program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}})]
{
    func main<ios17>(tensor<fp32, [1, 128, 233]> mel, tensor<int32, [1]> mel_length) {
            tensor<int32, [3]> x_1_perm_0 = const()[name = tensor<string, []>("x_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> mel_to_fp16_dtype_0 = const()[name = tensor<string, []>("mel_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 233]> mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = tensor<string, []>("cast_11")];
            tensor<fp16, [1, 233, 128]> x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = tensor<string, []>("transpose_1")];
            tensor<fp16, [1, 1, 233, 128]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = tensor<string, []>("tensor_1_cast_fp16")];
            tensor<int32, [1, 233]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 233]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232]])];
            tensor<int32, [1]> var_44_axes_0 = const()[name = tensor<string, []>("op_44_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = mel_length)[name = tensor<string, []>("op_44")];
            tensor<bool, [1, 233]> time_mask_1 = less(x = expand_dims_0, y = var_44)[name = tensor<string, []>("time_mask_1")];
            tensor<int32, [1]> var_46_axes_0 = const()[name = tensor<string, []>("op_46_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 233, 1]> var_46 = expand_dims(axes = var_46_axes_0, x = time_mask_1)[name = tensor<string, []>("op_46")];
            tensor<int32, [3]> var_48_reps_0 = const()[name = tensor<string, []>("op_48_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
            tensor<bool, [1, 233, 128]> var_48 = tile(reps = var_48_reps_0, x = var_46)[name = tensor<string, []>("op_48")];
            tensor<int32, [1]> var_54_axes_0 = const()[name = tensor<string, []>("op_54_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<string, []> mask_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("mask_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 233, 128]> var_48_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_48)[name = tensor<string, []>("cast_10")];
            tensor<fp16, [1, 1, 233, 128]> var_54_cast_fp16 = expand_dims(axes = var_54_axes_0, x = var_48_to_fp16)[name = tensor<string, []>("op_54_cast_fp16")];
            tensor<fp16, [1, 1, 233, 128]> input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_54_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
            tensor<int32, [8]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 2, 1, 2, 1])];
            tensor<string, []> input_3_mode_0 = const()[name = tensor<string, []>("input_3_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1, 236, 131]> input_3_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
            tensor<string, []> tensor_3_pad_type_0 = const()[name = tensor<string, []>("tensor_3_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> tensor_3_strides_0 = const()[name = tensor<string, []>("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [4]> tensor_3_pad_0 = const()[name = tensor<string, []>("tensor_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = tensor<string, []>("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> tensor_3_groups_0 = const()[name = tensor<string, []>("tensor_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1, 3, 3]> encoder_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2752))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> encoder_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("encoder_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3328)))];
            tensor<fp16, [1, 256, 117, 65]> tensor_3_cast_fp16 = conv(bias = encoder_pre_encode_conv_0_bias_to_fp16, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = encoder_pre_encode_conv_0_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = tensor<string, []>("tensor_3_cast_fp16")];
            tensor<string, []> current_lengths_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("current_lengths_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, []> var_67_promoted_to_fp16 = const()[name = tensor<string, []>("op_67_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1]> mel_length_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = mel_length)[name = tensor<string, []>("cast_9")];
            tensor<fp16, [1]> var_68_cast_fp16 = add(x = mel_length_to_fp16, y = var_67_promoted_to_fp16)[name = tensor<string, []>("op_68_cast_fp16")];
            tensor<fp16, []> var_69_promoted_to_fp16 = const()[name = tensor<string, []>("op_69_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> var_70_cast_fp16 = add(x = var_68_cast_fp16, y = var_69_promoted_to_fp16)[name = tensor<string, []>("op_70_cast_fp16")];
            tensor<fp16, []> var_71_promoted_to_fp16 = const()[name = tensor<string, []>("op_71_promoted_to_fp16"), val = tensor<fp16, []>(0x1.8p+1)];
            tensor<fp16, [1]> var_72_cast_fp16 = sub(x = var_70_cast_fp16, y = var_71_promoted_to_fp16)[name = tensor<string, []>("op_72_cast_fp16")];
