program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}})]
{
    func main<ios18>(tensor<fp32, [1, 128, 1501]> mel, tensor<int32, [1]> mel_length) {
            int32 var_25 = const()[name = string("op_25"), val = int32(-1)];
            tensor<int32, [3]> x_1_perm_0 = const()[name = string("x_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 1501]> mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_10")];
            tensor<fp16, [1, 1501, 128]> x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = mel_to_fp16)[name = string("transpose_317")];
            tensor<fp16, [1, 1, 1501, 128]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_1_cast_fp16)[name = string("tensor_1_cast_fp16")];
            tensor<int32, [1, 1501]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<int32, [1]> var_104_axes_0 = const()[name = string("op_104_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1, 1]> var_104 = expand_dims(axes = var_104_axes_0, x = mel_length)[name = string("op_104")];
            tensor<bool, [1, 1501]> time_mask_1 = less(x = expand_dims_0, y = var_104)[name = string("time_mask_1")];
            tensor<int32, [1]> var_106_axes_0 = const()[name = string("op_106_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 1501, 1]> var_106 = expand_dims(axes = var_106_axes_0, x = time_mask_1)[name = string("op_106")];
            tensor<int32, [3]> var_108_reps_0 = const()[name = string("op_108_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
            tensor<bool, [1, 1501, 128]> var_108 = tile(reps = var_108_reps_0, x = var_106)[name = string("op_108")];
            tensor<int32, [1]> var_114_axes_0 = const()[name = string("op_114_axes_0"), val = tensor<int32, [1]>([1])];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1, 1501, 128]> var_108_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_108)[name = string("cast_9")];
            tensor<fp16, [1, 1, 1501, 128]> var_114_cast_fp16 = expand_dims(axes = var_114_axes_0, x = var_108_to_fp16)[name = string("op_114_cast_fp16")];
            tensor<fp16, [1, 1, 1501, 128]> input_1_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_114_cast_fp16)[name = string("input_1_cast_fp16")];
            string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)];
            tensor<fp16, [256, 1, 3, 3]> encoder_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6144))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8512))))[name = string("encoder_pre_encode_conv_0_weight_to_fp16_quantized")];
            tensor<fp16, [256]> encoder_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9088)))];
            tensor<fp16, [1, 256, 751, 64]> 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_1_cast_fp16)[name = string("tensor_3_cast_fp16")];
            string current_lengths_1_to_fp16_dtype_0 = const()[name = string("current_lengths_1_to_fp16_dtype_0"), val = string("fp16")];
            fp16 var_125_promoted_to_fp16 = const()[name = string("op_125_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> mel_length_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = mel_length)[name = string("cast_8")];
            tensor<fp16, [1]> var_126_cast_fp16 = add(x = mel_length_to_fp16, y = var_125_promoted_to_fp16)[name = string("op_126_cast_fp16")];
            fp16 var_127_promoted_to_fp16 = const()[name = string("op_127_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> var_128_cast_fp16 = add(x = var_126_cast_fp16, y = var_127_promoted_to_fp16)[name = string("op_128_cast_fp16")];
            fp16 var_129_promoted_to_fp16 = const()[name = string("op_129_promoted_to_fp16"), val = fp16(0x1.8p+1)];
            tensor<fp16, [1]> var_130_cast_fp16 = sub(x = var_128_cast_fp16, y = var_129_promoted_to_fp16)[name = string("op_130_cast_fp16")];
            fp16 var_23_promoted_to_fp16 = const()[name = string("op_23_promoted_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1]> floor_div_0_cast_fp16 = floor_div(x = var_130_cast_fp16, y = var_23_promoted_to_fp16)[name = string("floor_div_0_cast_fp16")];
            fp16 var_132_promoted_to_fp16 = const()[name = string("op_132_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_0_cast_fp16, y = var_132_promoted_to_fp16)[name = string("current_lengths_3_cast_fp16")];
            string lengths_19_dtype_0 = const()[name = string("lengths_19_dtype_0"), val = string("int32")];
            tensor<int32, [1, 751]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1, 751]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9664)))];
            tensor<int32, [1]> var_141_axes_0 = const()[name = string("op_141_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_7")];
