const_2860_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_334_cast_fp16 = add(x = mean_132_cast_fp16, y = const_2860_to_fp16)[name = tensor<string, []>("add_334_cast_fp16")];
            tensor<fp32, []> rsqrt_132_epsilon_0 = const()[name = tensor<string, []>("rsqrt_132_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_132_cast_fp16 = rsqrt(epsilon = rsqrt_132_epsilon_0, x = add_334_cast_fp16)[name = tensor<string, []>("rsqrt_132_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_312_cast_fp16 = mul(x = unsqueeze_295_cast_fp16, y = rsqrt_132_cast_fp16)[name = tensor<string, []>("mul_312_cast_fp16")];
            tensor<int32, [1]> squeeze_138_axes_0 = const()[name = tensor<string, []>("squeeze_138_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_138_cast_fp16 = squeeze(axes = squeeze_138_axes_0, x = mul_312_cast_fp16)[name = tensor<string, []>("squeeze_138_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_208_to_fp16 = const()[name = tensor<string, []>("view_208_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32410560)))];
            tensor<fp16, [1, 256, 801]> mul_313_cast_fp16 = mul(x = squeeze_138_cast_fp16, y = view_208_to_fp16)[name = tensor<string, []>("mul_313_cast_fp16")];
            tensor<string, []> conv1d_180_pad_type_0 = const()[name = tensor<string, []>("conv1d_180_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_180_strides_0 = const()[name = tensor<string, []>("conv1d_180_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_180_pad_0 = const()[name = tensor<string, []>("conv1d_180_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_180_dilations_0 = const()[name = tensor<string, []>("conv1d_180_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_180_groups_0 = const()[name = tensor<string, []>("conv1d_180_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_22_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_22_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32411136)))];
            tensor<fp16, [20]> p_output_22_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_22_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32421440)))];
            tensor<fp16, [1, 20, 801]> conv1d_180_cast_fp16 = conv(bias = p_output_22_1_bias_to_fp16, dilations = conv1d_180_dilations_0, groups = conv1d_180_groups_0, pad = conv1d_180_pad_0, pad_type = conv1d_180_pad_type_0, strides = conv1d_180_strides_0, weight = p_output_22_1_weight_to_fp16, x = mul_313_cast_fp16)[name = tensor<string, []>("conv1d_180_cast_fp16")];
            tensor<int32, []> glu_22_split_num_splits_0 = const()[name = tensor<string, []>("glu_22_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_22_split_axis_0 = const()[name = tensor<string, []>("glu_22_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_22_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_22_split_cast_fp16_1 = split(axis = glu_22_split_axis_0, num_splits = glu_22_split_num_splits_0, x = conv1d_180_cast_fp16)[name = tensor<string, []>("glu_22_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_22_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_22_split_cast_fp16_1)[name = tensor<string, []>("glu_22_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_22_cast_fp16 = mul(x = glu_22_split_cast_fp16_0, y = glu_22_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_22_cast_fp16")];
            tensor<int32, [4]> const_2864 = const()[name = tensor<string, []>("const_2864"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_209_cast_fp16 = reshape(shape = const_2864, x = glu_22_cast_fp16)[name = tensor<string, []>("view_209_cast_fp16")];
            tensor<int32, [4]> select_69_begin_0 = const()[name = tensor<string, []>("select_69_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_69_end_0 = const()[name = tensor<string, []>("select_69_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_69_end_mask_0 = const()[name = tensor<string, []>("select_69_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_69_squeeze_mask_0 = const()[name = tensor<string, []>("select_69_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_69_cast_fp16 = slice_by_index(begin = select_69_begin_0, end = select_69_end_0, end_mask = select_69_end_mask_0, squeeze_mask = select_69_squeeze_mask_0, x = view_209_cast_fp16)[name = tensor<string, []>("select_69_cast_fp16")];
            tensor<int32, [4]> select_70_begin_0 = const()[name = tensor<string, []>("select_70_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_70_end_0 = const()[name = tensor<string, []>("select_70_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_70_end_mask_0 = const()[name = tensor<string, []>("select_70_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_70_squeeze_mask_0 = const()[name = tensor<string, []>("select_70_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_70_cast_fp16 = slice_by_index(begin = select_70_begin_0, end = select_70_end_0, end_mask = select_70_end_mask_0, squeeze_mask = select_70_squeeze_mask_0, x = view_209_cast_fp16)[name = tensor<string, []>("select_70_cast_fp16")];
            tensor<int32, [4]> select_71_begin_0 = const()[name = tensor<string, []>("select_71_begin_0"), val = tensor<int32, [4]>([0, 23, 0, 0])];
            tensor<int32, [4]> select_71_end_0 = const()[name = tensor<string, []>("select_71_end_0"), val = tensor<int32, [4]>([1, 24, 256, 801])];
            tensor<bool, [4]> select_71_end_mask_0 = const()[name = tensor<string, []>("select_71_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_71_squeeze_mask_0 = const()[name = tensor<string, []>("select_71_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_71_cast_fp16 = slice_by_index(begin = select_71_begin_0, end = select_71_end_0, end_mask = select_71_end_mask_0, squeeze_mask = select_71_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_71_cast_fp16")];
            tensor<int32, [1]> unsqueeze_297_axes_0 = const()[name = tensor<string, []>("unsqueeze_297_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_297_cast_fp16 = expand_dims(axes = unsqueeze_297_axes_0, x = select_71_cast_fp16)[name = tensor<string, []>("unsqueeze_297_cast_fp16")];
            tensor<fp16, []> const_2875_promoted_to_fp16 = const()[name = tensor<string, []>("const_2875_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_294_cast_fp16 = pow(x = unsqueeze_297_cast_fp16, y = const_2875_promoted_to_fp16)[name = tensor<string, []>("pow_294_cast_fp16")];
            tensor<int32, [1]> mean_133_axes_0 = const()[name = tensor<string, []>("mean_133_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_133_keep_dims_0 = const()[name = tensor<string, []>("mean_133_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_133_cast_fp16 = reduce_mean(axes = mean_133_axes_0, keep_dims = mean_133_keep_dims_0, x = pow_294_cast_fp16)[name = tensor<string, []>("mean_133_cast_fp16")];
            tensor<fp16, []> const_2878_to_fp16 = const()[name = tensor<string, []>("const_2878_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_335_cast_fp16 = add(x = mean_133_cast_fp16, y = const_2878_to_fp16)[name = tensor<string, []>("add_335_cast_fp16")];
            tensor<fp32, []> rsqrt_133_epsilon_0 = const()[name = tensor<string, []>("rsqrt_133_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_133_cast_fp16 = rsqrt(epsilon = rsqrt_133_epsilon_0, x = add_335_cast_fp16)[name = tensor<string, []>("rsqrt_133_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_314_cast_fp16 = mul(x = unsqueeze_297_cast_fp16, y = rsqrt_133_cast_fp16)[name = tensor<string, []>("mul_314_cast_fp16")];
            tensor<int32, [1]> squeeze_139_axes_0 = const()[name = tensor<string, []>("squeeze_139_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_139_cast_fp16 = squeeze(axes = squeeze_139_axes_0, x = mul_314_cast_fp16)[name = tensor<string, []>("squeeze_139_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_210_to_fp16 = const()[name = tensor<string, []>("view_210_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32421568)))];
            tensor<fp16, [1, 256, 801]> mul_315_cast_fp16 = mul(x = squeeze_139_cast_fp16, y = view_210_to_fp16)[name = tensor<string, []>("mul_315_cast_fp16")];
            tensor<string, []> conv1d_181_pad_type_0 = const()[name = tensor<string, []>("conv1d_181_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_181_strides_0 = const()[name = tensor<string, []>("conv1d_181_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_181_pad_0 = const()[name = tensor<string, []>("conv1d_181_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_181_dilations_0 = const()[name = tensor<string, []>("conv1d_181_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_181_groups_0 = const()[name = tensor<string, []>("conv1d_181_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_23_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_23_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32422144)))];
            tensor<fp16, [20]> p_output_23_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_23_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32432448)))];
            tensor<fp16, [1, 20, 801]> conv1d_181_cast_fp16 = conv(bias = p_output_23_1_bias_to_fp16, dilations = conv1d_181_dilations_0, groups = conv1d_181_groups_0, pad = conv1d_181_pad_0, pad_type = conv1d_181_pad_type_0, strides = conv1d_181_strides_0, weight = p_output_23_1_weight_to_fp16, x = mul_315_cast_fp16)[name = tensor<string, []>("conv1d_181_cast_fp16")];
            tensor<int32, []> glu_23_split_num_splits_0 = const()[name = tensor<string, []>("glu_23_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_23_split_axis_0 = const()[name = tensor<string, []>("glu_23_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_23_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_23_split_cast_fp16_1 = split(axis = glu_23_split_axis_0, num_splits = glu_23_split_num_splits_0, x = conv1d_181_cast_fp16)[name = tensor<string, []>("glu_23_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_23_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_23_split_cast_fp16_1)[name = tensor<string, []>("glu_23_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_23_cast_fp16 = mul(x = glu_23_split_cast_fp16_0, y = glu_23_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_23_cast_fp16")];
            tensor<int32, [4]> const_2882 = const()[name = tensor<string, []>("const_2882"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_211_cast_fp16 = reshape(shape = const_2882, x = glu_23_cast_fp16)[name = tensor<string, []>("view_211_cast_fp16")];
            tensor<int32, [4]> select_72_begin_0 = const()[name = tensor<string, []>("select_72_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_72_end_0 = const()[name = tensor<string, []>("select_72_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_72_end_mask_0 = const()[name = tensor<string, []>("select_72_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_72_squeeze_mask_0 = const()[name = tensor<string, []>("select_72_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_72_cast_fp16 = slice_by_index(begin = select_72_begin_0, end = select_72_end_0, end_mask = select_72_end_mask_0, squeeze_mask = select_72_squeeze_mask_0, x = view_211_cast_fp16)[name = tensor<string, []>("select_72_cast_fp16")];
            tensor<int32, [4]> select_73_begin_0 = const()[name = tensor<string, []>("select_73_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_73_end_0 = const()[name = tensor<string, []>("select_73_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_73_end_mask_0 = const()[name = tensor<string, []>("select_73_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_73_squeeze_mask_0 = const()[name = tensor<string, []>("select_73_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_73_cast_fp16 = slice_by_index(begin = select_73_begin_0, end = select_73_end_0, end_mask = select_73_end_mask_0, squeeze_mask = select_73_squeeze_mask_0, x = view_211_cast_fp16)[name = tensor<string, []>("select_73_cast_fp16")];
            tensor<int32, [4]> select_74_begin_0 = const()[name = tensor<string, []>("select_74_begin_0"), val = tensor<int32, [4]>([0, 24, 0, 0])];
            tensor<int32, [4]> select_74_end_0 = const()[name = tensor<string, []>("select_74_end_0"), val = tensor<int32, [4]>([1, 25, 256, 801])];
            tensor<bool, [4]> select_74_end_mask_0 = const()[name = tensor<string, []>("select_74_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_74_squeeze_mask_0 = const()[name = tensor<string, []>("select_74_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_74_cast_fp16 = slice_by_index(begin = select_74_begin_0, end = select_74_end_0, end_mask = select_74_end_mask_0, squeeze_mask = select_74_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_74_cast_fp16")];
            tensor<int32, [1]> unsqueeze_299_axes_0 = const()[name = tensor<string, []>("unsqueeze_299_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_299_cast_fp16 = expand_dims(axes = unsqueeze_299_axes_0, x = select_74_cast_fp16)[name = tensor<string, []>("unsqueeze_299_cast_fp16")];
            tensor<fp16, []> const_2893_promoted_to_fp16 = const()[name = tensor<string, []>("const_2893_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_295_cast_fp16 = pow(x = unsqueeze_299_cast_fp16, y = const_2893_promoted_to_fp16)[name = tensor<string, []>("pow_295_cast_fp16")];
            tensor<int32, [1]> mean_134_axes_0 = const()[name = tensor<string, []>("mean_134_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_134_keep_dims_0 = const()[name = tensor<string, []>("mean_134_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_134_cast_fp16 = reduce_mean(axes = mean_134_axes_0, keep_dims = mean_134_keep_dims_0, x = pow_295_cast_fp16)[name = tensor<string, []>("mean_134_cast_fp16")];
            tensor<fp16, []> const_2896_to_fp16 = const()[name = tensor<string, []>("const_2896_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_336_cast_fp16 = add(x = mean_134_cast_fp16, y = const_2896_to_fp16)[name = tensor<string, []>("add_336_cast_fp16")];
            tensor<fp32, []> rsqrt_134_epsilon_0 = const()[name = tensor<string, []>("rsqrt_134_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_134_cast_fp16 = rsqrt(epsilon = rsqrt_134_epsilon_0, x = add_336_cast_fp16)[name = tensor<string, []>("rsqrt_134_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_316_cast_fp16 = mul(x = unsqueeze_299_cast_fp16, y = rsqrt_134_cast_fp16)[name = tensor<string, []>("mul_316_cast_fp16")];
            tensor<int32, [1]> squeeze_140_axes_0 = const()[name = tensor<string, []>("squeeze_140_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_140_cast_fp16 = squeeze(axes = squeeze_140_axes_0, x = mul_316_cast_fp16)[name = tensor<string, []>("squeeze_140_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_212_to_fp16 = const()[name = tensor<string, []>("view_212_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32432576)))];
            tensor<fp16, [1, 256, 801]> mul_317_cast_fp16 = mul(x = squeeze_140_cast_fp16, y = view_212_to_fp16)[name = tensor<string, []>("mul_317_cast_fp16")];
            tensor<string, []> conv1d_182_pad_type_0 = const()[name = tensor<string, []>("conv1d_182_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_182_strides_0 = const()[name = tensor<string, []>("conv1d_182_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_182_pad_0 = const()[name = tensor<string, []>("conv1d_182_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_182_dilations_0 = const()[name = tensor<string, []>("conv1d_182_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_182_groups_0 = const()[name = tensor<string, []>("conv1d_182_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_24_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_24_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32433152)))];
            tensor<fp16, [20]> p_output_24_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_24_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32443456)))];
            tensor<fp16, [1, 20, 801]> conv1d_182_cast_fp16 = conv(bias = p_output_24_1_bias_to_fp16, dilations = conv1d_182_dilations_0, groups = conv1d_182_groups_0, pad = conv1d_182_pad_0, pad_type = conv1d_182_pad_type_0, strides = conv1d_182_strides_0, weight = p_output_24_1_weight_to_fp16, x = mul_317_cast_fp16)[name = tensor<string, []>("conv1d_182_cast_fp16")];
            tensor<int32, []> glu_24_split_num_splits_0 = const()[name = tensor<string, []>("glu_24_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_24_split_axis_0 = const()[name = tensor<string, []>("glu_24_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_24_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_24_split_cast_fp16_1 = split(axis = glu_24_split_axis_0, num_splits = glu_24_split_num_splits_0, x = conv1d_182_cast_fp16)[name = tensor<string, []>("glu_24_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_24_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_24_split_cast_fp16_1)[name = tensor<string, []>("glu_24_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_24_cast_fp16 = mul(x = glu_24_split_cast_fp16_0, y = glu_24_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_24_cast_fp16")];
            tensor<int32, [4]> const_2900 = const()[name = tensor<string, []>("const_2900"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_213_cast_fp16 = reshape(shape = const_2900, x = glu_24_cast_fp16)[name = tensor<string, []>("view_213_cast_fp16")];
            tensor<int32, [4]> select_75_begin_0 = const()[name = tensor<string, []>("select_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_75_end_0 = const()[name = tensor<string, []>("select_75_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_75_end_mask_0 = const()[name = tensor<string, []>("select_75_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_75_squeeze_mask_0 = const()[name = tensor<string, []>("select_75_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_75_cast_fp16 = slice_by_index(begin = select_75_begin_0, end = select_75_end_0, end_mask = select_75_end_mask_0, squeeze_mask = select_75_squeeze_mask_0, x = view_213_cast_fp16)[name = tensor<string, []>("select_75_cast_fp16")];
            tensor<int32, [4]> select_76_begin_0 = const()[name = tensor<string, []>("select_76_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_76_end_0 = const()[name = tensor<string, []>("select_76_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_76_end_mask_0 = const()[name = tensor<string, []>("select_76_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_76_squeeze_mask_0 = const()[name = tensor<string, []>("select_76_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_76_cast_fp16 = slice_by_index(begin = select_76_begin_0, end = select_76_end_0, end_mask = select_76_end_mask_0, squeeze_mask = select_76_squeeze_mask_0, x = view_213_cast_fp16)[name = tensor<string, []>("select_76_cast_fp16")];
            tensor<int32, [4]> select_77_begin_0 = const()[name = tensor<string, []>("select_77_begin_0"), val = tensor<int32, [4]>([0, 25, 0, 0])];
            tensor<int32, [4]> select_77_end_0 = const()[name = tensor<string, []>("select_77_end_0"), val = tensor<int32, [4]>([1, 26, 256, 801])];
            tensor<bool, [4]> select_77_end_mask_0 = const()[name = tensor<string, []>("select_77_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_77_squeeze_mask_0 = const()[name = tensor<string, []>("select_77_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_77_cast_fp16 = slice_by_index(begin = select_77_begin_0, end = select_77_end_0, end_mask = select_77_end_mask_0, squeeze_mask = select_77_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_77_cast_fp16")];
            tensor<int32, [1]> unsqueeze_301_axes_0 = const()[name = tensor<string, []>("unsqueeze_301_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_301_cast_fp16 = expand_dims(axes = unsqueeze_301_axes_0, x = select_77_cast_fp16)[name = tensor<string, []>("unsqueeze_301_cast_fp16")];
            tensor<fp16, []> const_2911_promoted_to_fp16 = const()[name = tensor<string, []>("const_2911_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_296_cast_fp16 = pow(x = unsqueeze_301_cast_fp16, y = const_2911_promoted_to_fp16)[name = tensor<string, []>("pow_296_cast_fp16")];
            tensor<int32, [1]> mean_135_axes_0 = const()[name = tensor<string, []>("mean_135_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_135_keep_dims_0 = const()[name = tensor<string, []>("mean_135_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_135_cast_fp16 = reduce_mean(axes = mean_135_axes_0, keep_dims = mean_135_keep_dims_0, x = pow_296_cast_fp16)[name = tensor<string, []>("mean_135_cast_fp16")];
            tensor<fp16, []> const_2914_to_fp16 = const()[name = tensor<string, []>("const_2914_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_337_cast_fp16 = add(x = mean_135_cast_fp16, y = const_2914_to_fp16)[name = tensor<string, []>("add_337_cast_fp16")];
            tensor<fp32, []> rsqrt_135_epsilon_0 = const()[name = tensor<string, []>("rsqrt_135_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_135_cast_fp16 = rsqrt(epsilon = rsqrt_135_epsilon_0, x = add_337_cast_fp16)[name = tensor<string, []>("rsqrt_135_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_318_cast_fp16 = mul(x = unsqueeze_301_cast_fp16, y = rsqrt_135_cast_fp16)[name = tensor<string, []>("mul_318_cast_fp16")];
            tensor<int32, [1]> squeeze_141_axes_0 = const()[name = tensor<string, []>("squeeze_141_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_141_cast_fp16 = squeeze(axes = squeeze_141_axes_0, x = mul_318_cast_fp16)[name = tensor<string, []>("squeeze_141_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_214_to_fp16 = const()[name = tensor<string, []>("view_214_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32443584)))];
            tensor<fp16, [1, 256, 801]> mul_319_cast_fp16 = mul(x = squeeze_141_cast_fp16, y = view_214_to_fp16)[name = tensor<string, []>("mul_319_cast_fp16")];
            tensor<string, []> conv1d_183_pad_type_0 = const()[name = tensor<string, []>("conv1d_183_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_183_strides_0 = const()[name = tensor<string, []>("conv1d_183_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_183_pad_0 = const()[name = tensor<string, []>("conv1d_183_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_183_dilations_0 = const()[name = tensor<string, []>("conv1d_183_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_183_groups_0 = const()[name = tensor<string, []>("conv1d_183_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_25_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_25_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32444160)))];
            tensor<fp16, [20]> p_output_25_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_25_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32454464)))];
            tensor<fp16, [1, 20, 801]> conv1d_183_cast_fp16 = conv(bias = p_output_25_1_bias_to_fp16, dilations = conv1d_183_dilations_0, groups = conv1d_183_groups_0, pad = conv1d_183_pad_0, pad_type = conv1d_183_pad_type_0, strides = conv1d_183_strides_0, weight = p_output_25_1_weight_to_fp16, x = mul_319_cast_fp16)[name = tensor<string, []>("conv1d_183_cast_fp16")];
            tensor<int32, []> glu_25_split_num_splits_0 = const()[name = tensor<string, []>("glu_25_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_25_split_axis_0 = const()[name = tensor<string, []>("glu_25_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_25_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_25_split_cast_fp16_1 = split(axis = glu_25_split_axis_0, num_splits = glu_25_split_num_splits_0, x = conv1d_183_cast_fp16)[name = tensor<string, []>("glu_25_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_25_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_25_split_cast_fp16_1)[name = tensor<string, []>("glu_25_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_25_cast_fp16 = mul(x = glu_25_split_cast_fp16_0, y = glu_25_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_25_cast_fp16")];
            tensor<int32, [4]> const_2918 = const()[name = tensor<string, []>("const_2918"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_215_cast_fp16 = reshape(shape = const_2918, x = glu_25_cast_fp16)[name = tensor<string, []>("view_215_cast_fp16")];
            tensor<int32, [4]> select_78_begin_0 = const()[name = tensor<string, []>("select_78_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_78_end_0 = const()[name = tensor<string, []>("select_78_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_78_end_mask_0 = const()[name = tensor<string, []>("select_78_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_78_squeeze_mask_0 = const()[name = tensor<string, []>("select_78_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_78_cast_fp16 = slice_by_index(begin = select_78_begin_0, end = select_78_end_0, end_mask = select_78_end_mask_0, squeeze_mask = select_78_squeeze_mask_0, x = view_215_cast_fp16)[name = tensor<string, []>("select_78_cast_fp16")];
            tensor<int32, [4]> select_79_begin_0 = const()[name = tensor<string, []>("select_79_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_79_end_0 = const()[name = tensor<string, []>("select_79_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_79_end_mask_0 = const()[name = tensor<string, []>("select_79_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_79_squeeze_mask_0 = const()[name = tensor<string, []>("select_79_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_79_cast_fp16 = slice_by_index(begin = select_79_begin_0, end = select_79_end_0, end_mask = select_79_end_mask_0, squeeze_mask = select_79_squeeze_mask_0, x = view_215_cast_fp16)[name = tensor<string, []>("select_79_cast_fp16")];
            tensor<int32, [4]> select_80_begin_0 = const()[name = tensor<string, []>("select_80_begin_0"), val = tensor<int32, [4]>([0, 26, 0, 0])];
            tensor<int32, [4]> select_80_end_0 = const()[name = tensor<string, []>("select_80_end_0"), val = tensor<int32, [4]>([1, 27, 256, 801])];
            tensor<bool, [4]> select_80_end_mask_0 = const()[name = tensor<string, []>("select_80_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_80_squeeze_mask_0 = const()[name = tensor<string, []>("select_80_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_80_cast_fp16 = slice_by_index(begin = select_80_begin_0, end = select_80_end_0, end_mask = select_80_end_mask_0, squeeze_mask = select_80_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_80_cast_fp16")];
            tensor<int32, [1]> unsqueeze_303_axes_0 = const()[name = tensor<string, []>("unsqueeze_303_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_303_cast_fp16 = expand_dims(axes = unsqueeze_303_axes_0, x = select_80_cast_fp16)[name = tensor<string, []>("unsqueeze_303_cast_fp16")];
            tensor<fp16, []> const_2929_promoted_to_fp16 = const()[name = tensor<string, []>("const_2929_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_297_cast_fp16 = pow(x = unsqueeze_303_cast_fp16, y = const_2929_promoted_to_fp16)[name = tensor<string, []>("pow_297_cast_fp16")];
            tensor<int32, [1]> mean_136_axes_0 = const()[name = tensor<string, []>("mean_136_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_136_keep_dims_0 = const()[name = tensor<string, []>("mean_136_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_136_cast_fp16 = reduce_mean(axes = mean_136_axes_0, keep_dims = mean_136_keep_dims_0, x = pow_297_cast_fp16)[name = tensor<string, []>("mean_136_cast_fp16")];
            tensor<fp16, []> const_2932_to_fp16 = const()[name = tensor<string, []>("const_2932_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_338_cast_fp16 = add(x = mean_136_cast_fp16, y = const_2932_to_fp16)[name = tensor<string, []>("add_338_cast_fp16")];
            tensor<fp32, []> rsqrt_136_epsilon_0 = const()[name = tensor<string, []>("rsqrt_136_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_136_cast_fp16 = rsqrt(epsilon = rsqrt_136_epsilon_0, x = add_338_cast_fp16)[name = tensor<string, []>("rsqrt_136_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_320_cast_fp16 = mul(x = unsqueeze_303_cast_fp16, y = rsqrt_136_cast_fp16)[name = tensor<string, []>("mul_320_cast_fp16")];
            tensor<int32, [1]> squeeze_142_axes_0 = const()[name = tensor<string, []>("squeeze_142_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_142_cast_fp16 = squeeze(axes = squeeze_142_axes_0, x = mul_320_cast_fp16)[name = tensor<string, []>("squeeze_142_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_216_to_fp16 = const()[name = tensor<string, []>("view_216_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32454592)))];
            tensor<fp16, [1, 256, 801]> mul_321_cast_fp16 = mul(x = squeeze_142_cast_fp16, y = view_216_to_fp16)[name = tensor<string, []>("mul_321_cast_fp16")];
            tensor<string, []> conv1d_184_pad_type_0 = const()[name = tensor<string, []>("conv1d_184_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_184_strides_0 = const()[name = tensor<string, []>("conv1d_184_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_184_pad_0 = const()[name = tensor<string, []>("conv1d_184_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_184_dilations_0 = const()[name = tensor<string, []>("conv1d_184_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_184_groups_0 = const()[name = tensor<string, []>("conv1d_184_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_26_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_26_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32455168)))];
            tensor<fp16, [20]> p_output_26_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_26_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32465472)))];
            tensor<fp16, [1, 20, 801]> conv1d_184_cast_fp16 = conv(bias = p_output_26_1_bias_to_fp16, dilations = conv1d_184_dilations_0, groups = conv1d_184_groups_0, pad = conv1d_184_pad_0, pad_type = conv1d_184_pad_type_0, strides = conv1d_184_strides_0, weight = p_output_26_1_weight_to_fp16, x = mul_321_cast_fp16)[name = tensor<string, []>("conv1d_184_cast_fp16")];
            tensor<int32, []> glu_26_split_num_splits_0 = const()[name = tensor<string, []>("glu_26_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_26_split_axis_0 = const()[name = tensor<string, []>("glu_26_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_26_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_26_split_cast_fp16_1 = split(axis = glu_26_split_axis_0, num_splits = glu_26_split_num_splits_0, x = conv1d_184_cast_fp16)[name = tensor<string, []>("glu_26_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_26_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_26_split_cast_fp16_1)[name = tensor<string, []>("glu_26_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_26_cast_fp16 = mul(x = glu_26_split_cast_fp16_0, y = glu_26_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_26_cast_fp16")];
            tensor<int32, [4]> const_2936 = const()[name = tensor<string, []>("const_2936"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_217_cast_fp16 = reshape(shape = const_2936, x = glu_26_cast_fp16)[name = tensor<string, []>("view_217_cast_fp16")];
            tensor<int32, [4]> select_81_begin_0 = const()[name = tensor<string, []>("select_81_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_81_end_0 = const()[name = tensor<string, []>("select_81_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_81_end_mask_0 = const()[name = tensor<string, []>("select_81_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_81_squeeze_mask_0 = const()[name = tensor<string, []>("select_81_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_81_cast_fp16 = slice_by_index(begin = select_81_begin_0, end = select_81_end_0, end_mask = select_81_end_mask_0, squeeze_mask = select_81_squeeze_mask_0, x = view_217_cast_fp16)[name = tensor<string, []>("select_81_cast_fp16")];
            tensor<int32, [4]> select_82_begin_0 = const()[name = tensor<string, []>("select_82_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_82_end_0 = const()[name = tensor<string, []>("select_82_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_82_end_mask_0 = const()[name = tensor<string, []>("select_82_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_82_squeeze_mask_0 = const()[name = tensor<string, []>("select_82_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_82_cast_fp16 = slice_by_index(begin = select_82_begin_0, end = select_82_end_0, end_mask = select_82_end_mask_0, squeeze_mask = select_82_squeeze_mask_0, x = view_217_cast_fp16)[name = tensor<string, []>("select_82_cast_fp16")];
            tensor<int32, [4]> select_83_begin_0 = const()[name = tensor<string, []>("select_83_begin_0"), val = tensor<int32, [4]>([0, 27, 0, 0])];
            tensor<int32, [4]> select_83_end_0 = const()[name = tensor<string, []>("select_83_end_0"), val = tensor<int32, [4]>([1, 28, 256, 801])];
            tensor<bool, [4]> select_83_end_mask_0 = const()[name = tensor<string, []>("select_83_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_83_squeeze_mask_0 = const()[name = tensor<string, []>("select_83_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_83_cast_fp16 = slice_by_index(begin = select_83_begin_0, end = select_83_end_0, end_mask = select_83_end_mask_0, squeeze_mask = select_83_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_83_cast_fp16")];
            tensor<int32, [1]> unsqueeze_305_axes_0 = const()[name = tensor<string, []>("unsqueeze_305_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_305_cast_fp16 = expand_dims(axes = unsqueeze_305_axes_0, x = select_83_cast_fp16)[name = tensor<string, []>("unsqueeze_305_cast_fp16")];
            tensor<fp16, []> const_2947_promoted_to_fp16 = const()[name = tensor<string, []>("const_2947_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_298_cast_fp16 = pow(x = unsqueeze_305_cast_fp16, y = const_2947_promoted_to_fp16)[name = tensor<string, []>("pow_298_cast_fp16")];
            tensor<int32, [1]> mean_137_axes_0 = const()[name = tensor<string, []>("mean_137_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_137_keep_dims_0 = const()[name = tensor<string, []>("mean_137_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_137_cast_fp16 = reduce_mean(axes = mean_137_axes_0, keep_dims = mean_137_keep_dims_0, x = pow_298_cast_fp16)[name = tensor<string, []>("mean_137_cast_fp16")];
            tensor<fp16, []> const_2950_to_fp16 = const()[name = tensor<string, []>("const_2950_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_339_cast_fp16 = add(x = mean_137_cast_fp16, y = const_2950_to_fp16)[name = tensor<string, []>("add_339_cast_fp16")];
            tensor<fp32, []> rsqrt_137_epsilon_0 = const()[name = tensor<string, []>("rsqrt_137_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_137_cast_fp16 = rsqrt(epsilon = rsqrt_137_epsilon_0, x = add_339_cast_fp16)[name = tensor<string, []>("rsqrt_137_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_322_cast_fp16 = mul(x = unsqueeze_305_cast_fp16, y = rsqrt_137_cast_fp16)[name = tensor<string, []>("mul_322_cast_fp16")];
            tensor<int32, [1]> squeeze_143_axes_0 = const()[name = tensor<string, []>("squeeze_143_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_143_cast_fp16 = squeeze(axes = squeeze_143_axes_0, x = mul_322_cast_fp16)[name = tensor<string, []>("squeeze_143_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_218_to_fp16 = const()[name = tensor<string, []>("view_218_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32465600)))];
            tensor<fp16, [1, 256, 801]> mul_323_cast_fp16 = mul(x = squeeze_143_cast_fp16, y = view_218_to_fp16)[name = tensor<string, []>("mul_323_cast_fp16")];
            tensor<string, []> conv1d_185_pad_type_0 = const()[name = tensor<string, []>("conv1d_185_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_185_strides_0 = const()[name = tensor<string, []>("conv1d_185_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_185_pad_0 = const()[name = tensor<string, []>("conv1d_185_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_185_dilations_0 = const()[name = tensor<string, []>("conv1d_185_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_185_groups_0 = const()[name = tensor<string, []>("conv1d_185_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_27_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_27_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32466176)))];
            tensor<fp16, [20]> p_output_27_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_27_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32476480)))];
            tensor<fp16, [1, 20, 801]> conv1d_185_cast_fp16 = conv(bias = p_output_27_1_bias_to_fp16, dilations = conv1d_185_dilations_0, groups = conv1d_185_groups_0, pad = conv1d_185_pad_0, pad_type = conv1d_185_pad_type_0, strides = conv1d_185_strides_0, weight = p_output_27_1_weight_to_fp16, x = mul_323_cast_fp16)[name = tensor<string, []>("conv1d_185_cast_fp16")];
            tensor<int32, []> glu_27_split_num_splits_0 = const()[name = tensor<string, []>("glu_27_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_27_split_axis_0 = const()[name = tensor<string, []>("glu_27_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_27_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_27_split_cast_fp16_1 = split(axis = glu_27_split_axis_0, num_splits = glu_27_split_num_splits_0, x = conv1d_185_cast_fp16)[name = tensor<string, []>("glu_27_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_27_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_27_split_cast_fp16_1)[name = tensor<string, []>("glu_27_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_27_cast_fp16 = mul(x = glu_27_split_cast_fp16_0, y = glu_27_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_27_cast_fp16")];
            tensor<int32, [4]> const_2954 = const()[name = tensor<string, []>("const_2954"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_219_cast_fp16 = reshape(shape = const_2954, x = glu_27_cast_fp16)[name = tensor<string, []>("view_219_cast_fp16")];
            tensor<int32, [4]> select_84_begin_0 = const()[name = tensor<string, []>("select_84_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_84_end_0 = const()[name = tensor<string, []>("select_84_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_84_end_mask_0 = const()[name = tensor<string, []>("select_84_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_84_squeeze_mask_0 = const()[name = tensor<string, []>("select_84_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_84_cast_fp16 = slice_by_index(begin = select_84_begin_0, end = select_84_end_0, end_mask = select_84_end_mask_0, squeeze_mask = select_84_squeeze_mask_0, x = view_219_cast_fp16)[name = tensor<string, []>("select_84_cast_fp16")];
            tensor<int32, [4]> select_85_begin_0 = const()[name = tensor<string, []>("select_85_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_85_end_0 = const()[name = tensor<string, []>("select_85_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_85_end_mask_0 = const()[name = tensor<string, []>("select_85_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_85_squeeze_mask_0 = const()[name = tensor<string, []>("select_85_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_85_cast_fp16 = slice_by_index(begin = select_85_begin_0, end = select_85_end_0, end_mask = select_85_end_mask_0, squeeze_mask = select_85_squeeze_mask_0, x = view_219_cast_fp16)[name = tensor<string, []>("select_85_cast_fp16")];
            tensor<int32, [4]> select_86_begin_0 = const()[name = tensor<string, []>("select_86_begin_0"), val = tensor<int32, [4]>([0, 28, 0, 0])];
            tensor<int32, [4]> select_86_end_0 = const()[name = tensor<string, []>("select_86_end_0"), val = tensor<int32, [4]>([1, 29, 256, 801])];
            tensor<bool, [4]> select_86_end_mask_0 = const()[name = tensor<string, []>("select_86_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_86_squeeze_mask_0 = const()[name = tensor<string, []>("select_86_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_86_cast_fp16 = slice_by_index(begin = select_86_begin_0, end = select_86_end_0, end_mask = select_86_end_mask_0, squeeze_mask = select_86_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_86_cast_fp16")];
            tensor<int32, [1]> unsqueeze_307_axes_0 = const()[name = tensor<string, []>("unsqueeze_307_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_307_cast_fp16 = expand_dims(axes = unsqueeze_307_axes_0, x = select_86_cast_fp16)[name = tensor<string, []>("unsqueeze_307_cast_fp16")];
            tensor<fp16, []> const_2965_promoted_to_fp16 = const()[name = tensor<string, []>("const_2965_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_299_cast_fp16 = pow(x = unsqueeze_307_cast_fp16, y = const_2965_promoted_to_fp16)[name = tensor<string, []>("pow_299_cast_fp16")];
            tensor<int32, [1]> mean_138_axes_0 = const()[name = tensor<string, []>("mean_138_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_138_keep_dims_0 = const()[name = tensor<string, []>("mean_138_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_138_cast_fp16 = reduce_mean(axes = mean_138_axes_0, keep_dims = mean_138_keep_dims_0, x = pow_299_cast_fp16)[name = tensor<string, []>("mean_138_cast_fp16")];
            tensor<fp16, []> const_2968_to_fp16 = const()[name = tensor<string, []>("const_2968_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_340_cast_fp16 = add(x = mean_138_cast_fp16, y = const_2968_to_fp16)[name = tensor<string, []>("add_340_cast_fp16")];
            tensor<fp32, []> rsqrt_138_epsilon_0 = const()[name = tensor<string, []>("rsqrt_138_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_138_cast_fp16 = rsqrt(epsilon = rsqrt_138_epsilon_0, x = add_340_cast_fp16)[name = tensor<string, []>("rsqrt_138_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_324_cast_fp16 = mul(x = unsqueeze_307_cast_fp16, y = rsqrt_138_cast_fp16)[name = tensor<string, []>("mul_324_cast_fp16")];
            tensor<int32, [1]> squeeze_144_axes_0 = const()[name = tensor<string, []>("squeeze_144_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_144_cast_fp16 = squeeze(axes = squeeze_144_axes_0, x = mul_324_cast_fp16)[name = tensor<string, []>("squeeze_144_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_220_to_fp16 = const()[name = tensor<string, []>("view_220_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32476608)))];
            tensor<fp16, [1, 256, 801]> mul_325_cast_fp16 = mul(x = squeeze_144_cast_fp16, y = view_220_to_fp16)[name = tensor<string, []>("mul_325_cast_fp16")];
            tensor<string, []> conv1d_186_pad_type_0 = const()[name = tensor<string, []>("conv1d_186_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_186_strides_0 = const()[name = tensor<string, []>("conv1d_186_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_186_pad_0 = const()[name = tensor<string, []>("conv1d_186_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_186_dilations_0 = const()[name = tensor<string, []>("conv1d_186_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_186_groups_0 = const()[name = tensor<string, []>("conv1d_186_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_28_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_28_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32477184)))];
            tensor<fp16, [20]> p_output_28_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_28_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32487488)))];
            tensor<fp16, [1, 20, 801]> conv1d_186_cast_fp16 = conv(bias = p_output_28_1_bias_to_fp16, dilations = conv1d_186_dilations_0, groups = conv1d_186_groups_0, pad = conv1d_186_pad_0, pad_type = conv1d_186_pad_type_0, strides = conv1d_186_strides_0, weight = p_output_28_1_weight_to_fp16, x = mul_325_cast_fp16)[name = tensor<string, []>("conv1d_186_cast_fp16")];
            tensor<int32, []> glu_28_split_num_splits_0 = const()[name = tensor<string, []>("glu_28_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_28_split_axis_0 = const()[name = tensor<string, []>("glu_28_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_28_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_28_split_cast_fp16_1 = split(axis = glu_28_split_axis_0, num_splits = glu_28_split_num_splits_0, x = conv1d_186_cast_fp16)[name = tensor<string, []>("glu_28_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_28_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_28_split_cast_fp16_1)[name = tensor<string, []>("glu_28_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_28_cast_fp16 = mul(x = glu_28_split_cast_fp16_0, y = glu_28_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_28_cast_fp16")];
            tensor<int32, [4]> const_2972 = const()[name = tensor<string, []>("const_2972"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_221_cast_fp16 = reshape(shape = const_2972, x = glu_28_cast_fp16)[name = tensor<string, []>("view_221_cast_fp16")];
            tensor<int32, [4]> select_87_begin_0 = const()[name = tensor<string, []>("select_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_87_end_0 = const()[name = tensor<string, []>("select_87_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_87_end_mask_0 = const()[name = tensor<string, []>("select_87_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_87_squeeze_mask_0 = const()[name = tensor<string, []>("select_87_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_87_cast_fp16 = slice_by_index(begin = select_87_begin_0, end = select_87_end_0, end_mask = select_87_end_mask_0, squeeze_mask = select_87_squeeze_mask_0, x = view_221_cast_fp16)[name = tensor<string, []>("select_87_cast_fp16")];
            tensor<int32, [4]> select_88_begin_0 = const()[name = tensor<string, []>("select_88_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_88_end_0 = const()[name = tensor<string, []>("select_88_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_88_end_mask_0 = const()[name = tensor<string, []>("select_88_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_88_squeeze_mask_0 = const()[name = tensor<string, []>("select_88_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_88_cast_fp16 = slice_by_index(begin = select_88_begin_0, end = select_88_end_0, end_mask = select_88_end_mask_0, squeeze_mask = select_88_squeeze_mask_0, x = view_221_cast_fp16)[name = tensor<string, []>("select_88_cast_fp16")];
            tensor<int32, [4]> select_89_begin_0 = const()[name = tensor<string, []>("select_89_begin_0"), val = tensor<int32, [4]>([0, 29, 0, 0])];
            tensor<int32, [4]> select_89_end_0 = const()[name = tensor<string, []>("select_89_end_0"), val = tensor<int32, [4]>([1, 30, 256, 801])];
            tensor<bool, [4]> select_89_end_mask_0 = const()[name = tensor<string, []>("select_89_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_89_squeeze_mask_0 = const()[name = tensor<string, []>("select_89_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_89_cast_fp16 = slice_by_index(begin = select_89_begin_0, end = select_89_end_0, end_mask = select_89_end_mask_0, squeeze_mask = select_89_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_89_cast_fp16")];
            tensor<int32, [1]> unsqueeze_309_axes_0 = const()[name = tensor<string, []>("unsqueeze_309_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_309_cast_fp16 = expand_dims(axes = unsqueeze_309_axes_0, x = select_89_cast_fp16)[name = tensor<string, []>("unsqueeze_309_cast_fp16")];
            tensor<fp16, []> const_2983_promoted_to_fp16 = const()[name = tensor<string, []>("const_2983_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_300_cast_fp16 = pow(x = unsqueeze_309_cast_fp16, y = const_2983_promoted_to_fp16)[name = tensor<string, []>("pow_300_cast_fp16")];
            tensor<int32, [1]> mean_139_axes_0 = const()[name = tensor<string, []>("mean_139_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_139_keep_dims_0 = const()[name = tensor<string, []>("mean_139_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_139_cast_fp16 = reduce_mean(axes = mean_139_axes_0, keep_dims = mean_139_keep_dims_0, x = pow_300_cast_fp16)[name = tensor<string, []>("mean_139_cast_fp16")];
            tensor<fp16, []> const_2986_to_fp16 = const()[name = tensor<string, []>("const_2986_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_341_cast_fp16 = add(x = mean_139_cast_fp16, y = const_2986_to_fp16)[name = tensor<string, []>("add_341_cast_fp16")];
            tensor<fp32, []> rsqrt_139_epsilon_0 = const()[name = tensor<string, []>("rsqrt_139_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_139_cast_fp16 = rsqrt(epsilon = rsqrt_139_epsilon_0, x = add_341_cast_fp16)[name = tensor<string, []>("rsqrt_139_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_326_cast_fp16 = mul(x = unsqueeze_309_cast_fp16, y = rsqrt_139_cast_fp16)[name = tensor<string, []>("mul_326_cast_fp16")];
            tensor<int32, [1]> squeeze_145_axes_0 = const()[name = tensor<string, []>("squeeze_145_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_145_cast_fp16 = squeeze(axes = squeeze_145_axes_0, x = mul_326_cast_fp16)[name = tensor<string, []>("squeeze_145_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_222_to_fp16 = const()[name = tensor<string, []>("view_222_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32487616)))];
            tensor<fp16, [1, 256, 801]> mul_327_cast_fp16 = mul(x = squeeze_145_cast_fp16, y = view_222_to_fp16)[name = tensor<string, []>("mul_327_cast_fp16")];
            tensor<string, []> conv1d_187_pad_type_0 = const()[name = tensor<string, []>("conv1d_187_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_187_strides_0 = const()[name = tensor<string, []>("conv1d_187_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_187_pad_0 = const()[name = tensor<string, []>("conv1d_187_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_187_dilations_0 = const()[name = tensor<string, []>("conv1d_187_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_187_groups_0 = const()[name = tensor<string, []>("conv1d_187_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_29_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_29_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32488192)))];
            tensor<fp16, [20]> p_output_29_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_29_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32498496)))];
            tensor<fp16, [1, 20, 801]> conv1d_187_cast_fp16 = conv(bias = p_output_29_1_bias_to_fp16, dilations = conv1d_187_dilations_0, groups = conv1d_187_groups_0, pad = conv1d_187_pad_0, pad_type = conv1d_187_pad_type_0, strides = conv1d_187_strides_0, weight = p_output_29_1_weight_to_fp16, x = mul_327_cast_fp16)[name = tensor<string, []>("conv1d_187_cast_fp16")];