            tensor<fp16, []> var_25_promoted_to_fp16 = const()[name = tensor<string, []>("op_25_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1]> floor_div_0_cast_fp16 = floor_div(x = var_72_cast_fp16, y = var_25_promoted_to_fp16)[name = tensor<string, []>("floor_div_0_cast_fp16")];
            tensor<fp16, []> var_74_promoted_to_fp16 = const()[name = tensor<string, []>("op_74_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_74_promoted_to_fp16)[name = tensor<string, []>("current_lengths_3_cast_fp16")];
            tensor<string, []> lengths_21_dtype_0 = const()[name = tensor<string, []>("lengths_21_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, [1, 117]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<int32, [1, 117]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116]])];
            tensor<int32, [1]> var_83_axes_0 = const()[name = tensor<string, []>("op_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_3_cast_fp16)[name = tensor<string, []>("cast_8")];
            tensor<int32, [1, 1]> var_83 = expand_dims(axes = var_83_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = tensor<string, []>("op_83")];
            tensor<bool, [1, 117]> time_mask_3 = less(x = expand_dims_1, y = var_83)[name = tensor<string, []>("time_mask_3")];
            tensor<int32, [1]> var_85_axes_0 = const()[name = tensor<string, []>("op_85_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 117, 1]> var_85 = expand_dims(axes = var_85_axes_0, x = time_mask_3)[name = tensor<string, []>("op_85")];
            tensor<int32, [3]> var_87_reps_0 = const()[name = tensor<string, []>("op_87_reps_0"), val = tensor<int32, [3]>([1, 1, 65])];
            tensor<bool, [1, 117, 65]> var_87 = tile(reps = var_87_reps_0, x = var_85)[name = tensor<string, []>("op_87")];
            tensor<int32, [1]> var_93_axes_0 = const()[name = tensor<string, []>("op_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<string, []> mask_3_to_fp16_dtype_0 = const()[name = tensor<string, []>("mask_3_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 117, 65]> var_87_to_fp16 = cast(dtype = mask_3_to_fp16_dtype_0, x = var_87)[name = tensor<string, []>("cast_7")];
            tensor<fp16, [1, 1, 117, 65]> var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = var_87_to_fp16)[name = tensor<string, []>("op_93_cast_fp16")];
            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = tensor<string, []>("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
            tensor<fp16, [1, 256, 117, 65]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_93_cast_fp16)[name = tensor<string, []>("expanded_mask_3_cast_fp16")];
            tensor<fp16, [1, 256, 117, 65]> input_5_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
            tensor<fp16, [1, 256, 117, 65]> tensor_5_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor<string, []>("tensor_5_cast_fp16")];
            tensor<fp16, [1, 256, 117, 65]> input_7_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
            tensor<int32, [8]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 2, 1, 2, 1])];
            tensor<string, []> input_9_mode_0 = const()[name = tensor<string, []>("input_9_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_23_to_fp16 = const()[name = tensor<string, []>("const_23_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 256, 120, 68]> input_9_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_9_mode_0, pad = input_9_pad_0, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
            tensor<string, []> tensor_7_pad_type_0 = const()[name = tensor<string, []>("tensor_7_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> tensor_7_strides_0 = const()[name = tensor<string, []>("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, []> tensor_7_groups_0 = const()[name = tensor<string, []>("tensor_7_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [4]> tensor_7_pad_0 = const()[name = tensor<string, []>("tensor_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = tensor<string, []>("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> encoder_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3904))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6272))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> encoder_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6848)))];
            tensor<fp16, [1, 256, 59, 33]> tensor_7_cast_fp16 = conv(bias = encoder_pre_encode_conv_2_bias_to_fp16, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = encoder_pre_encode_conv_2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = tensor<string, []>("tensor_7_cast_fp16")];
            tensor<fp16, []> var_115_promoted_to_fp16 = const()[name = tensor<string, []>("op_115_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1]> var_116_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_115_promoted_to_fp16)[name = tensor<string, []>("op_116_cast_fp16")];
            tensor<fp16, []> var_117_promoted_to_fp16 = const()[name = tensor<string, []>("op_117_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> var_118_cast_fp16 = add(x = var_116_cast_fp16, y = var_117_promoted_to_fp16)[name = tensor<string, []>("op_118_cast_fp16")];