            tensor<int32, [1, 1]> var_141 = expand_dims(axes = var_141_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_141")];
            tensor<bool, [1, 751]> time_mask_3 = less(x = expand_dims_1, y = var_141)[name = string("time_mask_3")];
            tensor<int32, [1]> var_143_axes_0 = const()[name = string("op_143_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 751, 1]> var_143 = expand_dims(axes = var_143_axes_0, x = time_mask_3)[name = string("op_143")];
            tensor<int32, [3]> var_145_reps_0 = const()[name = string("op_145_reps_0"), val = tensor<int32, [3]>([1, 1, 64])];
            tensor<bool, [1, 751, 64]> var_145 = tile(reps = var_145_reps_0, x = var_143)[name = string("op_145")];
            tensor<int32, [1]> var_151_axes_0 = const()[name = string("op_151_axes_0"), val = tensor<int32, [1]>([1])];
            string mask_3_to_fp16_dtype_0 = const()[name = string("mask_3_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1, 751, 64]> var_145_to_fp16 = cast(dtype = mask_3_to_fp16_dtype_0, x = var_145)[name = string("cast_6")];
            tensor<fp16, [1, 1, 751, 64]> var_151_cast_fp16 = expand_dims(axes = var_151_axes_0, x = var_145_to_fp16)[name = string("op_151_cast_fp16")];
            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
            tensor<fp16, [1, 256, 751, 64]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_151_cast_fp16)[name = string("expanded_mask_3_cast_fp16")];
            tensor<fp16, [1, 256, 751, 64]> input_3_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 256, 751, 64]> tensor_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = string("tensor_5_cast_fp16")];
            tensor<fp16, [1, 256, 751, 64]> input_5_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_5_cast_fp16")];
            string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)];
            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = 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_blockwise_shift_scale(data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12736))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15104))))[name = string("encoder_pre_encode_conv_2_weight_to_fp16_quantized")];
            tensor<fp16, [256]> encoder_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15680)))];
            tensor<fp16, [1, 256, 376, 32]> 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_5_cast_fp16)[name = string("tensor_7_cast_fp16")];
            fp16 var_171_promoted_to_fp16 = const()[name = string("op_171_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> var_172_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_171_promoted_to_fp16)[name = string("op_172_cast_fp16")];
            fp16 var_173_promoted_to_fp16 = const()[name = string("op_173_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> var_174_cast_fp16 = add(x = var_172_cast_fp16, y = var_173_promoted_to_fp16)[name = string("op_174_cast_fp16")];
            fp16 var_175_promoted_to_fp16 = const()[name = string("op_175_promoted_to_fp16"), val = fp16(0x1.8p+1)];
            tensor<fp16, [1]> var_176_cast_fp16 = sub(x = var_174_cast_fp16, y = var_175_promoted_to_fp16)[name = string("op_176_cast_fp16")];
            fp16 var_23_promoted_1_to_fp16 = const()[name = string("op_23_promoted_1_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_176_cast_fp16, y = var_23_promoted_1_to_fp16)[name = string("floor_div_1_cast_fp16")];
            fp16 var_178_promoted_to_fp16 = const()[name = string("op_178_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_178_promoted_to_fp16)[name = string("current_lengths_5_cast_fp16")];
            string lengths_21_dtype_0 = const()[name = string("lengths_21_dtype_0"), val = string("int32")];
            tensor<int32, [1, 376]> expand_dims_2 = const()[name = string("expand_dims_2"), val = tensor<int32, [1, 376]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16256)))];
            tensor<int32, [1]> var_187_axes_0 = const()[name = string("op_187_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_5")];
            tensor<int32, [1, 1]> var_187 = expand_dims(axes = var_187_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_187")];
            tensor<bool, [1, 376]> time_mask_5 = less(x = expand_dims_2, y = var_187)[name = string("time_mask_5")];
            tensor<int32, [1]> var_189_axes_0 = const()[name = string("op_189_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 376, 1]> var_189 = expand_dims(axes = var_189_axes_0, x = time_mask_5)[name = string("op_189")];
            tensor<int32, [3]> var_191_reps_0 = const()[name = string("op_191_reps_0"), val = tensor<int32, [3]>([1, 1, 32])];
            tensor<bool, [1, 376, 32]> var_191 = tile(reps = var_191_reps_0, x = var_189)[name = string("op_191")];