            tensor<int32, []> glu_29_split_num_splits_0 = const()[name = tensor<string, []>("glu_29_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_29_split_axis_0 = const()[name = tensor<string, []>("glu_29_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_29_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_29_split_cast_fp16_1 = split(axis = glu_29_split_axis_0, num_splits = glu_29_split_num_splits_0, x = conv1d_187_cast_fp16)[name = tensor<string, []>("glu_29_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_29_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_29_split_cast_fp16_1)[name = tensor<string, []>("glu_29_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_29_cast_fp16 = mul(x = glu_29_split_cast_fp16_0, y = glu_29_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_29_cast_fp16")];
            tensor<int32, [4]> const_2990 = const()[name = tensor<string, []>("const_2990"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_223_cast_fp16 = reshape(shape = const_2990, x = glu_29_cast_fp16)[name = tensor<string, []>("view_223_cast_fp16")];
            tensor<int32, [4]> select_90_begin_0 = const()[name = tensor<string, []>("select_90_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_90_end_0 = const()[name = tensor<string, []>("select_90_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_90_end_mask_0 = const()[name = tensor<string, []>("select_90_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_90_squeeze_mask_0 = const()[name = tensor<string, []>("select_90_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_90_cast_fp16 = slice_by_index(begin = select_90_begin_0, end = select_90_end_0, end_mask = select_90_end_mask_0, squeeze_mask = select_90_squeeze_mask_0, x = view_223_cast_fp16)[name = tensor<string, []>("select_90_cast_fp16")];
            tensor<int32, [4]> select_91_begin_0 = const()[name = tensor<string, []>("select_91_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_91_end_0 = const()[name = tensor<string, []>("select_91_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_91_end_mask_0 = const()[name = tensor<string, []>("select_91_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_91_squeeze_mask_0 = const()[name = tensor<string, []>("select_91_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_91_cast_fp16 = slice_by_index(begin = select_91_begin_0, end = select_91_end_0, end_mask = select_91_end_mask_0, squeeze_mask = select_91_squeeze_mask_0, x = view_223_cast_fp16)[name = tensor<string, []>("select_91_cast_fp16")];
            tensor<int32, [4]> select_92_begin_0 = const()[name = tensor<string, []>("select_92_begin_0"), val = tensor<int32, [4]>([0, 30, 0, 0])];
            tensor<int32, [4]> select_92_end_0 = const()[name = tensor<string, []>("select_92_end_0"), val = tensor<int32, [4]>([1, 31, 256, 801])];
            tensor<bool, [4]> select_92_end_mask_0 = const()[name = tensor<string, []>("select_92_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_92_squeeze_mask_0 = const()[name = tensor<string, []>("select_92_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_92_cast_fp16 = slice_by_index(begin = select_92_begin_0, end = select_92_end_0, end_mask = select_92_end_mask_0, squeeze_mask = select_92_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_92_cast_fp16")];
            tensor<int32, [1]> unsqueeze_311_axes_0 = const()[name = tensor<string, []>("unsqueeze_311_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_311_cast_fp16 = expand_dims(axes = unsqueeze_311_axes_0, x = select_92_cast_fp16)[name = tensor<string, []>("unsqueeze_311_cast_fp16")];
            tensor<fp16, []> const_3001_promoted_to_fp16 = const()[name = tensor<string, []>("const_3001_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_301_cast_fp16 = pow(x = unsqueeze_311_cast_fp16, y = const_3001_promoted_to_fp16)[name = tensor<string, []>("pow_301_cast_fp16")];
            tensor<int32, [1]> mean_140_axes_0 = const()[name = tensor<string, []>("mean_140_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_140_keep_dims_0 = const()[name = tensor<string, []>("mean_140_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_140_cast_fp16 = reduce_mean(axes = mean_140_axes_0, keep_dims = mean_140_keep_dims_0, x = pow_301_cast_fp16)[name = tensor<string, []>("mean_140_cast_fp16")];
            tensor<fp16, []> const_3004_to_fp16 = const()[name = tensor<string, []>("const_3004_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_342_cast_fp16 = add(x = mean_140_cast_fp16, y = const_3004_to_fp16)[name = tensor<string, []>("add_342_cast_fp16")];
            tensor<fp32, []> rsqrt_140_epsilon_0 = const()[name = tensor<string, []>("rsqrt_140_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_140_cast_fp16 = rsqrt(epsilon = rsqrt_140_epsilon_0, x = add_342_cast_fp16)[name = tensor<string, []>("rsqrt_140_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_328_cast_fp16 = mul(x = unsqueeze_311_cast_fp16, y = rsqrt_140_cast_fp16)[name = tensor<string, []>("mul_328_cast_fp16")];
            tensor<int32, [1]> squeeze_146_axes_0 = const()[name = tensor<string, []>("squeeze_146_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_146_cast_fp16 = squeeze(axes = squeeze_146_axes_0, x = mul_328_cast_fp16)[name = tensor<string, []>("squeeze_146_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_224_to_fp16 = const()[name = tensor<string, []>("view_224_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32498624)))];
            tensor<fp16, [1, 256, 801]> mul_329_cast_fp16 = mul(x = squeeze_146_cast_fp16, y = view_224_to_fp16)[name = tensor<string, []>("mul_329_cast_fp16")];
            tensor<string, []> conv1d_188_pad_type_0 = const()[name = tensor<string, []>("conv1d_188_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_188_strides_0 = const()[name = tensor<string, []>("conv1d_188_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_188_pad_0 = const()[name = tensor<string, []>("conv1d_188_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_188_dilations_0 = const()[name = tensor<string, []>("conv1d_188_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_188_groups_0 = const()[name = tensor<string, []>("conv1d_188_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_30_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_30_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32499200)))];
            tensor<fp16, [20]> p_output_30_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_30_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32509504)))];
            tensor<fp16, [1, 20, 801]> conv1d_188_cast_fp16 = conv(bias = p_output_30_1_bias_to_fp16, dilations = conv1d_188_dilations_0, groups = conv1d_188_groups_0, pad = conv1d_188_pad_0, pad_type = conv1d_188_pad_type_0, strides = conv1d_188_strides_0, weight = p_output_30_1_weight_to_fp16, x = mul_329_cast_fp16)[name = tensor<string, []>("conv1d_188_cast_fp16")];
            tensor<int32, []> glu_30_split_num_splits_0 = const()[name = tensor<string, []>("glu_30_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_30_split_axis_0 = const()[name = tensor<string, []>("glu_30_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_30_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_30_split_cast_fp16_1 = split(axis = glu_30_split_axis_0, num_splits = glu_30_split_num_splits_0, x = conv1d_188_cast_fp16)[name = tensor<string, []>("glu_30_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_30_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_30_split_cast_fp16_1)[name = tensor<string, []>("glu_30_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_30_cast_fp16 = mul(x = glu_30_split_cast_fp16_0, y = glu_30_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_30_cast_fp16")];
            tensor<int32, [4]> const_3008 = const()[name = tensor<string, []>("const_3008"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_225_cast_fp16 = reshape(shape = const_3008, x = glu_30_cast_fp16)[name = tensor<string, []>("view_225_cast_fp16")];
            tensor<int32, [4]> select_93_begin_0 = const()[name = tensor<string, []>("select_93_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_93_end_0 = const()[name = tensor<string, []>("select_93_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_93_end_mask_0 = const()[name = tensor<string, []>("select_93_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_93_squeeze_mask_0 = const()[name = tensor<string, []>("select_93_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_93_cast_fp16 = slice_by_index(begin = select_93_begin_0, end = select_93_end_0, end_mask = select_93_end_mask_0, squeeze_mask = select_93_squeeze_mask_0, x = view_225_cast_fp16)[name = tensor<string, []>("select_93_cast_fp16")];
            tensor<int32, [4]> select_94_begin_0 = const()[name = tensor<string, []>("select_94_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_94_end_0 = const()[name = tensor<string, []>("select_94_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_94_end_mask_0 = const()[name = tensor<string, []>("select_94_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_94_squeeze_mask_0 = const()[name = tensor<string, []>("select_94_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_94_cast_fp16 = slice_by_index(begin = select_94_begin_0, end = select_94_end_0, end_mask = select_94_end_mask_0, squeeze_mask = select_94_squeeze_mask_0, x = view_225_cast_fp16)[name = tensor<string, []>("select_94_cast_fp16")];
            tensor<int32, [4]> select_95_begin_0 = const()[name = tensor<string, []>("select_95_begin_0"), val = tensor<int32, [4]>([0, 31, 0, 0])];
            tensor<int32, [4]> select_95_end_0 = const()[name = tensor<string, []>("select_95_end_0"), val = tensor<int32, [4]>([1, 32, 256, 801])];
            tensor<bool, [4]> select_95_end_mask_0 = const()[name = tensor<string, []>("select_95_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_95_squeeze_mask_0 = const()[name = tensor<string, []>("select_95_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_95_cast_fp16 = slice_by_index(begin = select_95_begin_0, end = select_95_end_0, end_mask = select_95_end_mask_0, squeeze_mask = select_95_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_95_cast_fp16")];
            tensor<int32, [1]> unsqueeze_313_axes_0 = const()[name = tensor<string, []>("unsqueeze_313_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_313_cast_fp16 = expand_dims(axes = unsqueeze_313_axes_0, x = select_95_cast_fp16)[name = tensor<string, []>("unsqueeze_313_cast_fp16")];
            tensor<fp16, []> const_3019_promoted_to_fp16 = const()[name = tensor<string, []>("const_3019_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_302_cast_fp16 = pow(x = unsqueeze_313_cast_fp16, y = const_3019_promoted_to_fp16)[name = tensor<string, []>("pow_302_cast_fp16")];
            tensor<int32, [1]> mean_141_axes_0 = const()[name = tensor<string, []>("mean_141_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_141_keep_dims_0 = const()[name = tensor<string, []>("mean_141_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_141_cast_fp16 = reduce_mean(axes = mean_141_axes_0, keep_dims = mean_141_keep_dims_0, x = pow_302_cast_fp16)[name = tensor<string, []>("mean_141_cast_fp16")];
            tensor<fp16, []> const_3022_to_fp16 = const()[name = tensor<string, []>("const_3022_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_343_cast_fp16 = add(x = mean_141_cast_fp16, y = const_3022_to_fp16)[name = tensor<string, []>("add_343_cast_fp16")];
            tensor<fp32, []> rsqrt_141_epsilon_0 = const()[name = tensor<string, []>("rsqrt_141_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_141_cast_fp16 = rsqrt(epsilon = rsqrt_141_epsilon_0, x = add_343_cast_fp16)[name = tensor<string, []>("rsqrt_141_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_330_cast_fp16 = mul(x = unsqueeze_313_cast_fp16, y = rsqrt_141_cast_fp16)[name = tensor<string, []>("mul_330_cast_fp16")];
            tensor<int32, [1]> squeeze_147_axes_0 = const()[name = tensor<string, []>("squeeze_147_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_147_cast_fp16 = squeeze(axes = squeeze_147_axes_0, x = mul_330_cast_fp16)[name = tensor<string, []>("squeeze_147_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_226_to_fp16 = const()[name = tensor<string, []>("view_226_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32509632)))];
            tensor<fp16, [1, 256, 801]> mul_331_cast_fp16 = mul(x = squeeze_147_cast_fp16, y = view_226_to_fp16)[name = tensor<string, []>("mul_331_cast_fp16")];
            tensor<string, []> conv1d_189_pad_type_0 = const()[name = tensor<string, []>("conv1d_189_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_189_strides_0 = const()[name = tensor<string, []>("conv1d_189_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_189_pad_0 = const()[name = tensor<string, []>("conv1d_189_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_189_dilations_0 = const()[name = tensor<string, []>("conv1d_189_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_189_groups_0 = const()[name = tensor<string, []>("conv1d_189_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_31_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_31_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32510208)))];
            tensor<fp16, [20]> p_output_31_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_31_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32520512)))];
            tensor<fp16, [1, 20, 801]> conv1d_189_cast_fp16 = conv(bias = p_output_31_1_bias_to_fp16, dilations = conv1d_189_dilations_0, groups = conv1d_189_groups_0, pad = conv1d_189_pad_0, pad_type = conv1d_189_pad_type_0, strides = conv1d_189_strides_0, weight = p_output_31_1_weight_to_fp16, x = mul_331_cast_fp16)[name = tensor<string, []>("conv1d_189_cast_fp16")];
            tensor<int32, []> glu_31_split_num_splits_0 = const()[name = tensor<string, []>("glu_31_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_31_split_axis_0 = const()[name = tensor<string, []>("glu_31_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_31_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_31_split_cast_fp16_1 = split(axis = glu_31_split_axis_0, num_splits = glu_31_split_num_splits_0, x = conv1d_189_cast_fp16)[name = tensor<string, []>("glu_31_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_31_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_31_split_cast_fp16_1)[name = tensor<string, []>("glu_31_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_31_cast_fp16 = mul(x = glu_31_split_cast_fp16_0, y = glu_31_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_31_cast_fp16")];
            tensor<int32, [4]> const_3026 = const()[name = tensor<string, []>("const_3026"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_227_cast_fp16 = reshape(shape = const_3026, x = glu_31_cast_fp16)[name = tensor<string, []>("view_227_cast_fp16")];
            tensor<int32, [4]> select_96_begin_0 = const()[name = tensor<string, []>("select_96_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_96_end_0 = const()[name = tensor<string, []>("select_96_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_96_end_mask_0 = const()[name = tensor<string, []>("select_96_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_96_squeeze_mask_0 = const()[name = tensor<string, []>("select_96_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_96_cast_fp16 = slice_by_index(begin = select_96_begin_0, end = select_96_end_0, end_mask = select_96_end_mask_0, squeeze_mask = select_96_squeeze_mask_0, x = view_227_cast_fp16)[name = tensor<string, []>("select_96_cast_fp16")];
            tensor<int32, [4]> select_97_begin_0 = const()[name = tensor<string, []>("select_97_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_97_end_0 = const()[name = tensor<string, []>("select_97_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_97_end_mask_0 = const()[name = tensor<string, []>("select_97_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_97_squeeze_mask_0 = const()[name = tensor<string, []>("select_97_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_97_cast_fp16 = slice_by_index(begin = select_97_begin_0, end = select_97_end_0, end_mask = select_97_end_mask_0, squeeze_mask = select_97_squeeze_mask_0, x = view_227_cast_fp16)[name = tensor<string, []>("select_97_cast_fp16")];
            tensor<int32, [4]> select_98_begin_0 = const()[name = tensor<string, []>("select_98_begin_0"), val = tensor<int32, [4]>([0, 32, 0, 0])];
            tensor<int32, [4]> select_98_end_0 = const()[name = tensor<string, []>("select_98_end_0"), val = tensor<int32, [4]>([1, 33, 256, 801])];
            tensor<bool, [4]> select_98_end_mask_0 = const()[name = tensor<string, []>("select_98_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_98_squeeze_mask_0 = const()[name = tensor<string, []>("select_98_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_98_cast_fp16 = slice_by_index(begin = select_98_begin_0, end = select_98_end_0, end_mask = select_98_end_mask_0, squeeze_mask = select_98_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_98_cast_fp16")];
            tensor<int32, [1]> unsqueeze_315_axes_0 = const()[name = tensor<string, []>("unsqueeze_315_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_315_cast_fp16 = expand_dims(axes = unsqueeze_315_axes_0, x = select_98_cast_fp16)[name = tensor<string, []>("unsqueeze_315_cast_fp16")];
            tensor<fp16, []> const_3037_promoted_to_fp16 = const()[name = tensor<string, []>("const_3037_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_303_cast_fp16 = pow(x = unsqueeze_315_cast_fp16, y = const_3037_promoted_to_fp16)[name = tensor<string, []>("pow_303_cast_fp16")];
            tensor<int32, [1]> mean_142_axes_0 = const()[name = tensor<string, []>("mean_142_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_142_keep_dims_0 = const()[name = tensor<string, []>("mean_142_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_142_cast_fp16 = reduce_mean(axes = mean_142_axes_0, keep_dims = mean_142_keep_dims_0, x = pow_303_cast_fp16)[name = tensor<string, []>("mean_142_cast_fp16")];
            tensor<fp16, []> const_3040_to_fp16 = const()[name = tensor<string, []>("const_3040_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_344_cast_fp16 = add(x = mean_142_cast_fp16, y = const_3040_to_fp16)[name = tensor<string, []>("add_344_cast_fp16")];
            tensor<fp32, []> rsqrt_142_epsilon_0 = const()[name = tensor<string, []>("rsqrt_142_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_142_cast_fp16 = rsqrt(epsilon = rsqrt_142_epsilon_0, x = add_344_cast_fp16)[name = tensor<string, []>("rsqrt_142_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_332_cast_fp16 = mul(x = unsqueeze_315_cast_fp16, y = rsqrt_142_cast_fp16)[name = tensor<string, []>("mul_332_cast_fp16")];
            tensor<int32, [1]> squeeze_148_axes_0 = const()[name = tensor<string, []>("squeeze_148_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_148_cast_fp16 = squeeze(axes = squeeze_148_axes_0, x = mul_332_cast_fp16)[name = tensor<string, []>("squeeze_148_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_228_to_fp16 = const()[name = tensor<string, []>("view_228_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32520640)))];
            tensor<fp16, [1, 256, 801]> mul_333_cast_fp16 = mul(x = squeeze_148_cast_fp16, y = view_228_to_fp16)[name = tensor<string, []>("mul_333_cast_fp16")];
            tensor<string, []> conv1d_190_pad_type_0 = const()[name = tensor<string, []>("conv1d_190_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_190_strides_0 = const()[name = tensor<string, []>("conv1d_190_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_190_pad_0 = const()[name = tensor<string, []>("conv1d_190_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_190_dilations_0 = const()[name = tensor<string, []>("conv1d_190_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_190_groups_0 = const()[name = tensor<string, []>("conv1d_190_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_32_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_32_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32521216)))];
            tensor<fp16, [20]> p_output_32_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_32_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32531520)))];
            tensor<fp16, [1, 20, 801]> conv1d_190_cast_fp16 = conv(bias = p_output_32_1_bias_to_fp16, dilations = conv1d_190_dilations_0, groups = conv1d_190_groups_0, pad = conv1d_190_pad_0, pad_type = conv1d_190_pad_type_0, strides = conv1d_190_strides_0, weight = p_output_32_1_weight_to_fp16, x = mul_333_cast_fp16)[name = tensor<string, []>("conv1d_190_cast_fp16")];
            tensor<int32, []> glu_32_split_num_splits_0 = const()[name = tensor<string, []>("glu_32_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_32_split_axis_0 = const()[name = tensor<string, []>("glu_32_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_32_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_32_split_cast_fp16_1 = split(axis = glu_32_split_axis_0, num_splits = glu_32_split_num_splits_0, x = conv1d_190_cast_fp16)[name = tensor<string, []>("glu_32_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_32_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_32_split_cast_fp16_1)[name = tensor<string, []>("glu_32_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_32_cast_fp16 = mul(x = glu_32_split_cast_fp16_0, y = glu_32_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_32_cast_fp16")];
            tensor<int32, [4]> const_3044 = const()[name = tensor<string, []>("const_3044"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_229_cast_fp16 = reshape(shape = const_3044, x = glu_32_cast_fp16)[name = tensor<string, []>("view_229_cast_fp16")];
            tensor<int32, [4]> select_99_begin_0 = const()[name = tensor<string, []>("select_99_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_99_end_0 = const()[name = tensor<string, []>("select_99_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_99_end_mask_0 = const()[name = tensor<string, []>("select_99_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_99_squeeze_mask_0 = const()[name = tensor<string, []>("select_99_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_99_cast_fp16 = slice_by_index(begin = select_99_begin_0, end = select_99_end_0, end_mask = select_99_end_mask_0, squeeze_mask = select_99_squeeze_mask_0, x = view_229_cast_fp16)[name = tensor<string, []>("select_99_cast_fp16")];
            tensor<int32, [4]> select_100_begin_0 = const()[name = tensor<string, []>("select_100_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_100_end_0 = const()[name = tensor<string, []>("select_100_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_100_end_mask_0 = const()[name = tensor<string, []>("select_100_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_100_squeeze_mask_0 = const()[name = tensor<string, []>("select_100_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_100_cast_fp16 = slice_by_index(begin = select_100_begin_0, end = select_100_end_0, end_mask = select_100_end_mask_0, squeeze_mask = select_100_squeeze_mask_0, x = view_229_cast_fp16)[name = tensor<string, []>("select_100_cast_fp16")];
            tensor<int32, [4]> select_101_begin_0 = const()[name = tensor<string, []>("select_101_begin_0"), val = tensor<int32, [4]>([0, 33, 0, 0])];
            tensor<int32, [4]> select_101_end_0 = const()[name = tensor<string, []>("select_101_end_0"), val = tensor<int32, [4]>([1, 34, 256, 801])];
            tensor<bool, [4]> select_101_end_mask_0 = const()[name = tensor<string, []>("select_101_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_101_squeeze_mask_0 = const()[name = tensor<string, []>("select_101_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_101_cast_fp16 = slice_by_index(begin = select_101_begin_0, end = select_101_end_0, end_mask = select_101_end_mask_0, squeeze_mask = select_101_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_101_cast_fp16")];
            tensor<int32, [1]> unsqueeze_317_axes_0 = const()[name = tensor<string, []>("unsqueeze_317_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_317_cast_fp16 = expand_dims(axes = unsqueeze_317_axes_0, x = select_101_cast_fp16)[name = tensor<string, []>("unsqueeze_317_cast_fp16")];
            tensor<fp16, []> const_3055_promoted_to_fp16 = const()[name = tensor<string, []>("const_3055_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_304_cast_fp16 = pow(x = unsqueeze_317_cast_fp16, y = const_3055_promoted_to_fp16)[name = tensor<string, []>("pow_304_cast_fp16")];
            tensor<int32, [1]> mean_143_axes_0 = const()[name = tensor<string, []>("mean_143_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_143_keep_dims_0 = const()[name = tensor<string, []>("mean_143_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_143_cast_fp16 = reduce_mean(axes = mean_143_axes_0, keep_dims = mean_143_keep_dims_0, x = pow_304_cast_fp16)[name = tensor<string, []>("mean_143_cast_fp16")];
            tensor<fp16, []> const_3058_to_fp16 = const()[name = tensor<string, []>("const_3058_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_345_cast_fp16 = add(x = mean_143_cast_fp16, y = const_3058_to_fp16)[name = tensor<string, []>("add_345_cast_fp16")];
            tensor<fp32, []> rsqrt_143_epsilon_0 = const()[name = tensor<string, []>("rsqrt_143_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_143_cast_fp16 = rsqrt(epsilon = rsqrt_143_epsilon_0, x = add_345_cast_fp16)[name = tensor<string, []>("rsqrt_143_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_334_cast_fp16 = mul(x = unsqueeze_317_cast_fp16, y = rsqrt_143_cast_fp16)[name = tensor<string, []>("mul_334_cast_fp16")];
            tensor<int32, [1]> squeeze_149_axes_0 = const()[name = tensor<string, []>("squeeze_149_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_149_cast_fp16 = squeeze(axes = squeeze_149_axes_0, x = mul_334_cast_fp16)[name = tensor<string, []>("squeeze_149_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_230_to_fp16 = const()[name = tensor<string, []>("view_230_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32531648)))];
            tensor<fp16, [1, 256, 801]> mul_335_cast_fp16 = mul(x = squeeze_149_cast_fp16, y = view_230_to_fp16)[name = tensor<string, []>("mul_335_cast_fp16")];
            tensor<string, []> conv1d_191_pad_type_0 = const()[name = tensor<string, []>("conv1d_191_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_191_strides_0 = const()[name = tensor<string, []>("conv1d_191_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_191_pad_0 = const()[name = tensor<string, []>("conv1d_191_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_191_dilations_0 = const()[name = tensor<string, []>("conv1d_191_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_191_groups_0 = const()[name = tensor<string, []>("conv1d_191_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_33_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_33_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32532224)))];
            tensor<fp16, [20]> p_output_33_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_33_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32542528)))];
            tensor<fp16, [1, 20, 801]> conv1d_191_cast_fp16 = conv(bias = p_output_33_1_bias_to_fp16, dilations = conv1d_191_dilations_0, groups = conv1d_191_groups_0, pad = conv1d_191_pad_0, pad_type = conv1d_191_pad_type_0, strides = conv1d_191_strides_0, weight = p_output_33_1_weight_to_fp16, x = mul_335_cast_fp16)[name = tensor<string, []>("conv1d_191_cast_fp16")];
            tensor<int32, []> glu_33_split_num_splits_0 = const()[name = tensor<string, []>("glu_33_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_33_split_axis_0 = const()[name = tensor<string, []>("glu_33_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_33_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_33_split_cast_fp16_1 = split(axis = glu_33_split_axis_0, num_splits = glu_33_split_num_splits_0, x = conv1d_191_cast_fp16)[name = tensor<string, []>("glu_33_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_33_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_33_split_cast_fp16_1)[name = tensor<string, []>("glu_33_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_33_cast_fp16 = mul(x = glu_33_split_cast_fp16_0, y = glu_33_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_33_cast_fp16")];
            tensor<int32, [4]> const_3062 = const()[name = tensor<string, []>("const_3062"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_231_cast_fp16 = reshape(shape = const_3062, x = glu_33_cast_fp16)[name = tensor<string, []>("view_231_cast_fp16")];
            tensor<int32, [4]> select_102_begin_0 = const()[name = tensor<string, []>("select_102_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_102_end_0 = const()[name = tensor<string, []>("select_102_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_102_end_mask_0 = const()[name = tensor<string, []>("select_102_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_102_squeeze_mask_0 = const()[name = tensor<string, []>("select_102_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_102_cast_fp16 = slice_by_index(begin = select_102_begin_0, end = select_102_end_0, end_mask = select_102_end_mask_0, squeeze_mask = select_102_squeeze_mask_0, x = view_231_cast_fp16)[name = tensor<string, []>("select_102_cast_fp16")];
            tensor<int32, [4]> select_103_begin_0 = const()[name = tensor<string, []>("select_103_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_103_end_0 = const()[name = tensor<string, []>("select_103_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_103_end_mask_0 = const()[name = tensor<string, []>("select_103_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_103_squeeze_mask_0 = const()[name = tensor<string, []>("select_103_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_103_cast_fp16 = slice_by_index(begin = select_103_begin_0, end = select_103_end_0, end_mask = select_103_end_mask_0, squeeze_mask = select_103_squeeze_mask_0, x = view_231_cast_fp16)[name = tensor<string, []>("select_103_cast_fp16")];
            tensor<int32, [4]> select_104_begin_0 = const()[name = tensor<string, []>("select_104_begin_0"), val = tensor<int32, [4]>([0, 34, 0, 0])];
            tensor<int32, [4]> select_104_end_0 = const()[name = tensor<string, []>("select_104_end_0"), val = tensor<int32, [4]>([1, 35, 256, 801])];
            tensor<bool, [4]> select_104_end_mask_0 = const()[name = tensor<string, []>("select_104_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_104_squeeze_mask_0 = const()[name = tensor<string, []>("select_104_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_104_cast_fp16 = slice_by_index(begin = select_104_begin_0, end = select_104_end_0, end_mask = select_104_end_mask_0, squeeze_mask = select_104_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_104_cast_fp16")];
            tensor<int32, [1]> unsqueeze_319_axes_0 = const()[name = tensor<string, []>("unsqueeze_319_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_319_cast_fp16 = expand_dims(axes = unsqueeze_319_axes_0, x = select_104_cast_fp16)[name = tensor<string, []>("unsqueeze_319_cast_fp16")];
            tensor<fp16, []> const_3073_promoted_to_fp16 = const()[name = tensor<string, []>("const_3073_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_305_cast_fp16 = pow(x = unsqueeze_319_cast_fp16, y = const_3073_promoted_to_fp16)[name = tensor<string, []>("pow_305_cast_fp16")];
            tensor<int32, [1]> mean_144_axes_0 = const()[name = tensor<string, []>("mean_144_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_144_keep_dims_0 = const()[name = tensor<string, []>("mean_144_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_144_cast_fp16 = reduce_mean(axes = mean_144_axes_0, keep_dims = mean_144_keep_dims_0, x = pow_305_cast_fp16)[name = tensor<string, []>("mean_144_cast_fp16")];
            tensor<fp16, []> const_3076_to_fp16 = const()[name = tensor<string, []>("const_3076_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_346_cast_fp16 = add(x = mean_144_cast_fp16, y = const_3076_to_fp16)[name = tensor<string, []>("add_346_cast_fp16")];
            tensor<fp32, []> rsqrt_144_epsilon_0 = const()[name = tensor<string, []>("rsqrt_144_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_144_cast_fp16 = rsqrt(epsilon = rsqrt_144_epsilon_0, x = add_346_cast_fp16)[name = tensor<string, []>("rsqrt_144_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_336_cast_fp16 = mul(x = unsqueeze_319_cast_fp16, y = rsqrt_144_cast_fp16)[name = tensor<string, []>("mul_336_cast_fp16")];
            tensor<int32, [1]> squeeze_150_axes_0 = const()[name = tensor<string, []>("squeeze_150_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_150_cast_fp16 = squeeze(axes = squeeze_150_axes_0, x = mul_336_cast_fp16)[name = tensor<string, []>("squeeze_150_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_232_to_fp16 = const()[name = tensor<string, []>("view_232_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32542656)))];
            tensor<fp16, [1, 256, 801]> mul_337_cast_fp16 = mul(x = squeeze_150_cast_fp16, y = view_232_to_fp16)[name = tensor<string, []>("mul_337_cast_fp16")];
            tensor<string, []> conv1d_192_pad_type_0 = const()[name = tensor<string, []>("conv1d_192_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_192_strides_0 = const()[name = tensor<string, []>("conv1d_192_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_192_pad_0 = const()[name = tensor<string, []>("conv1d_192_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_192_dilations_0 = const()[name = tensor<string, []>("conv1d_192_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_192_groups_0 = const()[name = tensor<string, []>("conv1d_192_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_34_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_34_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32543232)))];
            tensor<fp16, [20]> p_output_34_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_34_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32553536)))];
            tensor<fp16, [1, 20, 801]> conv1d_192_cast_fp16 = conv(bias = p_output_34_1_bias_to_fp16, dilations = conv1d_192_dilations_0, groups = conv1d_192_groups_0, pad = conv1d_192_pad_0, pad_type = conv1d_192_pad_type_0, strides = conv1d_192_strides_0, weight = p_output_34_1_weight_to_fp16, x = mul_337_cast_fp16)[name = tensor<string, []>("conv1d_192_cast_fp16")];
            tensor<int32, []> glu_34_split_num_splits_0 = const()[name = tensor<string, []>("glu_34_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_34_split_axis_0 = const()[name = tensor<string, []>("glu_34_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_34_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_34_split_cast_fp16_1 = split(axis = glu_34_split_axis_0, num_splits = glu_34_split_num_splits_0, x = conv1d_192_cast_fp16)[name = tensor<string, []>("glu_34_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_34_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_34_split_cast_fp16_1)[name = tensor<string, []>("glu_34_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_34_cast_fp16 = mul(x = glu_34_split_cast_fp16_0, y = glu_34_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_34_cast_fp16")];
            tensor<int32, [4]> const_3080 = const()[name = tensor<string, []>("const_3080"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_233_cast_fp16 = reshape(shape = const_3080, x = glu_34_cast_fp16)[name = tensor<string, []>("view_233_cast_fp16")];
            tensor<int32, [4]> select_105_begin_0 = const()[name = tensor<string, []>("select_105_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_105_end_0 = const()[name = tensor<string, []>("select_105_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_105_end_mask_0 = const()[name = tensor<string, []>("select_105_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_105_squeeze_mask_0 = const()[name = tensor<string, []>("select_105_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_105_cast_fp16 = slice_by_index(begin = select_105_begin_0, end = select_105_end_0, end_mask = select_105_end_mask_0, squeeze_mask = select_105_squeeze_mask_0, x = view_233_cast_fp16)[name = tensor<string, []>("select_105_cast_fp16")];
            tensor<int32, [4]> select_106_begin_0 = const()[name = tensor<string, []>("select_106_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_106_end_0 = const()[name = tensor<string, []>("select_106_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_106_end_mask_0 = const()[name = tensor<string, []>("select_106_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_106_squeeze_mask_0 = const()[name = tensor<string, []>("select_106_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_106_cast_fp16 = slice_by_index(begin = select_106_begin_0, end = select_106_end_0, end_mask = select_106_end_mask_0, squeeze_mask = select_106_squeeze_mask_0, x = view_233_cast_fp16)[name = tensor<string, []>("select_106_cast_fp16")];
            tensor<int32, [4]> select_107_begin_0 = const()[name = tensor<string, []>("select_107_begin_0"), val = tensor<int32, [4]>([0, 35, 0, 0])];
            tensor<int32, [4]> select_107_end_0 = const()[name = tensor<string, []>("select_107_end_0"), val = tensor<int32, [4]>([1, 36, 256, 801])];
            tensor<bool, [4]> select_107_end_mask_0 = const()[name = tensor<string, []>("select_107_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_107_squeeze_mask_0 = const()[name = tensor<string, []>("select_107_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_107_cast_fp16 = slice_by_index(begin = select_107_begin_0, end = select_107_end_0, end_mask = select_107_end_mask_0, squeeze_mask = select_107_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_107_cast_fp16")];
            tensor<int32, [1]> unsqueeze_321_axes_0 = const()[name = tensor<string, []>("unsqueeze_321_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_321_cast_fp16 = expand_dims(axes = unsqueeze_321_axes_0, x = select_107_cast_fp16)[name = tensor<string, []>("unsqueeze_321_cast_fp16")];
            tensor<fp16, []> const_3091_promoted_to_fp16 = const()[name = tensor<string, []>("const_3091_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_306_cast_fp16 = pow(x = unsqueeze_321_cast_fp16, y = const_3091_promoted_to_fp16)[name = tensor<string, []>("pow_306_cast_fp16")];
            tensor<int32, [1]> mean_145_axes_0 = const()[name = tensor<string, []>("mean_145_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_145_keep_dims_0 = const()[name = tensor<string, []>("mean_145_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_145_cast_fp16 = reduce_mean(axes = mean_145_axes_0, keep_dims = mean_145_keep_dims_0, x = pow_306_cast_fp16)[name = tensor<string, []>("mean_145_cast_fp16")];
            tensor<fp16, []> const_3094_to_fp16 = const()[name = tensor<string, []>("const_3094_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_347_cast_fp16 = add(x = mean_145_cast_fp16, y = const_3094_to_fp16)[name = tensor<string, []>("add_347_cast_fp16")];
            tensor<fp32, []> rsqrt_145_epsilon_0 = const()[name = tensor<string, []>("rsqrt_145_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_145_cast_fp16 = rsqrt(epsilon = rsqrt_145_epsilon_0, x = add_347_cast_fp16)[name = tensor<string, []>("rsqrt_145_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_338_cast_fp16 = mul(x = unsqueeze_321_cast_fp16, y = rsqrt_145_cast_fp16)[name = tensor<string, []>("mul_338_cast_fp16")];
            tensor<int32, [1]> squeeze_151_axes_0 = const()[name = tensor<string, []>("squeeze_151_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_151_cast_fp16 = squeeze(axes = squeeze_151_axes_0, x = mul_338_cast_fp16)[name = tensor<string, []>("squeeze_151_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_234_to_fp16 = const()[name = tensor<string, []>("view_234_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32553664)))];
            tensor<fp16, [1, 256, 801]> mul_339_cast_fp16 = mul(x = squeeze_151_cast_fp16, y = view_234_to_fp16)[name = tensor<string, []>("mul_339_cast_fp16")];
            tensor<string, []> conv1d_193_pad_type_0 = const()[name = tensor<string, []>("conv1d_193_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_193_strides_0 = const()[name = tensor<string, []>("conv1d_193_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_193_pad_0 = const()[name = tensor<string, []>("conv1d_193_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_193_dilations_0 = const()[name = tensor<string, []>("conv1d_193_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_193_groups_0 = const()[name = tensor<string, []>("conv1d_193_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_35_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_35_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32554240)))];
            tensor<fp16, [20]> p_output_35_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_35_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32564544)))];
            tensor<fp16, [1, 20, 801]> conv1d_193_cast_fp16 = conv(bias = p_output_35_1_bias_to_fp16, dilations = conv1d_193_dilations_0, groups = conv1d_193_groups_0, pad = conv1d_193_pad_0, pad_type = conv1d_193_pad_type_0, strides = conv1d_193_strides_0, weight = p_output_35_1_weight_to_fp16, x = mul_339_cast_fp16)[name = tensor<string, []>("conv1d_193_cast_fp16")];
            tensor<int32, []> glu_35_split_num_splits_0 = const()[name = tensor<string, []>("glu_35_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_35_split_axis_0 = const()[name = tensor<string, []>("glu_35_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_35_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_35_split_cast_fp16_1 = split(axis = glu_35_split_axis_0, num_splits = glu_35_split_num_splits_0, x = conv1d_193_cast_fp16)[name = tensor<string, []>("glu_35_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_35_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_35_split_cast_fp16_1)[name = tensor<string, []>("glu_35_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_35_cast_fp16 = mul(x = glu_35_split_cast_fp16_0, y = glu_35_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_35_cast_fp16")];
            tensor<int32, [4]> const_3098 = const()[name = tensor<string, []>("const_3098"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_235_cast_fp16 = reshape(shape = const_3098, x = glu_35_cast_fp16)[name = tensor<string, []>("view_235_cast_fp16")];
            tensor<int32, [4]> select_108_begin_0 = const()[name = tensor<string, []>("select_108_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_108_end_0 = const()[name = tensor<string, []>("select_108_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_108_end_mask_0 = const()[name = tensor<string, []>("select_108_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_108_squeeze_mask_0 = const()[name = tensor<string, []>("select_108_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_108_cast_fp16 = slice_by_index(begin = select_108_begin_0, end = select_108_end_0, end_mask = select_108_end_mask_0, squeeze_mask = select_108_squeeze_mask_0, x = view_235_cast_fp16)[name = tensor<string, []>("select_108_cast_fp16")];
            tensor<int32, [4]> select_109_begin_0 = const()[name = tensor<string, []>("select_109_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_109_end_0 = const()[name = tensor<string, []>("select_109_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_109_end_mask_0 = const()[name = tensor<string, []>("select_109_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_109_squeeze_mask_0 = const()[name = tensor<string, []>("select_109_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_109_cast_fp16 = slice_by_index(begin = select_109_begin_0, end = select_109_end_0, end_mask = select_109_end_mask_0, squeeze_mask = select_109_squeeze_mask_0, x = view_235_cast_fp16)[name = tensor<string, []>("select_109_cast_fp16")];
            tensor<int32, [4]> select_110_begin_0 = const()[name = tensor<string, []>("select_110_begin_0"), val = tensor<int32, [4]>([0, 36, 0, 0])];
            tensor<int32, [4]> select_110_end_0 = const()[name = tensor<string, []>("select_110_end_0"), val = tensor<int32, [4]>([1, 37, 256, 801])];
            tensor<bool, [4]> select_110_end_mask_0 = const()[name = tensor<string, []>("select_110_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_110_squeeze_mask_0 = const()[name = tensor<string, []>("select_110_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_110_cast_fp16 = slice_by_index(begin = select_110_begin_0, end = select_110_end_0, end_mask = select_110_end_mask_0, squeeze_mask = select_110_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_110_cast_fp16")];
            tensor<int32, [1]> unsqueeze_323_axes_0 = const()[name = tensor<string, []>("unsqueeze_323_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_323_cast_fp16 = expand_dims(axes = unsqueeze_323_axes_0, x = select_110_cast_fp16)[name = tensor<string, []>("unsqueeze_323_cast_fp16")];
            tensor<fp16, []> const_3109_promoted_to_fp16 = const()[name = tensor<string, []>("const_3109_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_307_cast_fp16 = pow(x = unsqueeze_323_cast_fp16, y = const_3109_promoted_to_fp16)[name = tensor<string, []>("pow_307_cast_fp16")];
            tensor<int32, [1]> mean_146_axes_0 = const()[name = tensor<string, []>("mean_146_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_146_keep_dims_0 = const()[name = tensor<string, []>("mean_146_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_146_cast_fp16 = reduce_mean(axes = mean_146_axes_0, keep_dims = mean_146_keep_dims_0, x = pow_307_cast_fp16)[name = tensor<string, []>("mean_146_cast_fp16")];
            tensor<fp16, []> const_3112_to_fp16 = const()[name = tensor<string, []>("const_3112_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_348_cast_fp16 = add(x = mean_146_cast_fp16, y = const_3112_to_fp16)[name = tensor<string, []>("add_348_cast_fp16")];
            tensor<fp32, []> rsqrt_146_epsilon_0 = const()[name = tensor<string, []>("rsqrt_146_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_146_cast_fp16 = rsqrt(epsilon = rsqrt_146_epsilon_0, x = add_348_cast_fp16)[name = tensor<string, []>("rsqrt_146_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_340_cast_fp16 = mul(x = unsqueeze_323_cast_fp16, y = rsqrt_146_cast_fp16)[name = tensor<string, []>("mul_340_cast_fp16")];
            tensor<int32, [1]> squeeze_152_axes_0 = const()[name = tensor<string, []>("squeeze_152_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_152_cast_fp16 = squeeze(axes = squeeze_152_axes_0, x = mul_340_cast_fp16)[name = tensor<string, []>("squeeze_152_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_236_to_fp16 = const()[name = tensor<string, []>("view_236_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32564672)))];
            tensor<fp16, [1, 256, 801]> mul_341_cast_fp16 = mul(x = squeeze_152_cast_fp16, y = view_236_to_fp16)[name = tensor<string, []>("mul_341_cast_fp16")];
            tensor<string, []> conv1d_194_pad_type_0 = const()[name = tensor<string, []>("conv1d_194_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_194_strides_0 = const()[name = tensor<string, []>("conv1d_194_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_194_pad_0 = const()[name = tensor<string, []>("conv1d_194_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_194_dilations_0 = const()[name = tensor<string, []>("conv1d_194_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_194_groups_0 = const()[name = tensor<string, []>("conv1d_194_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_36_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_36_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32565248)))];
            tensor<fp16, [20]> p_output_36_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_36_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32575552)))];
            tensor<fp16, [1, 20, 801]> conv1d_194_cast_fp16 = conv(bias = p_output_36_1_bias_to_fp16, dilations = conv1d_194_dilations_0, groups = conv1d_194_groups_0, pad = conv1d_194_pad_0, pad_type = conv1d_194_pad_type_0, strides = conv1d_194_strides_0, weight = p_output_36_1_weight_to_fp16, x = mul_341_cast_fp16)[name = tensor<string, []>("conv1d_194_cast_fp16")];
            tensor<int32, []> glu_36_split_num_splits_0 = const()[name = tensor<string, []>("glu_36_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_36_split_axis_0 = const()[name = tensor<string, []>("glu_36_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_36_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_36_split_cast_fp16_1 = split(axis = glu_36_split_axis_0, num_splits = glu_36_split_num_splits_0, x = conv1d_194_cast_fp16)[name = tensor<string, []>("glu_36_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_36_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_36_split_cast_fp16_1)[name = tensor<string, []>("glu_36_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_36_cast_fp16 = mul(x = glu_36_split_cast_fp16_0, y = glu_36_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_36_cast_fp16")];
            tensor<int32, [4]> const_3116 = const()[name = tensor<string, []>("const_3116"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_237_cast_fp16 = reshape(shape = const_3116, x = glu_36_cast_fp16)[name = tensor<string, []>("view_237_cast_fp16")];
            tensor<int32, [4]> select_111_begin_0 = const()[name = tensor<string, []>("select_111_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_111_end_0 = const()[name = tensor<string, []>("select_111_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_111_end_mask_0 = const()[name = tensor<string, []>("select_111_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_111_squeeze_mask_0 = const()[name = tensor<string, []>("select_111_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_111_cast_fp16 = slice_by_index(begin = select_111_begin_0, end = select_111_end_0, end_mask = select_111_end_mask_0, squeeze_mask = select_111_squeeze_mask_0, x = view_237_cast_fp16)[name = tensor<string, []>("select_111_cast_fp16")];