            tensor<fp16, []> var_119_promoted_to_fp16 = const()[name = tensor<string, []>("op_119_promoted_to_fp16"), val = tensor<fp16, []>(0x1.8p+1)];
            tensor<fp16, [1]> var_120_cast_fp16 = sub(x = var_118_cast_fp16, y = var_119_promoted_to_fp16)[name = tensor<string, []>("op_120_cast_fp16")];
            tensor<fp16, []> var_25_promoted_1_to_fp16 = const()[name = tensor<string, []>("op_25_promoted_1_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_120_cast_fp16, y = var_25_promoted_1_to_fp16)[name = tensor<string, []>("floor_div_1_cast_fp16")];
            tensor<fp16, []> var_122_promoted_to_fp16 = const()[name = tensor<string, []>("op_122_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_122_promoted_to_fp16)[name = tensor<string, []>("current_lengths_5_cast_fp16")];
            tensor<string, []> lengths_23_dtype_0 = const()[name = tensor<string, []>("lengths_23_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, [1, 59]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<int32, [1, 59]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]])];
            tensor<int32, [1]> var_131_axes_0 = const()[name = tensor<string, []>("op_131_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_23_dtype_0, x = current_lengths_5_cast_fp16)[name = tensor<string, []>("cast_6")];
            tensor<int32, [1, 1]> var_131 = expand_dims(axes = var_131_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = tensor<string, []>("op_131")];
            tensor<bool, [1, 59]> time_mask_5 = less(x = expand_dims_2, y = var_131)[name = tensor<string, []>("time_mask_5")];
            tensor<int32, [1]> var_133_axes_0 = const()[name = tensor<string, []>("op_133_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 59, 1]> var_133 = expand_dims(axes = var_133_axes_0, x = time_mask_5)[name = tensor<string, []>("op_133")];
            tensor<int32, [3]> var_135_reps_0 = const()[name = tensor<string, []>("op_135_reps_0"), val = tensor<int32, [3]>([1, 1, 33])];
            tensor<bool, [1, 59, 33]> var_135 = tile(reps = var_135_reps_0, x = var_133)[name = tensor<string, []>("op_135")];
            tensor<int32, [1]> var_141_axes_0 = const()[name = tensor<string, []>("op_141_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<string, []> mask_5_to_fp16_dtype_0 = const()[name = tensor<string, []>("mask_5_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 59, 33]> var_135_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_135)[name = tensor<string, []>("cast_5")];
            tensor<fp16, [1, 1, 59, 33]> var_141_cast_fp16 = expand_dims(axes = var_141_axes_0, x = var_135_to_fp16)[name = tensor<string, []>("op_141_cast_fp16")];
            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = tensor<string, []>("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
            tensor<fp16, [1, 256, 59, 33]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_141_cast_fp16)[name = tensor<string, []>("expanded_mask_7_cast_fp16")];
            tensor<fp16, [1, 256, 59, 33]> input_11_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
            tensor<string, []> tensor_9_pad_type_0 = const()[name = tensor<string, []>("tensor_9_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> tensor_9_strides_0 = const()[name = tensor<string, []>("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> tensor_9_pad_0 = const()[name = tensor<string, []>("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = tensor<string, []>("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> tensor_9_groups_0 = const()[name = tensor<string, []>("tensor_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1, 1]> encoder_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_3_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7424))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73024))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> encoder_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor<string, []>("encoder_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73600)))];
            tensor<fp16, [1, 256, 59, 33]> tensor_9_cast_fp16 = conv(bias = encoder_pre_encode_conv_3_bias_to_fp16, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = encoder_pre_encode_conv_3_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = tensor<string, []>("tensor_9_cast_fp16")];
            tensor<fp16, [1, 256, 59, 33]> input_13_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
            tensor<fp16, [1, 256, 59, 33]> tensor_11_cast_fp16 = relu(x = input_13_cast_fp16)[name = tensor<string, []>("tensor_11_cast_fp16")];
            tensor<fp16, [1, 256, 59, 33]> input_15_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
            tensor<int32, [8]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 2, 1, 2, 1])];
            tensor<string, []> input_17_mode_0 = const()[name = tensor<string, []>("input_17_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_41_to_fp16 = const()[name = tensor<string, []>("const_41_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 256, 62, 36]> input_17_cast_fp16 = pad(constant_val = const_41_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