            tensor<int32, [4]> select_112_begin_0 = const()[name = tensor<string, []>("select_112_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_112_end_0 = const()[name = tensor<string, []>("select_112_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_112_end_mask_0 = const()[name = tensor<string, []>("select_112_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_112_squeeze_mask_0 = const()[name = tensor<string, []>("select_112_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_112_cast_fp16 = slice_by_index(begin = select_112_begin_0, end = select_112_end_0, end_mask = select_112_end_mask_0, squeeze_mask = select_112_squeeze_mask_0, x = view_237_cast_fp16)[name = tensor<string, []>("select_112_cast_fp16")];
            tensor<int32, [4]> select_113_begin_0 = const()[name = tensor<string, []>("select_113_begin_0"), val = tensor<int32, [4]>([0, 37, 0, 0])];
            tensor<int32, [4]> select_113_end_0 = const()[name = tensor<string, []>("select_113_end_0"), val = tensor<int32, [4]>([1, 38, 256, 801])];
            tensor<bool, [4]> select_113_end_mask_0 = const()[name = tensor<string, []>("select_113_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_113_squeeze_mask_0 = const()[name = tensor<string, []>("select_113_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_113_cast_fp16 = slice_by_index(begin = select_113_begin_0, end = select_113_end_0, end_mask = select_113_end_mask_0, squeeze_mask = select_113_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_113_cast_fp16")];
            tensor<int32, [1]> unsqueeze_325_axes_0 = const()[name = tensor<string, []>("unsqueeze_325_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_325_cast_fp16 = expand_dims(axes = unsqueeze_325_axes_0, x = select_113_cast_fp16)[name = tensor<string, []>("unsqueeze_325_cast_fp16")];
            tensor<fp16, []> const_3127_promoted_to_fp16 = const()[name = tensor<string, []>("const_3127_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_308_cast_fp16 = pow(x = unsqueeze_325_cast_fp16, y = const_3127_promoted_to_fp16)[name = tensor<string, []>("pow_308_cast_fp16")];
            tensor<int32, [1]> mean_147_axes_0 = const()[name = tensor<string, []>("mean_147_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_147_keep_dims_0 = const()[name = tensor<string, []>("mean_147_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_147_cast_fp16 = reduce_mean(axes = mean_147_axes_0, keep_dims = mean_147_keep_dims_0, x = pow_308_cast_fp16)[name = tensor<string, []>("mean_147_cast_fp16")];
            tensor<fp16, []> const_3130_to_fp16 = const()[name = tensor<string, []>("const_3130_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_349_cast_fp16 = add(x = mean_147_cast_fp16, y = const_3130_to_fp16)[name = tensor<string, []>("add_349_cast_fp16")];
            tensor<fp32, []> rsqrt_147_epsilon_0 = const()[name = tensor<string, []>("rsqrt_147_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_147_cast_fp16 = rsqrt(epsilon = rsqrt_147_epsilon_0, x = add_349_cast_fp16)[name = tensor<string, []>("rsqrt_147_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_342_cast_fp16 = mul(x = unsqueeze_325_cast_fp16, y = rsqrt_147_cast_fp16)[name = tensor<string, []>("mul_342_cast_fp16")];
            tensor<int32, [1]> squeeze_153_axes_0 = const()[name = tensor<string, []>("squeeze_153_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_153_cast_fp16 = squeeze(axes = squeeze_153_axes_0, x = mul_342_cast_fp16)[name = tensor<string, []>("squeeze_153_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_238_to_fp16 = const()[name = tensor<string, []>("view_238_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32575680)))];
            tensor<fp16, [1, 256, 801]> mul_343_cast_fp16 = mul(x = squeeze_153_cast_fp16, y = view_238_to_fp16)[name = tensor<string, []>("mul_343_cast_fp16")];
            tensor<string, []> conv1d_195_pad_type_0 = const()[name = tensor<string, []>("conv1d_195_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_195_strides_0 = const()[name = tensor<string, []>("conv1d_195_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_195_pad_0 = const()[name = tensor<string, []>("conv1d_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_195_dilations_0 = const()[name = tensor<string, []>("conv1d_195_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_195_groups_0 = const()[name = tensor<string, []>("conv1d_195_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_37_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_37_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32576256)))];
            tensor<fp16, [20]> p_output_37_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_37_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32586560)))];
            tensor<fp16, [1, 20, 801]> conv1d_195_cast_fp16 = conv(bias = p_output_37_1_bias_to_fp16, dilations = conv1d_195_dilations_0, groups = conv1d_195_groups_0, pad = conv1d_195_pad_0, pad_type = conv1d_195_pad_type_0, strides = conv1d_195_strides_0, weight = p_output_37_1_weight_to_fp16, x = mul_343_cast_fp16)[name = tensor<string, []>("conv1d_195_cast_fp16")];
            tensor<int32, []> glu_37_split_num_splits_0 = const()[name = tensor<string, []>("glu_37_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_37_split_axis_0 = const()[name = tensor<string, []>("glu_37_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_37_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_37_split_cast_fp16_1 = split(axis = glu_37_split_axis_0, num_splits = glu_37_split_num_splits_0, x = conv1d_195_cast_fp16)[name = tensor<string, []>("glu_37_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_37_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_37_split_cast_fp16_1)[name = tensor<string, []>("glu_37_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_37_cast_fp16 = mul(x = glu_37_split_cast_fp16_0, y = glu_37_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_37_cast_fp16")];
            tensor<int32, [4]> const_3134 = const()[name = tensor<string, []>("const_3134"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_239_cast_fp16 = reshape(shape = const_3134, x = glu_37_cast_fp16)[name = tensor<string, []>("view_239_cast_fp16")];
            tensor<int32, [4]> select_114_begin_0 = const()[name = tensor<string, []>("select_114_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_114_end_0 = const()[name = tensor<string, []>("select_114_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_114_end_mask_0 = const()[name = tensor<string, []>("select_114_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_114_squeeze_mask_0 = const()[name = tensor<string, []>("select_114_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_114_cast_fp16 = slice_by_index(begin = select_114_begin_0, end = select_114_end_0, end_mask = select_114_end_mask_0, squeeze_mask = select_114_squeeze_mask_0, x = view_239_cast_fp16)[name = tensor<string, []>("select_114_cast_fp16")];
            tensor<int32, [4]> select_115_begin_0 = const()[name = tensor<string, []>("select_115_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_115_end_0 = const()[name = tensor<string, []>("select_115_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_115_end_mask_0 = const()[name = tensor<string, []>("select_115_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_115_squeeze_mask_0 = const()[name = tensor<string, []>("select_115_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_115_cast_fp16 = slice_by_index(begin = select_115_begin_0, end = select_115_end_0, end_mask = select_115_end_mask_0, squeeze_mask = select_115_squeeze_mask_0, x = view_239_cast_fp16)[name = tensor<string, []>("select_115_cast_fp16")];
            tensor<int32, [4]> select_116_begin_0 = const()[name = tensor<string, []>("select_116_begin_0"), val = tensor<int32, [4]>([0, 38, 0, 0])];
            tensor<int32, [4]> select_116_end_0 = const()[name = tensor<string, []>("select_116_end_0"), val = tensor<int32, [4]>([1, 39, 256, 801])];
            tensor<bool, [4]> select_116_end_mask_0 = const()[name = tensor<string, []>("select_116_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_116_squeeze_mask_0 = const()[name = tensor<string, []>("select_116_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_116_cast_fp16 = slice_by_index(begin = select_116_begin_0, end = select_116_end_0, end_mask = select_116_end_mask_0, squeeze_mask = select_116_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_116_cast_fp16")];
            tensor<int32, [1]> unsqueeze_327_axes_0 = const()[name = tensor<string, []>("unsqueeze_327_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_327_cast_fp16 = expand_dims(axes = unsqueeze_327_axes_0, x = select_116_cast_fp16)[name = tensor<string, []>("unsqueeze_327_cast_fp16")];
            tensor<fp16, []> const_3145_promoted_to_fp16 = const()[name = tensor<string, []>("const_3145_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_309_cast_fp16 = pow(x = unsqueeze_327_cast_fp16, y = const_3145_promoted_to_fp16)[name = tensor<string, []>("pow_309_cast_fp16")];
            tensor<int32, [1]> mean_148_axes_0 = const()[name = tensor<string, []>("mean_148_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_148_keep_dims_0 = const()[name = tensor<string, []>("mean_148_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_148_cast_fp16 = reduce_mean(axes = mean_148_axes_0, keep_dims = mean_148_keep_dims_0, x = pow_309_cast_fp16)[name = tensor<string, []>("mean_148_cast_fp16")];
            tensor<fp16, []> const_3148_to_fp16 = const()[name = tensor<string, []>("const_3148_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_350_cast_fp16 = add(x = mean_148_cast_fp16, y = const_3148_to_fp16)[name = tensor<string, []>("add_350_cast_fp16")];
            tensor<fp32, []> rsqrt_148_epsilon_0 = const()[name = tensor<string, []>("rsqrt_148_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_148_cast_fp16 = rsqrt(epsilon = rsqrt_148_epsilon_0, x = add_350_cast_fp16)[name = tensor<string, []>("rsqrt_148_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_344_cast_fp16 = mul(x = unsqueeze_327_cast_fp16, y = rsqrt_148_cast_fp16)[name = tensor<string, []>("mul_344_cast_fp16")];
            tensor<int32, [1]> squeeze_154_axes_0 = const()[name = tensor<string, []>("squeeze_154_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_154_cast_fp16 = squeeze(axes = squeeze_154_axes_0, x = mul_344_cast_fp16)[name = tensor<string, []>("squeeze_154_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_240_to_fp16 = const()[name = tensor<string, []>("view_240_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32586688)))];
            tensor<fp16, [1, 256, 801]> mul_345_cast_fp16 = mul(x = squeeze_154_cast_fp16, y = view_240_to_fp16)[name = tensor<string, []>("mul_345_cast_fp16")];
            tensor<string, []> conv1d_196_pad_type_0 = const()[name = tensor<string, []>("conv1d_196_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_196_strides_0 = const()[name = tensor<string, []>("conv1d_196_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_196_pad_0 = const()[name = tensor<string, []>("conv1d_196_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_196_dilations_0 = const()[name = tensor<string, []>("conv1d_196_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_196_groups_0 = const()[name = tensor<string, []>("conv1d_196_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_38_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_38_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32587264)))];
            tensor<fp16, [20]> p_output_38_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_38_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32597568)))];
            tensor<fp16, [1, 20, 801]> conv1d_196_cast_fp16 = conv(bias = p_output_38_1_bias_to_fp16, dilations = conv1d_196_dilations_0, groups = conv1d_196_groups_0, pad = conv1d_196_pad_0, pad_type = conv1d_196_pad_type_0, strides = conv1d_196_strides_0, weight = p_output_38_1_weight_to_fp16, x = mul_345_cast_fp16)[name = tensor<string, []>("conv1d_196_cast_fp16")];
            tensor<int32, []> glu_38_split_num_splits_0 = const()[name = tensor<string, []>("glu_38_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_38_split_axis_0 = const()[name = tensor<string, []>("glu_38_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_38_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_38_split_cast_fp16_1 = split(axis = glu_38_split_axis_0, num_splits = glu_38_split_num_splits_0, x = conv1d_196_cast_fp16)[name = tensor<string, []>("glu_38_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_38_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_38_split_cast_fp16_1)[name = tensor<string, []>("glu_38_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_38_cast_fp16 = mul(x = glu_38_split_cast_fp16_0, y = glu_38_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_38_cast_fp16")];
            tensor<int32, [4]> const_3152 = const()[name = tensor<string, []>("const_3152"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_241_cast_fp16 = reshape(shape = const_3152, x = glu_38_cast_fp16)[name = tensor<string, []>("view_241_cast_fp16")];
            tensor<int32, [4]> select_117_begin_0 = const()[name = tensor<string, []>("select_117_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_117_end_0 = const()[name = tensor<string, []>("select_117_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_117_end_mask_0 = const()[name = tensor<string, []>("select_117_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_117_squeeze_mask_0 = const()[name = tensor<string, []>("select_117_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_117_cast_fp16 = slice_by_index(begin = select_117_begin_0, end = select_117_end_0, end_mask = select_117_end_mask_0, squeeze_mask = select_117_squeeze_mask_0, x = view_241_cast_fp16)[name = tensor<string, []>("select_117_cast_fp16")];
            tensor<int32, [4]> select_118_begin_0 = const()[name = tensor<string, []>("select_118_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_118_end_0 = const()[name = tensor<string, []>("select_118_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_118_end_mask_0 = const()[name = tensor<string, []>("select_118_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_118_squeeze_mask_0 = const()[name = tensor<string, []>("select_118_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_118_cast_fp16 = slice_by_index(begin = select_118_begin_0, end = select_118_end_0, end_mask = select_118_end_mask_0, squeeze_mask = select_118_squeeze_mask_0, x = view_241_cast_fp16)[name = tensor<string, []>("select_118_cast_fp16")];
            tensor<int32, [4]> select_119_begin_0 = const()[name = tensor<string, []>("select_119_begin_0"), val = tensor<int32, [4]>([0, 39, 0, 0])];
            tensor<int32, [4]> select_119_end_0 = const()[name = tensor<string, []>("select_119_end_0"), val = tensor<int32, [4]>([1, 40, 256, 801])];
            tensor<bool, [4]> select_119_end_mask_0 = const()[name = tensor<string, []>("select_119_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_119_squeeze_mask_0 = const()[name = tensor<string, []>("select_119_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_119_cast_fp16 = slice_by_index(begin = select_119_begin_0, end = select_119_end_0, end_mask = select_119_end_mask_0, squeeze_mask = select_119_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_119_cast_fp16")];
            tensor<int32, [1]> unsqueeze_329_axes_0 = const()[name = tensor<string, []>("unsqueeze_329_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_329_cast_fp16 = expand_dims(axes = unsqueeze_329_axes_0, x = select_119_cast_fp16)[name = tensor<string, []>("unsqueeze_329_cast_fp16")];
            tensor<fp16, []> const_3163_promoted_to_fp16 = const()[name = tensor<string, []>("const_3163_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_310_cast_fp16 = pow(x = unsqueeze_329_cast_fp16, y = const_3163_promoted_to_fp16)[name = tensor<string, []>("pow_310_cast_fp16")];
            tensor<int32, [1]> mean_149_axes_0 = const()[name = tensor<string, []>("mean_149_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_149_keep_dims_0 = const()[name = tensor<string, []>("mean_149_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_149_cast_fp16 = reduce_mean(axes = mean_149_axes_0, keep_dims = mean_149_keep_dims_0, x = pow_310_cast_fp16)[name = tensor<string, []>("mean_149_cast_fp16")];
            tensor<fp16, []> const_3166_to_fp16 = const()[name = tensor<string, []>("const_3166_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_351_cast_fp16 = add(x = mean_149_cast_fp16, y = const_3166_to_fp16)[name = tensor<string, []>("add_351_cast_fp16")];
            tensor<fp32, []> rsqrt_149_epsilon_0 = const()[name = tensor<string, []>("rsqrt_149_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_149_cast_fp16 = rsqrt(epsilon = rsqrt_149_epsilon_0, x = add_351_cast_fp16)[name = tensor<string, []>("rsqrt_149_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_346_cast_fp16 = mul(x = unsqueeze_329_cast_fp16, y = rsqrt_149_cast_fp16)[name = tensor<string, []>("mul_346_cast_fp16")];
            tensor<int32, [1]> squeeze_155_axes_0 = const()[name = tensor<string, []>("squeeze_155_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_155_cast_fp16 = squeeze(axes = squeeze_155_axes_0, x = mul_346_cast_fp16)[name = tensor<string, []>("squeeze_155_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_242_to_fp16 = const()[name = tensor<string, []>("view_242_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32597696)))];
            tensor<fp16, [1, 256, 801]> mul_347_cast_fp16 = mul(x = squeeze_155_cast_fp16, y = view_242_to_fp16)[name = tensor<string, []>("mul_347_cast_fp16")];
            tensor<string, []> conv1d_197_pad_type_0 = const()[name = tensor<string, []>("conv1d_197_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_197_strides_0 = const()[name = tensor<string, []>("conv1d_197_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_197_pad_0 = const()[name = tensor<string, []>("conv1d_197_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_197_dilations_0 = const()[name = tensor<string, []>("conv1d_197_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_197_groups_0 = const()[name = tensor<string, []>("conv1d_197_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_39_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_39_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32598272)))];
            tensor<fp16, [20]> p_output_39_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_39_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32608576)))];
            tensor<fp16, [1, 20, 801]> conv1d_197_cast_fp16 = conv(bias = p_output_39_1_bias_to_fp16, dilations = conv1d_197_dilations_0, groups = conv1d_197_groups_0, pad = conv1d_197_pad_0, pad_type = conv1d_197_pad_type_0, strides = conv1d_197_strides_0, weight = p_output_39_1_weight_to_fp16, x = mul_347_cast_fp16)[name = tensor<string, []>("conv1d_197_cast_fp16")];
            tensor<int32, []> glu_39_split_num_splits_0 = const()[name = tensor<string, []>("glu_39_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_39_split_axis_0 = const()[name = tensor<string, []>("glu_39_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_39_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_39_split_cast_fp16_1 = split(axis = glu_39_split_axis_0, num_splits = glu_39_split_num_splits_0, x = conv1d_197_cast_fp16)[name = tensor<string, []>("glu_39_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_39_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_39_split_cast_fp16_1)[name = tensor<string, []>("glu_39_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_39_cast_fp16 = mul(x = glu_39_split_cast_fp16_0, y = glu_39_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_39_cast_fp16")];
            tensor<int32, [4]> const_3170 = const()[name = tensor<string, []>("const_3170"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_243_cast_fp16 = reshape(shape = const_3170, x = glu_39_cast_fp16)[name = tensor<string, []>("view_243_cast_fp16")];
            tensor<int32, [4]> select_120_begin_0 = const()[name = tensor<string, []>("select_120_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_120_end_0 = const()[name = tensor<string, []>("select_120_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_120_end_mask_0 = const()[name = tensor<string, []>("select_120_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_120_squeeze_mask_0 = const()[name = tensor<string, []>("select_120_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_120_cast_fp16 = slice_by_index(begin = select_120_begin_0, end = select_120_end_0, end_mask = select_120_end_mask_0, squeeze_mask = select_120_squeeze_mask_0, x = view_243_cast_fp16)[name = tensor<string, []>("select_120_cast_fp16")];
            tensor<int32, [4]> select_121_begin_0 = const()[name = tensor<string, []>("select_121_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_121_end_0 = const()[name = tensor<string, []>("select_121_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_121_end_mask_0 = const()[name = tensor<string, []>("select_121_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_121_squeeze_mask_0 = const()[name = tensor<string, []>("select_121_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_121_cast_fp16 = slice_by_index(begin = select_121_begin_0, end = select_121_end_0, end_mask = select_121_end_mask_0, squeeze_mask = select_121_squeeze_mask_0, x = view_243_cast_fp16)[name = tensor<string, []>("select_121_cast_fp16")];
            tensor<int32, [4]> select_122_begin_0 = const()[name = tensor<string, []>("select_122_begin_0"), val = tensor<int32, [4]>([0, 40, 0, 0])];
            tensor<int32, [4]> select_122_end_0 = const()[name = tensor<string, []>("select_122_end_0"), val = tensor<int32, [4]>([1, 41, 256, 801])];
            tensor<bool, [4]> select_122_end_mask_0 = const()[name = tensor<string, []>("select_122_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_122_squeeze_mask_0 = const()[name = tensor<string, []>("select_122_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_122_cast_fp16 = slice_by_index(begin = select_122_begin_0, end = select_122_end_0, end_mask = select_122_end_mask_0, squeeze_mask = select_122_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_122_cast_fp16")];
            tensor<int32, [1]> unsqueeze_331_axes_0 = const()[name = tensor<string, []>("unsqueeze_331_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_331_cast_fp16 = expand_dims(axes = unsqueeze_331_axes_0, x = select_122_cast_fp16)[name = tensor<string, []>("unsqueeze_331_cast_fp16")];
            tensor<fp16, []> const_3181_promoted_to_fp16 = const()[name = tensor<string, []>("const_3181_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_311_cast_fp16 = pow(x = unsqueeze_331_cast_fp16, y = const_3181_promoted_to_fp16)[name = tensor<string, []>("pow_311_cast_fp16")];
            tensor<int32, [1]> mean_150_axes_0 = const()[name = tensor<string, []>("mean_150_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_150_keep_dims_0 = const()[name = tensor<string, []>("mean_150_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_150_cast_fp16 = reduce_mean(axes = mean_150_axes_0, keep_dims = mean_150_keep_dims_0, x = pow_311_cast_fp16)[name = tensor<string, []>("mean_150_cast_fp16")];
            tensor<fp16, []> const_3184_to_fp16 = const()[name = tensor<string, []>("const_3184_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_352_cast_fp16 = add(x = mean_150_cast_fp16, y = const_3184_to_fp16)[name = tensor<string, []>("add_352_cast_fp16")];
            tensor<fp32, []> rsqrt_150_epsilon_0 = const()[name = tensor<string, []>("rsqrt_150_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_150_cast_fp16 = rsqrt(epsilon = rsqrt_150_epsilon_0, x = add_352_cast_fp16)[name = tensor<string, []>("rsqrt_150_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_348_cast_fp16 = mul(x = unsqueeze_331_cast_fp16, y = rsqrt_150_cast_fp16)[name = tensor<string, []>("mul_348_cast_fp16")];
            tensor<int32, [1]> squeeze_156_axes_0 = const()[name = tensor<string, []>("squeeze_156_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_156_cast_fp16 = squeeze(axes = squeeze_156_axes_0, x = mul_348_cast_fp16)[name = tensor<string, []>("squeeze_156_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_244_to_fp16 = const()[name = tensor<string, []>("view_244_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32608704)))];
            tensor<fp16, [1, 256, 801]> mul_349_cast_fp16 = mul(x = squeeze_156_cast_fp16, y = view_244_to_fp16)[name = tensor<string, []>("mul_349_cast_fp16")];
            tensor<string, []> conv1d_198_pad_type_0 = const()[name = tensor<string, []>("conv1d_198_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_198_strides_0 = const()[name = tensor<string, []>("conv1d_198_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_198_pad_0 = const()[name = tensor<string, []>("conv1d_198_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_198_dilations_0 = const()[name = tensor<string, []>("conv1d_198_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_198_groups_0 = const()[name = tensor<string, []>("conv1d_198_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_40_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_40_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32609280)))];
            tensor<fp16, [20]> p_output_40_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_40_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32619584)))];
            tensor<fp16, [1, 20, 801]> conv1d_198_cast_fp16 = conv(bias = p_output_40_1_bias_to_fp16, dilations = conv1d_198_dilations_0, groups = conv1d_198_groups_0, pad = conv1d_198_pad_0, pad_type = conv1d_198_pad_type_0, strides = conv1d_198_strides_0, weight = p_output_40_1_weight_to_fp16, x = mul_349_cast_fp16)[name = tensor<string, []>("conv1d_198_cast_fp16")];
            tensor<int32, []> glu_40_split_num_splits_0 = const()[name = tensor<string, []>("glu_40_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_40_split_axis_0 = const()[name = tensor<string, []>("glu_40_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_40_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_40_split_cast_fp16_1 = split(axis = glu_40_split_axis_0, num_splits = glu_40_split_num_splits_0, x = conv1d_198_cast_fp16)[name = tensor<string, []>("glu_40_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_40_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_40_split_cast_fp16_1)[name = tensor<string, []>("glu_40_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_40_cast_fp16 = mul(x = glu_40_split_cast_fp16_0, y = glu_40_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_40_cast_fp16")];
            tensor<int32, [4]> const_3188 = const()[name = tensor<string, []>("const_3188"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_245_cast_fp16 = reshape(shape = const_3188, x = glu_40_cast_fp16)[name = tensor<string, []>("view_245_cast_fp16")];
            tensor<int32, [4]> select_123_begin_0 = const()[name = tensor<string, []>("select_123_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_123_end_0 = const()[name = tensor<string, []>("select_123_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_123_end_mask_0 = const()[name = tensor<string, []>("select_123_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_123_squeeze_mask_0 = const()[name = tensor<string, []>("select_123_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_123_cast_fp16 = slice_by_index(begin = select_123_begin_0, end = select_123_end_0, end_mask = select_123_end_mask_0, squeeze_mask = select_123_squeeze_mask_0, x = view_245_cast_fp16)[name = tensor<string, []>("select_123_cast_fp16")];
            tensor<int32, [4]> select_124_begin_0 = const()[name = tensor<string, []>("select_124_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_124_end_0 = const()[name = tensor<string, []>("select_124_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_124_end_mask_0 = const()[name = tensor<string, []>("select_124_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_124_squeeze_mask_0 = const()[name = tensor<string, []>("select_124_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_124_cast_fp16 = slice_by_index(begin = select_124_begin_0, end = select_124_end_0, end_mask = select_124_end_mask_0, squeeze_mask = select_124_squeeze_mask_0, x = view_245_cast_fp16)[name = tensor<string, []>("select_124_cast_fp16")];
            tensor<int32, [4]> select_125_begin_0 = const()[name = tensor<string, []>("select_125_begin_0"), val = tensor<int32, [4]>([0, 41, 0, 0])];
            tensor<int32, [4]> select_125_end_0 = const()[name = tensor<string, []>("select_125_end_0"), val = tensor<int32, [4]>([1, 42, 256, 801])];
            tensor<bool, [4]> select_125_end_mask_0 = const()[name = tensor<string, []>("select_125_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_125_squeeze_mask_0 = const()[name = tensor<string, []>("select_125_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_125_cast_fp16 = slice_by_index(begin = select_125_begin_0, end = select_125_end_0, end_mask = select_125_end_mask_0, squeeze_mask = select_125_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_125_cast_fp16")];
            tensor<int32, [1]> unsqueeze_333_axes_0 = const()[name = tensor<string, []>("unsqueeze_333_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_333_cast_fp16 = expand_dims(axes = unsqueeze_333_axes_0, x = select_125_cast_fp16)[name = tensor<string, []>("unsqueeze_333_cast_fp16")];
            tensor<fp16, []> const_3199_promoted_to_fp16 = const()[name = tensor<string, []>("const_3199_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_312_cast_fp16 = pow(x = unsqueeze_333_cast_fp16, y = const_3199_promoted_to_fp16)[name = tensor<string, []>("pow_312_cast_fp16")];
            tensor<int32, [1]> mean_151_axes_0 = const()[name = tensor<string, []>("mean_151_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_151_keep_dims_0 = const()[name = tensor<string, []>("mean_151_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_151_cast_fp16 = reduce_mean(axes = mean_151_axes_0, keep_dims = mean_151_keep_dims_0, x = pow_312_cast_fp16)[name = tensor<string, []>("mean_151_cast_fp16")];
            tensor<fp16, []> const_3202_to_fp16 = const()[name = tensor<string, []>("const_3202_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_353_cast_fp16 = add(x = mean_151_cast_fp16, y = const_3202_to_fp16)[name = tensor<string, []>("add_353_cast_fp16")];
            tensor<fp32, []> rsqrt_151_epsilon_0 = const()[name = tensor<string, []>("rsqrt_151_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_151_cast_fp16 = rsqrt(epsilon = rsqrt_151_epsilon_0, x = add_353_cast_fp16)[name = tensor<string, []>("rsqrt_151_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_350_cast_fp16 = mul(x = unsqueeze_333_cast_fp16, y = rsqrt_151_cast_fp16)[name = tensor<string, []>("mul_350_cast_fp16")];
            tensor<int32, [1]> squeeze_157_axes_0 = const()[name = tensor<string, []>("squeeze_157_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_157_cast_fp16 = squeeze(axes = squeeze_157_axes_0, x = mul_350_cast_fp16)[name = tensor<string, []>("squeeze_157_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_246_to_fp16 = const()[name = tensor<string, []>("view_246_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32619712)))];
            tensor<fp16, [1, 256, 801]> mul_351_cast_fp16 = mul(x = squeeze_157_cast_fp16, y = view_246_to_fp16)[name = tensor<string, []>("mul_351_cast_fp16")];
            tensor<string, []> conv1d_199_pad_type_0 = const()[name = tensor<string, []>("conv1d_199_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_199_strides_0 = const()[name = tensor<string, []>("conv1d_199_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_199_pad_0 = const()[name = tensor<string, []>("conv1d_199_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_199_dilations_0 = const()[name = tensor<string, []>("conv1d_199_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_199_groups_0 = const()[name = tensor<string, []>("conv1d_199_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_41_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_41_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32620288)))];
            tensor<fp16, [20]> p_output_41_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_41_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32630592)))];
            tensor<fp16, [1, 20, 801]> conv1d_199_cast_fp16 = conv(bias = p_output_41_1_bias_to_fp16, dilations = conv1d_199_dilations_0, groups = conv1d_199_groups_0, pad = conv1d_199_pad_0, pad_type = conv1d_199_pad_type_0, strides = conv1d_199_strides_0, weight = p_output_41_1_weight_to_fp16, x = mul_351_cast_fp16)[name = tensor<string, []>("conv1d_199_cast_fp16")];
            tensor<int32, []> glu_41_split_num_splits_0 = const()[name = tensor<string, []>("glu_41_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_41_split_axis_0 = const()[name = tensor<string, []>("glu_41_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_41_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_41_split_cast_fp16_1 = split(axis = glu_41_split_axis_0, num_splits = glu_41_split_num_splits_0, x = conv1d_199_cast_fp16)[name = tensor<string, []>("glu_41_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_41_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_41_split_cast_fp16_1)[name = tensor<string, []>("glu_41_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_41_cast_fp16 = mul(x = glu_41_split_cast_fp16_0, y = glu_41_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_41_cast_fp16")];
            tensor<int32, [4]> const_3206 = const()[name = tensor<string, []>("const_3206"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_247_cast_fp16 = reshape(shape = const_3206, x = glu_41_cast_fp16)[name = tensor<string, []>("view_247_cast_fp16")];
            tensor<int32, [4]> select_126_begin_0 = const()[name = tensor<string, []>("select_126_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_126_end_0 = const()[name = tensor<string, []>("select_126_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_126_end_mask_0 = const()[name = tensor<string, []>("select_126_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_126_squeeze_mask_0 = const()[name = tensor<string, []>("select_126_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_126_cast_fp16 = slice_by_index(begin = select_126_begin_0, end = select_126_end_0, end_mask = select_126_end_mask_0, squeeze_mask = select_126_squeeze_mask_0, x = view_247_cast_fp16)[name = tensor<string, []>("select_126_cast_fp16")];
            tensor<int32, [4]> select_127_begin_0 = const()[name = tensor<string, []>("select_127_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_127_end_0 = const()[name = tensor<string, []>("select_127_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_127_end_mask_0 = const()[name = tensor<string, []>("select_127_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_127_squeeze_mask_0 = const()[name = tensor<string, []>("select_127_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_127_cast_fp16 = slice_by_index(begin = select_127_begin_0, end = select_127_end_0, end_mask = select_127_end_mask_0, squeeze_mask = select_127_squeeze_mask_0, x = view_247_cast_fp16)[name = tensor<string, []>("select_127_cast_fp16")];
            tensor<int32, [4]> select_128_begin_0 = const()[name = tensor<string, []>("select_128_begin_0"), val = tensor<int32, [4]>([0, 42, 0, 0])];
            tensor<int32, [4]> select_128_end_0 = const()[name = tensor<string, []>("select_128_end_0"), val = tensor<int32, [4]>([1, 43, 256, 801])];
            tensor<bool, [4]> select_128_end_mask_0 = const()[name = tensor<string, []>("select_128_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_128_squeeze_mask_0 = const()[name = tensor<string, []>("select_128_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_128_cast_fp16 = slice_by_index(begin = select_128_begin_0, end = select_128_end_0, end_mask = select_128_end_mask_0, squeeze_mask = select_128_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_128_cast_fp16")];
            tensor<int32, [1]> unsqueeze_335_axes_0 = const()[name = tensor<string, []>("unsqueeze_335_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_335_cast_fp16 = expand_dims(axes = unsqueeze_335_axes_0, x = select_128_cast_fp16)[name = tensor<string, []>("unsqueeze_335_cast_fp16")];
            tensor<fp16, []> const_3217_promoted_to_fp16 = const()[name = tensor<string, []>("const_3217_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_313_cast_fp16 = pow(x = unsqueeze_335_cast_fp16, y = const_3217_promoted_to_fp16)[name = tensor<string, []>("pow_313_cast_fp16")];
            tensor<int32, [1]> mean_152_axes_0 = const()[name = tensor<string, []>("mean_152_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_152_keep_dims_0 = const()[name = tensor<string, []>("mean_152_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_152_cast_fp16 = reduce_mean(axes = mean_152_axes_0, keep_dims = mean_152_keep_dims_0, x = pow_313_cast_fp16)[name = tensor<string, []>("mean_152_cast_fp16")];
            tensor<fp16, []> const_3220_to_fp16 = const()[name = tensor<string, []>("const_3220_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_354_cast_fp16 = add(x = mean_152_cast_fp16, y = const_3220_to_fp16)[name = tensor<string, []>("add_354_cast_fp16")];
            tensor<fp32, []> rsqrt_152_epsilon_0 = const()[name = tensor<string, []>("rsqrt_152_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_152_cast_fp16 = rsqrt(epsilon = rsqrt_152_epsilon_0, x = add_354_cast_fp16)[name = tensor<string, []>("rsqrt_152_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_352_cast_fp16 = mul(x = unsqueeze_335_cast_fp16, y = rsqrt_152_cast_fp16)[name = tensor<string, []>("mul_352_cast_fp16")];
            tensor<int32, [1]> squeeze_158_axes_0 = const()[name = tensor<string, []>("squeeze_158_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_158_cast_fp16 = squeeze(axes = squeeze_158_axes_0, x = mul_352_cast_fp16)[name = tensor<string, []>("squeeze_158_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_248_to_fp16 = const()[name = tensor<string, []>("view_248_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32630720)))];
            tensor<fp16, [1, 256, 801]> mul_353_cast_fp16 = mul(x = squeeze_158_cast_fp16, y = view_248_to_fp16)[name = tensor<string, []>("mul_353_cast_fp16")];
            tensor<string, []> conv1d_200_pad_type_0 = const()[name = tensor<string, []>("conv1d_200_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_200_strides_0 = const()[name = tensor<string, []>("conv1d_200_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_200_pad_0 = const()[name = tensor<string, []>("conv1d_200_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_200_dilations_0 = const()[name = tensor<string, []>("conv1d_200_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_200_groups_0 = const()[name = tensor<string, []>("conv1d_200_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_42_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_42_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32631296)))];
            tensor<fp16, [20]> p_output_42_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_42_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32641600)))];
            tensor<fp16, [1, 20, 801]> conv1d_200_cast_fp16 = conv(bias = p_output_42_1_bias_to_fp16, dilations = conv1d_200_dilations_0, groups = conv1d_200_groups_0, pad = conv1d_200_pad_0, pad_type = conv1d_200_pad_type_0, strides = conv1d_200_strides_0, weight = p_output_42_1_weight_to_fp16, x = mul_353_cast_fp16)[name = tensor<string, []>("conv1d_200_cast_fp16")];
            tensor<int32, []> glu_42_split_num_splits_0 = const()[name = tensor<string, []>("glu_42_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_42_split_axis_0 = const()[name = tensor<string, []>("glu_42_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_42_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_42_split_cast_fp16_1 = split(axis = glu_42_split_axis_0, num_splits = glu_42_split_num_splits_0, x = conv1d_200_cast_fp16)[name = tensor<string, []>("glu_42_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_42_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_42_split_cast_fp16_1)[name = tensor<string, []>("glu_42_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_42_cast_fp16 = mul(x = glu_42_split_cast_fp16_0, y = glu_42_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_42_cast_fp16")];
            tensor<int32, [4]> const_3224 = const()[name = tensor<string, []>("const_3224"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_249_cast_fp16 = reshape(shape = const_3224, x = glu_42_cast_fp16)[name = tensor<string, []>("view_249_cast_fp16")];
            tensor<int32, [4]> select_129_begin_0 = const()[name = tensor<string, []>("select_129_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_129_end_0 = const()[name = tensor<string, []>("select_129_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_129_end_mask_0 = const()[name = tensor<string, []>("select_129_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_129_squeeze_mask_0 = const()[name = tensor<string, []>("select_129_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_129_cast_fp16 = slice_by_index(begin = select_129_begin_0, end = select_129_end_0, end_mask = select_129_end_mask_0, squeeze_mask = select_129_squeeze_mask_0, x = view_249_cast_fp16)[name = tensor<string, []>("select_129_cast_fp16")];
            tensor<int32, [4]> select_130_begin_0 = const()[name = tensor<string, []>("select_130_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_130_end_0 = const()[name = tensor<string, []>("select_130_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_130_end_mask_0 = const()[name = tensor<string, []>("select_130_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_130_squeeze_mask_0 = const()[name = tensor<string, []>("select_130_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_130_cast_fp16 = slice_by_index(begin = select_130_begin_0, end = select_130_end_0, end_mask = select_130_end_mask_0, squeeze_mask = select_130_squeeze_mask_0, x = view_249_cast_fp16)[name = tensor<string, []>("select_130_cast_fp16")];
            tensor<int32, [4]> select_131_begin_0 = const()[name = tensor<string, []>("select_131_begin_0"), val = tensor<int32, [4]>([0, 43, 0, 0])];
            tensor<int32, [4]> select_131_end_0 = const()[name = tensor<string, []>("select_131_end_0"), val = tensor<int32, [4]>([1, 44, 256, 801])];
            tensor<bool, [4]> select_131_end_mask_0 = const()[name = tensor<string, []>("select_131_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_131_squeeze_mask_0 = const()[name = tensor<string, []>("select_131_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_131_cast_fp16 = slice_by_index(begin = select_131_begin_0, end = select_131_end_0, end_mask = select_131_end_mask_0, squeeze_mask = select_131_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_131_cast_fp16")];
            tensor<int32, [1]> unsqueeze_337_axes_0 = const()[name = tensor<string, []>("unsqueeze_337_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_337_cast_fp16 = expand_dims(axes = unsqueeze_337_axes_0, x = select_131_cast_fp16)[name = tensor<string, []>("unsqueeze_337_cast_fp16")];
            tensor<fp16, []> const_3235_promoted_to_fp16 = const()[name = tensor<string, []>("const_3235_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_314_cast_fp16 = pow(x = unsqueeze_337_cast_fp16, y = const_3235_promoted_to_fp16)[name = tensor<string, []>("pow_314_cast_fp16")];
            tensor<int32, [1]> mean_153_axes_0 = const()[name = tensor<string, []>("mean_153_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_153_keep_dims_0 = const()[name = tensor<string, []>("mean_153_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_153_cast_fp16 = reduce_mean(axes = mean_153_axes_0, keep_dims = mean_153_keep_dims_0, x = pow_314_cast_fp16)[name = tensor<string, []>("mean_153_cast_fp16")];
            tensor<fp16, []> const_3238_to_fp16 = const()[name = tensor<string, []>("const_3238_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_355_cast_fp16 = add(x = mean_153_cast_fp16, y = const_3238_to_fp16)[name = tensor<string, []>("add_355_cast_fp16")];
            tensor<fp32, []> rsqrt_153_epsilon_0 = const()[name = tensor<string, []>("rsqrt_153_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_153_cast_fp16 = rsqrt(epsilon = rsqrt_153_epsilon_0, x = add_355_cast_fp16)[name = tensor<string, []>("rsqrt_153_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_354_cast_fp16 = mul(x = unsqueeze_337_cast_fp16, y = rsqrt_153_cast_fp16)[name = tensor<string, []>("mul_354_cast_fp16")];
            tensor<int32, [1]> squeeze_159_axes_0 = const()[name = tensor<string, []>("squeeze_159_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_159_cast_fp16 = squeeze(axes = squeeze_159_axes_0, x = mul_354_cast_fp16)[name = tensor<string, []>("squeeze_159_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_250_to_fp16 = const()[name = tensor<string, []>("view_250_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32641728)))];
            tensor<fp16, [1, 256, 801]> mul_355_cast_fp16 = mul(x = squeeze_159_cast_fp16, y = view_250_to_fp16)[name = tensor<string, []>("mul_355_cast_fp16")];
            tensor<string, []> conv1d_201_pad_type_0 = const()[name = tensor<string, []>("conv1d_201_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_201_strides_0 = const()[name = tensor<string, []>("conv1d_201_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_201_pad_0 = const()[name = tensor<string, []>("conv1d_201_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_201_dilations_0 = const()[name = tensor<string, []>("conv1d_201_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_201_groups_0 = const()[name = tensor<string, []>("conv1d_201_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_43_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_43_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32642304)))];
            tensor<fp16, [20]> p_output_43_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_43_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32652608)))];
            tensor<fp16, [1, 20, 801]> conv1d_201_cast_fp16 = conv(bias = p_output_43_1_bias_to_fp16, dilations = conv1d_201_dilations_0, groups = conv1d_201_groups_0, pad = conv1d_201_pad_0, pad_type = conv1d_201_pad_type_0, strides = conv1d_201_strides_0, weight = p_output_43_1_weight_to_fp16, x = mul_355_cast_fp16)[name = tensor<string, []>("conv1d_201_cast_fp16")];
            tensor<int32, []> glu_43_split_num_splits_0 = const()[name = tensor<string, []>("glu_43_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_43_split_axis_0 = const()[name = tensor<string, []>("glu_43_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_43_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_43_split_cast_fp16_1 = split(axis = glu_43_split_axis_0, num_splits = glu_43_split_num_splits_0, x = conv1d_201_cast_fp16)[name = tensor<string, []>("glu_43_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_43_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_43_split_cast_fp16_1)[name = tensor<string, []>("glu_43_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_43_cast_fp16 = mul(x = glu_43_split_cast_fp16_0, y = glu_43_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_43_cast_fp16")];
            tensor<int32, [4]> const_3242 = const()[name = tensor<string, []>("const_3242"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_251_cast_fp16 = reshape(shape = const_3242, x = glu_43_cast_fp16)[name = tensor<string, []>("view_251_cast_fp16")];
            tensor<int32, [4]> select_132_begin_0 = const()[name = tensor<string, []>("select_132_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_132_end_0 = const()[name = tensor<string, []>("select_132_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_132_end_mask_0 = const()[name = tensor<string, []>("select_132_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_132_squeeze_mask_0 = const()[name = tensor<string, []>("select_132_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_132_cast_fp16 = slice_by_index(begin = select_132_begin_0, end = select_132_end_0, end_mask = select_132_end_mask_0, squeeze_mask = select_132_squeeze_mask_0, x = view_251_cast_fp16)[name = tensor<string, []>("select_132_cast_fp16")];
            tensor<int32, [4]> select_133_begin_0 = const()[name = tensor<string, []>("select_133_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_133_end_0 = const()[name = tensor<string, []>("select_133_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_133_end_mask_0 = const()[name = tensor<string, []>("select_133_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_133_squeeze_mask_0 = const()[name = tensor<string, []>("select_133_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_133_cast_fp16 = slice_by_index(begin = select_133_begin_0, end = select_133_end_0, end_mask = select_133_end_mask_0, squeeze_mask = select_133_squeeze_mask_0, x = view_251_cast_fp16)[name = tensor<string, []>("select_133_cast_fp16")];
            tensor<int32, [4]> select_134_begin_0 = const()[name = tensor<string, []>("select_134_begin_0"), val = tensor<int32, [4]>([0, 44, 0, 0])];
            tensor<int32, [4]> select_134_end_0 = const()[name = tensor<string, []>("select_134_end_0"), val = tensor<int32, [4]>([1, 45, 256, 801])];
            tensor<bool, [4]> select_134_end_mask_0 = const()[name = tensor<string, []>("select_134_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_134_squeeze_mask_0 = const()[name = tensor<string, []>("select_134_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_134_cast_fp16 = slice_by_index(begin = select_134_begin_0, end = select_134_end_0, end_mask = select_134_end_mask_0, squeeze_mask = select_134_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_134_cast_fp16")];
            tensor<int32, [1]> unsqueeze_339_axes_0 = const()[name = tensor<string, []>("unsqueeze_339_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_339_cast_fp16 = expand_dims(axes = unsqueeze_339_axes_0, x = select_134_cast_fp16)[name = tensor<string, []>("unsqueeze_339_cast_fp16")];
            tensor<fp16, []> const_3253_promoted_to_fp16 = const()[name = tensor<string, []>("const_3253_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_315_cast_fp16 = pow(x = unsqueeze_339_cast_fp16, y = const_3253_promoted_to_fp16)[name = tensor<string, []>("pow_315_cast_fp16")];
            tensor<int32, [1]> mean_154_axes_0 = const()[name = tensor<string, []>("mean_154_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_154_keep_dims_0 = const()[name = tensor<string, []>("mean_154_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_154_cast_fp16 = reduce_mean(axes = mean_154_axes_0, keep_dims = mean_154_keep_dims_0, x = pow_315_cast_fp16)[name = tensor<string, []>("mean_154_cast_fp16")];
            tensor<fp16, []> const_3256_to_fp16 = const()[name = tensor<string, []>("const_3256_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_356_cast_fp16 = add(x = mean_154_cast_fp16, y = const_3256_to_fp16)[name = tensor<string, []>("add_356_cast_fp16")];
            tensor<fp32, []> rsqrt_154_epsilon_0 = const()[name = tensor<string, []>("rsqrt_154_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_154_cast_fp16 = rsqrt(epsilon = rsqrt_154_epsilon_0, x = add_356_cast_fp16)[name = tensor<string, []>("rsqrt_154_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_356_cast_fp16 = mul(x = unsqueeze_339_cast_fp16, y = rsqrt_154_cast_fp16)[name = tensor<string, []>("mul_356_cast_fp16")];
            tensor<int32, [1]> squeeze_160_axes_0 = const()[name = tensor<string, []>("squeeze_160_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_160_cast_fp16 = squeeze(axes = squeeze_160_axes_0, x = mul_356_cast_fp16)[name = tensor<string, []>("squeeze_160_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_252_to_fp16 = const()[name = tensor<string, []>("view_252_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32652736)))];
            tensor<fp16, [1, 256, 801]> mul_357_cast_fp16 = mul(x = squeeze_160_cast_fp16, y = view_252_to_fp16)[name = tensor<string, []>("mul_357_cast_fp16")];
            tensor<string, []> conv1d_202_pad_type_0 = const()[name = tensor<string, []>("conv1d_202_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_202_strides_0 = const()[name = tensor<string, []>("conv1d_202_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_202_pad_0 = const()[name = tensor<string, []>("conv1d_202_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_202_dilations_0 = const()[name = tensor<string, []>("conv1d_202_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_202_groups_0 = const()[name = tensor<string, []>("conv1d_202_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_44_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_44_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32653312)))];
            tensor<fp16, [20]> p_output_44_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_44_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32663616)))];
            tensor<fp16, [1, 20, 801]> conv1d_202_cast_fp16 = conv(bias = p_output_44_1_bias_to_fp16, dilations = conv1d_202_dilations_0, groups = conv1d_202_groups_0, pad = conv1d_202_pad_0, pad_type = conv1d_202_pad_type_0, strides = conv1d_202_strides_0, weight = p_output_44_1_weight_to_fp16, x = mul_357_cast_fp16)[name = tensor<string, []>("conv1d_202_cast_fp16")];
            tensor<int32, []> glu_44_split_num_splits_0 = const()[name = tensor<string, []>("glu_44_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_44_split_axis_0 = const()[name = tensor<string, []>("glu_44_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_44_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_44_split_cast_fp16_1 = split(axis = glu_44_split_axis_0, num_splits = glu_44_split_num_splits_0, x = conv1d_202_cast_fp16)[name = tensor<string, []>("glu_44_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_44_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_44_split_cast_fp16_1)[name = tensor<string, []>("glu_44_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_44_cast_fp16 = mul(x = glu_44_split_cast_fp16_0, y = glu_44_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_44_cast_fp16")];
            tensor<int32, [4]> const_3260 = const()[name = tensor<string, []>("const_3260"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_253_cast_fp16 = reshape(shape = const_3260, x = glu_44_cast_fp16)[name = tensor<string, []>("view_253_cast_fp16")];
            tensor<int32, [4]> select_135_begin_0 = const()[name = tensor<string, []>("select_135_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_135_end_0 = const()[name = tensor<string, []>("select_135_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_135_end_mask_0 = const()[name = tensor<string, []>("select_135_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_135_squeeze_mask_0 = const()[name = tensor<string, []>("select_135_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_135_cast_fp16 = slice_by_index(begin = select_135_begin_0, end = select_135_end_0, end_mask = select_135_end_mask_0, squeeze_mask = select_135_squeeze_mask_0, x = view_253_cast_fp16)[name = tensor<string, []>("select_135_cast_fp16")];
            tensor<int32, [4]> select_136_begin_0 = const()[name = tensor<string, []>("select_136_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_136_end_0 = const()[name = tensor<string, []>("select_136_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_136_end_mask_0 = const()[name = tensor<string, []>("select_136_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_136_squeeze_mask_0 = const()[name = tensor<string, []>("select_136_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_136_cast_fp16 = slice_by_index(begin = select_136_begin_0, end = select_136_end_0, end_mask = select_136_end_mask_0, squeeze_mask = select_136_squeeze_mask_0, x = view_253_cast_fp16)[name = tensor<string, []>("select_136_cast_fp16")];
            tensor<int32, [4]> select_137_begin_0 = const()[name = tensor<string, []>("select_137_begin_0"), val = tensor<int32, [4]>([0, 45, 0, 0])];
            tensor<int32, [4]> select_137_end_0 = const()[name = tensor<string, []>("select_137_end_0"), val = tensor<int32, [4]>([1, 46, 256, 801])];
            tensor<bool, [4]> select_137_end_mask_0 = const()[name = tensor<string, []>("select_137_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_137_squeeze_mask_0 = const()[name = tensor<string, []>("select_137_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_137_cast_fp16 = slice_by_index(begin = select_137_begin_0, end = select_137_end_0, end_mask = select_137_end_mask_0, squeeze_mask = select_137_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_137_cast_fp16")];
            tensor<int32, [1]> unsqueeze_341_axes_0 = const()[name = tensor<string, []>("unsqueeze_341_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_341_cast_fp16 = expand_dims(axes = unsqueeze_341_axes_0, x = select_137_cast_fp16)[name = tensor<string, []>("unsqueeze_341_cast_fp16")];
            tensor<fp16, []> const_3271_promoted_to_fp16 = const()[name = tensor<string, []>("const_3271_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_316_cast_fp16 = pow(x = unsqueeze_341_cast_fp16, y = const_3271_promoted_to_fp16)[name = tensor<string, []>("pow_316_cast_fp16")];
            tensor<int32, [1]> mean_155_axes_0 = const()[name = tensor<string, []>("mean_155_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_155_keep_dims_0 = const()[name = tensor<string, []>("mean_155_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_155_cast_fp16 = reduce_mean(axes = mean_155_axes_0, keep_dims = mean_155_keep_dims_0, x = pow_316_cast_fp16)[name = tensor<string, []>("mean_155_cast_fp16")];
            tensor<fp16, []> const_3274_to_fp16 = const()[name = tensor<string, []>("const_3274_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_357_cast_fp16 = add(x = mean_155_cast_fp16, y = const_3274_to_fp16)[name = tensor<string, []>("add_357_cast_fp16")];
            tensor<fp32, []> rsqrt_155_epsilon_0 = const()[name = tensor<string, []>("rsqrt_155_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_155_cast_fp16 = rsqrt(epsilon = rsqrt_155_epsilon_0, x = add_357_cast_fp16)[name = tensor<string, []>("rsqrt_155_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_358_cast_fp16 = mul(x = unsqueeze_341_cast_fp16, y = rsqrt_155_cast_fp16)[name = tensor<string, []>("mul_358_cast_fp16")];
            tensor<int32, [1]> squeeze_161_axes_0 = const()[name = tensor<string, []>("squeeze_161_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_161_cast_fp16 = squeeze(axes = squeeze_161_axes_0, x = mul_358_cast_fp16)[name = tensor<string, []>("squeeze_161_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_254_to_fp16 = const()[name = tensor<string, []>("view_254_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32663744)))];
            tensor<fp16, [1, 256, 801]> mul_359_cast_fp16 = mul(x = squeeze_161_cast_fp16, y = view_254_to_fp16)[name = tensor<string, []>("mul_359_cast_fp16")];
            tensor<string, []> conv1d_203_pad_type_0 = const()[name = tensor<string, []>("conv1d_203_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_203_strides_0 = const()[name = tensor<string, []>("conv1d_203_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_203_pad_0 = const()[name = tensor<string, []>("conv1d_203_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_203_dilations_0 = const()[name = tensor<string, []>("conv1d_203_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_203_groups_0 = const()[name = tensor<string, []>("conv1d_203_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_45_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_45_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32664320)))];
            tensor<fp16, [20]> p_output_45_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_45_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32674624)))];
            tensor<fp16, [1, 20, 801]> conv1d_203_cast_fp16 = conv(bias = p_output_45_1_bias_to_fp16, dilations = conv1d_203_dilations_0, groups = conv1d_203_groups_0, pad = conv1d_203_pad_0, pad_type = conv1d_203_pad_type_0, strides = conv1d_203_strides_0, weight = p_output_45_1_weight_to_fp16, x = mul_359_cast_fp16)[name = tensor<string, []>("conv1d_203_cast_fp16")];
            tensor<int32, []> glu_45_split_num_splits_0 = const()[name = tensor<string, []>("glu_45_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_45_split_axis_0 = const()[name = tensor<string, []>("glu_45_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_45_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_45_split_cast_fp16_1 = split(axis = glu_45_split_axis_0, num_splits = glu_45_split_num_splits_0, x = conv1d_203_cast_fp16)[name = tensor<string, []>("glu_45_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_45_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_45_split_cast_fp16_1)[name = tensor<string, []>("glu_45_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_45_cast_fp16 = mul(x = glu_45_split_cast_fp16_0, y = glu_45_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_45_cast_fp16")];
            tensor<int32, [4]> const_3278 = const()[name = tensor<string, []>("const_3278"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_255_cast_fp16 = reshape(shape = const_3278, x = glu_45_cast_fp16)[name = tensor<string, []>("view_255_cast_fp16")];
            tensor<int32, [4]> select_138_begin_0 = const()[name = tensor<string, []>("select_138_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_138_end_0 = const()[name = tensor<string, []>("select_138_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_138_end_mask_0 = const()[name = tensor<string, []>("select_138_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_138_squeeze_mask_0 = const()[name = tensor<string, []>("select_138_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_138_cast_fp16 = slice_by_index(begin = select_138_begin_0, end = select_138_end_0, end_mask = select_138_end_mask_0, squeeze_mask = select_138_squeeze_mask_0, x = view_255_cast_fp16)[name = tensor<string, []>("select_138_cast_fp16")];
            tensor<int32, [4]> select_139_begin_0 = const()[name = tensor<string, []>("select_139_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_139_end_0 = const()[name = tensor<string, []>("select_139_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_139_end_mask_0 = const()[name = tensor<string, []>("select_139_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_139_squeeze_mask_0 = const()[name = tensor<string, []>("select_139_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_139_cast_fp16 = slice_by_index(begin = select_139_begin_0, end = select_139_end_0, end_mask = select_139_end_mask_0, squeeze_mask = select_139_squeeze_mask_0, x = view_255_cast_fp16)[name = tensor<string, []>("select_139_cast_fp16")];
            tensor<int32, [4]> select_140_begin_0 = const()[name = tensor<string, []>("select_140_begin_0"), val = tensor<int32, [4]>([0, 46, 0, 0])];
            tensor<int32, [4]> select_140_end_0 = const()[name = tensor<string, []>("select_140_end_0"), val = tensor<int32, [4]>([1, 47, 256, 801])];
            tensor<bool, [4]> select_140_end_mask_0 = const()[name = tensor<string, []>("select_140_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_140_squeeze_mask_0 = const()[name = tensor<string, []>("select_140_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_140_cast_fp16 = slice_by_index(begin = select_140_begin_0, end = select_140_end_0, end_mask = select_140_end_mask_0, squeeze_mask = select_140_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_140_cast_fp16")];
            tensor<int32, [1]> unsqueeze_343_axes_0 = const()[name = tensor<string, []>("unsqueeze_343_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_343_cast_fp16 = expand_dims(axes = unsqueeze_343_axes_0, x = select_140_cast_fp16)[name = tensor<string, []>("unsqueeze_343_cast_fp16")];
            tensor<fp16, []> const_3289_promoted_to_fp16 = const()[name = tensor<string, []>("const_3289_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_317_cast_fp16 = pow(x = unsqueeze_343_cast_fp16, y = const_3289_promoted_to_fp16)[name = tensor<string, []>("pow_317_cast_fp16")];
            tensor<int32, [1]> mean_156_axes_0 = const()[name = tensor<string, []>("mean_156_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_156_keep_dims_0 = const()[name = tensor<string, []>("mean_156_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_156_cast_fp16 = reduce_mean(axes = mean_156_axes_0, keep_dims = mean_156_keep_dims_0, x = pow_317_cast_fp16)[name = tensor<string, []>("mean_156_cast_fp16")];
            tensor<fp16, []> const_3292_to_fp16 = const()[name = tensor<string, []>("const_3292_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_358_cast_fp16 = add(x = mean_156_cast_fp16, y = const_3292_to_fp16)[name = tensor<string, []>("add_358_cast_fp16")];
            tensor<fp32, []> rsqrt_156_epsilon_0 = const()[name = tensor<string, []>("rsqrt_156_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_156_cast_fp16 = rsqrt(epsilon = rsqrt_156_epsilon_0, x = add_358_cast_fp16)[name = tensor<string, []>("rsqrt_156_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_360_cast_fp16 = mul(x = unsqueeze_343_cast_fp16, y = rsqrt_156_cast_fp16)[name = tensor<string, []>("mul_360_cast_fp16")];
            tensor<int32, [1]> squeeze_162_axes_0 = const()[name = tensor<string, []>("squeeze_162_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_162_cast_fp16 = squeeze(axes = squeeze_162_axes_0, x = mul_360_cast_fp16)[name = tensor<string, []>("squeeze_162_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_256_to_fp16 = const()[name = tensor<string, []>("view_256_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32674752)))];
            tensor<fp16, [1, 256, 801]> mul_361_cast_fp16 = mul(x = squeeze_162_cast_fp16, y = view_256_to_fp16)[name = tensor<string, []>("mul_361_cast_fp16")];
            tensor<string, []> conv1d_204_pad_type_0 = const()[name = tensor<string, []>("conv1d_204_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_204_strides_0 = const()[name = tensor<string, []>("conv1d_204_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_204_pad_0 = const()[name = tensor<string, []>("conv1d_204_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_204_dilations_0 = const()[name = tensor<string, []>("conv1d_204_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_204_groups_0 = const()[name = tensor<string, []>("conv1d_204_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_46_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_46_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32675328)))];
            tensor<fp16, [20]> p_output_46_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_46_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32685632)))];
            tensor<fp16, [1, 20, 801]> conv1d_204_cast_fp16 = conv(bias = p_output_46_1_bias_to_fp16, dilations = conv1d_204_dilations_0, groups = conv1d_204_groups_0, pad = conv1d_204_pad_0, pad_type = conv1d_204_pad_type_0, strides = conv1d_204_strides_0, weight = p_output_46_1_weight_to_fp16, x = mul_361_cast_fp16)[name = tensor<string, []>("conv1d_204_cast_fp16")];
            tensor<int32, []> glu_46_split_num_splits_0 = const()[name = tensor<string, []>("glu_46_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_46_split_axis_0 = const()[name = tensor<string, []>("glu_46_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_46_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_46_split_cast_fp16_1 = split(axis = glu_46_split_axis_0, num_splits = glu_46_split_num_splits_0, x = conv1d_204_cast_fp16)[name = tensor<string, []>("glu_46_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_46_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_46_split_cast_fp16_1)[name = tensor<string, []>("glu_46_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_46_cast_fp16 = mul(x = glu_46_split_cast_fp16_0, y = glu_46_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_46_cast_fp16")];
            tensor<int32, [4]> const_3296 = const()[name = tensor<string, []>("const_3296"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_257_cast_fp16 = reshape(shape = const_3296, x = glu_46_cast_fp16)[name = tensor<string, []>("view_257_cast_fp16")];
            tensor<int32, [4]> select_141_begin_0 = const()[name = tensor<string, []>("select_141_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_141_end_0 = const()[name = tensor<string, []>("select_141_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_141_end_mask_0 = const()[name = tensor<string, []>("select_141_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_141_squeeze_mask_0 = const()[name = tensor<string, []>("select_141_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_141_cast_fp16 = slice_by_index(begin = select_141_begin_0, end = select_141_end_0, end_mask = select_141_end_mask_0, squeeze_mask = select_141_squeeze_mask_0, x = view_257_cast_fp16)[name = tensor<string, []>("select_141_cast_fp16")];
            tensor<int32, [4]> select_142_begin_0 = const()[name = tensor<string, []>("select_142_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_142_end_0 = const()[name = tensor<string, []>("select_142_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_142_end_mask_0 = const()[name = tensor<string, []>("select_142_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_142_squeeze_mask_0 = const()[name = tensor<string, []>("select_142_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_142_cast_fp16 = slice_by_index(begin = select_142_begin_0, end = select_142_end_0, end_mask = select_142_end_mask_0, squeeze_mask = select_142_squeeze_mask_0, x = view_257_cast_fp16)[name = tensor<string, []>("select_142_cast_fp16")];
            tensor<int32, [4]> select_143_begin_0 = const()[name = tensor<string, []>("select_143_begin_0"), val = tensor<int32, [4]>([0, 47, 0, 0])];
            tensor<int32, [4]> select_143_end_0 = const()[name = tensor<string, []>("select_143_end_0"), val = tensor<int32, [4]>([1, 48, 256, 801])];
            tensor<bool, [4]> select_143_end_mask_0 = const()[name = tensor<string, []>("select_143_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_143_squeeze_mask_0 = const()[name = tensor<string, []>("select_143_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_143_cast_fp16 = slice_by_index(begin = select_143_begin_0, end = select_143_end_0, end_mask = select_143_end_mask_0, squeeze_mask = select_143_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_143_cast_fp16")];
            tensor<int32, [1]> unsqueeze_345_axes_0 = const()[name = tensor<string, []>("unsqueeze_345_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_345_cast_fp16 = expand_dims(axes = unsqueeze_345_axes_0, x = select_143_cast_fp16)[name = tensor<string, []>("unsqueeze_345_cast_fp16")];
            tensor<fp16, []> const_3307_promoted_to_fp16 = const()[name = tensor<string, []>("const_3307_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_318_cast_fp16 = pow(x = unsqueeze_345_cast_fp16, y = const_3307_promoted_to_fp16)[name = tensor<string, []>("pow_318_cast_fp16")];
            tensor<int32, [1]> mean_157_axes_0 = const()[name = tensor<string, []>("mean_157_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_157_keep_dims_0 = const()[name = tensor<string, []>("mean_157_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_157_cast_fp16 = reduce_mean(axes = mean_157_axes_0, keep_dims = mean_157_keep_dims_0, x = pow_318_cast_fp16)[name = tensor<string, []>("mean_157_cast_fp16")];
            tensor<fp16, []> const_3310_to_fp16 = const()[name = tensor<string, []>("const_3310_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_359_cast_fp16 = add(x = mean_157_cast_fp16, y = const_3310_to_fp16)[name = tensor<string, []>("add_359_cast_fp16")];
            tensor<fp32, []> rsqrt_157_epsilon_0 = const()[name = tensor<string, []>("rsqrt_157_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_157_cast_fp16 = rsqrt(epsilon = rsqrt_157_epsilon_0, x = add_359_cast_fp16)[name = tensor<string, []>("rsqrt_157_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_362_cast_fp16 = mul(x = unsqueeze_345_cast_fp16, y = rsqrt_157_cast_fp16)[name = tensor<string, []>("mul_362_cast_fp16")];
            tensor<int32, [1]> squeeze_163_axes_0 = const()[name = tensor<string, []>("squeeze_163_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_163_cast_fp16 = squeeze(axes = squeeze_163_axes_0, x = mul_362_cast_fp16)[name = tensor<string, []>("squeeze_163_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_258_to_fp16 = const()[name = tensor<string, []>("view_258_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32685760)))];
            tensor<fp16, [1, 256, 801]> mul_363_cast_fp16 = mul(x = squeeze_163_cast_fp16, y = view_258_to_fp16)[name = tensor<string, []>("mul_363_cast_fp16")];
            tensor<string, []> conv1d_205_pad_type_0 = const()[name = tensor<string, []>("conv1d_205_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_205_strides_0 = const()[name = tensor<string, []>("conv1d_205_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_205_pad_0 = const()[name = tensor<string, []>("conv1d_205_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_205_dilations_0 = const()[name = tensor<string, []>("conv1d_205_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_205_groups_0 = const()[name = tensor<string, []>("conv1d_205_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_47_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_47_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32686336)))];
            tensor<fp16, [20]> p_output_47_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_47_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32696640)))];
            tensor<fp16, [1, 20, 801]> conv1d_205_cast_fp16 = conv(bias = p_output_47_1_bias_to_fp16, dilations = conv1d_205_dilations_0, groups = conv1d_205_groups_0, pad = conv1d_205_pad_0, pad_type = conv1d_205_pad_type_0, strides = conv1d_205_strides_0, weight = p_output_47_1_weight_to_fp16, x = mul_363_cast_fp16)[name = tensor<string, []>("conv1d_205_cast_fp16")];
            tensor<int32, []> glu_47_split_num_splits_0 = const()[name = tensor<string, []>("glu_47_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_47_split_axis_0 = const()[name = tensor<string, []>("glu_47_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_47_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_47_split_cast_fp16_1 = split(axis = glu_47_split_axis_0, num_splits = glu_47_split_num_splits_0, x = conv1d_205_cast_fp16)[name = tensor<string, []>("glu_47_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_47_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_47_split_cast_fp16_1)[name = tensor<string, []>("glu_47_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_47_cast_fp16 = mul(x = glu_47_split_cast_fp16_0, y = glu_47_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_47_cast_fp16")];
            tensor<int32, [4]> const_3314 = const()[name = tensor<string, []>("const_3314"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_259_cast_fp16 = reshape(shape = const_3314, x = glu_47_cast_fp16)[name = tensor<string, []>("view_259_cast_fp16")];
            tensor<int32, [4]> select_144_begin_0 = const()[name = tensor<string, []>("select_144_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_144_end_0 = const()[name = tensor<string, []>("select_144_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_144_end_mask_0 = const()[name = tensor<string, []>("select_144_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_144_squeeze_mask_0 = const()[name = tensor<string, []>("select_144_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_144_cast_fp16 = slice_by_index(begin = select_144_begin_0, end = select_144_end_0, end_mask = select_144_end_mask_0, squeeze_mask = select_144_squeeze_mask_0, x = view_259_cast_fp16)[name = tensor<string, []>("select_144_cast_fp16")];
            tensor<int32, [4]> select_145_begin_0 = const()[name = tensor<string, []>("select_145_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_145_end_0 = const()[name = tensor<string, []>("select_145_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_145_end_mask_0 = const()[name = tensor<string, []>("select_145_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_145_squeeze_mask_0 = const()[name = tensor<string, []>("select_145_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_145_cast_fp16 = slice_by_index(begin = select_145_begin_0, end = select_145_end_0, end_mask = select_145_end_mask_0, squeeze_mask = select_145_squeeze_mask_0, x = view_259_cast_fp16)[name = tensor<string, []>("select_145_cast_fp16")];
            tensor<int32, [4]> select_146_begin_0 = const()[name = tensor<string, []>("select_146_begin_0"), val = tensor<int32, [4]>([0, 48, 0, 0])];
            tensor<int32, [4]> select_146_end_0 = const()[name = tensor<string, []>("select_146_end_0"), val = tensor<int32, [4]>([1, 49, 256, 801])];
            tensor<bool, [4]> select_146_end_mask_0 = const()[name = tensor<string, []>("select_146_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_146_squeeze_mask_0 = const()[name = tensor<string, []>("select_146_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_146_cast_fp16 = slice_by_index(begin = select_146_begin_0, end = select_146_end_0, end_mask = select_146_end_mask_0, squeeze_mask = select_146_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_146_cast_fp16")];
            tensor<int32, [1]> unsqueeze_347_axes_0 = const()[name = tensor<string, []>("unsqueeze_347_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_347_cast_fp16 = expand_dims(axes = unsqueeze_347_axes_0, x = select_146_cast_fp16)[name = tensor<string, []>("unsqueeze_347_cast_fp16")];
            tensor<fp16, []> const_3325_promoted_to_fp16 = const()[name = tensor<string, []>("const_3325_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_319_cast_fp16 = pow(x = unsqueeze_347_cast_fp16, y = const_3325_promoted_to_fp16)[name = tensor<string, []>("pow_319_cast_fp16")];
            tensor<int32, [1]> mean_158_axes_0 = const()[name = tensor<string, []>("mean_158_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_158_keep_dims_0 = const()[name = tensor<string, []>("mean_158_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_158_cast_fp16 = reduce_mean(axes = mean_158_axes_0, keep_dims = mean_158_keep_dims_0, x = pow_319_cast_fp16)[name = tensor<string, []>("mean_158_cast_fp16")];
            tensor<fp16, []> const_3328_to_fp16 = const()[name = tensor<string, []>("const_3328_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_360_cast_fp16 = add(x = mean_158_cast_fp16, y = const_3328_to_fp16)[name = tensor<string, []>("add_360_cast_fp16")];
            tensor<fp32, []> rsqrt_158_epsilon_0 = const()[name = tensor<string, []>("rsqrt_158_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_158_cast_fp16 = rsqrt(epsilon = rsqrt_158_epsilon_0, x = add_360_cast_fp16)[name = tensor<string, []>("rsqrt_158_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_364_cast_fp16 = mul(x = unsqueeze_347_cast_fp16, y = rsqrt_158_cast_fp16)[name = tensor<string, []>("mul_364_cast_fp16")];
            tensor<int32, [1]> squeeze_164_axes_0 = const()[name = tensor<string, []>("squeeze_164_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_164_cast_fp16 = squeeze(axes = squeeze_164_axes_0, x = mul_364_cast_fp16)[name = tensor<string, []>("squeeze_164_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_260_to_fp16 = const()[name = tensor<string, []>("view_260_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32696768)))];
            tensor<fp16, [1, 256, 801]> mul_365_cast_fp16 = mul(x = squeeze_164_cast_fp16, y = view_260_to_fp16)[name = tensor<string, []>("mul_365_cast_fp16")];
            tensor<string, []> conv1d_206_pad_type_0 = const()[name = tensor<string, []>("conv1d_206_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_206_strides_0 = const()[name = tensor<string, []>("conv1d_206_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_206_pad_0 = const()[name = tensor<string, []>("conv1d_206_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_206_dilations_0 = const()[name = tensor<string, []>("conv1d_206_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_206_groups_0 = const()[name = tensor<string, []>("conv1d_206_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_48_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_48_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32697344)))];
            tensor<fp16, [20]> p_output_48_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_48_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32707648)))];
            tensor<fp16, [1, 20, 801]> conv1d_206_cast_fp16 = conv(bias = p_output_48_1_bias_to_fp16, dilations = conv1d_206_dilations_0, groups = conv1d_206_groups_0, pad = conv1d_206_pad_0, pad_type = conv1d_206_pad_type_0, strides = conv1d_206_strides_0, weight = p_output_48_1_weight_to_fp16, x = mul_365_cast_fp16)[name = tensor<string, []>("conv1d_206_cast_fp16")];
            tensor<int32, []> glu_48_split_num_splits_0 = const()[name = tensor<string, []>("glu_48_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_48_split_axis_0 = const()[name = tensor<string, []>("glu_48_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_48_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_48_split_cast_fp16_1 = split(axis = glu_48_split_axis_0, num_splits = glu_48_split_num_splits_0, x = conv1d_206_cast_fp16)[name = tensor<string, []>("glu_48_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_48_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_48_split_cast_fp16_1)[name = tensor<string, []>("glu_48_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_48_cast_fp16 = mul(x = glu_48_split_cast_fp16_0, y = glu_48_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_48_cast_fp16")];
            tensor<int32, [4]> const_3332 = const()[name = tensor<string, []>("const_3332"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_261_cast_fp16 = reshape(shape = const_3332, x = glu_48_cast_fp16)[name = tensor<string, []>("view_261_cast_fp16")];
            tensor<int32, [4]> select_147_begin_0 = const()[name = tensor<string, []>("select_147_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_147_end_0 = const()[name = tensor<string, []>("select_147_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_147_end_mask_0 = const()[name = tensor<string, []>("select_147_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_147_squeeze_mask_0 = const()[name = tensor<string, []>("select_147_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_147_cast_fp16 = slice_by_index(begin = select_147_begin_0, end = select_147_end_0, end_mask = select_147_end_mask_0, squeeze_mask = select_147_squeeze_mask_0, x = view_261_cast_fp16)[name = tensor<string, []>("select_147_cast_fp16")];
            tensor<int32, [4]> select_148_begin_0 = const()[name = tensor<string, []>("select_148_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_148_end_0 = const()[name = tensor<string, []>("select_148_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_148_end_mask_0 = const()[name = tensor<string, []>("select_148_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_148_squeeze_mask_0 = const()[name = tensor<string, []>("select_148_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_148_cast_fp16 = slice_by_index(begin = select_148_begin_0, end = select_148_end_0, end_mask = select_148_end_mask_0, squeeze_mask = select_148_squeeze_mask_0, x = view_261_cast_fp16)[name = tensor<string, []>("select_148_cast_fp16")];
            tensor<int32, [4]> select_149_begin_0 = const()[name = tensor<string, []>("select_149_begin_0"), val = tensor<int32, [4]>([0, 49, 0, 0])];
            tensor<int32, [4]> select_149_end_0 = const()[name = tensor<string, []>("select_149_end_0"), val = tensor<int32, [4]>([1, 50, 256, 801])];
            tensor<bool, [4]> select_149_end_mask_0 = const()[name = tensor<string, []>("select_149_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_149_squeeze_mask_0 = const()[name = tensor<string, []>("select_149_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_149_cast_fp16 = slice_by_index(begin = select_149_begin_0, end = select_149_end_0, end_mask = select_149_end_mask_0, squeeze_mask = select_149_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_149_cast_fp16")];
            tensor<int32, [1]> unsqueeze_349_axes_0 = const()[name = tensor<string, []>("unsqueeze_349_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_349_cast_fp16 = expand_dims(axes = unsqueeze_349_axes_0, x = select_149_cast_fp16)[name = tensor<string, []>("unsqueeze_349_cast_fp16")];
            tensor<fp16, []> const_3343_promoted_to_fp16 = const()[name = tensor<string, []>("const_3343_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_320_cast_fp16 = pow(x = unsqueeze_349_cast_fp16, y = const_3343_promoted_to_fp16)[name = tensor<string, []>("pow_320_cast_fp16")];
            tensor<int32, [1]> mean_159_axes_0 = const()[name = tensor<string, []>("mean_159_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_159_keep_dims_0 = const()[name = tensor<string, []>("mean_159_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_159_cast_fp16 = reduce_mean(axes = mean_159_axes_0, keep_dims = mean_159_keep_dims_0, x = pow_320_cast_fp16)[name = tensor<string, []>("mean_159_cast_fp16")];
            tensor<fp16, []> const_3346_to_fp16 = const()[name = tensor<string, []>("const_3346_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_361_cast_fp16 = add(x = mean_159_cast_fp16, y = const_3346_to_fp16)[name = tensor<string, []>("add_361_cast_fp16")];
            tensor<fp32, []> rsqrt_159_epsilon_0 = const()[name = tensor<string, []>("rsqrt_159_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_159_cast_fp16 = rsqrt(epsilon = rsqrt_159_epsilon_0, x = add_361_cast_fp16)[name = tensor<string, []>("rsqrt_159_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_366_cast_fp16 = mul(x = unsqueeze_349_cast_fp16, y = rsqrt_159_cast_fp16)[name = tensor<string, []>("mul_366_cast_fp16")];
            tensor<int32, [1]> squeeze_165_axes_0 = const()[name = tensor<string, []>("squeeze_165_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_165_cast_fp16 = squeeze(axes = squeeze_165_axes_0, x = mul_366_cast_fp16)[name = tensor<string, []>("squeeze_165_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_262_to_fp16 = const()[name = tensor<string, []>("view_262_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32707776)))];
            tensor<fp16, [1, 256, 801]> mul_367_cast_fp16 = mul(x = squeeze_165_cast_fp16, y = view_262_to_fp16)[name = tensor<string, []>("mul_367_cast_fp16")];
            tensor<string, []> conv1d_207_pad_type_0 = const()[name = tensor<string, []>("conv1d_207_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_207_strides_0 = const()[name = tensor<string, []>("conv1d_207_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_207_pad_0 = const()[name = tensor<string, []>("conv1d_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_207_dilations_0 = const()[name = tensor<string, []>("conv1d_207_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_207_groups_0 = const()[name = tensor<string, []>("conv1d_207_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_49_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_49_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32708352)))];
            tensor<fp16, [20]> p_output_49_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_49_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32718656)))];
            tensor<fp16, [1, 20, 801]> conv1d_207_cast_fp16 = conv(bias = p_output_49_1_bias_to_fp16, dilations = conv1d_207_dilations_0, groups = conv1d_207_groups_0, pad = conv1d_207_pad_0, pad_type = conv1d_207_pad_type_0, strides = conv1d_207_strides_0, weight = p_output_49_1_weight_to_fp16, x = mul_367_cast_fp16)[name = tensor<string, []>("conv1d_207_cast_fp16")];
            tensor<int32, []> glu_49_split_num_splits_0 = const()[name = tensor<string, []>("glu_49_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_49_split_axis_0 = const()[name = tensor<string, []>("glu_49_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_49_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_49_split_cast_fp16_1 = split(axis = glu_49_split_axis_0, num_splits = glu_49_split_num_splits_0, x = conv1d_207_cast_fp16)[name = tensor<string, []>("glu_49_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_49_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_49_split_cast_fp16_1)[name = tensor<string, []>("glu_49_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_49_cast_fp16 = mul(x = glu_49_split_cast_fp16_0, y = glu_49_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_49_cast_fp16")];
            tensor<int32, [4]> const_3350 = const()[name = tensor<string, []>("const_3350"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_263_cast_fp16 = reshape(shape = const_3350, x = glu_49_cast_fp16)[name = tensor<string, []>("view_263_cast_fp16")];
            tensor<int32, [4]> select_150_begin_0 = const()[name = tensor<string, []>("select_150_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_150_end_0 = const()[name = tensor<string, []>("select_150_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_150_end_mask_0 = const()[name = tensor<string, []>("select_150_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_150_squeeze_mask_0 = const()[name = tensor<string, []>("select_150_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_150_cast_fp16 = slice_by_index(begin = select_150_begin_0, end = select_150_end_0, end_mask = select_150_end_mask_0, squeeze_mask = select_150_squeeze_mask_0, x = view_263_cast_fp16)[name = tensor<string, []>("select_150_cast_fp16")];
            tensor<int32, [4]> select_151_begin_0 = const()[name = tensor<string, []>("select_151_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_151_end_0 = const()[name = tensor<string, []>("select_151_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_151_end_mask_0 = const()[name = tensor<string, []>("select_151_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_151_squeeze_mask_0 = const()[name = tensor<string, []>("select_151_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_151_cast_fp16 = slice_by_index(begin = select_151_begin_0, end = select_151_end_0, end_mask = select_151_end_mask_0, squeeze_mask = select_151_squeeze_mask_0, x = view_263_cast_fp16)[name = tensor<string, []>("select_151_cast_fp16")];
            tensor<int32, [4]> select_152_begin_0 = const()[name = tensor<string, []>("select_152_begin_0"), val = tensor<int32, [4]>([0, 50, 0, 0])];
            tensor<int32, [4]> select_152_end_0 = const()[name = tensor<string, []>("select_152_end_0"), val = tensor<int32, [4]>([1, 51, 256, 801])];
            tensor<bool, [4]> select_152_end_mask_0 = const()[name = tensor<string, []>("select_152_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_152_squeeze_mask_0 = const()[name = tensor<string, []>("select_152_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_152_cast_fp16 = slice_by_index(begin = select_152_begin_0, end = select_152_end_0, end_mask = select_152_end_mask_0, squeeze_mask = select_152_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_152_cast_fp16")];
            tensor<int32, [1]> unsqueeze_351_axes_0 = const()[name = tensor<string, []>("unsqueeze_351_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_351_cast_fp16 = expand_dims(axes = unsqueeze_351_axes_0, x = select_152_cast_fp16)[name = tensor<string, []>("unsqueeze_351_cast_fp16")];
            tensor<fp16, []> const_3361_promoted_to_fp16 = const()[name = tensor<string, []>("const_3361_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_321_cast_fp16 = pow(x = unsqueeze_351_cast_fp16, y = const_3361_promoted_to_fp16)[name = tensor<string, []>("pow_321_cast_fp16")];
            tensor<int32, [1]> mean_160_axes_0 = const()[name = tensor<string, []>("mean_160_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_160_keep_dims_0 = const()[name = tensor<string, []>("mean_160_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_160_cast_fp16 = reduce_mean(axes = mean_160_axes_0, keep_dims = mean_160_keep_dims_0, x = pow_321_cast_fp16)[name = tensor<string, []>("mean_160_cast_fp16")];
            tensor<fp16, []> const_3364_to_fp16 = const()[name = tensor<string, []>("const_3364_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_362_cast_fp16 = add(x = mean_160_cast_fp16, y = const_3364_to_fp16)[name = tensor<string, []>("add_362_cast_fp16")];
            tensor<fp32, []> rsqrt_160_epsilon_0 = const()[name = tensor<string, []>("rsqrt_160_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_160_cast_fp16 = rsqrt(epsilon = rsqrt_160_epsilon_0, x = add_362_cast_fp16)[name = tensor<string, []>("rsqrt_160_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_368_cast_fp16 = mul(x = unsqueeze_351_cast_fp16, y = rsqrt_160_cast_fp16)[name = tensor<string, []>("mul_368_cast_fp16")];
            tensor<int32, [1]> squeeze_166_axes_0 = const()[name = tensor<string, []>("squeeze_166_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_166_cast_fp16 = squeeze(axes = squeeze_166_axes_0, x = mul_368_cast_fp16)[name = tensor<string, []>("squeeze_166_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_264_to_fp16 = const()[name = tensor<string, []>("view_264_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32718784)))];
            tensor<fp16, [1, 256, 801]> mul_369_cast_fp16 = mul(x = squeeze_166_cast_fp16, y = view_264_to_fp16)[name = tensor<string, []>("mul_369_cast_fp16")];
            tensor<string, []> conv1d_208_pad_type_0 = const()[name = tensor<string, []>("conv1d_208_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_208_strides_0 = const()[name = tensor<string, []>("conv1d_208_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_208_pad_0 = const()[name = tensor<string, []>("conv1d_208_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_208_dilations_0 = const()[name = tensor<string, []>("conv1d_208_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_208_groups_0 = const()[name = tensor<string, []>("conv1d_208_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_50_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_50_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32719360)))];
            tensor<fp16, [20]> p_output_50_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_50_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32729664)))];
            tensor<fp16, [1, 20, 801]> conv1d_208_cast_fp16 = conv(bias = p_output_50_1_bias_to_fp16, dilations = conv1d_208_dilations_0, groups = conv1d_208_groups_0, pad = conv1d_208_pad_0, pad_type = conv1d_208_pad_type_0, strides = conv1d_208_strides_0, weight = p_output_50_1_weight_to_fp16, x = mul_369_cast_fp16)[name = tensor<string, []>("conv1d_208_cast_fp16")];
            tensor<int32, []> glu_50_split_num_splits_0 = const()[name = tensor<string, []>("glu_50_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_50_split_axis_0 = const()[name = tensor<string, []>("glu_50_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_50_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_50_split_cast_fp16_1 = split(axis = glu_50_split_axis_0, num_splits = glu_50_split_num_splits_0, x = conv1d_208_cast_fp16)[name = tensor<string, []>("glu_50_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_50_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_50_split_cast_fp16_1)[name = tensor<string, []>("glu_50_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_50_cast_fp16 = mul(x = glu_50_split_cast_fp16_0, y = glu_50_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_50_cast_fp16")];
            tensor<int32, [4]> const_3368 = const()[name = tensor<string, []>("const_3368"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_265_cast_fp16 = reshape(shape = const_3368, x = glu_50_cast_fp16)[name = tensor<string, []>("view_265_cast_fp16")];
            tensor<int32, [4]> select_153_begin_0 = const()[name = tensor<string, []>("select_153_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_153_end_0 = const()[name = tensor<string, []>("select_153_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_153_end_mask_0 = const()[name = tensor<string, []>("select_153_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_153_squeeze_mask_0 = const()[name = tensor<string, []>("select_153_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_153_cast_fp16 = slice_by_index(begin = select_153_begin_0, end = select_153_end_0, end_mask = select_153_end_mask_0, squeeze_mask = select_153_squeeze_mask_0, x = view_265_cast_fp16)[name = tensor<string, []>("select_153_cast_fp16")];
            tensor<int32, [4]> select_154_begin_0 = const()[name = tensor<string, []>("select_154_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_154_end_0 = const()[name = tensor<string, []>("select_154_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_154_end_mask_0 = const()[name = tensor<string, []>("select_154_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_154_squeeze_mask_0 = const()[name = tensor<string, []>("select_154_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_154_cast_fp16 = slice_by_index(begin = select_154_begin_0, end = select_154_end_0, end_mask = select_154_end_mask_0, squeeze_mask = select_154_squeeze_mask_0, x = view_265_cast_fp16)[name = tensor<string, []>("select_154_cast_fp16")];
            tensor<int32, [4]> select_155_begin_0 = const()[name = tensor<string, []>("select_155_begin_0"), val = tensor<int32, [4]>([0, 51, 0, 0])];
            tensor<int32, [4]> select_155_end_0 = const()[name = tensor<string, []>("select_155_end_0"), val = tensor<int32, [4]>([1, 52, 256, 801])];
            tensor<bool, [4]> select_155_end_mask_0 = const()[name = tensor<string, []>("select_155_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_155_squeeze_mask_0 = const()[name = tensor<string, []>("select_155_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_155_cast_fp16 = slice_by_index(begin = select_155_begin_0, end = select_155_end_0, end_mask = select_155_end_mask_0, squeeze_mask = select_155_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_155_cast_fp16")];
            tensor<int32, [1]> unsqueeze_353_axes_0 = const()[name = tensor<string, []>("unsqueeze_353_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_353_cast_fp16 = expand_dims(axes = unsqueeze_353_axes_0, x = select_155_cast_fp16)[name = tensor<string, []>("unsqueeze_353_cast_fp16")];
            tensor<fp16, []> const_3379_promoted_to_fp16 = const()[name = tensor<string, []>("const_3379_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_322_cast_fp16 = pow(x = unsqueeze_353_cast_fp16, y = const_3379_promoted_to_fp16)[name = tensor<string, []>("pow_322_cast_fp16")];
            tensor<int32, [1]> mean_161_axes_0 = const()[name = tensor<string, []>("mean_161_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_161_keep_dims_0 = const()[name = tensor<string, []>("mean_161_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_161_cast_fp16 = reduce_mean(axes = mean_161_axes_0, keep_dims = mean_161_keep_dims_0, x = pow_322_cast_fp16)[name = tensor<string, []>("mean_161_cast_fp16")];
            tensor<fp16, []> const_3382_to_fp16 = const()[name = tensor<string, []>("const_3382_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_363_cast_fp16 = add(x = mean_161_cast_fp16, y = const_3382_to_fp16)[name = tensor<string, []>("add_363_cast_fp16")];
            tensor<fp32, []> rsqrt_161_epsilon_0 = const()[name = tensor<string, []>("rsqrt_161_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_161_cast_fp16 = rsqrt(epsilon = rsqrt_161_epsilon_0, x = add_363_cast_fp16)[name = tensor<string, []>("rsqrt_161_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_370_cast_fp16 = mul(x = unsqueeze_353_cast_fp16, y = rsqrt_161_cast_fp16)[name = tensor<string, []>("mul_370_cast_fp16")];
            tensor<int32, [1]> squeeze_167_axes_0 = const()[name = tensor<string, []>("squeeze_167_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_167_cast_fp16 = squeeze(axes = squeeze_167_axes_0, x = mul_370_cast_fp16)[name = tensor<string, []>("squeeze_167_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_266_to_fp16 = const()[name = tensor<string, []>("view_266_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32729792)))];
            tensor<fp16, [1, 256, 801]> mul_371_cast_fp16 = mul(x = squeeze_167_cast_fp16, y = view_266_to_fp16)[name = tensor<string, []>("mul_371_cast_fp16")];
            tensor<string, []> conv1d_209_pad_type_0 = const()[name = tensor<string, []>("conv1d_209_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_209_strides_0 = const()[name = tensor<string, []>("conv1d_209_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_209_pad_0 = const()[name = tensor<string, []>("conv1d_209_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_209_dilations_0 = const()[name = tensor<string, []>("conv1d_209_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_209_groups_0 = const()[name = tensor<string, []>("conv1d_209_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_51_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_51_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32730368)))];