            tensor<string, []> tensor_13_pad_type_0 = const()[name = tensor<string, []>("tensor_13_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> tensor_13_strides_0 = const()[name = tensor<string, []>("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, []> tensor_13_groups_0 = const()[name = tensor<string, []>("tensor_13_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [4]> tensor_13_pad_0 = const()[name = tensor<string, []>("tensor_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = tensor<string, []>("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> encoder_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_5_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74176))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76544))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> encoder_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor<string, []>("encoder_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77120)))];
            tensor<fp16, [1, 256, 30, 17]> tensor_13_cast_fp16 = conv(bias = encoder_pre_encode_conv_5_bias_to_fp16, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = encoder_pre_encode_conv_5_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = tensor<string, []>("tensor_13_cast_fp16")];
            tensor<fp16, []> var_178_promoted_to_fp16 = const()[name = tensor<string, []>("op_178_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1]> var_179_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_178_promoted_to_fp16)[name = tensor<string, []>("op_179_cast_fp16")];
            tensor<fp16, []> var_180_promoted_to_fp16 = const()[name = tensor<string, []>("op_180_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> var_181_cast_fp16 = add(x = var_179_cast_fp16, y = var_180_promoted_to_fp16)[name = tensor<string, []>("op_181_cast_fp16")];
            tensor<fp16, []> var_182_promoted_to_fp16 = const()[name = tensor<string, []>("op_182_promoted_to_fp16"), val = tensor<fp16, []>(0x1.8p+1)];
            tensor<fp16, [1]> var_183_cast_fp16 = sub(x = var_181_cast_fp16, y = var_182_promoted_to_fp16)[name = tensor<string, []>("op_183_cast_fp16")];
            tensor<fp16, []> var_25_promoted_2_to_fp16 = const()[name = tensor<string, []>("op_25_promoted_2_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_183_cast_fp16, y = var_25_promoted_2_to_fp16)[name = tensor<string, []>("floor_div_2_cast_fp16")];
            tensor<fp16, []> var_185_promoted_to_fp16 = const()[name = tensor<string, []>("op_185_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_185_promoted_to_fp16)[name = tensor<string, []>("current_lengths_cast_fp16")];
            tensor<string, []> lengths_dtype_0 = const()[name = tensor<string, []>("lengths_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, [1, 30]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1, 30]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])];
            tensor<int32, [1]> var_194_axes_0 = const()[name = tensor<string, []>("op_194_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = tensor<string, []>("cast_4")];
            tensor<int32, [1, 1]> var_194 = expand_dims(axes = var_194_axes_0, x = current_lengths_cast_fp16_to_int32)[name = tensor<string, []>("op_194")];
            tensor<bool, [1, 30]> time_mask = less(x = expand_dims_3, y = var_194)[name = tensor<string, []>("time_mask")];
            tensor<int32, [1]> var_196_axes_0 = const()[name = tensor<string, []>("op_196_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 30, 1]> var_196 = expand_dims(axes = var_196_axes_0, x = time_mask)[name = tensor<string, []>("op_196")];
            tensor<int32, [3]> var_198_reps_0 = const()[name = tensor<string, []>("op_198_reps_0"), val = tensor<int32, [3]>([1, 1, 17])];
            tensor<bool, [1, 30, 17]> var_198 = tile(reps = var_198_reps_0, x = var_196)[name = tensor<string, []>("op_198")];
            tensor<int32, [1]> var_204_axes_0 = const()[name = tensor<string, []>("op_204_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<string, []> mask_to_fp16_dtype_0 = const()[name = tensor<string, []>("mask_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [1, 30, 17]> var_198_to_fp16 = cast(dtype = mask_to_fp16_dtype_0, x = var_198)[name = tensor<string, []>("cast_3")];
            tensor<fp16, [1, 1, 30, 17]> var_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = var_198_to_fp16)[name = tensor<string, []>("op_204_cast_fp16")];
            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = tensor<string, []>("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
            tensor<fp16, [1, 256, 30, 17]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_204_cast_fp16)[name = tensor<string, []>("expanded_mask_13_cast_fp16")];
            tensor<fp16, [1, 256, 30, 17]> input_19_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