            tensor<fp16, [20]> p_output_51_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_51_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32740672)))];
            tensor<fp16, [1, 20, 801]> conv1d_209_cast_fp16 = conv(bias = p_output_51_1_bias_to_fp16, dilations = conv1d_209_dilations_0, groups = conv1d_209_groups_0, pad = conv1d_209_pad_0, pad_type = conv1d_209_pad_type_0, strides = conv1d_209_strides_0, weight = p_output_51_1_weight_to_fp16, x = mul_371_cast_fp16)[name = tensor<string, []>("conv1d_209_cast_fp16")];
            tensor<int32, []> glu_51_split_num_splits_0 = const()[name = tensor<string, []>("glu_51_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_51_split_axis_0 = const()[name = tensor<string, []>("glu_51_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_51_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_51_split_cast_fp16_1 = split(axis = glu_51_split_axis_0, num_splits = glu_51_split_num_splits_0, x = conv1d_209_cast_fp16)[name = tensor<string, []>("glu_51_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_51_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_51_split_cast_fp16_1)[name = tensor<string, []>("glu_51_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_51_cast_fp16 = mul(x = glu_51_split_cast_fp16_0, y = glu_51_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_51_cast_fp16")];
            tensor<int32, [4]> const_3386 = const()[name = tensor<string, []>("const_3386"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_267_cast_fp16 = reshape(shape = const_3386, x = glu_51_cast_fp16)[name = tensor<string, []>("view_267_cast_fp16")];
            tensor<int32, [4]> select_156_begin_0 = const()[name = tensor<string, []>("select_156_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_156_end_0 = const()[name = tensor<string, []>("select_156_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_156_end_mask_0 = const()[name = tensor<string, []>("select_156_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_156_squeeze_mask_0 = const()[name = tensor<string, []>("select_156_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_156_cast_fp16 = slice_by_index(begin = select_156_begin_0, end = select_156_end_0, end_mask = select_156_end_mask_0, squeeze_mask = select_156_squeeze_mask_0, x = view_267_cast_fp16)[name = tensor<string, []>("select_156_cast_fp16")];
            tensor<int32, [4]> select_157_begin_0 = const()[name = tensor<string, []>("select_157_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_157_end_0 = const()[name = tensor<string, []>("select_157_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_157_end_mask_0 = const()[name = tensor<string, []>("select_157_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_157_squeeze_mask_0 = const()[name = tensor<string, []>("select_157_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_157_cast_fp16 = slice_by_index(begin = select_157_begin_0, end = select_157_end_0, end_mask = select_157_end_mask_0, squeeze_mask = select_157_squeeze_mask_0, x = view_267_cast_fp16)[name = tensor<string, []>("select_157_cast_fp16")];
            tensor<int32, [4]> select_158_begin_0 = const()[name = tensor<string, []>("select_158_begin_0"), val = tensor<int32, [4]>([0, 52, 0, 0])];
            tensor<int32, [4]> select_158_end_0 = const()[name = tensor<string, []>("select_158_end_0"), val = tensor<int32, [4]>([1, 53, 256, 801])];
            tensor<bool, [4]> select_158_end_mask_0 = const()[name = tensor<string, []>("select_158_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_158_squeeze_mask_0 = const()[name = tensor<string, []>("select_158_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_158_cast_fp16 = slice_by_index(begin = select_158_begin_0, end = select_158_end_0, end_mask = select_158_end_mask_0, squeeze_mask = select_158_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_158_cast_fp16")];
            tensor<int32, [1]> unsqueeze_355_axes_0 = const()[name = tensor<string, []>("unsqueeze_355_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_355_cast_fp16 = expand_dims(axes = unsqueeze_355_axes_0, x = select_158_cast_fp16)[name = tensor<string, []>("unsqueeze_355_cast_fp16")];
            tensor<fp16, []> const_3397_promoted_to_fp16 = const()[name = tensor<string, []>("const_3397_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_323_cast_fp16 = pow(x = unsqueeze_355_cast_fp16, y = const_3397_promoted_to_fp16)[name = tensor<string, []>("pow_323_cast_fp16")];
            tensor<int32, [1]> mean_162_axes_0 = const()[name = tensor<string, []>("mean_162_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_162_keep_dims_0 = const()[name = tensor<string, []>("mean_162_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_162_cast_fp16 = reduce_mean(axes = mean_162_axes_0, keep_dims = mean_162_keep_dims_0, x = pow_323_cast_fp16)[name = tensor<string, []>("mean_162_cast_fp16")];
            tensor<fp16, []> const_3400_to_fp16 = const()[name = tensor<string, []>("const_3400_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_364_cast_fp16 = add(x = mean_162_cast_fp16, y = const_3400_to_fp16)[name = tensor<string, []>("add_364_cast_fp16")];
            tensor<fp32, []> rsqrt_162_epsilon_0 = const()[name = tensor<string, []>("rsqrt_162_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_162_cast_fp16 = rsqrt(epsilon = rsqrt_162_epsilon_0, x = add_364_cast_fp16)[name = tensor<string, []>("rsqrt_162_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_372_cast_fp16 = mul(x = unsqueeze_355_cast_fp16, y = rsqrt_162_cast_fp16)[name = tensor<string, []>("mul_372_cast_fp16")];
            tensor<int32, [1]> squeeze_168_axes_0 = const()[name = tensor<string, []>("squeeze_168_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_168_cast_fp16 = squeeze(axes = squeeze_168_axes_0, x = mul_372_cast_fp16)[name = tensor<string, []>("squeeze_168_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_268_to_fp16 = const()[name = tensor<string, []>("view_268_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32740800)))];
            tensor<fp16, [1, 256, 801]> mul_373_cast_fp16 = mul(x = squeeze_168_cast_fp16, y = view_268_to_fp16)[name = tensor<string, []>("mul_373_cast_fp16")];
            tensor<string, []> conv1d_210_pad_type_0 = const()[name = tensor<string, []>("conv1d_210_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_210_strides_0 = const()[name = tensor<string, []>("conv1d_210_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_210_pad_0 = const()[name = tensor<string, []>("conv1d_210_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_210_dilations_0 = const()[name = tensor<string, []>("conv1d_210_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_210_groups_0 = const()[name = tensor<string, []>("conv1d_210_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_52_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_52_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32741376)))];
            tensor<fp16, [20]> p_output_52_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_52_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32751680)))];
            tensor<fp16, [1, 20, 801]> conv1d_210_cast_fp16 = conv(bias = p_output_52_1_bias_to_fp16, dilations = conv1d_210_dilations_0, groups = conv1d_210_groups_0, pad = conv1d_210_pad_0, pad_type = conv1d_210_pad_type_0, strides = conv1d_210_strides_0, weight = p_output_52_1_weight_to_fp16, x = mul_373_cast_fp16)[name = tensor<string, []>("conv1d_210_cast_fp16")];
            tensor<int32, []> glu_52_split_num_splits_0 = const()[name = tensor<string, []>("glu_52_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_52_split_axis_0 = const()[name = tensor<string, []>("glu_52_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_52_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_52_split_cast_fp16_1 = split(axis = glu_52_split_axis_0, num_splits = glu_52_split_num_splits_0, x = conv1d_210_cast_fp16)[name = tensor<string, []>("glu_52_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_52_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_52_split_cast_fp16_1)[name = tensor<string, []>("glu_52_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_52_cast_fp16 = mul(x = glu_52_split_cast_fp16_0, y = glu_52_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_52_cast_fp16")];
            tensor<int32, [4]> const_3404 = const()[name = tensor<string, []>("const_3404"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_269_cast_fp16 = reshape(shape = const_3404, x = glu_52_cast_fp16)[name = tensor<string, []>("view_269_cast_fp16")];
            tensor<int32, [4]> select_159_begin_0 = const()[name = tensor<string, []>("select_159_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_159_end_0 = const()[name = tensor<string, []>("select_159_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_159_end_mask_0 = const()[name = tensor<string, []>("select_159_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_159_squeeze_mask_0 = const()[name = tensor<string, []>("select_159_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_159_cast_fp16 = slice_by_index(begin = select_159_begin_0, end = select_159_end_0, end_mask = select_159_end_mask_0, squeeze_mask = select_159_squeeze_mask_0, x = view_269_cast_fp16)[name = tensor<string, []>("select_159_cast_fp16")];
            tensor<int32, [4]> select_160_begin_0 = const()[name = tensor<string, []>("select_160_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_160_end_0 = const()[name = tensor<string, []>("select_160_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_160_end_mask_0 = const()[name = tensor<string, []>("select_160_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_160_squeeze_mask_0 = const()[name = tensor<string, []>("select_160_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_160_cast_fp16 = slice_by_index(begin = select_160_begin_0, end = select_160_end_0, end_mask = select_160_end_mask_0, squeeze_mask = select_160_squeeze_mask_0, x = view_269_cast_fp16)[name = tensor<string, []>("select_160_cast_fp16")];
            tensor<int32, [4]> select_161_begin_0 = const()[name = tensor<string, []>("select_161_begin_0"), val = tensor<int32, [4]>([0, 53, 0, 0])];
            tensor<int32, [4]> select_161_end_0 = const()[name = tensor<string, []>("select_161_end_0"), val = tensor<int32, [4]>([1, 54, 256, 801])];
            tensor<bool, [4]> select_161_end_mask_0 = const()[name = tensor<string, []>("select_161_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_161_squeeze_mask_0 = const()[name = tensor<string, []>("select_161_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_161_cast_fp16 = slice_by_index(begin = select_161_begin_0, end = select_161_end_0, end_mask = select_161_end_mask_0, squeeze_mask = select_161_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_161_cast_fp16")];
            tensor<int32, [1]> unsqueeze_357_axes_0 = const()[name = tensor<string, []>("unsqueeze_357_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_357_cast_fp16 = expand_dims(axes = unsqueeze_357_axes_0, x = select_161_cast_fp16)[name = tensor<string, []>("unsqueeze_357_cast_fp16")];
            tensor<fp16, []> const_3415_promoted_to_fp16 = const()[name = tensor<string, []>("const_3415_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_324_cast_fp16 = pow(x = unsqueeze_357_cast_fp16, y = const_3415_promoted_to_fp16)[name = tensor<string, []>("pow_324_cast_fp16")];
            tensor<int32, [1]> mean_163_axes_0 = const()[name = tensor<string, []>("mean_163_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_163_keep_dims_0 = const()[name = tensor<string, []>("mean_163_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_163_cast_fp16 = reduce_mean(axes = mean_163_axes_0, keep_dims = mean_163_keep_dims_0, x = pow_324_cast_fp16)[name = tensor<string, []>("mean_163_cast_fp16")];
            tensor<fp16, []> const_3418_to_fp16 = const()[name = tensor<string, []>("const_3418_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_365_cast_fp16 = add(x = mean_163_cast_fp16, y = const_3418_to_fp16)[name = tensor<string, []>("add_365_cast_fp16")];
            tensor<fp32, []> rsqrt_163_epsilon_0 = const()[name = tensor<string, []>("rsqrt_163_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_163_cast_fp16 = rsqrt(epsilon = rsqrt_163_epsilon_0, x = add_365_cast_fp16)[name = tensor<string, []>("rsqrt_163_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_374_cast_fp16 = mul(x = unsqueeze_357_cast_fp16, y = rsqrt_163_cast_fp16)[name = tensor<string, []>("mul_374_cast_fp16")];
            tensor<int32, [1]> squeeze_169_axes_0 = const()[name = tensor<string, []>("squeeze_169_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_169_cast_fp16 = squeeze(axes = squeeze_169_axes_0, x = mul_374_cast_fp16)[name = tensor<string, []>("squeeze_169_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_270_to_fp16 = const()[name = tensor<string, []>("view_270_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32751808)))];
            tensor<fp16, [1, 256, 801]> mul_375_cast_fp16 = mul(x = squeeze_169_cast_fp16, y = view_270_to_fp16)[name = tensor<string, []>("mul_375_cast_fp16")];
            tensor<string, []> conv1d_211_pad_type_0 = const()[name = tensor<string, []>("conv1d_211_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_211_strides_0 = const()[name = tensor<string, []>("conv1d_211_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_211_pad_0 = const()[name = tensor<string, []>("conv1d_211_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_211_dilations_0 = const()[name = tensor<string, []>("conv1d_211_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_211_groups_0 = const()[name = tensor<string, []>("conv1d_211_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_53_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_53_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32752384)))];
            tensor<fp16, [20]> p_output_53_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_53_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32762688)))];
            tensor<fp16, [1, 20, 801]> conv1d_211_cast_fp16 = conv(bias = p_output_53_1_bias_to_fp16, dilations = conv1d_211_dilations_0, groups = conv1d_211_groups_0, pad = conv1d_211_pad_0, pad_type = conv1d_211_pad_type_0, strides = conv1d_211_strides_0, weight = p_output_53_1_weight_to_fp16, x = mul_375_cast_fp16)[name = tensor<string, []>("conv1d_211_cast_fp16")];
            tensor<int32, []> glu_53_split_num_splits_0 = const()[name = tensor<string, []>("glu_53_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_53_split_axis_0 = const()[name = tensor<string, []>("glu_53_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_53_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_53_split_cast_fp16_1 = split(axis = glu_53_split_axis_0, num_splits = glu_53_split_num_splits_0, x = conv1d_211_cast_fp16)[name = tensor<string, []>("glu_53_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_53_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_53_split_cast_fp16_1)[name = tensor<string, []>("glu_53_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_53_cast_fp16 = mul(x = glu_53_split_cast_fp16_0, y = glu_53_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_53_cast_fp16")];
            tensor<int32, [4]> const_3422 = const()[name = tensor<string, []>("const_3422"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_271_cast_fp16 = reshape(shape = const_3422, x = glu_53_cast_fp16)[name = tensor<string, []>("view_271_cast_fp16")];
            tensor<int32, [4]> select_162_begin_0 = const()[name = tensor<string, []>("select_162_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_162_end_0 = const()[name = tensor<string, []>("select_162_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_162_end_mask_0 = const()[name = tensor<string, []>("select_162_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_162_squeeze_mask_0 = const()[name = tensor<string, []>("select_162_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_162_cast_fp16 = slice_by_index(begin = select_162_begin_0, end = select_162_end_0, end_mask = select_162_end_mask_0, squeeze_mask = select_162_squeeze_mask_0, x = view_271_cast_fp16)[name = tensor<string, []>("select_162_cast_fp16")];
            tensor<int32, [4]> select_163_begin_0 = const()[name = tensor<string, []>("select_163_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_163_end_0 = const()[name = tensor<string, []>("select_163_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_163_end_mask_0 = const()[name = tensor<string, []>("select_163_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_163_squeeze_mask_0 = const()[name = tensor<string, []>("select_163_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_163_cast_fp16 = slice_by_index(begin = select_163_begin_0, end = select_163_end_0, end_mask = select_163_end_mask_0, squeeze_mask = select_163_squeeze_mask_0, x = view_271_cast_fp16)[name = tensor<string, []>("select_163_cast_fp16")];
            tensor<int32, [4]> select_164_begin_0 = const()[name = tensor<string, []>("select_164_begin_0"), val = tensor<int32, [4]>([0, 54, 0, 0])];
            tensor<int32, [4]> select_164_end_0 = const()[name = tensor<string, []>("select_164_end_0"), val = tensor<int32, [4]>([1, 55, 256, 801])];
            tensor<bool, [4]> select_164_end_mask_0 = const()[name = tensor<string, []>("select_164_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_164_squeeze_mask_0 = const()[name = tensor<string, []>("select_164_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_164_cast_fp16 = slice_by_index(begin = select_164_begin_0, end = select_164_end_0, end_mask = select_164_end_mask_0, squeeze_mask = select_164_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_164_cast_fp16")];
            tensor<int32, [1]> unsqueeze_359_axes_0 = const()[name = tensor<string, []>("unsqueeze_359_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_359_cast_fp16 = expand_dims(axes = unsqueeze_359_axes_0, x = select_164_cast_fp16)[name = tensor<string, []>("unsqueeze_359_cast_fp16")];
            tensor<fp16, []> const_3433_promoted_to_fp16 = const()[name = tensor<string, []>("const_3433_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_325_cast_fp16 = pow(x = unsqueeze_359_cast_fp16, y = const_3433_promoted_to_fp16)[name = tensor<string, []>("pow_325_cast_fp16")];
            tensor<int32, [1]> mean_164_axes_0 = const()[name = tensor<string, []>("mean_164_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_164_keep_dims_0 = const()[name = tensor<string, []>("mean_164_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_164_cast_fp16 = reduce_mean(axes = mean_164_axes_0, keep_dims = mean_164_keep_dims_0, x = pow_325_cast_fp16)[name = tensor<string, []>("mean_164_cast_fp16")];
            tensor<fp16, []> const_3436_to_fp16 = const()[name = tensor<string, []>("const_3436_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_366_cast_fp16 = add(x = mean_164_cast_fp16, y = const_3436_to_fp16)[name = tensor<string, []>("add_366_cast_fp16")];
            tensor<fp32, []> rsqrt_164_epsilon_0 = const()[name = tensor<string, []>("rsqrt_164_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_164_cast_fp16 = rsqrt(epsilon = rsqrt_164_epsilon_0, x = add_366_cast_fp16)[name = tensor<string, []>("rsqrt_164_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_376_cast_fp16 = mul(x = unsqueeze_359_cast_fp16, y = rsqrt_164_cast_fp16)[name = tensor<string, []>("mul_376_cast_fp16")];
            tensor<int32, [1]> squeeze_170_axes_0 = const()[name = tensor<string, []>("squeeze_170_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_170_cast_fp16 = squeeze(axes = squeeze_170_axes_0, x = mul_376_cast_fp16)[name = tensor<string, []>("squeeze_170_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_272_to_fp16 = const()[name = tensor<string, []>("view_272_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32762816)))];
            tensor<fp16, [1, 256, 801]> mul_377_cast_fp16 = mul(x = squeeze_170_cast_fp16, y = view_272_to_fp16)[name = tensor<string, []>("mul_377_cast_fp16")];
            tensor<string, []> conv1d_212_pad_type_0 = const()[name = tensor<string, []>("conv1d_212_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_212_strides_0 = const()[name = tensor<string, []>("conv1d_212_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_212_pad_0 = const()[name = tensor<string, []>("conv1d_212_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_212_dilations_0 = const()[name = tensor<string, []>("conv1d_212_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_212_groups_0 = const()[name = tensor<string, []>("conv1d_212_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_54_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_54_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32763392)))];
            tensor<fp16, [20]> p_output_54_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_54_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32773696)))];
            tensor<fp16, [1, 20, 801]> conv1d_212_cast_fp16 = conv(bias = p_output_54_1_bias_to_fp16, dilations = conv1d_212_dilations_0, groups = conv1d_212_groups_0, pad = conv1d_212_pad_0, pad_type = conv1d_212_pad_type_0, strides = conv1d_212_strides_0, weight = p_output_54_1_weight_to_fp16, x = mul_377_cast_fp16)[name = tensor<string, []>("conv1d_212_cast_fp16")];
            tensor<int32, []> glu_54_split_num_splits_0 = const()[name = tensor<string, []>("glu_54_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_54_split_axis_0 = const()[name = tensor<string, []>("glu_54_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_54_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_54_split_cast_fp16_1 = split(axis = glu_54_split_axis_0, num_splits = glu_54_split_num_splits_0, x = conv1d_212_cast_fp16)[name = tensor<string, []>("glu_54_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_54_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_54_split_cast_fp16_1)[name = tensor<string, []>("glu_54_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_54_cast_fp16 = mul(x = glu_54_split_cast_fp16_0, y = glu_54_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_54_cast_fp16")];
            tensor<int32, [4]> const_3440 = const()[name = tensor<string, []>("const_3440"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_273_cast_fp16 = reshape(shape = const_3440, x = glu_54_cast_fp16)[name = tensor<string, []>("view_273_cast_fp16")];
            tensor<int32, [4]> select_165_begin_0 = const()[name = tensor<string, []>("select_165_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_165_end_0 = const()[name = tensor<string, []>("select_165_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_165_end_mask_0 = const()[name = tensor<string, []>("select_165_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_165_squeeze_mask_0 = const()[name = tensor<string, []>("select_165_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_165_cast_fp16 = slice_by_index(begin = select_165_begin_0, end = select_165_end_0, end_mask = select_165_end_mask_0, squeeze_mask = select_165_squeeze_mask_0, x = view_273_cast_fp16)[name = tensor<string, []>("select_165_cast_fp16")];
            tensor<int32, [4]> select_166_begin_0 = const()[name = tensor<string, []>("select_166_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_166_end_0 = const()[name = tensor<string, []>("select_166_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_166_end_mask_0 = const()[name = tensor<string, []>("select_166_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_166_squeeze_mask_0 = const()[name = tensor<string, []>("select_166_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_166_cast_fp16 = slice_by_index(begin = select_166_begin_0, end = select_166_end_0, end_mask = select_166_end_mask_0, squeeze_mask = select_166_squeeze_mask_0, x = view_273_cast_fp16)[name = tensor<string, []>("select_166_cast_fp16")];
            tensor<int32, [4]> select_167_begin_0 = const()[name = tensor<string, []>("select_167_begin_0"), val = tensor<int32, [4]>([0, 55, 0, 0])];
            tensor<int32, [4]> select_167_end_0 = const()[name = tensor<string, []>("select_167_end_0"), val = tensor<int32, [4]>([1, 56, 256, 801])];
            tensor<bool, [4]> select_167_end_mask_0 = const()[name = tensor<string, []>("select_167_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_167_squeeze_mask_0 = const()[name = tensor<string, []>("select_167_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_167_cast_fp16 = slice_by_index(begin = select_167_begin_0, end = select_167_end_0, end_mask = select_167_end_mask_0, squeeze_mask = select_167_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_167_cast_fp16")];
            tensor<int32, [1]> unsqueeze_361_axes_0 = const()[name = tensor<string, []>("unsqueeze_361_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_361_cast_fp16 = expand_dims(axes = unsqueeze_361_axes_0, x = select_167_cast_fp16)[name = tensor<string, []>("unsqueeze_361_cast_fp16")];
            tensor<fp16, []> const_3451_promoted_to_fp16 = const()[name = tensor<string, []>("const_3451_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_326_cast_fp16 = pow(x = unsqueeze_361_cast_fp16, y = const_3451_promoted_to_fp16)[name = tensor<string, []>("pow_326_cast_fp16")];
            tensor<int32, [1]> mean_165_axes_0 = const()[name = tensor<string, []>("mean_165_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_165_keep_dims_0 = const()[name = tensor<string, []>("mean_165_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_165_cast_fp16 = reduce_mean(axes = mean_165_axes_0, keep_dims = mean_165_keep_dims_0, x = pow_326_cast_fp16)[name = tensor<string, []>("mean_165_cast_fp16")];
            tensor<fp16, []> const_3454_to_fp16 = const()[name = tensor<string, []>("const_3454_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_367_cast_fp16 = add(x = mean_165_cast_fp16, y = const_3454_to_fp16)[name = tensor<string, []>("add_367_cast_fp16")];
            tensor<fp32, []> rsqrt_165_epsilon_0 = const()[name = tensor<string, []>("rsqrt_165_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_165_cast_fp16 = rsqrt(epsilon = rsqrt_165_epsilon_0, x = add_367_cast_fp16)[name = tensor<string, []>("rsqrt_165_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_378_cast_fp16 = mul(x = unsqueeze_361_cast_fp16, y = rsqrt_165_cast_fp16)[name = tensor<string, []>("mul_378_cast_fp16")];
            tensor<int32, [1]> squeeze_171_axes_0 = const()[name = tensor<string, []>("squeeze_171_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_171_cast_fp16 = squeeze(axes = squeeze_171_axes_0, x = mul_378_cast_fp16)[name = tensor<string, []>("squeeze_171_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_274_to_fp16 = const()[name = tensor<string, []>("view_274_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32773824)))];
            tensor<fp16, [1, 256, 801]> mul_379_cast_fp16 = mul(x = squeeze_171_cast_fp16, y = view_274_to_fp16)[name = tensor<string, []>("mul_379_cast_fp16")];
            tensor<string, []> conv1d_213_pad_type_0 = const()[name = tensor<string, []>("conv1d_213_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_213_strides_0 = const()[name = tensor<string, []>("conv1d_213_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_213_pad_0 = const()[name = tensor<string, []>("conv1d_213_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_213_dilations_0 = const()[name = tensor<string, []>("conv1d_213_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_213_groups_0 = const()[name = tensor<string, []>("conv1d_213_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_55_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_55_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32774400)))];
            tensor<fp16, [20]> p_output_55_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_55_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32784704)))];
            tensor<fp16, [1, 20, 801]> conv1d_213_cast_fp16 = conv(bias = p_output_55_1_bias_to_fp16, dilations = conv1d_213_dilations_0, groups = conv1d_213_groups_0, pad = conv1d_213_pad_0, pad_type = conv1d_213_pad_type_0, strides = conv1d_213_strides_0, weight = p_output_55_1_weight_to_fp16, x = mul_379_cast_fp16)[name = tensor<string, []>("conv1d_213_cast_fp16")];
            tensor<int32, []> glu_55_split_num_splits_0 = const()[name = tensor<string, []>("glu_55_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_55_split_axis_0 = const()[name = tensor<string, []>("glu_55_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_55_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_55_split_cast_fp16_1 = split(axis = glu_55_split_axis_0, num_splits = glu_55_split_num_splits_0, x = conv1d_213_cast_fp16)[name = tensor<string, []>("glu_55_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_55_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_55_split_cast_fp16_1)[name = tensor<string, []>("glu_55_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_55_cast_fp16 = mul(x = glu_55_split_cast_fp16_0, y = glu_55_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_55_cast_fp16")];
            tensor<int32, [4]> const_3458 = const()[name = tensor<string, []>("const_3458"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_275_cast_fp16 = reshape(shape = const_3458, x = glu_55_cast_fp16)[name = tensor<string, []>("view_275_cast_fp16")];
            tensor<int32, [4]> select_168_begin_0 = const()[name = tensor<string, []>("select_168_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_168_end_0 = const()[name = tensor<string, []>("select_168_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_168_end_mask_0 = const()[name = tensor<string, []>("select_168_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_168_squeeze_mask_0 = const()[name = tensor<string, []>("select_168_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_168_cast_fp16 = slice_by_index(begin = select_168_begin_0, end = select_168_end_0, end_mask = select_168_end_mask_0, squeeze_mask = select_168_squeeze_mask_0, x = view_275_cast_fp16)[name = tensor<string, []>("select_168_cast_fp16")];
            tensor<int32, [4]> select_169_begin_0 = const()[name = tensor<string, []>("select_169_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_169_end_0 = const()[name = tensor<string, []>("select_169_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_169_end_mask_0 = const()[name = tensor<string, []>("select_169_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_169_squeeze_mask_0 = const()[name = tensor<string, []>("select_169_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_169_cast_fp16 = slice_by_index(begin = select_169_begin_0, end = select_169_end_0, end_mask = select_169_end_mask_0, squeeze_mask = select_169_squeeze_mask_0, x = view_275_cast_fp16)[name = tensor<string, []>("select_169_cast_fp16")];
            tensor<int32, [4]> select_170_begin_0 = const()[name = tensor<string, []>("select_170_begin_0"), val = tensor<int32, [4]>([0, 56, 0, 0])];
            tensor<int32, [4]> select_170_end_0 = const()[name = tensor<string, []>("select_170_end_0"), val = tensor<int32, [4]>([1, 57, 256, 801])];
            tensor<bool, [4]> select_170_end_mask_0 = const()[name = tensor<string, []>("select_170_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_170_squeeze_mask_0 = const()[name = tensor<string, []>("select_170_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_170_cast_fp16 = slice_by_index(begin = select_170_begin_0, end = select_170_end_0, end_mask = select_170_end_mask_0, squeeze_mask = select_170_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_170_cast_fp16")];
            tensor<int32, [1]> unsqueeze_363_axes_0 = const()[name = tensor<string, []>("unsqueeze_363_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_363_cast_fp16 = expand_dims(axes = unsqueeze_363_axes_0, x = select_170_cast_fp16)[name = tensor<string, []>("unsqueeze_363_cast_fp16")];
            tensor<fp16, []> const_3469_promoted_to_fp16 = const()[name = tensor<string, []>("const_3469_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_327_cast_fp16 = pow(x = unsqueeze_363_cast_fp16, y = const_3469_promoted_to_fp16)[name = tensor<string, []>("pow_327_cast_fp16")];
            tensor<int32, [1]> mean_166_axes_0 = const()[name = tensor<string, []>("mean_166_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_166_keep_dims_0 = const()[name = tensor<string, []>("mean_166_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_166_cast_fp16 = reduce_mean(axes = mean_166_axes_0, keep_dims = mean_166_keep_dims_0, x = pow_327_cast_fp16)[name = tensor<string, []>("mean_166_cast_fp16")];
            tensor<fp16, []> const_3472_to_fp16 = const()[name = tensor<string, []>("const_3472_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_368_cast_fp16 = add(x = mean_166_cast_fp16, y = const_3472_to_fp16)[name = tensor<string, []>("add_368_cast_fp16")];
            tensor<fp32, []> rsqrt_166_epsilon_0 = const()[name = tensor<string, []>("rsqrt_166_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_166_cast_fp16 = rsqrt(epsilon = rsqrt_166_epsilon_0, x = add_368_cast_fp16)[name = tensor<string, []>("rsqrt_166_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_380_cast_fp16 = mul(x = unsqueeze_363_cast_fp16, y = rsqrt_166_cast_fp16)[name = tensor<string, []>("mul_380_cast_fp16")];
            tensor<int32, [1]> squeeze_172_axes_0 = const()[name = tensor<string, []>("squeeze_172_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_172_cast_fp16 = squeeze(axes = squeeze_172_axes_0, x = mul_380_cast_fp16)[name = tensor<string, []>("squeeze_172_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_276_to_fp16 = const()[name = tensor<string, []>("view_276_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32784832)))];
            tensor<fp16, [1, 256, 801]> mul_381_cast_fp16 = mul(x = squeeze_172_cast_fp16, y = view_276_to_fp16)[name = tensor<string, []>("mul_381_cast_fp16")];
            tensor<string, []> conv1d_214_pad_type_0 = const()[name = tensor<string, []>("conv1d_214_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_214_strides_0 = const()[name = tensor<string, []>("conv1d_214_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_214_pad_0 = const()[name = tensor<string, []>("conv1d_214_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_214_dilations_0 = const()[name = tensor<string, []>("conv1d_214_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_214_groups_0 = const()[name = tensor<string, []>("conv1d_214_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_56_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_56_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32785408)))];
            tensor<fp16, [20]> p_output_56_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_56_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32795712)))];
            tensor<fp16, [1, 20, 801]> conv1d_214_cast_fp16 = conv(bias = p_output_56_1_bias_to_fp16, dilations = conv1d_214_dilations_0, groups = conv1d_214_groups_0, pad = conv1d_214_pad_0, pad_type = conv1d_214_pad_type_0, strides = conv1d_214_strides_0, weight = p_output_56_1_weight_to_fp16, x = mul_381_cast_fp16)[name = tensor<string, []>("conv1d_214_cast_fp16")];
            tensor<int32, []> glu_56_split_num_splits_0 = const()[name = tensor<string, []>("glu_56_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_56_split_axis_0 = const()[name = tensor<string, []>("glu_56_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_56_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_56_split_cast_fp16_1 = split(axis = glu_56_split_axis_0, num_splits = glu_56_split_num_splits_0, x = conv1d_214_cast_fp16)[name = tensor<string, []>("glu_56_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_56_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_56_split_cast_fp16_1)[name = tensor<string, []>("glu_56_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_56_cast_fp16 = mul(x = glu_56_split_cast_fp16_0, y = glu_56_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_56_cast_fp16")];
            tensor<int32, [4]> const_3476 = const()[name = tensor<string, []>("const_3476"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_277_cast_fp16 = reshape(shape = const_3476, x = glu_56_cast_fp16)[name = tensor<string, []>("view_277_cast_fp16")];
            tensor<int32, [4]> select_171_begin_0 = const()[name = tensor<string, []>("select_171_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_171_end_0 = const()[name = tensor<string, []>("select_171_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_171_end_mask_0 = const()[name = tensor<string, []>("select_171_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_171_squeeze_mask_0 = const()[name = tensor<string, []>("select_171_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_171_cast_fp16 = slice_by_index(begin = select_171_begin_0, end = select_171_end_0, end_mask = select_171_end_mask_0, squeeze_mask = select_171_squeeze_mask_0, x = view_277_cast_fp16)[name = tensor<string, []>("select_171_cast_fp16")];
            tensor<int32, [4]> select_172_begin_0 = const()[name = tensor<string, []>("select_172_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_172_end_0 = const()[name = tensor<string, []>("select_172_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_172_end_mask_0 = const()[name = tensor<string, []>("select_172_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_172_squeeze_mask_0 = const()[name = tensor<string, []>("select_172_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_172_cast_fp16 = slice_by_index(begin = select_172_begin_0, end = select_172_end_0, end_mask = select_172_end_mask_0, squeeze_mask = select_172_squeeze_mask_0, x = view_277_cast_fp16)[name = tensor<string, []>("select_172_cast_fp16")];
            tensor<int32, [4]> select_173_begin_0 = const()[name = tensor<string, []>("select_173_begin_0"), val = tensor<int32, [4]>([0, 57, 0, 0])];
            tensor<int32, [4]> select_173_end_0 = const()[name = tensor<string, []>("select_173_end_0"), val = tensor<int32, [4]>([1, 58, 256, 801])];
            tensor<bool, [4]> select_173_end_mask_0 = const()[name = tensor<string, []>("select_173_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_173_squeeze_mask_0 = const()[name = tensor<string, []>("select_173_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_173_cast_fp16 = slice_by_index(begin = select_173_begin_0, end = select_173_end_0, end_mask = select_173_end_mask_0, squeeze_mask = select_173_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_173_cast_fp16")];
            tensor<int32, [1]> unsqueeze_365_axes_0 = const()[name = tensor<string, []>("unsqueeze_365_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_365_cast_fp16 = expand_dims(axes = unsqueeze_365_axes_0, x = select_173_cast_fp16)[name = tensor<string, []>("unsqueeze_365_cast_fp16")];
            tensor<fp16, []> const_3487_promoted_to_fp16 = const()[name = tensor<string, []>("const_3487_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_328_cast_fp16 = pow(x = unsqueeze_365_cast_fp16, y = const_3487_promoted_to_fp16)[name = tensor<string, []>("pow_328_cast_fp16")];
            tensor<int32, [1]> mean_167_axes_0 = const()[name = tensor<string, []>("mean_167_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_167_keep_dims_0 = const()[name = tensor<string, []>("mean_167_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_167_cast_fp16 = reduce_mean(axes = mean_167_axes_0, keep_dims = mean_167_keep_dims_0, x = pow_328_cast_fp16)[name = tensor<string, []>("mean_167_cast_fp16")];
            tensor<fp16, []> const_3490_to_fp16 = const()[name = tensor<string, []>("const_3490_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_369_cast_fp16 = add(x = mean_167_cast_fp16, y = const_3490_to_fp16)[name = tensor<string, []>("add_369_cast_fp16")];
            tensor<fp32, []> rsqrt_167_epsilon_0 = const()[name = tensor<string, []>("rsqrt_167_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_167_cast_fp16 = rsqrt(epsilon = rsqrt_167_epsilon_0, x = add_369_cast_fp16)[name = tensor<string, []>("rsqrt_167_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_382_cast_fp16 = mul(x = unsqueeze_365_cast_fp16, y = rsqrt_167_cast_fp16)[name = tensor<string, []>("mul_382_cast_fp16")];
            tensor<int32, [1]> squeeze_173_axes_0 = const()[name = tensor<string, []>("squeeze_173_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_173_cast_fp16 = squeeze(axes = squeeze_173_axes_0, x = mul_382_cast_fp16)[name = tensor<string, []>("squeeze_173_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_278_to_fp16 = const()[name = tensor<string, []>("view_278_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32795840)))];
            tensor<fp16, [1, 256, 801]> mul_383_cast_fp16 = mul(x = squeeze_173_cast_fp16, y = view_278_to_fp16)[name = tensor<string, []>("mul_383_cast_fp16")];
            tensor<string, []> conv1d_215_pad_type_0 = const()[name = tensor<string, []>("conv1d_215_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_215_strides_0 = const()[name = tensor<string, []>("conv1d_215_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_215_pad_0 = const()[name = tensor<string, []>("conv1d_215_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_215_dilations_0 = const()[name = tensor<string, []>("conv1d_215_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_215_groups_0 = const()[name = tensor<string, []>("conv1d_215_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_57_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_57_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32796416)))];
            tensor<fp16, [20]> p_output_57_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_57_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32806720)))];
            tensor<fp16, [1, 20, 801]> conv1d_215_cast_fp16 = conv(bias = p_output_57_1_bias_to_fp16, dilations = conv1d_215_dilations_0, groups = conv1d_215_groups_0, pad = conv1d_215_pad_0, pad_type = conv1d_215_pad_type_0, strides = conv1d_215_strides_0, weight = p_output_57_1_weight_to_fp16, x = mul_383_cast_fp16)[name = tensor<string, []>("conv1d_215_cast_fp16")];
            tensor<int32, []> glu_57_split_num_splits_0 = const()[name = tensor<string, []>("glu_57_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_57_split_axis_0 = const()[name = tensor<string, []>("glu_57_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_57_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_57_split_cast_fp16_1 = split(axis = glu_57_split_axis_0, num_splits = glu_57_split_num_splits_0, x = conv1d_215_cast_fp16)[name = tensor<string, []>("glu_57_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_57_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_57_split_cast_fp16_1)[name = tensor<string, []>("glu_57_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_57_cast_fp16 = mul(x = glu_57_split_cast_fp16_0, y = glu_57_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_57_cast_fp16")];
            tensor<int32, [4]> const_3494 = const()[name = tensor<string, []>("const_3494"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_279_cast_fp16 = reshape(shape = const_3494, x = glu_57_cast_fp16)[name = tensor<string, []>("view_279_cast_fp16")];
            tensor<int32, [4]> select_174_begin_0 = const()[name = tensor<string, []>("select_174_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_174_end_0 = const()[name = tensor<string, []>("select_174_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_174_end_mask_0 = const()[name = tensor<string, []>("select_174_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_174_squeeze_mask_0 = const()[name = tensor<string, []>("select_174_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_174_cast_fp16 = slice_by_index(begin = select_174_begin_0, end = select_174_end_0, end_mask = select_174_end_mask_0, squeeze_mask = select_174_squeeze_mask_0, x = view_279_cast_fp16)[name = tensor<string, []>("select_174_cast_fp16")];
            tensor<int32, [4]> select_175_begin_0 = const()[name = tensor<string, []>("select_175_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_175_end_0 = const()[name = tensor<string, []>("select_175_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_175_end_mask_0 = const()[name = tensor<string, []>("select_175_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_175_squeeze_mask_0 = const()[name = tensor<string, []>("select_175_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_175_cast_fp16 = slice_by_index(begin = select_175_begin_0, end = select_175_end_0, end_mask = select_175_end_mask_0, squeeze_mask = select_175_squeeze_mask_0, x = view_279_cast_fp16)[name = tensor<string, []>("select_175_cast_fp16")];
            tensor<int32, [4]> select_176_begin_0 = const()[name = tensor<string, []>("select_176_begin_0"), val = tensor<int32, [4]>([0, 58, 0, 0])];
            tensor<int32, [4]> select_176_end_0 = const()[name = tensor<string, []>("select_176_end_0"), val = tensor<int32, [4]>([1, 59, 256, 801])];
            tensor<bool, [4]> select_176_end_mask_0 = const()[name = tensor<string, []>("select_176_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_176_squeeze_mask_0 = const()[name = tensor<string, []>("select_176_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_176_cast_fp16 = slice_by_index(begin = select_176_begin_0, end = select_176_end_0, end_mask = select_176_end_mask_0, squeeze_mask = select_176_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_176_cast_fp16")];
            tensor<int32, [1]> unsqueeze_367_axes_0 = const()[name = tensor<string, []>("unsqueeze_367_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_367_cast_fp16 = expand_dims(axes = unsqueeze_367_axes_0, x = select_176_cast_fp16)[name = tensor<string, []>("unsqueeze_367_cast_fp16")];
            tensor<fp16, []> const_3505_promoted_to_fp16 = const()[name = tensor<string, []>("const_3505_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_329_cast_fp16 = pow(x = unsqueeze_367_cast_fp16, y = const_3505_promoted_to_fp16)[name = tensor<string, []>("pow_329_cast_fp16")];
            tensor<int32, [1]> mean_168_axes_0 = const()[name = tensor<string, []>("mean_168_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_168_keep_dims_0 = const()[name = tensor<string, []>("mean_168_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_168_cast_fp16 = reduce_mean(axes = mean_168_axes_0, keep_dims = mean_168_keep_dims_0, x = pow_329_cast_fp16)[name = tensor<string, []>("mean_168_cast_fp16")];
            tensor<fp16, []> const_3508_to_fp16 = const()[name = tensor<string, []>("const_3508_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_370_cast_fp16 = add(x = mean_168_cast_fp16, y = const_3508_to_fp16)[name = tensor<string, []>("add_370_cast_fp16")];
            tensor<fp32, []> rsqrt_168_epsilon_0 = const()[name = tensor<string, []>("rsqrt_168_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_168_cast_fp16 = rsqrt(epsilon = rsqrt_168_epsilon_0, x = add_370_cast_fp16)[name = tensor<string, []>("rsqrt_168_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_384_cast_fp16 = mul(x = unsqueeze_367_cast_fp16, y = rsqrt_168_cast_fp16)[name = tensor<string, []>("mul_384_cast_fp16")];
            tensor<int32, [1]> squeeze_174_axes_0 = const()[name = tensor<string, []>("squeeze_174_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_174_cast_fp16 = squeeze(axes = squeeze_174_axes_0, x = mul_384_cast_fp16)[name = tensor<string, []>("squeeze_174_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_280_to_fp16 = const()[name = tensor<string, []>("view_280_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32806848)))];
            tensor<fp16, [1, 256, 801]> mul_385_cast_fp16 = mul(x = squeeze_174_cast_fp16, y = view_280_to_fp16)[name = tensor<string, []>("mul_385_cast_fp16")];
            tensor<string, []> conv1d_216_pad_type_0 = const()[name = tensor<string, []>("conv1d_216_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_216_strides_0 = const()[name = tensor<string, []>("conv1d_216_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_216_pad_0 = const()[name = tensor<string, []>("conv1d_216_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_216_dilations_0 = const()[name = tensor<string, []>("conv1d_216_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_216_groups_0 = const()[name = tensor<string, []>("conv1d_216_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_58_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_58_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32807424)))];
            tensor<fp16, [20]> p_output_58_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_58_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32817728)))];
            tensor<fp16, [1, 20, 801]> conv1d_216_cast_fp16 = conv(bias = p_output_58_1_bias_to_fp16, dilations = conv1d_216_dilations_0, groups = conv1d_216_groups_0, pad = conv1d_216_pad_0, pad_type = conv1d_216_pad_type_0, strides = conv1d_216_strides_0, weight = p_output_58_1_weight_to_fp16, x = mul_385_cast_fp16)[name = tensor<string, []>("conv1d_216_cast_fp16")];
            tensor<int32, []> glu_58_split_num_splits_0 = const()[name = tensor<string, []>("glu_58_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_58_split_axis_0 = const()[name = tensor<string, []>("glu_58_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_58_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_58_split_cast_fp16_1 = split(axis = glu_58_split_axis_0, num_splits = glu_58_split_num_splits_0, x = conv1d_216_cast_fp16)[name = tensor<string, []>("glu_58_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_58_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_58_split_cast_fp16_1)[name = tensor<string, []>("glu_58_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_58_cast_fp16 = mul(x = glu_58_split_cast_fp16_0, y = glu_58_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_58_cast_fp16")];
            tensor<int32, [4]> const_3512 = const()[name = tensor<string, []>("const_3512"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_281_cast_fp16 = reshape(shape = const_3512, x = glu_58_cast_fp16)[name = tensor<string, []>("view_281_cast_fp16")];
            tensor<int32, [4]> select_177_begin_0 = const()[name = tensor<string, []>("select_177_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_177_end_0 = const()[name = tensor<string, []>("select_177_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_177_end_mask_0 = const()[name = tensor<string, []>("select_177_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_177_squeeze_mask_0 = const()[name = tensor<string, []>("select_177_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_177_cast_fp16 = slice_by_index(begin = select_177_begin_0, end = select_177_end_0, end_mask = select_177_end_mask_0, squeeze_mask = select_177_squeeze_mask_0, x = view_281_cast_fp16)[name = tensor<string, []>("select_177_cast_fp16")];
            tensor<int32, [4]> select_178_begin_0 = const()[name = tensor<string, []>("select_178_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_178_end_0 = const()[name = tensor<string, []>("select_178_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_178_end_mask_0 = const()[name = tensor<string, []>("select_178_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_178_squeeze_mask_0 = const()[name = tensor<string, []>("select_178_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_178_cast_fp16 = slice_by_index(begin = select_178_begin_0, end = select_178_end_0, end_mask = select_178_end_mask_0, squeeze_mask = select_178_squeeze_mask_0, x = view_281_cast_fp16)[name = tensor<string, []>("select_178_cast_fp16")];
            tensor<int32, [4]> select_179_begin_0 = const()[name = tensor<string, []>("select_179_begin_0"), val = tensor<int32, [4]>([0, 59, 0, 0])];
            tensor<int32, [4]> select_179_end_0 = const()[name = tensor<string, []>("select_179_end_0"), val = tensor<int32, [4]>([1, 60, 256, 801])];
            tensor<bool, [4]> select_179_end_mask_0 = const()[name = tensor<string, []>("select_179_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_179_squeeze_mask_0 = const()[name = tensor<string, []>("select_179_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_179_cast_fp16 = slice_by_index(begin = select_179_begin_0, end = select_179_end_0, end_mask = select_179_end_mask_0, squeeze_mask = select_179_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_179_cast_fp16")];
            tensor<int32, [1]> unsqueeze_369_axes_0 = const()[name = tensor<string, []>("unsqueeze_369_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_369_cast_fp16 = expand_dims(axes = unsqueeze_369_axes_0, x = select_179_cast_fp16)[name = tensor<string, []>("unsqueeze_369_cast_fp16")];
            tensor<fp16, []> const_3523_promoted_to_fp16 = const()[name = tensor<string, []>("const_3523_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_330_cast_fp16 = pow(x = unsqueeze_369_cast_fp16, y = const_3523_promoted_to_fp16)[name = tensor<string, []>("pow_330_cast_fp16")];
            tensor<int32, [1]> mean_169_axes_0 = const()[name = tensor<string, []>("mean_169_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_169_keep_dims_0 = const()[name = tensor<string, []>("mean_169_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_169_cast_fp16 = reduce_mean(axes = mean_169_axes_0, keep_dims = mean_169_keep_dims_0, x = pow_330_cast_fp16)[name = tensor<string, []>("mean_169_cast_fp16")];
            tensor<fp16, []> const_3526_to_fp16 = const()[name = tensor<string, []>("const_3526_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_371_cast_fp16 = add(x = mean_169_cast_fp16, y = const_3526_to_fp16)[name = tensor<string, []>("add_371_cast_fp16")];
            tensor<fp32, []> rsqrt_169_epsilon_0 = const()[name = tensor<string, []>("rsqrt_169_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_169_cast_fp16 = rsqrt(epsilon = rsqrt_169_epsilon_0, x = add_371_cast_fp16)[name = tensor<string, []>("rsqrt_169_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_386_cast_fp16 = mul(x = unsqueeze_369_cast_fp16, y = rsqrt_169_cast_fp16)[name = tensor<string, []>("mul_386_cast_fp16")];
            tensor<int32, [1]> squeeze_175_axes_0 = const()[name = tensor<string, []>("squeeze_175_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_175_cast_fp16 = squeeze(axes = squeeze_175_axes_0, x = mul_386_cast_fp16)[name = tensor<string, []>("squeeze_175_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_282_to_fp16 = const()[name = tensor<string, []>("view_282_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32817856)))];
            tensor<fp16, [1, 256, 801]> mul_387_cast_fp16 = mul(x = squeeze_175_cast_fp16, y = view_282_to_fp16)[name = tensor<string, []>("mul_387_cast_fp16")];
            tensor<string, []> conv1d_217_pad_type_0 = const()[name = tensor<string, []>("conv1d_217_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_217_strides_0 = const()[name = tensor<string, []>("conv1d_217_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_217_pad_0 = const()[name = tensor<string, []>("conv1d_217_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_217_dilations_0 = const()[name = tensor<string, []>("conv1d_217_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_217_groups_0 = const()[name = tensor<string, []>("conv1d_217_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_59_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_59_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32818432)))];
            tensor<fp16, [20]> p_output_59_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_59_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32828736)))];
            tensor<fp16, [1, 20, 801]> conv1d_217_cast_fp16 = conv(bias = p_output_59_1_bias_to_fp16, dilations = conv1d_217_dilations_0, groups = conv1d_217_groups_0, pad = conv1d_217_pad_0, pad_type = conv1d_217_pad_type_0, strides = conv1d_217_strides_0, weight = p_output_59_1_weight_to_fp16, x = mul_387_cast_fp16)[name = tensor<string, []>("conv1d_217_cast_fp16")];
            tensor<int32, []> glu_59_split_num_splits_0 = const()[name = tensor<string, []>("glu_59_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_59_split_axis_0 = const()[name = tensor<string, []>("glu_59_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_59_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_59_split_cast_fp16_1 = split(axis = glu_59_split_axis_0, num_splits = glu_59_split_num_splits_0, x = conv1d_217_cast_fp16)[name = tensor<string, []>("glu_59_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_59_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_59_split_cast_fp16_1)[name = tensor<string, []>("glu_59_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_59_cast_fp16 = mul(x = glu_59_split_cast_fp16_0, y = glu_59_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_59_cast_fp16")];
            tensor<int32, [4]> const_3530 = const()[name = tensor<string, []>("const_3530"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_283_cast_fp16 = reshape(shape = const_3530, x = glu_59_cast_fp16)[name = tensor<string, []>("view_283_cast_fp16")];
            tensor<int32, [4]> select_180_begin_0 = const()[name = tensor<string, []>("select_180_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_180_end_0 = const()[name = tensor<string, []>("select_180_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_180_end_mask_0 = const()[name = tensor<string, []>("select_180_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_180_squeeze_mask_0 = const()[name = tensor<string, []>("select_180_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_180_cast_fp16 = slice_by_index(begin = select_180_begin_0, end = select_180_end_0, end_mask = select_180_end_mask_0, squeeze_mask = select_180_squeeze_mask_0, x = view_283_cast_fp16)[name = tensor<string, []>("select_180_cast_fp16")];
            tensor<int32, [4]> select_181_begin_0 = const()[name = tensor<string, []>("select_181_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_181_end_0 = const()[name = tensor<string, []>("select_181_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_181_end_mask_0 = const()[name = tensor<string, []>("select_181_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_181_squeeze_mask_0 = const()[name = tensor<string, []>("select_181_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_181_cast_fp16 = slice_by_index(begin = select_181_begin_0, end = select_181_end_0, end_mask = select_181_end_mask_0, squeeze_mask = select_181_squeeze_mask_0, x = view_283_cast_fp16)[name = tensor<string, []>("select_181_cast_fp16")];
            tensor<int32, [4]> select_182_begin_0 = const()[name = tensor<string, []>("select_182_begin_0"), val = tensor<int32, [4]>([0, 60, 0, 0])];
            tensor<int32, [4]> select_182_end_0 = const()[name = tensor<string, []>("select_182_end_0"), val = tensor<int32, [4]>([1, 61, 256, 801])];
            tensor<bool, [4]> select_182_end_mask_0 = const()[name = tensor<string, []>("select_182_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_182_squeeze_mask_0 = const()[name = tensor<string, []>("select_182_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_182_cast_fp16 = slice_by_index(begin = select_182_begin_0, end = select_182_end_0, end_mask = select_182_end_mask_0, squeeze_mask = select_182_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_182_cast_fp16")];
            tensor<int32, [1]> unsqueeze_371_axes_0 = const()[name = tensor<string, []>("unsqueeze_371_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_371_cast_fp16 = expand_dims(axes = unsqueeze_371_axes_0, x = select_182_cast_fp16)[name = tensor<string, []>("unsqueeze_371_cast_fp16")];
            tensor<fp16, []> const_3541_promoted_to_fp16 = const()[name = tensor<string, []>("const_3541_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_331_cast_fp16 = pow(x = unsqueeze_371_cast_fp16, y = const_3541_promoted_to_fp16)[name = tensor<string, []>("pow_331_cast_fp16")];
            tensor<int32, [1]> mean_170_axes_0 = const()[name = tensor<string, []>("mean_170_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_170_keep_dims_0 = const()[name = tensor<string, []>("mean_170_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_170_cast_fp16 = reduce_mean(axes = mean_170_axes_0, keep_dims = mean_170_keep_dims_0, x = pow_331_cast_fp16)[name = tensor<string, []>("mean_170_cast_fp16")];
            tensor<fp16, []> const_3544_to_fp16 = const()[name = tensor<string, []>("const_3544_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_372_cast_fp16 = add(x = mean_170_cast_fp16, y = const_3544_to_fp16)[name = tensor<string, []>("add_372_cast_fp16")];
            tensor<fp32, []> rsqrt_170_epsilon_0 = const()[name = tensor<string, []>("rsqrt_170_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_170_cast_fp16 = rsqrt(epsilon = rsqrt_170_epsilon_0, x = add_372_cast_fp16)[name = tensor<string, []>("rsqrt_170_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_388_cast_fp16 = mul(x = unsqueeze_371_cast_fp16, y = rsqrt_170_cast_fp16)[name = tensor<string, []>("mul_388_cast_fp16")];
            tensor<int32, [1]> squeeze_176_axes_0 = const()[name = tensor<string, []>("squeeze_176_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_176_cast_fp16 = squeeze(axes = squeeze_176_axes_0, x = mul_388_cast_fp16)[name = tensor<string, []>("squeeze_176_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_284_to_fp16 = const()[name = tensor<string, []>("view_284_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32828864)))];
            tensor<fp16, [1, 256, 801]> mul_389_cast_fp16 = mul(x = squeeze_176_cast_fp16, y = view_284_to_fp16)[name = tensor<string, []>("mul_389_cast_fp16")];
            tensor<string, []> conv1d_218_pad_type_0 = const()[name = tensor<string, []>("conv1d_218_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_218_strides_0 = const()[name = tensor<string, []>("conv1d_218_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_218_pad_0 = const()[name = tensor<string, []>("conv1d_218_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_218_dilations_0 = const()[name = tensor<string, []>("conv1d_218_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_218_groups_0 = const()[name = tensor<string, []>("conv1d_218_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_60_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_60_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32829440)))];
            tensor<fp16, [20]> p_output_60_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_60_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32839744)))];
            tensor<fp16, [1, 20, 801]> conv1d_218_cast_fp16 = conv(bias = p_output_60_1_bias_to_fp16, dilations = conv1d_218_dilations_0, groups = conv1d_218_groups_0, pad = conv1d_218_pad_0, pad_type = conv1d_218_pad_type_0, strides = conv1d_218_strides_0, weight = p_output_60_1_weight_to_fp16, x = mul_389_cast_fp16)[name = tensor<string, []>("conv1d_218_cast_fp16")];
            tensor<int32, []> glu_60_split_num_splits_0 = const()[name = tensor<string, []>("glu_60_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_60_split_axis_0 = const()[name = tensor<string, []>("glu_60_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_60_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_60_split_cast_fp16_1 = split(axis = glu_60_split_axis_0, num_splits = glu_60_split_num_splits_0, x = conv1d_218_cast_fp16)[name = tensor<string, []>("glu_60_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_60_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_60_split_cast_fp16_1)[name = tensor<string, []>("glu_60_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_60_cast_fp16 = mul(x = glu_60_split_cast_fp16_0, y = glu_60_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_60_cast_fp16")];
            tensor<int32, [4]> const_3548 = const()[name = tensor<string, []>("const_3548"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_285_cast_fp16 = reshape(shape = const_3548, x = glu_60_cast_fp16)[name = tensor<string, []>("view_285_cast_fp16")];
            tensor<int32, [4]> select_183_begin_0 = const()[name = tensor<string, []>("select_183_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_183_end_0 = const()[name = tensor<string, []>("select_183_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_183_end_mask_0 = const()[name = tensor<string, []>("select_183_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_183_squeeze_mask_0 = const()[name = tensor<string, []>("select_183_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_183_cast_fp16 = slice_by_index(begin = select_183_begin_0, end = select_183_end_0, end_mask = select_183_end_mask_0, squeeze_mask = select_183_squeeze_mask_0, x = view_285_cast_fp16)[name = tensor<string, []>("select_183_cast_fp16")];
            tensor<int32, [4]> select_184_begin_0 = const()[name = tensor<string, []>("select_184_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_184_end_0 = const()[name = tensor<string, []>("select_184_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_184_end_mask_0 = const()[name = tensor<string, []>("select_184_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_184_squeeze_mask_0 = const()[name = tensor<string, []>("select_184_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_184_cast_fp16 = slice_by_index(begin = select_184_begin_0, end = select_184_end_0, end_mask = select_184_end_mask_0, squeeze_mask = select_184_squeeze_mask_0, x = view_285_cast_fp16)[name = tensor<string, []>("select_184_cast_fp16")];
            tensor<int32, [4]> select_185_begin_0 = const()[name = tensor<string, []>("select_185_begin_0"), val = tensor<int32, [4]>([0, 61, 0, 0])];
            tensor<int32, [4]> select_185_end_0 = const()[name = tensor<string, []>("select_185_end_0"), val = tensor<int32, [4]>([1, 62, 256, 801])];
            tensor<bool, [4]> select_185_end_mask_0 = const()[name = tensor<string, []>("select_185_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_185_squeeze_mask_0 = const()[name = tensor<string, []>("select_185_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_185_cast_fp16 = slice_by_index(begin = select_185_begin_0, end = select_185_end_0, end_mask = select_185_end_mask_0, squeeze_mask = select_185_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_185_cast_fp16")];
            tensor<int32, [1]> unsqueeze_373_axes_0 = const()[name = tensor<string, []>("unsqueeze_373_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_373_cast_fp16 = expand_dims(axes = unsqueeze_373_axes_0, x = select_185_cast_fp16)[name = tensor<string, []>("unsqueeze_373_cast_fp16")];
            tensor<fp16, []> const_3559_promoted_to_fp16 = const()[name = tensor<string, []>("const_3559_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_332_cast_fp16 = pow(x = unsqueeze_373_cast_fp16, y = const_3559_promoted_to_fp16)[name = tensor<string, []>("pow_332_cast_fp16")];
            tensor<int32, [1]> mean_171_axes_0 = const()[name = tensor<string, []>("mean_171_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_171_keep_dims_0 = const()[name = tensor<string, []>("mean_171_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_171_cast_fp16 = reduce_mean(axes = mean_171_axes_0, keep_dims = mean_171_keep_dims_0, x = pow_332_cast_fp16)[name = tensor<string, []>("mean_171_cast_fp16")];
            tensor<fp16, []> const_3562_to_fp16 = const()[name = tensor<string, []>("const_3562_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_373_cast_fp16 = add(x = mean_171_cast_fp16, y = const_3562_to_fp16)[name = tensor<string, []>("add_373_cast_fp16")];
            tensor<fp32, []> rsqrt_171_epsilon_0 = const()[name = tensor<string, []>("rsqrt_171_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_171_cast_fp16 = rsqrt(epsilon = rsqrt_171_epsilon_0, x = add_373_cast_fp16)[name = tensor<string, []>("rsqrt_171_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_390_cast_fp16 = mul(x = unsqueeze_373_cast_fp16, y = rsqrt_171_cast_fp16)[name = tensor<string, []>("mul_390_cast_fp16")];
            tensor<int32, [1]> squeeze_177_axes_0 = const()[name = tensor<string, []>("squeeze_177_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_177_cast_fp16 = squeeze(axes = squeeze_177_axes_0, x = mul_390_cast_fp16)[name = tensor<string, []>("squeeze_177_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_286_to_fp16 = const()[name = tensor<string, []>("view_286_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32839872)))];
            tensor<fp16, [1, 256, 801]> mul_391_cast_fp16 = mul(x = squeeze_177_cast_fp16, y = view_286_to_fp16)[name = tensor<string, []>("mul_391_cast_fp16")];
            tensor<string, []> conv1d_219_pad_type_0 = const()[name = tensor<string, []>("conv1d_219_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_219_strides_0 = const()[name = tensor<string, []>("conv1d_219_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_219_pad_0 = const()[name = tensor<string, []>("conv1d_219_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_219_dilations_0 = const()[name = tensor<string, []>("conv1d_219_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_219_groups_0 = const()[name = tensor<string, []>("conv1d_219_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_61_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_61_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32840448)))];
            tensor<fp16, [20]> p_output_61_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_61_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32850752)))];
            tensor<fp16, [1, 20, 801]> conv1d_219_cast_fp16 = conv(bias = p_output_61_1_bias_to_fp16, dilations = conv1d_219_dilations_0, groups = conv1d_219_groups_0, pad = conv1d_219_pad_0, pad_type = conv1d_219_pad_type_0, strides = conv1d_219_strides_0, weight = p_output_61_1_weight_to_fp16, x = mul_391_cast_fp16)[name = tensor<string, []>("conv1d_219_cast_fp16")];
            tensor<int32, []> glu_61_split_num_splits_0 = const()[name = tensor<string, []>("glu_61_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_61_split_axis_0 = const()[name = tensor<string, []>("glu_61_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_61_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_61_split_cast_fp16_1 = split(axis = glu_61_split_axis_0, num_splits = glu_61_split_num_splits_0, x = conv1d_219_cast_fp16)[name = tensor<string, []>("glu_61_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_61_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_61_split_cast_fp16_1)[name = tensor<string, []>("glu_61_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_61_cast_fp16 = mul(x = glu_61_split_cast_fp16_0, y = glu_61_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_61_cast_fp16")];
            tensor<int32, [4]> const_3566 = const()[name = tensor<string, []>("const_3566"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_287_cast_fp16 = reshape(shape = const_3566, x = glu_61_cast_fp16)[name = tensor<string, []>("view_287_cast_fp16")];
            tensor<int32, [4]> select_186_begin_0 = const()[name = tensor<string, []>("select_186_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_186_end_0 = const()[name = tensor<string, []>("select_186_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_186_end_mask_0 = const()[name = tensor<string, []>("select_186_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_186_squeeze_mask_0 = const()[name = tensor<string, []>("select_186_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_186_cast_fp16 = slice_by_index(begin = select_186_begin_0, end = select_186_end_0, end_mask = select_186_end_mask_0, squeeze_mask = select_186_squeeze_mask_0, x = view_287_cast_fp16)[name = tensor<string, []>("select_186_cast_fp16")];
            tensor<int32, [4]> select_187_begin_0 = const()[name = tensor<string, []>("select_187_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_187_end_0 = const()[name = tensor<string, []>("select_187_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_187_end_mask_0 = const()[name = tensor<string, []>("select_187_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_187_squeeze_mask_0 = const()[name = tensor<string, []>("select_187_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_187_cast_fp16 = slice_by_index(begin = select_187_begin_0, end = select_187_end_0, end_mask = select_187_end_mask_0, squeeze_mask = select_187_squeeze_mask_0, x = view_287_cast_fp16)[name = tensor<string, []>("select_187_cast_fp16")];
            tensor<int32, [4]> select_188_begin_0 = const()[name = tensor<string, []>("select_188_begin_0"), val = tensor<int32, [4]>([0, 62, 0, 0])];
            tensor<int32, [4]> select_188_end_0 = const()[name = tensor<string, []>("select_188_end_0"), val = tensor<int32, [4]>([1, 63, 256, 801])];
            tensor<bool, [4]> select_188_end_mask_0 = const()[name = tensor<string, []>("select_188_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_188_squeeze_mask_0 = const()[name = tensor<string, []>("select_188_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_188_cast_fp16 = slice_by_index(begin = select_188_begin_0, end = select_188_end_0, end_mask = select_188_end_mask_0, squeeze_mask = select_188_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_188_cast_fp16")];
            tensor<int32, [1]> unsqueeze_375_axes_0 = const()[name = tensor<string, []>("unsqueeze_375_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_375_cast_fp16 = expand_dims(axes = unsqueeze_375_axes_0, x = select_188_cast_fp16)[name = tensor<string, []>("unsqueeze_375_cast_fp16")];
            tensor<fp16, []> const_3577_promoted_to_fp16 = const()[name = tensor<string, []>("const_3577_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_333_cast_fp16 = pow(x = unsqueeze_375_cast_fp16, y = const_3577_promoted_to_fp16)[name = tensor<string, []>("pow_333_cast_fp16")];
            tensor<int32, [1]> mean_172_axes_0 = const()[name = tensor<string, []>("mean_172_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_172_keep_dims_0 = const()[name = tensor<string, []>("mean_172_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_172_cast_fp16 = reduce_mean(axes = mean_172_axes_0, keep_dims = mean_172_keep_dims_0, x = pow_333_cast_fp16)[name = tensor<string, []>("mean_172_cast_fp16")];
            tensor<fp16, []> const_3580_to_fp16 = const()[name = tensor<string, []>("const_3580_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_374_cast_fp16 = add(x = mean_172_cast_fp16, y = const_3580_to_fp16)[name = tensor<string, []>("add_374_cast_fp16")];
            tensor<fp32, []> rsqrt_172_epsilon_0 = const()[name = tensor<string, []>("rsqrt_172_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_172_cast_fp16 = rsqrt(epsilon = rsqrt_172_epsilon_0, x = add_374_cast_fp16)[name = tensor<string, []>("rsqrt_172_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_392_cast_fp16 = mul(x = unsqueeze_375_cast_fp16, y = rsqrt_172_cast_fp16)[name = tensor<string, []>("mul_392_cast_fp16")];
            tensor<int32, [1]> squeeze_178_axes_0 = const()[name = tensor<string, []>("squeeze_178_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_178_cast_fp16 = squeeze(axes = squeeze_178_axes_0, x = mul_392_cast_fp16)[name = tensor<string, []>("squeeze_178_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_288_to_fp16 = const()[name = tensor<string, []>("view_288_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32850880)))];
            tensor<fp16, [1, 256, 801]> mul_393_cast_fp16 = mul(x = squeeze_178_cast_fp16, y = view_288_to_fp16)[name = tensor<string, []>("mul_393_cast_fp16")];
            tensor<string, []> conv1d_220_pad_type_0 = const()[name = tensor<string, []>("conv1d_220_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_220_strides_0 = const()[name = tensor<string, []>("conv1d_220_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_220_pad_0 = const()[name = tensor<string, []>("conv1d_220_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_220_dilations_0 = const()[name = tensor<string, []>("conv1d_220_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_220_groups_0 = const()[name = tensor<string, []>("conv1d_220_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_62_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_62_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32851456)))];
            tensor<fp16, [20]> p_output_62_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_62_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32861760)))];
            tensor<fp16, [1, 20, 801]> conv1d_220_cast_fp16 = conv(bias = p_output_62_1_bias_to_fp16, dilations = conv1d_220_dilations_0, groups = conv1d_220_groups_0, pad = conv1d_220_pad_0, pad_type = conv1d_220_pad_type_0, strides = conv1d_220_strides_0, weight = p_output_62_1_weight_to_fp16, x = mul_393_cast_fp16)[name = tensor<string, []>("conv1d_220_cast_fp16")];
            tensor<int32, []> glu_62_split_num_splits_0 = const()[name = tensor<string, []>("glu_62_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_62_split_axis_0 = const()[name = tensor<string, []>("glu_62_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_62_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_62_split_cast_fp16_1 = split(axis = glu_62_split_axis_0, num_splits = glu_62_split_num_splits_0, x = conv1d_220_cast_fp16)[name = tensor<string, []>("glu_62_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_62_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_62_split_cast_fp16_1)[name = tensor<string, []>("glu_62_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_62_cast_fp16 = mul(x = glu_62_split_cast_fp16_0, y = glu_62_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_62_cast_fp16")];
            tensor<int32, [4]> const_3584 = const()[name = tensor<string, []>("const_3584"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_289_cast_fp16 = reshape(shape = const_3584, x = glu_62_cast_fp16)[name = tensor<string, []>("view_289_cast_fp16")];
            tensor<int32, [4]> select_189_begin_0 = const()[name = tensor<string, []>("select_189_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_189_end_0 = const()[name = tensor<string, []>("select_189_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_189_end_mask_0 = const()[name = tensor<string, []>("select_189_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_189_squeeze_mask_0 = const()[name = tensor<string, []>("select_189_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_189_cast_fp16 = slice_by_index(begin = select_189_begin_0, end = select_189_end_0, end_mask = select_189_end_mask_0, squeeze_mask = select_189_squeeze_mask_0, x = view_289_cast_fp16)[name = tensor<string, []>("select_189_cast_fp16")];
            tensor<int32, [4]> select_190_begin_0 = const()[name = tensor<string, []>("select_190_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_190_end_0 = const()[name = tensor<string, []>("select_190_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_190_end_mask_0 = const()[name = tensor<string, []>("select_190_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_190_squeeze_mask_0 = const()[name = tensor<string, []>("select_190_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_190_cast_fp16 = slice_by_index(begin = select_190_begin_0, end = select_190_end_0, end_mask = select_190_end_mask_0, squeeze_mask = select_190_squeeze_mask_0, x = view_289_cast_fp16)[name = tensor<string, []>("select_190_cast_fp16")];
            tensor<int32, [4]> select_191_begin_0 = const()[name = tensor<string, []>("select_191_begin_0"), val = tensor<int32, [4]>([0, 63, 0, 0])];
            tensor<int32, [4]> select_191_end_0 = const()[name = tensor<string, []>("select_191_end_0"), val = tensor<int32, [4]>([1, 64, 256, 801])];
            tensor<bool, [4]> select_191_end_mask_0 = const()[name = tensor<string, []>("select_191_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_191_squeeze_mask_0 = const()[name = tensor<string, []>("select_191_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_191_cast_fp16 = slice_by_index(begin = select_191_begin_0, end = select_191_end_0, end_mask = select_191_end_mask_0, squeeze_mask = select_191_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_191_cast_fp16")];
            tensor<int32, [1]> unsqueeze_377_axes_0 = const()[name = tensor<string, []>("unsqueeze_377_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_377_cast_fp16 = expand_dims(axes = unsqueeze_377_axes_0, x = select_191_cast_fp16)[name = tensor<string, []>("unsqueeze_377_cast_fp16")];
            tensor<fp16, []> const_3595_promoted_to_fp16 = const()[name = tensor<string, []>("const_3595_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_334_cast_fp16 = pow(x = unsqueeze_377_cast_fp16, y = const_3595_promoted_to_fp16)[name = tensor<string, []>("pow_334_cast_fp16")];
            tensor<int32, [1]> mean_173_axes_0 = const()[name = tensor<string, []>("mean_173_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_173_keep_dims_0 = const()[name = tensor<string, []>("mean_173_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_173_cast_fp16 = reduce_mean(axes = mean_173_axes_0, keep_dims = mean_173_keep_dims_0, x = pow_334_cast_fp16)[name = tensor<string, []>("mean_173_cast_fp16")];
            tensor<fp16, []> const_3598_to_fp16 = const()[name = tensor<string, []>("const_3598_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_375_cast_fp16 = add(x = mean_173_cast_fp16, y = const_3598_to_fp16)[name = tensor<string, []>("add_375_cast_fp16")];
            tensor<fp32, []> rsqrt_173_epsilon_0 = const()[name = tensor<string, []>("rsqrt_173_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_173_cast_fp16 = rsqrt(epsilon = rsqrt_173_epsilon_0, x = add_375_cast_fp16)[name = tensor<string, []>("rsqrt_173_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_394_cast_fp16 = mul(x = unsqueeze_377_cast_fp16, y = rsqrt_173_cast_fp16)[name = tensor<string, []>("mul_394_cast_fp16")];
            tensor<int32, [1]> squeeze_179_axes_0 = const()[name = tensor<string, []>("squeeze_179_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_179_cast_fp16 = squeeze(axes = squeeze_179_axes_0, x = mul_394_cast_fp16)[name = tensor<string, []>("squeeze_179_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_290_to_fp16 = const()[name = tensor<string, []>("view_290_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32861888)))];
            tensor<fp16, [1, 256, 801]> mul_395_cast_fp16 = mul(x = squeeze_179_cast_fp16, y = view_290_to_fp16)[name = tensor<string, []>("mul_395_cast_fp16")];
            tensor<string, []> conv1d_221_pad_type_0 = const()[name = tensor<string, []>("conv1d_221_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_221_strides_0 = const()[name = tensor<string, []>("conv1d_221_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_221_pad_0 = const()[name = tensor<string, []>("conv1d_221_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_221_dilations_0 = const()[name = tensor<string, []>("conv1d_221_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_221_groups_0 = const()[name = tensor<string, []>("conv1d_221_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_63_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_63_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32862464)))];
            tensor<fp16, [20]> p_output_63_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_63_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32872768)))];
            tensor<fp16, [1, 20, 801]> conv1d_221_cast_fp16 = conv(bias = p_output_63_1_bias_to_fp16, dilations = conv1d_221_dilations_0, groups = conv1d_221_groups_0, pad = conv1d_221_pad_0, pad_type = conv1d_221_pad_type_0, strides = conv1d_221_strides_0, weight = p_output_63_1_weight_to_fp16, x = mul_395_cast_fp16)[name = tensor<string, []>("conv1d_221_cast_fp16")];
            tensor<int32, []> glu_63_split_num_splits_0 = const()[name = tensor<string, []>("glu_63_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_63_split_axis_0 = const()[name = tensor<string, []>("glu_63_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_63_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_63_split_cast_fp16_1 = split(axis = glu_63_split_axis_0, num_splits = glu_63_split_num_splits_0, x = conv1d_221_cast_fp16)[name = tensor<string, []>("glu_63_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_63_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_63_split_cast_fp16_1)[name = tensor<string, []>("glu_63_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_63_cast_fp16 = mul(x = glu_63_split_cast_fp16_0, y = glu_63_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_63_cast_fp16")];
            tensor<int32, [4]> const_3602 = const()[name = tensor<string, []>("const_3602"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_291_cast_fp16 = reshape(shape = const_3602, x = glu_63_cast_fp16)[name = tensor<string, []>("view_291_cast_fp16")];
            tensor<int32, [4]> select_192_begin_0 = const()[name = tensor<string, []>("select_192_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_192_end_0 = const()[name = tensor<string, []>("select_192_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_192_end_mask_0 = const()[name = tensor<string, []>("select_192_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_192_squeeze_mask_0 = const()[name = tensor<string, []>("select_192_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_192_cast_fp16 = slice_by_index(begin = select_192_begin_0, end = select_192_end_0, end_mask = select_192_end_mask_0, squeeze_mask = select_192_squeeze_mask_0, x = view_291_cast_fp16)[name = tensor<string, []>("select_192_cast_fp16")];
            tensor<int32, [4]> select_193_begin_0 = const()[name = tensor<string, []>("select_193_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_193_end_0 = const()[name = tensor<string, []>("select_193_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_193_end_mask_0 = const()[name = tensor<string, []>("select_193_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_193_squeeze_mask_0 = const()[name = tensor<string, []>("select_193_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_193_cast_fp16 = slice_by_index(begin = select_193_begin_0, end = select_193_end_0, end_mask = select_193_end_mask_0, squeeze_mask = select_193_squeeze_mask_0, x = view_291_cast_fp16)[name = tensor<string, []>("select_193_cast_fp16")];
            tensor<int32, [4]> select_194_begin_0 = const()[name = tensor<string, []>("select_194_begin_0"), val = tensor<int32, [4]>([0, 64, 0, 0])];
            tensor<int32, [4]> select_194_end_0 = const()[name = tensor<string, []>("select_194_end_0"), val = tensor<int32, [4]>([1, 65, 256, 801])];
            tensor<bool, [4]> select_194_end_mask_0 = const()[name = tensor<string, []>("select_194_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_194_squeeze_mask_0 = const()[name = tensor<string, []>("select_194_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_194_cast_fp16 = slice_by_index(begin = select_194_begin_0, end = select_194_end_0, end_mask = select_194_end_mask_0, squeeze_mask = select_194_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_194_cast_fp16")];
            tensor<int32, [1]> unsqueeze_379_axes_0 = const()[name = tensor<string, []>("unsqueeze_379_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_379_cast_fp16 = expand_dims(axes = unsqueeze_379_axes_0, x = select_194_cast_fp16)[name = tensor<string, []>("unsqueeze_379_cast_fp16")];
            tensor<fp16, []> const_3613_promoted_to_fp16 = const()[name = tensor<string, []>("const_3613_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_335_cast_fp16 = pow(x = unsqueeze_379_cast_fp16, y = const_3613_promoted_to_fp16)[name = tensor<string, []>("pow_335_cast_fp16")];
            tensor<int32, [1]> mean_174_axes_0 = const()[name = tensor<string, []>("mean_174_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_174_keep_dims_0 = const()[name = tensor<string, []>("mean_174_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_174_cast_fp16 = reduce_mean(axes = mean_174_axes_0, keep_dims = mean_174_keep_dims_0, x = pow_335_cast_fp16)[name = tensor<string, []>("mean_174_cast_fp16")];
            tensor<fp16, []> const_3616_to_fp16 = const()[name = tensor<string, []>("const_3616_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_376_cast_fp16 = add(x = mean_174_cast_fp16, y = const_3616_to_fp16)[name = tensor<string, []>("add_376_cast_fp16")];
            tensor<fp32, []> rsqrt_174_epsilon_0 = const()[name = tensor<string, []>("rsqrt_174_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_174_cast_fp16 = rsqrt(epsilon = rsqrt_174_epsilon_0, x = add_376_cast_fp16)[name = tensor<string, []>("rsqrt_174_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_396_cast_fp16 = mul(x = unsqueeze_379_cast_fp16, y = rsqrt_174_cast_fp16)[name = tensor<string, []>("mul_396_cast_fp16")];
            tensor<int32, [1]> squeeze_180_axes_0 = const()[name = tensor<string, []>("squeeze_180_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_180_cast_fp16 = squeeze(axes = squeeze_180_axes_0, x = mul_396_cast_fp16)[name = tensor<string, []>("squeeze_180_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_292_to_fp16 = const()[name = tensor<string, []>("view_292_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32872896)))];
            tensor<fp16, [1, 256, 801]> mul_397_cast_fp16 = mul(x = squeeze_180_cast_fp16, y = view_292_to_fp16)[name = tensor<string, []>("mul_397_cast_fp16")];
            tensor<string, []> conv1d_222_pad_type_0 = const()[name = tensor<string, []>("conv1d_222_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_222_strides_0 = const()[name = tensor<string, []>("conv1d_222_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_222_pad_0 = const()[name = tensor<string, []>("conv1d_222_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_222_dilations_0 = const()[name = tensor<string, []>("conv1d_222_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_222_groups_0 = const()[name = tensor<string, []>("conv1d_222_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_64_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_64_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32873472)))];
            tensor<fp16, [20]> p_output_64_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_64_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32883776)))];
            tensor<fp16, [1, 20, 801]> conv1d_222_cast_fp16 = conv(bias = p_output_64_1_bias_to_fp16, dilations = conv1d_222_dilations_0, groups = conv1d_222_groups_0, pad = conv1d_222_pad_0, pad_type = conv1d_222_pad_type_0, strides = conv1d_222_strides_0, weight = p_output_64_1_weight_to_fp16, x = mul_397_cast_fp16)[name = tensor<string, []>("conv1d_222_cast_fp16")];
            tensor<int32, []> glu_64_split_num_splits_0 = const()[name = tensor<string, []>("glu_64_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_64_split_axis_0 = const()[name = tensor<string, []>("glu_64_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_64_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_64_split_cast_fp16_1 = split(axis = glu_64_split_axis_0, num_splits = glu_64_split_num_splits_0, x = conv1d_222_cast_fp16)[name = tensor<string, []>("glu_64_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_64_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_64_split_cast_fp16_1)[name = tensor<string, []>("glu_64_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_64_cast_fp16 = mul(x = glu_64_split_cast_fp16_0, y = glu_64_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_64_cast_fp16")];
            tensor<int32, [4]> const_3620 = const()[name = tensor<string, []>("const_3620"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_293_cast_fp16 = reshape(shape = const_3620, x = glu_64_cast_fp16)[name = tensor<string, []>("view_293_cast_fp16")];
            tensor<int32, [4]> select_195_begin_0 = const()[name = tensor<string, []>("select_195_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_195_end_0 = const()[name = tensor<string, []>("select_195_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_195_end_mask_0 = const()[name = tensor<string, []>("select_195_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_195_squeeze_mask_0 = const()[name = tensor<string, []>("select_195_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_195_cast_fp16 = slice_by_index(begin = select_195_begin_0, end = select_195_end_0, end_mask = select_195_end_mask_0, squeeze_mask = select_195_squeeze_mask_0, x = view_293_cast_fp16)[name = tensor<string, []>("select_195_cast_fp16")];
            tensor<int32, [4]> select_196_begin_0 = const()[name = tensor<string, []>("select_196_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_196_end_0 = const()[name = tensor<string, []>("select_196_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_196_end_mask_0 = const()[name = tensor<string, []>("select_196_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_196_squeeze_mask_0 = const()[name = tensor<string, []>("select_196_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_196_cast_fp16 = slice_by_index(begin = select_196_begin_0, end = select_196_end_0, end_mask = select_196_end_mask_0, squeeze_mask = select_196_squeeze_mask_0, x = view_293_cast_fp16)[name = tensor<string, []>("select_196_cast_fp16")];
            tensor<int32, [4]> select_197_begin_0 = const()[name = tensor<string, []>("select_197_begin_0"), val = tensor<int32, [4]>([0, 65, 0, 0])];
            tensor<int32, [4]> select_197_end_0 = const()[name = tensor<string, []>("select_197_end_0"), val = tensor<int32, [4]>([1, 66, 256, 801])];
            tensor<bool, [4]> select_197_end_mask_0 = const()[name = tensor<string, []>("select_197_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_197_squeeze_mask_0 = const()[name = tensor<string, []>("select_197_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_197_cast_fp16 = slice_by_index(begin = select_197_begin_0, end = select_197_end_0, end_mask = select_197_end_mask_0, squeeze_mask = select_197_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_197_cast_fp16")];
            tensor<int32, [1]> unsqueeze_381_axes_0 = const()[name = tensor<string, []>("unsqueeze_381_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_381_cast_fp16 = expand_dims(axes = unsqueeze_381_axes_0, x = select_197_cast_fp16)[name = tensor<string, []>("unsqueeze_381_cast_fp16")];
            tensor<fp16, []> const_3631_promoted_to_fp16 = const()[name = tensor<string, []>("const_3631_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_336_cast_fp16 = pow(x = unsqueeze_381_cast_fp16, y = const_3631_promoted_to_fp16)[name = tensor<string, []>("pow_336_cast_fp16")];
            tensor<int32, [1]> mean_175_axes_0 = const()[name = tensor<string, []>("mean_175_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_175_keep_dims_0 = const()[name = tensor<string, []>("mean_175_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_175_cast_fp16 = reduce_mean(axes = mean_175_axes_0, keep_dims = mean_175_keep_dims_0, x = pow_336_cast_fp16)[name = tensor<string, []>("mean_175_cast_fp16")];
            tensor<fp16, []> const_3634_to_fp16 = const()[name = tensor<string, []>("const_3634_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_377_cast_fp16 = add(x = mean_175_cast_fp16, y = const_3634_to_fp16)[name = tensor<string, []>("add_377_cast_fp16")];
            tensor<fp32, []> rsqrt_175_epsilon_0 = const()[name = tensor<string, []>("rsqrt_175_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_175_cast_fp16 = rsqrt(epsilon = rsqrt_175_epsilon_0, x = add_377_cast_fp16)[name = tensor<string, []>("rsqrt_175_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_398_cast_fp16 = mul(x = unsqueeze_381_cast_fp16, y = rsqrt_175_cast_fp16)[name = tensor<string, []>("mul_398_cast_fp16")];
            tensor<int32, [1]> squeeze_181_axes_0 = const()[name = tensor<string, []>("squeeze_181_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_181_cast_fp16 = squeeze(axes = squeeze_181_axes_0, x = mul_398_cast_fp16)[name = tensor<string, []>("squeeze_181_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_294_to_fp16 = const()[name = tensor<string, []>("view_294_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32883904)))];
            tensor<fp16, [1, 256, 801]> mul_399_cast_fp16 = mul(x = squeeze_181_cast_fp16, y = view_294_to_fp16)[name = tensor<string, []>("mul_399_cast_fp16")];
            tensor<string, []> conv1d_223_pad_type_0 = const()[name = tensor<string, []>("conv1d_223_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_223_strides_0 = const()[name = tensor<string, []>("conv1d_223_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_223_pad_0 = const()[name = tensor<string, []>("conv1d_223_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_223_dilations_0 = const()[name = tensor<string, []>("conv1d_223_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_223_groups_0 = const()[name = tensor<string, []>("conv1d_223_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_65_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_65_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32884480)))];
            tensor<fp16, [20]> p_output_65_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_65_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32894784)))];
            tensor<fp16, [1, 20, 801]> conv1d_223_cast_fp16 = conv(bias = p_output_65_1_bias_to_fp16, dilations = conv1d_223_dilations_0, groups = conv1d_223_groups_0, pad = conv1d_223_pad_0, pad_type = conv1d_223_pad_type_0, strides = conv1d_223_strides_0, weight = p_output_65_1_weight_to_fp16, x = mul_399_cast_fp16)[name = tensor<string, []>("conv1d_223_cast_fp16")];
            tensor<int32, []> glu_65_split_num_splits_0 = const()[name = tensor<string, []>("glu_65_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_65_split_axis_0 = const()[name = tensor<string, []>("glu_65_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_65_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_65_split_cast_fp16_1 = split(axis = glu_65_split_axis_0, num_splits = glu_65_split_num_splits_0, x = conv1d_223_cast_fp16)[name = tensor<string, []>("glu_65_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_65_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_65_split_cast_fp16_1)[name = tensor<string, []>("glu_65_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_65_cast_fp16 = mul(x = glu_65_split_cast_fp16_0, y = glu_65_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_65_cast_fp16")];
            tensor<int32, [4]> const_3638 = const()[name = tensor<string, []>("const_3638"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_295_cast_fp16 = reshape(shape = const_3638, x = glu_65_cast_fp16)[name = tensor<string, []>("view_295_cast_fp16")];
            tensor<int32, [4]> select_198_begin_0 = const()[name = tensor<string, []>("select_198_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_198_end_0 = const()[name = tensor<string, []>("select_198_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_198_end_mask_0 = const()[name = tensor<string, []>("select_198_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_198_squeeze_mask_0 = const()[name = tensor<string, []>("select_198_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_198_cast_fp16 = slice_by_index(begin = select_198_begin_0, end = select_198_end_0, end_mask = select_198_end_mask_0, squeeze_mask = select_198_squeeze_mask_0, x = view_295_cast_fp16)[name = tensor<string, []>("select_198_cast_fp16")];
            tensor<int32, [4]> select_199_begin_0 = const()[name = tensor<string, []>("select_199_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_199_end_0 = const()[name = tensor<string, []>("select_199_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_199_end_mask_0 = const()[name = tensor<string, []>("select_199_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_199_squeeze_mask_0 = const()[name = tensor<string, []>("select_199_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_199_cast_fp16 = slice_by_index(begin = select_199_begin_0, end = select_199_end_0, end_mask = select_199_end_mask_0, squeeze_mask = select_199_squeeze_mask_0, x = view_295_cast_fp16)[name = tensor<string, []>("select_199_cast_fp16")];
            tensor<int32, [4]> select_200_begin_0 = const()[name = tensor<string, []>("select_200_begin_0"), val = tensor<int32, [4]>([0, 66, 0, 0])];
            tensor<int32, [4]> select_200_end_0 = const()[name = tensor<string, []>("select_200_end_0"), val = tensor<int32, [4]>([1, 67, 256, 801])];
            tensor<bool, [4]> select_200_end_mask_0 = const()[name = tensor<string, []>("select_200_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_200_squeeze_mask_0 = const()[name = tensor<string, []>("select_200_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_200_cast_fp16 = slice_by_index(begin = select_200_begin_0, end = select_200_end_0, end_mask = select_200_end_mask_0, squeeze_mask = select_200_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_200_cast_fp16")];
            tensor<int32, [1]> unsqueeze_383_axes_0 = const()[name = tensor<string, []>("unsqueeze_383_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_383_cast_fp16 = expand_dims(axes = unsqueeze_383_axes_0, x = select_200_cast_fp16)[name = tensor<string, []>("unsqueeze_383_cast_fp16")];