            tensor<string, []> tensor_15_pad_type_0 = const()[name = tensor<string, []>("tensor_15_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> tensor_15_strides_0 = const()[name = tensor<string, []>("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> tensor_15_pad_0 = const()[name = tensor<string, []>("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = tensor<string, []>("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> tensor_15_groups_0 = const()[name = tensor<string, []>("tensor_15_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1, 1]> encoder_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_conv_6_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77696))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143296))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> encoder_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor<string, []>("encoder_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143872)))];
            tensor<fp16, [1, 256, 30, 17]> tensor_15_cast_fp16 = conv(bias = encoder_pre_encode_conv_6_bias_to_fp16, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = encoder_pre_encode_conv_6_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = tensor<string, []>("tensor_15_cast_fp16")];
            tensor<fp16, [1, 256, 30, 17]> input_21_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
            tensor<fp16, [1, 256, 30, 17]> tensor_cast_fp16 = relu(x = input_21_cast_fp16)[name = tensor<string, []>("tensor_cast_fp16")];
            tensor<fp16, [1, 256, 30, 17]> x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = tensor<string, []>("x_3_cast_fp16")];
            tensor<int32, [4]> var_238_perm_0 = const()[name = tensor<string, []>("op_238_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_239 = const()[name = tensor<string, []>("op_239"), val = tensor<int32, [3]>([1, 30, -1])];
            tensor<fp16, [1, 30, 256, 17]> var_238_cast_fp16 = transpose(perm = var_238_perm_0, x = x_3_cast_fp16)[name = tensor<string, []>("transpose_0")];
            tensor<fp16, [1, 30, 4352]> input_cast_fp16 = reshape(shape = var_239, x = var_238_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
            tensor<fp16, [1024, 4352]> encoder_pre_encode_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("encoder_pre_encode_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4352]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144448))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4602048))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4600960)))];
            tensor<fp16, [1024]> encoder_pre_encode_out_bias_to_fp16 = const()[name = tensor<string, []>("encoder_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4604160)))];
            tensor<fp16, [1, 30, 1024]> linear_0_cast_fp16 = linear(bias = encoder_pre_encode_out_bias_to_fp16, weight = encoder_pre_encode_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
            tensor<int32, [3]> var_261_begin_0 = const()[name = tensor<string, []>("op_261_begin_0"), val = tensor<int32, [3]>([0, 2, 0])];
            tensor<int32, [3]> var_261_end_0 = const()[name = tensor<string, []>("op_261_end_0"), val = tensor<int32, [3]>([1, 30, 1024])];
            tensor<bool, [3]> var_261_end_mask_0 = const()[name = tensor<string, []>("op_261_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 28, 1024]> var_261_cast_fp16 = slice_by_index(begin = var_261_begin_0, end = var_261_end_0, end_mask = var_261_end_mask_0, x = linear_0_cast_fp16)[name = tensor<string, []>("op_261_cast_fp16")];
            tensor<string, []> var_266_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_266_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, []> var_268 = const()[name = tensor<string, []>("op_268"), val = tensor<int32, []>(2)];
            tensor<int32, [1]> var_269 = sub(x = current_lengths_cast_fp16_to_int32, y = var_268)[name = tensor<string, []>("op_269")];
            tensor<string, []> var_269_promoted_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_269_promoted_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, []> var_270_promoted_to_fp16 = const()[name = tensor<string, []>("op_270_promoted_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, []> const_61_to_fp16 = const()[name = tensor<string, []>("const_61_to_fp16"), val = tensor<fp16, []>(inf)];
            tensor<fp16, [1]> var_269_to_fp16 = cast(dtype = var_269_promoted_to_fp16_dtype_0, x = var_269)[name = tensor<string, []>("cast_2")];
            tensor<fp16, [1]> clip_0_cast_fp16 = clip(alpha = var_270_promoted_to_fp16, beta = const_61_to_fp16, x = var_269_to_fp16)[name = tensor<string, []>("clip_0_cast_fp16")];
            tensor<string, []> var_277_dtype_0 = const()[name = tensor<string, []>("op_277_dtype_0"), val = tensor<string, []>("int32")];
            tensor<fp32, [1, 28, 1024]> pre_encoded = cast(dtype = var_266_cast_fp16_to_fp32_dtype_0, x = var_261_cast_fp16)[name = tensor<string, []>("cast_0")];
            tensor<int32, [1]> pre_encoded_length = cast(dtype = var_277_dtype_0, x = clip_0_cast_fp16)[name = tensor<string, []>("cast_1")];
        } -> (pre_encoded, pre_encoded_length);
}