            tensor<fp16, []> const_3649_promoted_to_fp16 = const()[name = tensor<string, []>("const_3649_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_337_cast_fp16 = pow(x = unsqueeze_383_cast_fp16, y = const_3649_promoted_to_fp16)[name = tensor<string, []>("pow_337_cast_fp16")];
            tensor<int32, [1]> mean_176_axes_0 = const()[name = tensor<string, []>("mean_176_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_176_keep_dims_0 = const()[name = tensor<string, []>("mean_176_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_176_cast_fp16 = reduce_mean(axes = mean_176_axes_0, keep_dims = mean_176_keep_dims_0, x = pow_337_cast_fp16)[name = tensor<string, []>("mean_176_cast_fp16")];
            tensor<fp16, []> const_3652_to_fp16 = const()[name = tensor<string, []>("const_3652_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_378_cast_fp16 = add(x = mean_176_cast_fp16, y = const_3652_to_fp16)[name = tensor<string, []>("add_378_cast_fp16")];
            tensor<fp32, []> rsqrt_176_epsilon_0 = const()[name = tensor<string, []>("rsqrt_176_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_176_cast_fp16 = rsqrt(epsilon = rsqrt_176_epsilon_0, x = add_378_cast_fp16)[name = tensor<string, []>("rsqrt_176_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_400_cast_fp16 = mul(x = unsqueeze_383_cast_fp16, y = rsqrt_176_cast_fp16)[name = tensor<string, []>("mul_400_cast_fp16")];
            tensor<int32, [1]> squeeze_182_axes_0 = const()[name = tensor<string, []>("squeeze_182_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_182_cast_fp16 = squeeze(axes = squeeze_182_axes_0, x = mul_400_cast_fp16)[name = tensor<string, []>("squeeze_182_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_296_to_fp16 = const()[name = tensor<string, []>("view_296_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32894912)))];
            tensor<fp16, [1, 256, 801]> mul_401_cast_fp16 = mul(x = squeeze_182_cast_fp16, y = view_296_to_fp16)[name = tensor<string, []>("mul_401_cast_fp16")];
            tensor<string, []> conv1d_224_pad_type_0 = const()[name = tensor<string, []>("conv1d_224_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_224_strides_0 = const()[name = tensor<string, []>("conv1d_224_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_224_pad_0 = const()[name = tensor<string, []>("conv1d_224_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_224_dilations_0 = const()[name = tensor<string, []>("conv1d_224_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_224_groups_0 = const()[name = tensor<string, []>("conv1d_224_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_66_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_66_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32895488)))];
            tensor<fp16, [20]> p_output_66_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_66_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32905792)))];
            tensor<fp16, [1, 20, 801]> conv1d_224_cast_fp16 = conv(bias = p_output_66_1_bias_to_fp16, dilations = conv1d_224_dilations_0, groups = conv1d_224_groups_0, pad = conv1d_224_pad_0, pad_type = conv1d_224_pad_type_0, strides = conv1d_224_strides_0, weight = p_output_66_1_weight_to_fp16, x = mul_401_cast_fp16)[name = tensor<string, []>("conv1d_224_cast_fp16")];
            tensor<int32, []> glu_66_split_num_splits_0 = const()[name = tensor<string, []>("glu_66_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_66_split_axis_0 = const()[name = tensor<string, []>("glu_66_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_66_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_66_split_cast_fp16_1 = split(axis = glu_66_split_axis_0, num_splits = glu_66_split_num_splits_0, x = conv1d_224_cast_fp16)[name = tensor<string, []>("glu_66_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_66_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_66_split_cast_fp16_1)[name = tensor<string, []>("glu_66_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_66_cast_fp16 = mul(x = glu_66_split_cast_fp16_0, y = glu_66_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_66_cast_fp16")];
            tensor<int32, [4]> const_3656 = const()[name = tensor<string, []>("const_3656"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_297_cast_fp16 = reshape(shape = const_3656, x = glu_66_cast_fp16)[name = tensor<string, []>("view_297_cast_fp16")];
            tensor<int32, [4]> select_201_begin_0 = const()[name = tensor<string, []>("select_201_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_201_end_0 = const()[name = tensor<string, []>("select_201_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_201_end_mask_0 = const()[name = tensor<string, []>("select_201_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_201_squeeze_mask_0 = const()[name = tensor<string, []>("select_201_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_201_cast_fp16 = slice_by_index(begin = select_201_begin_0, end = select_201_end_0, end_mask = select_201_end_mask_0, squeeze_mask = select_201_squeeze_mask_0, x = view_297_cast_fp16)[name = tensor<string, []>("select_201_cast_fp16")];
            tensor<int32, [4]> select_202_begin_0 = const()[name = tensor<string, []>("select_202_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_202_end_0 = const()[name = tensor<string, []>("select_202_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_202_end_mask_0 = const()[name = tensor<string, []>("select_202_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_202_squeeze_mask_0 = const()[name = tensor<string, []>("select_202_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_202_cast_fp16 = slice_by_index(begin = select_202_begin_0, end = select_202_end_0, end_mask = select_202_end_mask_0, squeeze_mask = select_202_squeeze_mask_0, x = view_297_cast_fp16)[name = tensor<string, []>("select_202_cast_fp16")];
            tensor<int32, [4]> select_203_begin_0 = const()[name = tensor<string, []>("select_203_begin_0"), val = tensor<int32, [4]>([0, 67, 0, 0])];
            tensor<int32, [4]> select_203_end_0 = const()[name = tensor<string, []>("select_203_end_0"), val = tensor<int32, [4]>([1, 68, 256, 801])];
            tensor<bool, [4]> select_203_end_mask_0 = const()[name = tensor<string, []>("select_203_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_203_squeeze_mask_0 = const()[name = tensor<string, []>("select_203_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_203_cast_fp16 = slice_by_index(begin = select_203_begin_0, end = select_203_end_0, end_mask = select_203_end_mask_0, squeeze_mask = select_203_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_203_cast_fp16")];
            tensor<int32, [1]> unsqueeze_385_axes_0 = const()[name = tensor<string, []>("unsqueeze_385_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_385_cast_fp16 = expand_dims(axes = unsqueeze_385_axes_0, x = select_203_cast_fp16)[name = tensor<string, []>("unsqueeze_385_cast_fp16")];
            tensor<fp16, []> const_3667_promoted_to_fp16 = const()[name = tensor<string, []>("const_3667_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_338_cast_fp16 = pow(x = unsqueeze_385_cast_fp16, y = const_3667_promoted_to_fp16)[name = tensor<string, []>("pow_338_cast_fp16")];
            tensor<int32, [1]> mean_177_axes_0 = const()[name = tensor<string, []>("mean_177_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_177_keep_dims_0 = const()[name = tensor<string, []>("mean_177_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_177_cast_fp16 = reduce_mean(axes = mean_177_axes_0, keep_dims = mean_177_keep_dims_0, x = pow_338_cast_fp16)[name = tensor<string, []>("mean_177_cast_fp16")];
            tensor<fp16, []> const_3670_to_fp16 = const()[name = tensor<string, []>("const_3670_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_379_cast_fp16 = add(x = mean_177_cast_fp16, y = const_3670_to_fp16)[name = tensor<string, []>("add_379_cast_fp16")];
            tensor<fp32, []> rsqrt_177_epsilon_0 = const()[name = tensor<string, []>("rsqrt_177_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_177_cast_fp16 = rsqrt(epsilon = rsqrt_177_epsilon_0, x = add_379_cast_fp16)[name = tensor<string, []>("rsqrt_177_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_402_cast_fp16 = mul(x = unsqueeze_385_cast_fp16, y = rsqrt_177_cast_fp16)[name = tensor<string, []>("mul_402_cast_fp16")];
            tensor<int32, [1]> squeeze_183_axes_0 = const()[name = tensor<string, []>("squeeze_183_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_183_cast_fp16 = squeeze(axes = squeeze_183_axes_0, x = mul_402_cast_fp16)[name = tensor<string, []>("squeeze_183_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_298_to_fp16 = const()[name = tensor<string, []>("view_298_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32905920)))];
            tensor<fp16, [1, 256, 801]> mul_403_cast_fp16 = mul(x = squeeze_183_cast_fp16, y = view_298_to_fp16)[name = tensor<string, []>("mul_403_cast_fp16")];
            tensor<string, []> conv1d_225_pad_type_0 = const()[name = tensor<string, []>("conv1d_225_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_225_strides_0 = const()[name = tensor<string, []>("conv1d_225_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_225_pad_0 = const()[name = tensor<string, []>("conv1d_225_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_225_dilations_0 = const()[name = tensor<string, []>("conv1d_225_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_225_groups_0 = const()[name = tensor<string, []>("conv1d_225_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_67_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_67_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32906496)))];
            tensor<fp16, [20]> p_output_67_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_67_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32916800)))];
            tensor<fp16, [1, 20, 801]> conv1d_225_cast_fp16 = conv(bias = p_output_67_1_bias_to_fp16, dilations = conv1d_225_dilations_0, groups = conv1d_225_groups_0, pad = conv1d_225_pad_0, pad_type = conv1d_225_pad_type_0, strides = conv1d_225_strides_0, weight = p_output_67_1_weight_to_fp16, x = mul_403_cast_fp16)[name = tensor<string, []>("conv1d_225_cast_fp16")];
            tensor<int32, []> glu_67_split_num_splits_0 = const()[name = tensor<string, []>("glu_67_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_67_split_axis_0 = const()[name = tensor<string, []>("glu_67_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_67_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_67_split_cast_fp16_1 = split(axis = glu_67_split_axis_0, num_splits = glu_67_split_num_splits_0, x = conv1d_225_cast_fp16)[name = tensor<string, []>("glu_67_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_67_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_67_split_cast_fp16_1)[name = tensor<string, []>("glu_67_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_67_cast_fp16 = mul(x = glu_67_split_cast_fp16_0, y = glu_67_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_67_cast_fp16")];
            tensor<int32, [4]> const_3674 = const()[name = tensor<string, []>("const_3674"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_299_cast_fp16 = reshape(shape = const_3674, x = glu_67_cast_fp16)[name = tensor<string, []>("view_299_cast_fp16")];
            tensor<int32, [4]> select_204_begin_0 = const()[name = tensor<string, []>("select_204_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_204_end_0 = const()[name = tensor<string, []>("select_204_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_204_end_mask_0 = const()[name = tensor<string, []>("select_204_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_204_squeeze_mask_0 = const()[name = tensor<string, []>("select_204_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_204_cast_fp16 = slice_by_index(begin = select_204_begin_0, end = select_204_end_0, end_mask = select_204_end_mask_0, squeeze_mask = select_204_squeeze_mask_0, x = view_299_cast_fp16)[name = tensor<string, []>("select_204_cast_fp16")];
            tensor<int32, [4]> select_205_begin_0 = const()[name = tensor<string, []>("select_205_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_205_end_0 = const()[name = tensor<string, []>("select_205_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_205_end_mask_0 = const()[name = tensor<string, []>("select_205_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_205_squeeze_mask_0 = const()[name = tensor<string, []>("select_205_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_205_cast_fp16 = slice_by_index(begin = select_205_begin_0, end = select_205_end_0, end_mask = select_205_end_mask_0, squeeze_mask = select_205_squeeze_mask_0, x = view_299_cast_fp16)[name = tensor<string, []>("select_205_cast_fp16")];
            tensor<int32, [4]> select_206_begin_0 = const()[name = tensor<string, []>("select_206_begin_0"), val = tensor<int32, [4]>([0, 68, 0, 0])];
            tensor<int32, [4]> select_206_end_0 = const()[name = tensor<string, []>("select_206_end_0"), val = tensor<int32, [4]>([1, 69, 256, 801])];
            tensor<bool, [4]> select_206_end_mask_0 = const()[name = tensor<string, []>("select_206_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_206_squeeze_mask_0 = const()[name = tensor<string, []>("select_206_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_206_cast_fp16 = slice_by_index(begin = select_206_begin_0, end = select_206_end_0, end_mask = select_206_end_mask_0, squeeze_mask = select_206_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_206_cast_fp16")];
            tensor<int32, [1]> unsqueeze_387_axes_0 = const()[name = tensor<string, []>("unsqueeze_387_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_387_cast_fp16 = expand_dims(axes = unsqueeze_387_axes_0, x = select_206_cast_fp16)[name = tensor<string, []>("unsqueeze_387_cast_fp16")];
            tensor<fp16, []> const_3685_promoted_to_fp16 = const()[name = tensor<string, []>("const_3685_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_339_cast_fp16 = pow(x = unsqueeze_387_cast_fp16, y = const_3685_promoted_to_fp16)[name = tensor<string, []>("pow_339_cast_fp16")];
            tensor<int32, [1]> mean_178_axes_0 = const()[name = tensor<string, []>("mean_178_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_178_keep_dims_0 = const()[name = tensor<string, []>("mean_178_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_178_cast_fp16 = reduce_mean(axes = mean_178_axes_0, keep_dims = mean_178_keep_dims_0, x = pow_339_cast_fp16)[name = tensor<string, []>("mean_178_cast_fp16")];
            tensor<fp16, []> const_3688_to_fp16 = const()[name = tensor<string, []>("const_3688_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_380_cast_fp16 = add(x = mean_178_cast_fp16, y = const_3688_to_fp16)[name = tensor<string, []>("add_380_cast_fp16")];
            tensor<fp32, []> rsqrt_178_epsilon_0 = const()[name = tensor<string, []>("rsqrt_178_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_178_cast_fp16 = rsqrt(epsilon = rsqrt_178_epsilon_0, x = add_380_cast_fp16)[name = tensor<string, []>("rsqrt_178_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_404_cast_fp16 = mul(x = unsqueeze_387_cast_fp16, y = rsqrt_178_cast_fp16)[name = tensor<string, []>("mul_404_cast_fp16")];
            tensor<int32, [1]> squeeze_184_axes_0 = const()[name = tensor<string, []>("squeeze_184_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_184_cast_fp16 = squeeze(axes = squeeze_184_axes_0, x = mul_404_cast_fp16)[name = tensor<string, []>("squeeze_184_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_300_to_fp16 = const()[name = tensor<string, []>("view_300_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32916928)))];
            tensor<fp16, [1, 256, 801]> mul_405_cast_fp16 = mul(x = squeeze_184_cast_fp16, y = view_300_to_fp16)[name = tensor<string, []>("mul_405_cast_fp16")];
            tensor<string, []> conv1d_226_pad_type_0 = const()[name = tensor<string, []>("conv1d_226_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_226_strides_0 = const()[name = tensor<string, []>("conv1d_226_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_226_pad_0 = const()[name = tensor<string, []>("conv1d_226_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_226_dilations_0 = const()[name = tensor<string, []>("conv1d_226_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_226_groups_0 = const()[name = tensor<string, []>("conv1d_226_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_68_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_68_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32917504)))];
            tensor<fp16, [20]> p_output_68_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_68_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32927808)))];
            tensor<fp16, [1, 20, 801]> conv1d_226_cast_fp16 = conv(bias = p_output_68_1_bias_to_fp16, dilations = conv1d_226_dilations_0, groups = conv1d_226_groups_0, pad = conv1d_226_pad_0, pad_type = conv1d_226_pad_type_0, strides = conv1d_226_strides_0, weight = p_output_68_1_weight_to_fp16, x = mul_405_cast_fp16)[name = tensor<string, []>("conv1d_226_cast_fp16")];
            tensor<int32, []> glu_68_split_num_splits_0 = const()[name = tensor<string, []>("glu_68_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_68_split_axis_0 = const()[name = tensor<string, []>("glu_68_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_68_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_68_split_cast_fp16_1 = split(axis = glu_68_split_axis_0, num_splits = glu_68_split_num_splits_0, x = conv1d_226_cast_fp16)[name = tensor<string, []>("glu_68_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_68_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_68_split_cast_fp16_1)[name = tensor<string, []>("glu_68_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_68_cast_fp16 = mul(x = glu_68_split_cast_fp16_0, y = glu_68_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_68_cast_fp16")];
            tensor<int32, [4]> const_3692 = const()[name = tensor<string, []>("const_3692"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_301_cast_fp16 = reshape(shape = const_3692, x = glu_68_cast_fp16)[name = tensor<string, []>("view_301_cast_fp16")];
            tensor<int32, [4]> select_207_begin_0 = const()[name = tensor<string, []>("select_207_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_207_end_0 = const()[name = tensor<string, []>("select_207_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_207_end_mask_0 = const()[name = tensor<string, []>("select_207_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_207_squeeze_mask_0 = const()[name = tensor<string, []>("select_207_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_207_cast_fp16 = slice_by_index(begin = select_207_begin_0, end = select_207_end_0, end_mask = select_207_end_mask_0, squeeze_mask = select_207_squeeze_mask_0, x = view_301_cast_fp16)[name = tensor<string, []>("select_207_cast_fp16")];
            tensor<int32, [4]> select_208_begin_0 = const()[name = tensor<string, []>("select_208_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_208_end_0 = const()[name = tensor<string, []>("select_208_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_208_end_mask_0 = const()[name = tensor<string, []>("select_208_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_208_squeeze_mask_0 = const()[name = tensor<string, []>("select_208_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_208_cast_fp16 = slice_by_index(begin = select_208_begin_0, end = select_208_end_0, end_mask = select_208_end_mask_0, squeeze_mask = select_208_squeeze_mask_0, x = view_301_cast_fp16)[name = tensor<string, []>("select_208_cast_fp16")];
            tensor<int32, [4]> select_209_begin_0 = const()[name = tensor<string, []>("select_209_begin_0"), val = tensor<int32, [4]>([0, 69, 0, 0])];
            tensor<int32, [4]> select_209_end_0 = const()[name = tensor<string, []>("select_209_end_0"), val = tensor<int32, [4]>([1, 70, 256, 801])];
            tensor<bool, [4]> select_209_end_mask_0 = const()[name = tensor<string, []>("select_209_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_209_squeeze_mask_0 = const()[name = tensor<string, []>("select_209_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_209_cast_fp16 = slice_by_index(begin = select_209_begin_0, end = select_209_end_0, end_mask = select_209_end_mask_0, squeeze_mask = select_209_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_209_cast_fp16")];
            tensor<int32, [1]> unsqueeze_389_axes_0 = const()[name = tensor<string, []>("unsqueeze_389_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_389_cast_fp16 = expand_dims(axes = unsqueeze_389_axes_0, x = select_209_cast_fp16)[name = tensor<string, []>("unsqueeze_389_cast_fp16")];
            tensor<fp16, []> const_3703_promoted_to_fp16 = const()[name = tensor<string, []>("const_3703_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_340_cast_fp16 = pow(x = unsqueeze_389_cast_fp16, y = const_3703_promoted_to_fp16)[name = tensor<string, []>("pow_340_cast_fp16")];
            tensor<int32, [1]> mean_179_axes_0 = const()[name = tensor<string, []>("mean_179_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_179_keep_dims_0 = const()[name = tensor<string, []>("mean_179_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_179_cast_fp16 = reduce_mean(axes = mean_179_axes_0, keep_dims = mean_179_keep_dims_0, x = pow_340_cast_fp16)[name = tensor<string, []>("mean_179_cast_fp16")];
            tensor<fp16, []> const_3706_to_fp16 = const()[name = tensor<string, []>("const_3706_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_381_cast_fp16 = add(x = mean_179_cast_fp16, y = const_3706_to_fp16)[name = tensor<string, []>("add_381_cast_fp16")];
            tensor<fp32, []> rsqrt_179_epsilon_0 = const()[name = tensor<string, []>("rsqrt_179_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_179_cast_fp16 = rsqrt(epsilon = rsqrt_179_epsilon_0, x = add_381_cast_fp16)[name = tensor<string, []>("rsqrt_179_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_406_cast_fp16 = mul(x = unsqueeze_389_cast_fp16, y = rsqrt_179_cast_fp16)[name = tensor<string, []>("mul_406_cast_fp16")];
            tensor<int32, [1]> squeeze_185_axes_0 = const()[name = tensor<string, []>("squeeze_185_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_185_cast_fp16 = squeeze(axes = squeeze_185_axes_0, x = mul_406_cast_fp16)[name = tensor<string, []>("squeeze_185_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_302_to_fp16 = const()[name = tensor<string, []>("view_302_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32927936)))];
            tensor<fp16, [1, 256, 801]> mul_407_cast_fp16 = mul(x = squeeze_185_cast_fp16, y = view_302_to_fp16)[name = tensor<string, []>("mul_407_cast_fp16")];
            tensor<string, []> conv1d_227_pad_type_0 = const()[name = tensor<string, []>("conv1d_227_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_227_strides_0 = const()[name = tensor<string, []>("conv1d_227_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_227_pad_0 = const()[name = tensor<string, []>("conv1d_227_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_227_dilations_0 = const()[name = tensor<string, []>("conv1d_227_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_227_groups_0 = const()[name = tensor<string, []>("conv1d_227_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_69_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_69_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32928512)))];
            tensor<fp16, [20]> p_output_69_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_69_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32938816)))];
            tensor<fp16, [1, 20, 801]> conv1d_227_cast_fp16 = conv(bias = p_output_69_1_bias_to_fp16, dilations = conv1d_227_dilations_0, groups = conv1d_227_groups_0, pad = conv1d_227_pad_0, pad_type = conv1d_227_pad_type_0, strides = conv1d_227_strides_0, weight = p_output_69_1_weight_to_fp16, x = mul_407_cast_fp16)[name = tensor<string, []>("conv1d_227_cast_fp16")];
            tensor<int32, []> glu_69_split_num_splits_0 = const()[name = tensor<string, []>("glu_69_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_69_split_axis_0 = const()[name = tensor<string, []>("glu_69_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_69_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_69_split_cast_fp16_1 = split(axis = glu_69_split_axis_0, num_splits = glu_69_split_num_splits_0, x = conv1d_227_cast_fp16)[name = tensor<string, []>("glu_69_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_69_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_69_split_cast_fp16_1)[name = tensor<string, []>("glu_69_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_69_cast_fp16 = mul(x = glu_69_split_cast_fp16_0, y = glu_69_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_69_cast_fp16")];
            tensor<int32, [4]> const_3710 = const()[name = tensor<string, []>("const_3710"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_303_cast_fp16 = reshape(shape = const_3710, x = glu_69_cast_fp16)[name = tensor<string, []>("view_303_cast_fp16")];
            tensor<int32, [4]> select_210_begin_0 = const()[name = tensor<string, []>("select_210_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_210_end_0 = const()[name = tensor<string, []>("select_210_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_210_end_mask_0 = const()[name = tensor<string, []>("select_210_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_210_squeeze_mask_0 = const()[name = tensor<string, []>("select_210_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_210_cast_fp16 = slice_by_index(begin = select_210_begin_0, end = select_210_end_0, end_mask = select_210_end_mask_0, squeeze_mask = select_210_squeeze_mask_0, x = view_303_cast_fp16)[name = tensor<string, []>("select_210_cast_fp16")];
            tensor<int32, [4]> select_211_begin_0 = const()[name = tensor<string, []>("select_211_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_211_end_0 = const()[name = tensor<string, []>("select_211_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_211_end_mask_0 = const()[name = tensor<string, []>("select_211_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_211_squeeze_mask_0 = const()[name = tensor<string, []>("select_211_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_211_cast_fp16 = slice_by_index(begin = select_211_begin_0, end = select_211_end_0, end_mask = select_211_end_mask_0, squeeze_mask = select_211_squeeze_mask_0, x = view_303_cast_fp16)[name = tensor<string, []>("select_211_cast_fp16")];
            tensor<int32, [4]> select_212_begin_0 = const()[name = tensor<string, []>("select_212_begin_0"), val = tensor<int32, [4]>([0, 70, 0, 0])];
            tensor<int32, [4]> select_212_end_0 = const()[name = tensor<string, []>("select_212_end_0"), val = tensor<int32, [4]>([1, 71, 256, 801])];
            tensor<bool, [4]> select_212_end_mask_0 = const()[name = tensor<string, []>("select_212_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_212_squeeze_mask_0 = const()[name = tensor<string, []>("select_212_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_212_cast_fp16 = slice_by_index(begin = select_212_begin_0, end = select_212_end_0, end_mask = select_212_end_mask_0, squeeze_mask = select_212_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_212_cast_fp16")];
            tensor<int32, [1]> unsqueeze_391_axes_0 = const()[name = tensor<string, []>("unsqueeze_391_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_391_cast_fp16 = expand_dims(axes = unsqueeze_391_axes_0, x = select_212_cast_fp16)[name = tensor<string, []>("unsqueeze_391_cast_fp16")];
            tensor<fp16, []> const_3721_promoted_to_fp16 = const()[name = tensor<string, []>("const_3721_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_341_cast_fp16 = pow(x = unsqueeze_391_cast_fp16, y = const_3721_promoted_to_fp16)[name = tensor<string, []>("pow_341_cast_fp16")];
            tensor<int32, [1]> mean_180_axes_0 = const()[name = tensor<string, []>("mean_180_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_180_keep_dims_0 = const()[name = tensor<string, []>("mean_180_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_180_cast_fp16 = reduce_mean(axes = mean_180_axes_0, keep_dims = mean_180_keep_dims_0, x = pow_341_cast_fp16)[name = tensor<string, []>("mean_180_cast_fp16")];
            tensor<fp16, []> const_3724_to_fp16 = const()[name = tensor<string, []>("const_3724_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_382_cast_fp16 = add(x = mean_180_cast_fp16, y = const_3724_to_fp16)[name = tensor<string, []>("add_382_cast_fp16")];
            tensor<fp32, []> rsqrt_180_epsilon_0 = const()[name = tensor<string, []>("rsqrt_180_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_180_cast_fp16 = rsqrt(epsilon = rsqrt_180_epsilon_0, x = add_382_cast_fp16)[name = tensor<string, []>("rsqrt_180_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_408_cast_fp16 = mul(x = unsqueeze_391_cast_fp16, y = rsqrt_180_cast_fp16)[name = tensor<string, []>("mul_408_cast_fp16")];
            tensor<int32, [1]> squeeze_186_axes_0 = const()[name = tensor<string, []>("squeeze_186_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_186_cast_fp16 = squeeze(axes = squeeze_186_axes_0, x = mul_408_cast_fp16)[name = tensor<string, []>("squeeze_186_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_304_to_fp16 = const()[name = tensor<string, []>("view_304_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32938944)))];
            tensor<fp16, [1, 256, 801]> mul_409_cast_fp16 = mul(x = squeeze_186_cast_fp16, y = view_304_to_fp16)[name = tensor<string, []>("mul_409_cast_fp16")];
            tensor<string, []> conv1d_228_pad_type_0 = const()[name = tensor<string, []>("conv1d_228_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_228_strides_0 = const()[name = tensor<string, []>("conv1d_228_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_228_pad_0 = const()[name = tensor<string, []>("conv1d_228_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_228_dilations_0 = const()[name = tensor<string, []>("conv1d_228_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_228_groups_0 = const()[name = tensor<string, []>("conv1d_228_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_70_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_70_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32939520)))];
            tensor<fp16, [20]> p_output_70_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_70_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32949824)))];
            tensor<fp16, [1, 20, 801]> conv1d_228_cast_fp16 = conv(bias = p_output_70_1_bias_to_fp16, dilations = conv1d_228_dilations_0, groups = conv1d_228_groups_0, pad = conv1d_228_pad_0, pad_type = conv1d_228_pad_type_0, strides = conv1d_228_strides_0, weight = p_output_70_1_weight_to_fp16, x = mul_409_cast_fp16)[name = tensor<string, []>("conv1d_228_cast_fp16")];
            tensor<int32, []> glu_70_split_num_splits_0 = const()[name = tensor<string, []>("glu_70_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_70_split_axis_0 = const()[name = tensor<string, []>("glu_70_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_70_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_70_split_cast_fp16_1 = split(axis = glu_70_split_axis_0, num_splits = glu_70_split_num_splits_0, x = conv1d_228_cast_fp16)[name = tensor<string, []>("glu_70_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_70_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_70_split_cast_fp16_1)[name = tensor<string, []>("glu_70_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_70_cast_fp16 = mul(x = glu_70_split_cast_fp16_0, y = glu_70_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_70_cast_fp16")];
            tensor<int32, [4]> const_3728 = const()[name = tensor<string, []>("const_3728"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_305_cast_fp16 = reshape(shape = const_3728, x = glu_70_cast_fp16)[name = tensor<string, []>("view_305_cast_fp16")];
            tensor<int32, [4]> select_213_begin_0 = const()[name = tensor<string, []>("select_213_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_213_end_0 = const()[name = tensor<string, []>("select_213_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_213_end_mask_0 = const()[name = tensor<string, []>("select_213_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_213_squeeze_mask_0 = const()[name = tensor<string, []>("select_213_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_213_cast_fp16 = slice_by_index(begin = select_213_begin_0, end = select_213_end_0, end_mask = select_213_end_mask_0, squeeze_mask = select_213_squeeze_mask_0, x = view_305_cast_fp16)[name = tensor<string, []>("select_213_cast_fp16")];
            tensor<int32, [4]> select_214_begin_0 = const()[name = tensor<string, []>("select_214_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_214_end_0 = const()[name = tensor<string, []>("select_214_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_214_end_mask_0 = const()[name = tensor<string, []>("select_214_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_214_squeeze_mask_0 = const()[name = tensor<string, []>("select_214_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_214_cast_fp16 = slice_by_index(begin = select_214_begin_0, end = select_214_end_0, end_mask = select_214_end_mask_0, squeeze_mask = select_214_squeeze_mask_0, x = view_305_cast_fp16)[name = tensor<string, []>("select_214_cast_fp16")];
            tensor<int32, [4]> select_215_begin_0 = const()[name = tensor<string, []>("select_215_begin_0"), val = tensor<int32, [4]>([0, 71, 0, 0])];
            tensor<int32, [4]> select_215_end_0 = const()[name = tensor<string, []>("select_215_end_0"), val = tensor<int32, [4]>([1, 72, 256, 801])];
            tensor<bool, [4]> select_215_end_mask_0 = const()[name = tensor<string, []>("select_215_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_215_squeeze_mask_0 = const()[name = tensor<string, []>("select_215_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_215_cast_fp16 = slice_by_index(begin = select_215_begin_0, end = select_215_end_0, end_mask = select_215_end_mask_0, squeeze_mask = select_215_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_215_cast_fp16")];
            tensor<int32, [1]> unsqueeze_393_axes_0 = const()[name = tensor<string, []>("unsqueeze_393_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_393_cast_fp16 = expand_dims(axes = unsqueeze_393_axes_0, x = select_215_cast_fp16)[name = tensor<string, []>("unsqueeze_393_cast_fp16")];
            tensor<fp16, []> const_3739_promoted_to_fp16 = const()[name = tensor<string, []>("const_3739_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_342_cast_fp16 = pow(x = unsqueeze_393_cast_fp16, y = const_3739_promoted_to_fp16)[name = tensor<string, []>("pow_342_cast_fp16")];
            tensor<int32, [1]> mean_181_axes_0 = const()[name = tensor<string, []>("mean_181_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_181_keep_dims_0 = const()[name = tensor<string, []>("mean_181_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_181_cast_fp16 = reduce_mean(axes = mean_181_axes_0, keep_dims = mean_181_keep_dims_0, x = pow_342_cast_fp16)[name = tensor<string, []>("mean_181_cast_fp16")];
            tensor<fp16, []> const_3742_to_fp16 = const()[name = tensor<string, []>("const_3742_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_383_cast_fp16 = add(x = mean_181_cast_fp16, y = const_3742_to_fp16)[name = tensor<string, []>("add_383_cast_fp16")];
            tensor<fp32, []> rsqrt_181_epsilon_0 = const()[name = tensor<string, []>("rsqrt_181_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_181_cast_fp16 = rsqrt(epsilon = rsqrt_181_epsilon_0, x = add_383_cast_fp16)[name = tensor<string, []>("rsqrt_181_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_410_cast_fp16 = mul(x = unsqueeze_393_cast_fp16, y = rsqrt_181_cast_fp16)[name = tensor<string, []>("mul_410_cast_fp16")];
            tensor<int32, [1]> squeeze_187_axes_0 = const()[name = tensor<string, []>("squeeze_187_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_187_cast_fp16 = squeeze(axes = squeeze_187_axes_0, x = mul_410_cast_fp16)[name = tensor<string, []>("squeeze_187_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_306_to_fp16 = const()[name = tensor<string, []>("view_306_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32949952)))];
            tensor<fp16, [1, 256, 801]> mul_411_cast_fp16 = mul(x = squeeze_187_cast_fp16, y = view_306_to_fp16)[name = tensor<string, []>("mul_411_cast_fp16")];
            tensor<string, []> conv1d_229_pad_type_0 = const()[name = tensor<string, []>("conv1d_229_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_229_strides_0 = const()[name = tensor<string, []>("conv1d_229_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_229_pad_0 = const()[name = tensor<string, []>("conv1d_229_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_229_dilations_0 = const()[name = tensor<string, []>("conv1d_229_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_229_groups_0 = const()[name = tensor<string, []>("conv1d_229_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_71_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_71_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32950528)))];
            tensor<fp16, [20]> p_output_71_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_71_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32960832)))];
            tensor<fp16, [1, 20, 801]> conv1d_229_cast_fp16 = conv(bias = p_output_71_1_bias_to_fp16, dilations = conv1d_229_dilations_0, groups = conv1d_229_groups_0, pad = conv1d_229_pad_0, pad_type = conv1d_229_pad_type_0, strides = conv1d_229_strides_0, weight = p_output_71_1_weight_to_fp16, x = mul_411_cast_fp16)[name = tensor<string, []>("conv1d_229_cast_fp16")];
            tensor<int32, []> glu_71_split_num_splits_0 = const()[name = tensor<string, []>("glu_71_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_71_split_axis_0 = const()[name = tensor<string, []>("glu_71_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_71_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_71_split_cast_fp16_1 = split(axis = glu_71_split_axis_0, num_splits = glu_71_split_num_splits_0, x = conv1d_229_cast_fp16)[name = tensor<string, []>("glu_71_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_71_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_71_split_cast_fp16_1)[name = tensor<string, []>("glu_71_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_71_cast_fp16 = mul(x = glu_71_split_cast_fp16_0, y = glu_71_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_71_cast_fp16")];
            tensor<int32, [4]> const_3746 = const()[name = tensor<string, []>("const_3746"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_307_cast_fp16 = reshape(shape = const_3746, x = glu_71_cast_fp16)[name = tensor<string, []>("view_307_cast_fp16")];
            tensor<int32, [4]> select_216_begin_0 = const()[name = tensor<string, []>("select_216_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_216_end_0 = const()[name = tensor<string, []>("select_216_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_216_end_mask_0 = const()[name = tensor<string, []>("select_216_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_216_squeeze_mask_0 = const()[name = tensor<string, []>("select_216_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_216_cast_fp16 = slice_by_index(begin = select_216_begin_0, end = select_216_end_0, end_mask = select_216_end_mask_0, squeeze_mask = select_216_squeeze_mask_0, x = view_307_cast_fp16)[name = tensor<string, []>("select_216_cast_fp16")];
            tensor<int32, [4]> select_217_begin_0 = const()[name = tensor<string, []>("select_217_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_217_end_0 = const()[name = tensor<string, []>("select_217_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_217_end_mask_0 = const()[name = tensor<string, []>("select_217_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_217_squeeze_mask_0 = const()[name = tensor<string, []>("select_217_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_217_cast_fp16 = slice_by_index(begin = select_217_begin_0, end = select_217_end_0, end_mask = select_217_end_mask_0, squeeze_mask = select_217_squeeze_mask_0, x = view_307_cast_fp16)[name = tensor<string, []>("select_217_cast_fp16")];
            tensor<int32, [4]> select_218_begin_0 = const()[name = tensor<string, []>("select_218_begin_0"), val = tensor<int32, [4]>([0, 72, 0, 0])];
            tensor<int32, [4]> select_218_end_0 = const()[name = tensor<string, []>("select_218_end_0"), val = tensor<int32, [4]>([1, 73, 256, 801])];
            tensor<bool, [4]> select_218_end_mask_0 = const()[name = tensor<string, []>("select_218_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_218_squeeze_mask_0 = const()[name = tensor<string, []>("select_218_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_218_cast_fp16 = slice_by_index(begin = select_218_begin_0, end = select_218_end_0, end_mask = select_218_end_mask_0, squeeze_mask = select_218_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_218_cast_fp16")];
            tensor<int32, [1]> unsqueeze_395_axes_0 = const()[name = tensor<string, []>("unsqueeze_395_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_395_cast_fp16 = expand_dims(axes = unsqueeze_395_axes_0, x = select_218_cast_fp16)[name = tensor<string, []>("unsqueeze_395_cast_fp16")];
            tensor<fp16, []> const_3757_promoted_to_fp16 = const()[name = tensor<string, []>("const_3757_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_343_cast_fp16 = pow(x = unsqueeze_395_cast_fp16, y = const_3757_promoted_to_fp16)[name = tensor<string, []>("pow_343_cast_fp16")];
            tensor<int32, [1]> mean_182_axes_0 = const()[name = tensor<string, []>("mean_182_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_182_keep_dims_0 = const()[name = tensor<string, []>("mean_182_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_182_cast_fp16 = reduce_mean(axes = mean_182_axes_0, keep_dims = mean_182_keep_dims_0, x = pow_343_cast_fp16)[name = tensor<string, []>("mean_182_cast_fp16")];
            tensor<fp16, []> const_3760_to_fp16 = const()[name = tensor<string, []>("const_3760_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_384_cast_fp16 = add(x = mean_182_cast_fp16, y = const_3760_to_fp16)[name = tensor<string, []>("add_384_cast_fp16")];
            tensor<fp32, []> rsqrt_182_epsilon_0 = const()[name = tensor<string, []>("rsqrt_182_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_182_cast_fp16 = rsqrt(epsilon = rsqrt_182_epsilon_0, x = add_384_cast_fp16)[name = tensor<string, []>("rsqrt_182_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_412_cast_fp16 = mul(x = unsqueeze_395_cast_fp16, y = rsqrt_182_cast_fp16)[name = tensor<string, []>("mul_412_cast_fp16")];
            tensor<int32, [1]> squeeze_188_axes_0 = const()[name = tensor<string, []>("squeeze_188_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_188_cast_fp16 = squeeze(axes = squeeze_188_axes_0, x = mul_412_cast_fp16)[name = tensor<string, []>("squeeze_188_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_308_to_fp16 = const()[name = tensor<string, []>("view_308_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32960960)))];
            tensor<fp16, [1, 256, 801]> mul_413_cast_fp16 = mul(x = squeeze_188_cast_fp16, y = view_308_to_fp16)[name = tensor<string, []>("mul_413_cast_fp16")];
            tensor<string, []> conv1d_230_pad_type_0 = const()[name = tensor<string, []>("conv1d_230_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_230_strides_0 = const()[name = tensor<string, []>("conv1d_230_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_230_pad_0 = const()[name = tensor<string, []>("conv1d_230_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_230_dilations_0 = const()[name = tensor<string, []>("conv1d_230_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_230_groups_0 = const()[name = tensor<string, []>("conv1d_230_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_72_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_72_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32961536)))];
            tensor<fp16, [20]> p_output_72_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_72_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32971840)))];
            tensor<fp16, [1, 20, 801]> conv1d_230_cast_fp16 = conv(bias = p_output_72_1_bias_to_fp16, dilations = conv1d_230_dilations_0, groups = conv1d_230_groups_0, pad = conv1d_230_pad_0, pad_type = conv1d_230_pad_type_0, strides = conv1d_230_strides_0, weight = p_output_72_1_weight_to_fp16, x = mul_413_cast_fp16)[name = tensor<string, []>("conv1d_230_cast_fp16")];
            tensor<int32, []> glu_72_split_num_splits_0 = const()[name = tensor<string, []>("glu_72_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_72_split_axis_0 = const()[name = tensor<string, []>("glu_72_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_72_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_72_split_cast_fp16_1 = split(axis = glu_72_split_axis_0, num_splits = glu_72_split_num_splits_0, x = conv1d_230_cast_fp16)[name = tensor<string, []>("glu_72_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_72_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_72_split_cast_fp16_1)[name = tensor<string, []>("glu_72_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_72_cast_fp16 = mul(x = glu_72_split_cast_fp16_0, y = glu_72_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_72_cast_fp16")];
            tensor<int32, [4]> const_3764 = const()[name = tensor<string, []>("const_3764"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_309_cast_fp16 = reshape(shape = const_3764, x = glu_72_cast_fp16)[name = tensor<string, []>("view_309_cast_fp16")];
            tensor<int32, [4]> select_219_begin_0 = const()[name = tensor<string, []>("select_219_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_219_end_0 = const()[name = tensor<string, []>("select_219_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_219_end_mask_0 = const()[name = tensor<string, []>("select_219_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_219_squeeze_mask_0 = const()[name = tensor<string, []>("select_219_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_219_cast_fp16 = slice_by_index(begin = select_219_begin_0, end = select_219_end_0, end_mask = select_219_end_mask_0, squeeze_mask = select_219_squeeze_mask_0, x = view_309_cast_fp16)[name = tensor<string, []>("select_219_cast_fp16")];
            tensor<int32, [4]> select_220_begin_0 = const()[name = tensor<string, []>("select_220_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_220_end_0 = const()[name = tensor<string, []>("select_220_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_220_end_mask_0 = const()[name = tensor<string, []>("select_220_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_220_squeeze_mask_0 = const()[name = tensor<string, []>("select_220_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_220_cast_fp16 = slice_by_index(begin = select_220_begin_0, end = select_220_end_0, end_mask = select_220_end_mask_0, squeeze_mask = select_220_squeeze_mask_0, x = view_309_cast_fp16)[name = tensor<string, []>("select_220_cast_fp16")];
            tensor<int32, [4]> select_221_begin_0 = const()[name = tensor<string, []>("select_221_begin_0"), val = tensor<int32, [4]>([0, 73, 0, 0])];
            tensor<int32, [4]> select_221_end_0 = const()[name = tensor<string, []>("select_221_end_0"), val = tensor<int32, [4]>([1, 74, 256, 801])];
            tensor<bool, [4]> select_221_end_mask_0 = const()[name = tensor<string, []>("select_221_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_221_squeeze_mask_0 = const()[name = tensor<string, []>("select_221_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_221_cast_fp16 = slice_by_index(begin = select_221_begin_0, end = select_221_end_0, end_mask = select_221_end_mask_0, squeeze_mask = select_221_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_221_cast_fp16")];
            tensor<int32, [1]> unsqueeze_397_axes_0 = const()[name = tensor<string, []>("unsqueeze_397_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_397_cast_fp16 = expand_dims(axes = unsqueeze_397_axes_0, x = select_221_cast_fp16)[name = tensor<string, []>("unsqueeze_397_cast_fp16")];
            tensor<fp16, []> const_3775_promoted_to_fp16 = const()[name = tensor<string, []>("const_3775_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_344_cast_fp16 = pow(x = unsqueeze_397_cast_fp16, y = const_3775_promoted_to_fp16)[name = tensor<string, []>("pow_344_cast_fp16")];
            tensor<int32, [1]> mean_183_axes_0 = const()[name = tensor<string, []>("mean_183_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_183_keep_dims_0 = const()[name = tensor<string, []>("mean_183_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_183_cast_fp16 = reduce_mean(axes = mean_183_axes_0, keep_dims = mean_183_keep_dims_0, x = pow_344_cast_fp16)[name = tensor<string, []>("mean_183_cast_fp16")];
            tensor<fp16, []> const_3778_to_fp16 = const()[name = tensor<string, []>("const_3778_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_385_cast_fp16 = add(x = mean_183_cast_fp16, y = const_3778_to_fp16)[name = tensor<string, []>("add_385_cast_fp16")];
            tensor<fp32, []> rsqrt_183_epsilon_0 = const()[name = tensor<string, []>("rsqrt_183_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_183_cast_fp16 = rsqrt(epsilon = rsqrt_183_epsilon_0, x = add_385_cast_fp16)[name = tensor<string, []>("rsqrt_183_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_414_cast_fp16 = mul(x = unsqueeze_397_cast_fp16, y = rsqrt_183_cast_fp16)[name = tensor<string, []>("mul_414_cast_fp16")];
            tensor<int32, [1]> squeeze_189_axes_0 = const()[name = tensor<string, []>("squeeze_189_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_189_cast_fp16 = squeeze(axes = squeeze_189_axes_0, x = mul_414_cast_fp16)[name = tensor<string, []>("squeeze_189_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_310_to_fp16 = const()[name = tensor<string, []>("view_310_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32971968)))];
            tensor<fp16, [1, 256, 801]> mul_415_cast_fp16 = mul(x = squeeze_189_cast_fp16, y = view_310_to_fp16)[name = tensor<string, []>("mul_415_cast_fp16")];
            tensor<string, []> conv1d_231_pad_type_0 = const()[name = tensor<string, []>("conv1d_231_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_231_strides_0 = const()[name = tensor<string, []>("conv1d_231_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_231_pad_0 = const()[name = tensor<string, []>("conv1d_231_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_231_dilations_0 = const()[name = tensor<string, []>("conv1d_231_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_231_groups_0 = const()[name = tensor<string, []>("conv1d_231_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_73_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_73_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32972544)))];
            tensor<fp16, [20]> p_output_73_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_73_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32982848)))];
            tensor<fp16, [1, 20, 801]> conv1d_231_cast_fp16 = conv(bias = p_output_73_1_bias_to_fp16, dilations = conv1d_231_dilations_0, groups = conv1d_231_groups_0, pad = conv1d_231_pad_0, pad_type = conv1d_231_pad_type_0, strides = conv1d_231_strides_0, weight = p_output_73_1_weight_to_fp16, x = mul_415_cast_fp16)[name = tensor<string, []>("conv1d_231_cast_fp16")];
            tensor<int32, []> glu_73_split_num_splits_0 = const()[name = tensor<string, []>("glu_73_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_73_split_axis_0 = const()[name = tensor<string, []>("glu_73_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_73_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_73_split_cast_fp16_1 = split(axis = glu_73_split_axis_0, num_splits = glu_73_split_num_splits_0, x = conv1d_231_cast_fp16)[name = tensor<string, []>("glu_73_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_73_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_73_split_cast_fp16_1)[name = tensor<string, []>("glu_73_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_73_cast_fp16 = mul(x = glu_73_split_cast_fp16_0, y = glu_73_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_73_cast_fp16")];
            tensor<int32, [4]> const_3782 = const()[name = tensor<string, []>("const_3782"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_311_cast_fp16 = reshape(shape = const_3782, x = glu_73_cast_fp16)[name = tensor<string, []>("view_311_cast_fp16")];
            tensor<int32, [4]> select_222_begin_0 = const()[name = tensor<string, []>("select_222_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_222_end_0 = const()[name = tensor<string, []>("select_222_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_222_end_mask_0 = const()[name = tensor<string, []>("select_222_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_222_squeeze_mask_0 = const()[name = tensor<string, []>("select_222_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_222_cast_fp16 = slice_by_index(begin = select_222_begin_0, end = select_222_end_0, end_mask = select_222_end_mask_0, squeeze_mask = select_222_squeeze_mask_0, x = view_311_cast_fp16)[name = tensor<string, []>("select_222_cast_fp16")];
            tensor<int32, [4]> select_223_begin_0 = const()[name = tensor<string, []>("select_223_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_223_end_0 = const()[name = tensor<string, []>("select_223_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_223_end_mask_0 = const()[name = tensor<string, []>("select_223_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_223_squeeze_mask_0 = const()[name = tensor<string, []>("select_223_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_223_cast_fp16 = slice_by_index(begin = select_223_begin_0, end = select_223_end_0, end_mask = select_223_end_mask_0, squeeze_mask = select_223_squeeze_mask_0, x = view_311_cast_fp16)[name = tensor<string, []>("select_223_cast_fp16")];
            tensor<int32, [4]> select_224_begin_0 = const()[name = tensor<string, []>("select_224_begin_0"), val = tensor<int32, [4]>([0, 74, 0, 0])];
            tensor<int32, [4]> select_224_end_0 = const()[name = tensor<string, []>("select_224_end_0"), val = tensor<int32, [4]>([1, 75, 256, 801])];
            tensor<bool, [4]> select_224_end_mask_0 = const()[name = tensor<string, []>("select_224_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_224_squeeze_mask_0 = const()[name = tensor<string, []>("select_224_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_224_cast_fp16 = slice_by_index(begin = select_224_begin_0, end = select_224_end_0, end_mask = select_224_end_mask_0, squeeze_mask = select_224_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_224_cast_fp16")];
            tensor<int32, [1]> unsqueeze_399_axes_0 = const()[name = tensor<string, []>("unsqueeze_399_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_399_cast_fp16 = expand_dims(axes = unsqueeze_399_axes_0, x = select_224_cast_fp16)[name = tensor<string, []>("unsqueeze_399_cast_fp16")];
            tensor<fp16, []> const_3793_promoted_to_fp16 = const()[name = tensor<string, []>("const_3793_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_345_cast_fp16 = pow(x = unsqueeze_399_cast_fp16, y = const_3793_promoted_to_fp16)[name = tensor<string, []>("pow_345_cast_fp16")];
            tensor<int32, [1]> mean_184_axes_0 = const()[name = tensor<string, []>("mean_184_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_184_keep_dims_0 = const()[name = tensor<string, []>("mean_184_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_184_cast_fp16 = reduce_mean(axes = mean_184_axes_0, keep_dims = mean_184_keep_dims_0, x = pow_345_cast_fp16)[name = tensor<string, []>("mean_184_cast_fp16")];
            tensor<fp16, []> const_3796_to_fp16 = const()[name = tensor<string, []>("const_3796_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_386_cast_fp16 = add(x = mean_184_cast_fp16, y = const_3796_to_fp16)[name = tensor<string, []>("add_386_cast_fp16")];
            tensor<fp32, []> rsqrt_184_epsilon_0 = const()[name = tensor<string, []>("rsqrt_184_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_184_cast_fp16 = rsqrt(epsilon = rsqrt_184_epsilon_0, x = add_386_cast_fp16)[name = tensor<string, []>("rsqrt_184_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_416_cast_fp16 = mul(x = unsqueeze_399_cast_fp16, y = rsqrt_184_cast_fp16)[name = tensor<string, []>("mul_416_cast_fp16")];
            tensor<int32, [1]> squeeze_190_axes_0 = const()[name = tensor<string, []>("squeeze_190_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_190_cast_fp16 = squeeze(axes = squeeze_190_axes_0, x = mul_416_cast_fp16)[name = tensor<string, []>("squeeze_190_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_312_to_fp16 = const()[name = tensor<string, []>("view_312_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32982976)))];
            tensor<fp16, [1, 256, 801]> mul_417_cast_fp16 = mul(x = squeeze_190_cast_fp16, y = view_312_to_fp16)[name = tensor<string, []>("mul_417_cast_fp16")];
            tensor<string, []> conv1d_232_pad_type_0 = const()[name = tensor<string, []>("conv1d_232_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_232_strides_0 = const()[name = tensor<string, []>("conv1d_232_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_232_pad_0 = const()[name = tensor<string, []>("conv1d_232_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_232_dilations_0 = const()[name = tensor<string, []>("conv1d_232_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_232_groups_0 = const()[name = tensor<string, []>("conv1d_232_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_74_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_74_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32983552)))];
            tensor<fp16, [20]> p_output_74_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_74_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32993856)))];
            tensor<fp16, [1, 20, 801]> conv1d_232_cast_fp16 = conv(bias = p_output_74_1_bias_to_fp16, dilations = conv1d_232_dilations_0, groups = conv1d_232_groups_0, pad = conv1d_232_pad_0, pad_type = conv1d_232_pad_type_0, strides = conv1d_232_strides_0, weight = p_output_74_1_weight_to_fp16, x = mul_417_cast_fp16)[name = tensor<string, []>("conv1d_232_cast_fp16")];
            tensor<int32, []> glu_74_split_num_splits_0 = const()[name = tensor<string, []>("glu_74_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_74_split_axis_0 = const()[name = tensor<string, []>("glu_74_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_74_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_74_split_cast_fp16_1 = split(axis = glu_74_split_axis_0, num_splits = glu_74_split_num_splits_0, x = conv1d_232_cast_fp16)[name = tensor<string, []>("glu_74_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_74_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_74_split_cast_fp16_1)[name = tensor<string, []>("glu_74_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_74_cast_fp16 = mul(x = glu_74_split_cast_fp16_0, y = glu_74_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_74_cast_fp16")];
            tensor<int32, [4]> const_3800 = const()[name = tensor<string, []>("const_3800"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_313_cast_fp16 = reshape(shape = const_3800, x = glu_74_cast_fp16)[name = tensor<string, []>("view_313_cast_fp16")];
            tensor<int32, [4]> select_225_begin_0 = const()[name = tensor<string, []>("select_225_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_225_end_0 = const()[name = tensor<string, []>("select_225_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_225_end_mask_0 = const()[name = tensor<string, []>("select_225_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_225_squeeze_mask_0 = const()[name = tensor<string, []>("select_225_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_225_cast_fp16 = slice_by_index(begin = select_225_begin_0, end = select_225_end_0, end_mask = select_225_end_mask_0, squeeze_mask = select_225_squeeze_mask_0, x = view_313_cast_fp16)[name = tensor<string, []>("select_225_cast_fp16")];
            tensor<int32, [4]> select_226_begin_0 = const()[name = tensor<string, []>("select_226_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_226_end_0 = const()[name = tensor<string, []>("select_226_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_226_end_mask_0 = const()[name = tensor<string, []>("select_226_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_226_squeeze_mask_0 = const()[name = tensor<string, []>("select_226_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_226_cast_fp16 = slice_by_index(begin = select_226_begin_0, end = select_226_end_0, end_mask = select_226_end_mask_0, squeeze_mask = select_226_squeeze_mask_0, x = view_313_cast_fp16)[name = tensor<string, []>("select_226_cast_fp16")];
            tensor<int32, [4]> select_227_begin_0 = const()[name = tensor<string, []>("select_227_begin_0"), val = tensor<int32, [4]>([0, 75, 0, 0])];
            tensor<int32, [4]> select_227_end_0 = const()[name = tensor<string, []>("select_227_end_0"), val = tensor<int32, [4]>([1, 76, 256, 801])];
            tensor<bool, [4]> select_227_end_mask_0 = const()[name = tensor<string, []>("select_227_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_227_squeeze_mask_0 = const()[name = tensor<string, []>("select_227_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_227_cast_fp16 = slice_by_index(begin = select_227_begin_0, end = select_227_end_0, end_mask = select_227_end_mask_0, squeeze_mask = select_227_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_227_cast_fp16")];
            tensor<int32, [1]> unsqueeze_401_axes_0 = const()[name = tensor<string, []>("unsqueeze_401_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_401_cast_fp16 = expand_dims(axes = unsqueeze_401_axes_0, x = select_227_cast_fp16)[name = tensor<string, []>("unsqueeze_401_cast_fp16")];
            tensor<fp16, []> const_3811_promoted_to_fp16 = const()[name = tensor<string, []>("const_3811_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_346_cast_fp16 = pow(x = unsqueeze_401_cast_fp16, y = const_3811_promoted_to_fp16)[name = tensor<string, []>("pow_346_cast_fp16")];
            tensor<int32, [1]> mean_185_axes_0 = const()[name = tensor<string, []>("mean_185_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_185_keep_dims_0 = const()[name = tensor<string, []>("mean_185_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_185_cast_fp16 = reduce_mean(axes = mean_185_axes_0, keep_dims = mean_185_keep_dims_0, x = pow_346_cast_fp16)[name = tensor<string, []>("mean_185_cast_fp16")];
            tensor<fp16, []> const_3814_to_fp16 = const()[name = tensor<string, []>("const_3814_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_387_cast_fp16 = add(x = mean_185_cast_fp16, y = const_3814_to_fp16)[name = tensor<string, []>("add_387_cast_fp16")];
            tensor<fp32, []> rsqrt_185_epsilon_0 = const()[name = tensor<string, []>("rsqrt_185_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_185_cast_fp16 = rsqrt(epsilon = rsqrt_185_epsilon_0, x = add_387_cast_fp16)[name = tensor<string, []>("rsqrt_185_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_418_cast_fp16 = mul(x = unsqueeze_401_cast_fp16, y = rsqrt_185_cast_fp16)[name = tensor<string, []>("mul_418_cast_fp16")];
            tensor<int32, [1]> squeeze_191_axes_0 = const()[name = tensor<string, []>("squeeze_191_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_191_cast_fp16 = squeeze(axes = squeeze_191_axes_0, x = mul_418_cast_fp16)[name = tensor<string, []>("squeeze_191_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_314_to_fp16 = const()[name = tensor<string, []>("view_314_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32993984)))];
            tensor<fp16, [1, 256, 801]> mul_419_cast_fp16 = mul(x = squeeze_191_cast_fp16, y = view_314_to_fp16)[name = tensor<string, []>("mul_419_cast_fp16")];
            tensor<string, []> conv1d_233_pad_type_0 = const()[name = tensor<string, []>("conv1d_233_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_233_strides_0 = const()[name = tensor<string, []>("conv1d_233_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_233_pad_0 = const()[name = tensor<string, []>("conv1d_233_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_233_dilations_0 = const()[name = tensor<string, []>("conv1d_233_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_233_groups_0 = const()[name = tensor<string, []>("conv1d_233_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_75_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_75_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32994560)))];
            tensor<fp16, [20]> p_output_75_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_75_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33004864)))];
            tensor<fp16, [1, 20, 801]> conv1d_233_cast_fp16 = conv(bias = p_output_75_1_bias_to_fp16, dilations = conv1d_233_dilations_0, groups = conv1d_233_groups_0, pad = conv1d_233_pad_0, pad_type = conv1d_233_pad_type_0, strides = conv1d_233_strides_0, weight = p_output_75_1_weight_to_fp16, x = mul_419_cast_fp16)[name = tensor<string, []>("conv1d_233_cast_fp16")];
            tensor<int32, []> glu_75_split_num_splits_0 = const()[name = tensor<string, []>("glu_75_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_75_split_axis_0 = const()[name = tensor<string, []>("glu_75_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_75_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_75_split_cast_fp16_1 = split(axis = glu_75_split_axis_0, num_splits = glu_75_split_num_splits_0, x = conv1d_233_cast_fp16)[name = tensor<string, []>("glu_75_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_75_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_75_split_cast_fp16_1)[name = tensor<string, []>("glu_75_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_75_cast_fp16 = mul(x = glu_75_split_cast_fp16_0, y = glu_75_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_75_cast_fp16")];
            tensor<int32, [4]> const_3818 = const()[name = tensor<string, []>("const_3818"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_315_cast_fp16 = reshape(shape = const_3818, x = glu_75_cast_fp16)[name = tensor<string, []>("view_315_cast_fp16")];
            tensor<int32, [4]> select_228_begin_0 = const()[name = tensor<string, []>("select_228_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_228_end_0 = const()[name = tensor<string, []>("select_228_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_228_end_mask_0 = const()[name = tensor<string, []>("select_228_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_228_squeeze_mask_0 = const()[name = tensor<string, []>("select_228_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_228_cast_fp16 = slice_by_index(begin = select_228_begin_0, end = select_228_end_0, end_mask = select_228_end_mask_0, squeeze_mask = select_228_squeeze_mask_0, x = view_315_cast_fp16)[name = tensor<string, []>("select_228_cast_fp16")];
            tensor<int32, [4]> select_229_begin_0 = const()[name = tensor<string, []>("select_229_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_229_end_0 = const()[name = tensor<string, []>("select_229_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_229_end_mask_0 = const()[name = tensor<string, []>("select_229_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_229_squeeze_mask_0 = const()[name = tensor<string, []>("select_229_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_229_cast_fp16 = slice_by_index(begin = select_229_begin_0, end = select_229_end_0, end_mask = select_229_end_mask_0, squeeze_mask = select_229_squeeze_mask_0, x = view_315_cast_fp16)[name = tensor<string, []>("select_229_cast_fp16")];
            tensor<int32, [4]> select_230_begin_0 = const()[name = tensor<string, []>("select_230_begin_0"), val = tensor<int32, [4]>([0, 76, 0, 0])];
            tensor<int32, [4]> select_230_end_0 = const()[name = tensor<string, []>("select_230_end_0"), val = tensor<int32, [4]>([1, 77, 256, 801])];
            tensor<bool, [4]> select_230_end_mask_0 = const()[name = tensor<string, []>("select_230_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_230_squeeze_mask_0 = const()[name = tensor<string, []>("select_230_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_230_cast_fp16 = slice_by_index(begin = select_230_begin_0, end = select_230_end_0, end_mask = select_230_end_mask_0, squeeze_mask = select_230_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_230_cast_fp16")];
            tensor<int32, [1]> unsqueeze_403_axes_0 = const()[name = tensor<string, []>("unsqueeze_403_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_403_cast_fp16 = expand_dims(axes = unsqueeze_403_axes_0, x = select_230_cast_fp16)[name = tensor<string, []>("unsqueeze_403_cast_fp16")];
            tensor<fp16, []> const_3829_promoted_to_fp16 = const()[name = tensor<string, []>("const_3829_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_347_cast_fp16 = pow(x = unsqueeze_403_cast_fp16, y = const_3829_promoted_to_fp16)[name = tensor<string, []>("pow_347_cast_fp16")];
            tensor<int32, [1]> mean_186_axes_0 = const()[name = tensor<string, []>("mean_186_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_186_keep_dims_0 = const()[name = tensor<string, []>("mean_186_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_186_cast_fp16 = reduce_mean(axes = mean_186_axes_0, keep_dims = mean_186_keep_dims_0, x = pow_347_cast_fp16)[name = tensor<string, []>("mean_186_cast_fp16")];
            tensor<fp16, []> const_3832_to_fp16 = const()[name = tensor<string, []>("const_3832_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_388_cast_fp16 = add(x = mean_186_cast_fp16, y = const_3832_to_fp16)[name = tensor<string, []>("add_388_cast_fp16")];
            tensor<fp32, []> rsqrt_186_epsilon_0 = const()[name = tensor<string, []>("rsqrt_186_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_186_cast_fp16 = rsqrt(epsilon = rsqrt_186_epsilon_0, x = add_388_cast_fp16)[name = tensor<string, []>("rsqrt_186_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_420_cast_fp16 = mul(x = unsqueeze_403_cast_fp16, y = rsqrt_186_cast_fp16)[name = tensor<string, []>("mul_420_cast_fp16")];
            tensor<int32, [1]> squeeze_192_axes_0 = const()[name = tensor<string, []>("squeeze_192_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_192_cast_fp16 = squeeze(axes = squeeze_192_axes_0, x = mul_420_cast_fp16)[name = tensor<string, []>("squeeze_192_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_316_to_fp16 = const()[name = tensor<string, []>("view_316_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33004992)))];
            tensor<fp16, [1, 256, 801]> mul_421_cast_fp16 = mul(x = squeeze_192_cast_fp16, y = view_316_to_fp16)[name = tensor<string, []>("mul_421_cast_fp16")];
            tensor<string, []> conv1d_234_pad_type_0 = const()[name = tensor<string, []>("conv1d_234_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_234_strides_0 = const()[name = tensor<string, []>("conv1d_234_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_234_pad_0 = const()[name = tensor<string, []>("conv1d_234_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_234_dilations_0 = const()[name = tensor<string, []>("conv1d_234_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_234_groups_0 = const()[name = tensor<string, []>("conv1d_234_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_76_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_76_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33005568)))];
            tensor<fp16, [20]> p_output_76_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_76_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33015872)))];
            tensor<fp16, [1, 20, 801]> conv1d_234_cast_fp16 = conv(bias = p_output_76_1_bias_to_fp16, dilations = conv1d_234_dilations_0, groups = conv1d_234_groups_0, pad = conv1d_234_pad_0, pad_type = conv1d_234_pad_type_0, strides = conv1d_234_strides_0, weight = p_output_76_1_weight_to_fp16, x = mul_421_cast_fp16)[name = tensor<string, []>("conv1d_234_cast_fp16")];
            tensor<int32, []> glu_76_split_num_splits_0 = const()[name = tensor<string, []>("glu_76_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_76_split_axis_0 = const()[name = tensor<string, []>("glu_76_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_76_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_76_split_cast_fp16_1 = split(axis = glu_76_split_axis_0, num_splits = glu_76_split_num_splits_0, x = conv1d_234_cast_fp16)[name = tensor<string, []>("glu_76_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_76_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_76_split_cast_fp16_1)[name = tensor<string, []>("glu_76_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_76_cast_fp16 = mul(x = glu_76_split_cast_fp16_0, y = glu_76_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_76_cast_fp16")];
            tensor<int32, [4]> const_3836 = const()[name = tensor<string, []>("const_3836"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_317_cast_fp16 = reshape(shape = const_3836, x = glu_76_cast_fp16)[name = tensor<string, []>("view_317_cast_fp16")];
            tensor<int32, [4]> select_231_begin_0 = const()[name = tensor<string, []>("select_231_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_231_end_0 = const()[name = tensor<string, []>("select_231_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_231_end_mask_0 = const()[name = tensor<string, []>("select_231_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_231_squeeze_mask_0 = const()[name = tensor<string, []>("select_231_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_231_cast_fp16 = slice_by_index(begin = select_231_begin_0, end = select_231_end_0, end_mask = select_231_end_mask_0, squeeze_mask = select_231_squeeze_mask_0, x = view_317_cast_fp16)[name = tensor<string, []>("select_231_cast_fp16")];
            tensor<int32, [4]> select_232_begin_0 = const()[name = tensor<string, []>("select_232_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_232_end_0 = const()[name = tensor<string, []>("select_232_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_232_end_mask_0 = const()[name = tensor<string, []>("select_232_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_232_squeeze_mask_0 = const()[name = tensor<string, []>("select_232_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_232_cast_fp16 = slice_by_index(begin = select_232_begin_0, end = select_232_end_0, end_mask = select_232_end_mask_0, squeeze_mask = select_232_squeeze_mask_0, x = view_317_cast_fp16)[name = tensor<string, []>("select_232_cast_fp16")];
            tensor<int32, [4]> select_233_begin_0 = const()[name = tensor<string, []>("select_233_begin_0"), val = tensor<int32, [4]>([0, 77, 0, 0])];
            tensor<int32, [4]> select_233_end_0 = const()[name = tensor<string, []>("select_233_end_0"), val = tensor<int32, [4]>([1, 78, 256, 801])];
            tensor<bool, [4]> select_233_end_mask_0 = const()[name = tensor<string, []>("select_233_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_233_squeeze_mask_0 = const()[name = tensor<string, []>("select_233_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_233_cast_fp16 = slice_by_index(begin = select_233_begin_0, end = select_233_end_0, end_mask = select_233_end_mask_0, squeeze_mask = select_233_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_233_cast_fp16")];
            tensor<int32, [1]> unsqueeze_405_axes_0 = const()[name = tensor<string, []>("unsqueeze_405_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_405_cast_fp16 = expand_dims(axes = unsqueeze_405_axes_0, x = select_233_cast_fp16)[name = tensor<string, []>("unsqueeze_405_cast_fp16")];
            tensor<fp16, []> const_3847_promoted_to_fp16 = const()[name = tensor<string, []>("const_3847_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_348_cast_fp16 = pow(x = unsqueeze_405_cast_fp16, y = const_3847_promoted_to_fp16)[name = tensor<string, []>("pow_348_cast_fp16")];
            tensor<int32, [1]> mean_187_axes_0 = const()[name = tensor<string, []>("mean_187_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_187_keep_dims_0 = const()[name = tensor<string, []>("mean_187_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_187_cast_fp16 = reduce_mean(axes = mean_187_axes_0, keep_dims = mean_187_keep_dims_0, x = pow_348_cast_fp16)[name = tensor<string, []>("mean_187_cast_fp16")];
            tensor<fp16, []> const_3850_to_fp16 = const()[name = tensor<string, []>("const_3850_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_389_cast_fp16 = add(x = mean_187_cast_fp16, y = const_3850_to_fp16)[name = tensor<string, []>("add_389_cast_fp16")];
            tensor<fp32, []> rsqrt_187_epsilon_0 = const()[name = tensor<string, []>("rsqrt_187_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_187_cast_fp16 = rsqrt(epsilon = rsqrt_187_epsilon_0, x = add_389_cast_fp16)[name = tensor<string, []>("rsqrt_187_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_422_cast_fp16 = mul(x = unsqueeze_405_cast_fp16, y = rsqrt_187_cast_fp16)[name = tensor<string, []>("mul_422_cast_fp16")];
            tensor<int32, [1]> squeeze_193_axes_0 = const()[name = tensor<string, []>("squeeze_193_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_193_cast_fp16 = squeeze(axes = squeeze_193_axes_0, x = mul_422_cast_fp16)[name = tensor<string, []>("squeeze_193_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_318_to_fp16 = const()[name = tensor<string, []>("view_318_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33016000)))];
            tensor<fp16, [1, 256, 801]> mul_423_cast_fp16 = mul(x = squeeze_193_cast_fp16, y = view_318_to_fp16)[name = tensor<string, []>("mul_423_cast_fp16")];
            tensor<string, []> conv1d_235_pad_type_0 = const()[name = tensor<string, []>("conv1d_235_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_235_strides_0 = const()[name = tensor<string, []>("conv1d_235_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_235_pad_0 = const()[name = tensor<string, []>("conv1d_235_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_235_dilations_0 = const()[name = tensor<string, []>("conv1d_235_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_235_groups_0 = const()[name = tensor<string, []>("conv1d_235_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_77_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_77_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33016576)))];
            tensor<fp16, [20]> p_output_77_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_77_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33026880)))];
            tensor<fp16, [1, 20, 801]> conv1d_235_cast_fp16 = conv(bias = p_output_77_1_bias_to_fp16, dilations = conv1d_235_dilations_0, groups = conv1d_235_groups_0, pad = conv1d_235_pad_0, pad_type = conv1d_235_pad_type_0, strides = conv1d_235_strides_0, weight = p_output_77_1_weight_to_fp16, x = mul_423_cast_fp16)[name = tensor<string, []>("conv1d_235_cast_fp16")];
            tensor<int32, []> glu_77_split_num_splits_0 = const()[name = tensor<string, []>("glu_77_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_77_split_axis_0 = const()[name = tensor<string, []>("glu_77_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_77_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_77_split_cast_fp16_1 = split(axis = glu_77_split_axis_0, num_splits = glu_77_split_num_splits_0, x = conv1d_235_cast_fp16)[name = tensor<string, []>("glu_77_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_77_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_77_split_cast_fp16_1)[name = tensor<string, []>("glu_77_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_77_cast_fp16 = mul(x = glu_77_split_cast_fp16_0, y = glu_77_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_77_cast_fp16")];
            tensor<int32, [4]> const_3854 = const()[name = tensor<string, []>("const_3854"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_319_cast_fp16 = reshape(shape = const_3854, x = glu_77_cast_fp16)[name = tensor<string, []>("view_319_cast_fp16")];
            tensor<int32, [4]> select_234_begin_0 = const()[name = tensor<string, []>("select_234_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_234_end_0 = const()[name = tensor<string, []>("select_234_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_234_end_mask_0 = const()[name = tensor<string, []>("select_234_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_234_squeeze_mask_0 = const()[name = tensor<string, []>("select_234_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_234_cast_fp16 = slice_by_index(begin = select_234_begin_0, end = select_234_end_0, end_mask = select_234_end_mask_0, squeeze_mask = select_234_squeeze_mask_0, x = view_319_cast_fp16)[name = tensor<string, []>("select_234_cast_fp16")];
            tensor<int32, [4]> select_235_begin_0 = const()[name = tensor<string, []>("select_235_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_235_end_0 = const()[name = tensor<string, []>("select_235_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_235_end_mask_0 = const()[name = tensor<string, []>("select_235_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_235_squeeze_mask_0 = const()[name = tensor<string, []>("select_235_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_235_cast_fp16 = slice_by_index(begin = select_235_begin_0, end = select_235_end_0, end_mask = select_235_end_mask_0, squeeze_mask = select_235_squeeze_mask_0, x = view_319_cast_fp16)[name = tensor<string, []>("select_235_cast_fp16")];
            tensor<int32, [4]> select_236_begin_0 = const()[name = tensor<string, []>("select_236_begin_0"), val = tensor<int32, [4]>([0, 78, 0, 0])];
            tensor<int32, [4]> select_236_end_0 = const()[name = tensor<string, []>("select_236_end_0"), val = tensor<int32, [4]>([1, 79, 256, 801])];
            tensor<bool, [4]> select_236_end_mask_0 = const()[name = tensor<string, []>("select_236_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_236_squeeze_mask_0 = const()[name = tensor<string, []>("select_236_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_236_cast_fp16 = slice_by_index(begin = select_236_begin_0, end = select_236_end_0, end_mask = select_236_end_mask_0, squeeze_mask = select_236_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_236_cast_fp16")];
            tensor<int32, [1]> unsqueeze_407_axes_0 = const()[name = tensor<string, []>("unsqueeze_407_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_407_cast_fp16 = expand_dims(axes = unsqueeze_407_axes_0, x = select_236_cast_fp16)[name = tensor<string, []>("unsqueeze_407_cast_fp16")];
            tensor<fp16, []> const_3865_promoted_to_fp16 = const()[name = tensor<string, []>("const_3865_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_349_cast_fp16 = pow(x = unsqueeze_407_cast_fp16, y = const_3865_promoted_to_fp16)[name = tensor<string, []>("pow_349_cast_fp16")];
            tensor<int32, [1]> mean_188_axes_0 = const()[name = tensor<string, []>("mean_188_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_188_keep_dims_0 = const()[name = tensor<string, []>("mean_188_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_188_cast_fp16 = reduce_mean(axes = mean_188_axes_0, keep_dims = mean_188_keep_dims_0, x = pow_349_cast_fp16)[name = tensor<string, []>("mean_188_cast_fp16")];
            tensor<fp16, []> const_3868_to_fp16 = const()[name = tensor<string, []>("const_3868_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_390_cast_fp16 = add(x = mean_188_cast_fp16, y = const_3868_to_fp16)[name = tensor<string, []>("add_390_cast_fp16")];
            tensor<fp32, []> rsqrt_188_epsilon_0 = const()[name = tensor<string, []>("rsqrt_188_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_188_cast_fp16 = rsqrt(epsilon = rsqrt_188_epsilon_0, x = add_390_cast_fp16)[name = tensor<string, []>("rsqrt_188_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_424_cast_fp16 = mul(x = unsqueeze_407_cast_fp16, y = rsqrt_188_cast_fp16)[name = tensor<string, []>("mul_424_cast_fp16")];
            tensor<int32, [1]> squeeze_194_axes_0 = const()[name = tensor<string, []>("squeeze_194_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_194_cast_fp16 = squeeze(axes = squeeze_194_axes_0, x = mul_424_cast_fp16)[name = tensor<string, []>("squeeze_194_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_320_to_fp16 = const()[name = tensor<string, []>("view_320_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33027008)))];
            tensor<fp16, [1, 256, 801]> mul_425_cast_fp16 = mul(x = squeeze_194_cast_fp16, y = view_320_to_fp16)[name = tensor<string, []>("mul_425_cast_fp16")];
            tensor<string, []> conv1d_236_pad_type_0 = const()[name = tensor<string, []>("conv1d_236_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_236_strides_0 = const()[name = tensor<string, []>("conv1d_236_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_236_pad_0 = const()[name = tensor<string, []>("conv1d_236_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_236_dilations_0 = const()[name = tensor<string, []>("conv1d_236_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_236_groups_0 = const()[name = tensor<string, []>("conv1d_236_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [20, 256, 1]> p_output_78_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_78_1_weight_to_fp16"), val = tensor<fp16, [20, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33027584)))];
            tensor<fp16, [20]> p_output_78_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_78_1_bias_to_fp16"), val = tensor<fp16, [20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33037888)))];
            tensor<fp16, [1, 20, 801]> conv1d_236_cast_fp16 = conv(bias = p_output_78_1_bias_to_fp16, dilations = conv1d_236_dilations_0, groups = conv1d_236_groups_0, pad = conv1d_236_pad_0, pad_type = conv1d_236_pad_type_0, strides = conv1d_236_strides_0, weight = p_output_78_1_weight_to_fp16, x = mul_425_cast_fp16)[name = tensor<string, []>("conv1d_236_cast_fp16")];
            tensor<int32, []> glu_78_split_num_splits_0 = const()[name = tensor<string, []>("glu_78_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_78_split_axis_0 = const()[name = tensor<string, []>("glu_78_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 10, 801]> glu_78_split_cast_fp16_0, tensor<fp16, [1, 10, 801]> glu_78_split_cast_fp16_1 = split(axis = glu_78_split_axis_0, num_splits = glu_78_split_num_splits_0, x = conv1d_236_cast_fp16)[name = tensor<string, []>("glu_78_split_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_78_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_78_split_cast_fp16_1)[name = tensor<string, []>("glu_78_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 10, 801]> glu_78_cast_fp16 = mul(x = glu_78_split_cast_fp16_0, y = glu_78_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_78_cast_fp16")];
            tensor<int32, [4]> const_3872 = const()[name = tensor<string, []>("const_3872"), val = tensor<int32, [4]>([1, 2, 5, -1])];
            tensor<fp16, [1, 2, 5, 801]> view_321_cast_fp16 = reshape(shape = const_3872, x = glu_78_cast_fp16)[name = tensor<string, []>("view_321_cast_fp16")];
            tensor<int32, [4]> select_237_begin_0 = const()[name = tensor<string, []>("select_237_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_237_end_0 = const()[name = tensor<string, []>("select_237_end_0"), val = tensor<int32, [4]>([1, 1, 5, 801])];
            tensor<bool, [4]> select_237_end_mask_0 = const()[name = tensor<string, []>("select_237_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_237_squeeze_mask_0 = const()[name = tensor<string, []>("select_237_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_237_cast_fp16 = slice_by_index(begin = select_237_begin_0, end = select_237_end_0, end_mask = select_237_end_mask_0, squeeze_mask = select_237_squeeze_mask_0, x = view_321_cast_fp16)[name = tensor<string, []>("select_237_cast_fp16")];
            tensor<int32, [4]> select_238_begin_0 = const()[name = tensor<string, []>("select_238_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_238_end_0 = const()[name = tensor<string, []>("select_238_end_0"), val = tensor<int32, [4]>([1, 2, 5, 801])];
            tensor<bool, [4]> select_238_end_mask_0 = const()[name = tensor<string, []>("select_238_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_238_squeeze_mask_0 = const()[name = tensor<string, []>("select_238_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 5, 801]> select_238_cast_fp16 = slice_by_index(begin = select_238_begin_0, end = select_238_end_0, end_mask = select_238_end_mask_0, squeeze_mask = select_238_squeeze_mask_0, x = view_321_cast_fp16)[name = tensor<string, []>("select_238_cast_fp16")];
            tensor<int32, [4]> select_239_begin_0 = const()[name = tensor<string, []>("select_239_begin_0"), val = tensor<int32, [4]>([0, 79, 0, 0])];
            tensor<int32, [4]> select_239_end_0 = const()[name = tensor<string, []>("select_239_end_0"), val = tensor<int32, [4]>([1, 80, 256, 801])];
            tensor<bool, [4]> select_239_end_mask_0 = const()[name = tensor<string, []>("select_239_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_239_squeeze_mask_0 = const()[name = tensor<string, []>("select_239_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 256, 801]> select_239_cast_fp16 = slice_by_index(begin = select_239_begin_0, end = select_239_end_0, end_mask = select_239_end_mask_0, squeeze_mask = select_239_squeeze_mask_0, x = unsqueeze_249_cast_fp16)[name = tensor<string, []>("select_239_cast_fp16")];
            tensor<int32, [1]> unsqueeze_409_axes_0 = const()[name = tensor<string, []>("unsqueeze_409_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 801]> unsqueeze_409_cast_fp16 = expand_dims(axes = unsqueeze_409_axes_0, x = select_239_cast_fp16)[name = tensor<string, []>("unsqueeze_409_cast_fp16")];
            tensor<fp16, []> const_3883_promoted_to_fp16 = const()[name = tensor<string, []>("const_3883_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 1, 256, 801]> pow_350_cast_fp16 = pow(x = unsqueeze_409_cast_fp16, y = const_3883_promoted_to_fp16)[name = tensor<string, []>("pow_350_cast_fp16")];
            tensor<int32, [1]> mean_189_axes_0 = const()[name = tensor<string, []>("mean_189_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<bool, []> mean_189_keep_dims_0 = const()[name = tensor<string, []>("mean_189_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1, 1, 801]> mean_189_cast_fp16 = reduce_mean(axes = mean_189_axes_0, keep_dims = mean_189_keep_dims_0, x = pow_350_cast_fp16)[name = tensor<string, []>("mean_189_cast_fp16")];
            tensor<fp16, []> const_3886_to_fp16 = const()[name = tensor<string, []>("const_3886_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1, 1, 801]> add_391_cast_fp16 = add(x = mean_189_cast_fp16, y = const_3886_to_fp16)[name = tensor<string, []>("add_391_cast_fp16")];
            tensor<fp32, []> rsqrt_189_epsilon_0 = const()[name = tensor<string, []>("rsqrt_189_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp16, [1, 1, 1, 801]> rsqrt_189_cast_fp16 = rsqrt(epsilon = rsqrt_189_epsilon_0, x = add_391_cast_fp16)[name = tensor<string, []>("rsqrt_189_cast_fp16")];
            tensor<fp16, [1, 1, 256, 801]> mul_426_cast_fp16 = mul(x = unsqueeze_409_cast_fp16, y = rsqrt_189_cast_fp16)[name = tensor<string, []>("mul_426_cast_fp16")];
            tensor<int32, [1]> squeeze_195_axes_0 = const()[name = tensor<string, []>("squeeze_195_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 256, 801]> squeeze_195_cast_fp16 = squeeze(axes = squeeze_195_axes_0, x = mul_426_cast_fp16)[name = tensor<string, []>("squeeze_195_cast_fp16")];
            tensor<fp16, [1, 256, 1]> view_322_to_fp16 = const()[name = tensor<string, []>("view_322_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33038016)))];
            tensor<fp16, [1, 256, 801]> mul_427_cast_fp16 = mul(x = squeeze_195_cast_fp16, y = view_322_to_fp16)[name = tensor<string, []>("mul_427_cast_fp16")];
            tensor<string, []> conv1d_237_pad_type_0 = const()[name = tensor<string, []>("conv1d_237_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_237_strides_0 = const()[name = tensor<string, []>("conv1d_237_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_237_pad_0 = const()[name = tensor<string, []>("conv1d_237_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_237_dilations_0 = const()[name = tensor<string, []>("conv1d_237_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_237_groups_0 = const()[name = tensor<string, []>("conv1d_237_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [188, 256, 1]> p_output_79_1_weight_to_fp16 = const()[name = tensor<string, []>("p_output_79_1_weight_to_fp16"), val = tensor<fp16, [188, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33038592)))];
            tensor<fp16, [188]> p_output_79_1_bias_to_fp16 = const()[name = tensor<string, []>("p_output_79_1_bias_to_fp16"), val = tensor<fp16, [188]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33134912)))];
            tensor<fp16, [1, 188, 801]> conv1d_237_cast_fp16 = conv(bias = p_output_79_1_bias_to_fp16, dilations = conv1d_237_dilations_0, groups = conv1d_237_groups_0, pad = conv1d_237_pad_0, pad_type = conv1d_237_pad_type_0, strides = conv1d_237_strides_0, weight = p_output_79_1_weight_to_fp16, x = mul_427_cast_fp16)[name = tensor<string, []>("conv1d_237_cast_fp16")];
            tensor<int32, []> glu_79_split_num_splits_0 = const()[name = tensor<string, []>("glu_79_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> glu_79_split_axis_0 = const()[name = tensor<string, []>("glu_79_split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 94, 801]> glu_79_split_cast_fp16_0, tensor<fp16, [1, 94, 801]> glu_79_split_cast_fp16_1 = split(axis = glu_79_split_axis_0, num_splits = glu_79_split_num_splits_0, x = conv1d_237_cast_fp16)[name = tensor<string, []>("glu_79_split_cast_fp16")];
            tensor<fp16, [1, 94, 801]> glu_79_split_1_sigmoid_cast_fp16 = sigmoid(x = glu_79_split_cast_fp16_1)[name = tensor<string, []>("glu_79_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 94, 801]> glu_79_cast_fp16 = mul(x = glu_79_split_cast_fp16_0, y = glu_79_split_1_sigmoid_cast_fp16)[name = tensor<string, []>("glu_79_cast_fp16")];
            tensor<int32, [4]> const_3890 = const()[name = tensor<string, []>("const_3890"), val = tensor<int32, [4]>([1, 2, 47, -1])];
            tensor<fp16, [1, 2, 47, 801]> view_323_cast_fp16 = reshape(shape = const_3890, x = glu_79_cast_fp16)[name = tensor<string, []>("view_323_cast_fp16")];
            tensor<int32, [4]> select_240_begin_0 = const()[name = tensor<string, []>("select_240_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> select_240_end_0 = const()[name = tensor<string, []>("select_240_end_0"), val = tensor<int32, [4]>([1, 1, 47, 801])];
            tensor<bool, [4]> select_240_end_mask_0 = const()[name = tensor<string, []>("select_240_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_240_squeeze_mask_0 = const()[name = tensor<string, []>("select_240_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 47, 801]> select_240_cast_fp16 = slice_by_index(begin = select_240_begin_0, end = select_240_end_0, end_mask = select_240_end_mask_0, squeeze_mask = select_240_squeeze_mask_0, x = view_323_cast_fp16)[name = tensor<string, []>("select_240_cast_fp16")];
            tensor<int32, [4]> select_241_begin_0 = const()[name = tensor<string, []>("select_241_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
            tensor<int32, [4]> select_241_end_0 = const()[name = tensor<string, []>("select_241_end_0"), val = tensor<int32, [4]>([1, 2, 47, 801])];
            tensor<bool, [4]> select_241_end_mask_0 = const()[name = tensor<string, []>("select_241_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
            tensor<bool, [4]> select_241_squeeze_mask_0 = const()[name = tensor<string, []>("select_241_squeeze_mask_0"), val = tensor<bool, [4]>([false, true, false, false])];
            tensor<fp16, [1, 47, 801]> select_241_cast_fp16 = slice_by_index(begin = select_241_begin_0, end = select_241_end_0, end_mask = select_241_end_mask_0, squeeze_mask = select_241_squeeze_mask_0, x = view_323_cast_fp16)[name = tensor<string, []>("select_241_cast_fp16")];
            tensor<int32, []> const_3895 = const()[name = tensor<string, []>("const_3895"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_80_interleave_0 = const()[name = tensor<string, []>("cat_80_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 442, 801]> cat_80_cast_fp16 = concat(axis = const_3895, interleave = cat_80_interleave_0, values = (select_3_cast_fp16, select_6_cast_fp16, select_9_cast_fp16, select_12_cast_fp16, select_15_cast_fp16, select_18_cast_fp16, select_21_cast_fp16, select_24_cast_fp16, select_27_cast_fp16, select_30_cast_fp16, select_33_cast_fp16, select_36_cast_fp16, select_39_cast_fp16, select_42_cast_fp16, select_45_cast_fp16, select_48_cast_fp16, select_51_cast_fp16, select_54_cast_fp16, select_57_cast_fp16, select_60_cast_fp16, select_63_cast_fp16, select_66_cast_fp16, select_69_cast_fp16, select_72_cast_fp16, select_75_cast_fp16, select_78_cast_fp16, select_81_cast_fp16, select_84_cast_fp16, select_87_cast_fp16, select_90_cast_fp16, select_93_cast_fp16, select_96_cast_fp16, select_99_cast_fp16, select_102_cast_fp16, select_105_cast_fp16, select_108_cast_fp16, select_111_cast_fp16, select_114_cast_fp16, select_117_cast_fp16, select_120_cast_fp16, select_123_cast_fp16, select_126_cast_fp16, select_129_cast_fp16, select_132_cast_fp16, select_135_cast_fp16, select_138_cast_fp16, select_141_cast_fp16, select_144_cast_fp16, select_147_cast_fp16, select_150_cast_fp16, select_153_cast_fp16, select_156_cast_fp16, select_159_cast_fp16, select_162_cast_fp16, select_165_cast_fp16, select_168_cast_fp16, select_171_cast_fp16, select_174_cast_fp16, select_177_cast_fp16, select_180_cast_fp16, select_183_cast_fp16, select_186_cast_fp16, select_189_cast_fp16, select_192_cast_fp16, select_195_cast_fp16, select_198_cast_fp16, select_201_cast_fp16, select_204_cast_fp16, select_207_cast_fp16, select_210_cast_fp16, select_213_cast_fp16, select_216_cast_fp16, select_219_cast_fp16, select_222_cast_fp16, select_225_cast_fp16, select_228_cast_fp16, select_231_cast_fp16, select_234_cast_fp16, select_237_cast_fp16, select_240_cast_fp16))[name = tensor<string, []>("cat_80_cast_fp16")];
            tensor<int32, []> const_3896 = const()[name = tensor<string, []>("const_3896"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_81_interleave_0 = const()[name = tensor<string, []>("cat_81_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 442, 801]> cat_81_cast_fp16 = concat(axis = const_3896, interleave = cat_81_interleave_0, values = (select_4_cast_fp16, select_7_cast_fp16, select_10_cast_fp16, select_13_cast_fp16, select_16_cast_fp16, select_19_cast_fp16, select_22_cast_fp16, select_25_cast_fp16, select_28_cast_fp16, select_31_cast_fp16, select_34_cast_fp16, select_37_cast_fp16, select_40_cast_fp16, select_43_cast_fp16, select_46_cast_fp16, select_49_cast_fp16, select_52_cast_fp16, select_55_cast_fp16, select_58_cast_fp16, select_61_cast_fp16, select_64_cast_fp16, select_67_cast_fp16, select_70_cast_fp16, select_73_cast_fp16, select_76_cast_fp16, select_79_cast_fp16, select_82_cast_fp16, select_85_cast_fp16, select_88_cast_fp16, select_91_cast_fp16, select_94_cast_fp16, select_97_cast_fp16, select_100_cast_fp16, select_103_cast_fp16, select_106_cast_fp16, select_109_cast_fp16, select_112_cast_fp16, select_115_cast_fp16, select_118_cast_fp16, select_121_cast_fp16, select_124_cast_fp16, select_127_cast_fp16, select_130_cast_fp16, select_133_cast_fp16, select_136_cast_fp16, select_139_cast_fp16, select_142_cast_fp16, select_145_cast_fp16, select_148_cast_fp16, select_151_cast_fp16, select_154_cast_fp16, select_157_cast_fp16, select_160_cast_fp16, select_163_cast_fp16, select_166_cast_fp16, select_169_cast_fp16, select_172_cast_fp16, select_175_cast_fp16, select_178_cast_fp16, select_181_cast_fp16, select_184_cast_fp16, select_187_cast_fp16, select_190_cast_fp16, select_193_cast_fp16, select_196_cast_fp16, select_199_cast_fp16, select_202_cast_fp16, select_205_cast_fp16, select_208_cast_fp16, select_211_cast_fp16, select_214_cast_fp16, select_217_cast_fp16, select_220_cast_fp16, select_223_cast_fp16, select_226_cast_fp16, select_229_cast_fp16, select_232_cast_fp16, select_235_cast_fp16, select_238_cast_fp16, select_241_cast_fp16))[name = tensor<string, []>("cat_81_cast_fp16")];
            tensor<int32, []> stack_1_axis_0 = const()[name = tensor<string, []>("stack_1_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 2, 442, 801]> stack_1 = stack(axis = stack_1_axis_0, values = (cat_80_cast_fp16, cat_81_cast_fp16))[name = tensor<string, []>("stack_1_cast_fp16")];
        } -> (stack_1);
}