            tensor<int32, [1]> var_197_axes_0 = const()[name = string("op_197_axes_0"), val = tensor<int32, [1]>([1])];
            string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1, 376, 32]> var_191_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_191)[name = string("cast_4")];
            tensor<fp16, [1, 1, 376, 32]> var_197_cast_fp16 = expand_dims(axes = var_197_axes_0, x = var_191_to_fp16)[name = string("op_197_cast_fp16")];
            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
            tensor<fp16, [1, 256, 376, 32]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_197_cast_fp16)[name = string("expanded_mask_7_cast_fp16")];
            tensor<fp16, [1, 256, 376, 32]> input_7_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_7_cast_fp16")];
            string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> encoder_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17856))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83456))))[name = string("encoder_pre_encode_conv_3_weight_to_fp16_quantized")];
            tensor<fp16, [256]> encoder_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84032)))];
            tensor<fp16, [1, 256, 376, 32]> 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_7_cast_fp16)[name = string("tensor_9_cast_fp16")];
            tensor<fp16, [1, 256, 376, 32]> input_9_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 256, 376, 32]> tensor_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = string("tensor_11_cast_fp16")];
            tensor<fp16, [1, 256, 376, 32]> input_11_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_11_cast_fp16")];
            string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)];
            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = 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_blockwise_shift_scale(data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84608))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86976))))[name = string("encoder_pre_encode_conv_5_weight_to_fp16_quantized")];
            tensor<fp16, [256]> encoder_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87552)))];
            tensor<fp16, [1, 256, 188, 16]> 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_11_cast_fp16)[name = string("tensor_13_cast_fp16")];
            fp16 var_232_promoted_to_fp16 = const()[name = string("op_232_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> var_233_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_232_promoted_to_fp16)[name = string("op_233_cast_fp16")];
            fp16 var_234_promoted_to_fp16 = const()[name = string("op_234_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> var_235_cast_fp16 = add(x = var_233_cast_fp16, y = var_234_promoted_to_fp16)[name = string("op_235_cast_fp16")];
            fp16 var_236_promoted_to_fp16 = const()[name = string("op_236_promoted_to_fp16"), val = fp16(0x1.8p+1)];
            tensor<fp16, [1]> var_237_cast_fp16 = sub(x = var_235_cast_fp16, y = var_236_promoted_to_fp16)[name = string("op_237_cast_fp16")];
            fp16 var_23_promoted_2_to_fp16 = const()[name = string("op_23_promoted_2_to_fp16"), val = fp16(0x1p+1)];
            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_237_cast_fp16, y = var_23_promoted_2_to_fp16)[name = string("floor_div_2_cast_fp16")];
            fp16 var_239_promoted_to_fp16 = const()[name = string("op_239_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_239_promoted_to_fp16)[name = string("current_lengths_cast_fp16")];
            string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")];
            tensor<int32, [1, 188]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1, 188]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88128)))];
            tensor<int32, [1]> var_248_axes_0 = const()[name = string("op_248_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 = string("cast_3")];
            tensor<int32, [1, 1]> var_248 = expand_dims(axes = var_248_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_248")];
            tensor<bool, [1, 188]> time_mask = less(x = expand_dims_3, y = var_248)[name = string("time_mask")];
            tensor<int32, [1]> var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 188, 1]> var_250 = expand_dims(axes = var_250_axes_0, x = time_mask)[name = string("op_250")];
            tensor<int32, [3]> var_252_reps_0 = const()[name = string("op_252_reps_0"), val = tensor<int32, [3]>([1, 1, 16])];
            tensor<bool, [1, 188, 16]> var_252 = tile(reps = var_252_reps_0, x = var_250)[name = string("op_252")];
            tensor<int32, [1]> var_258_axes_0 = const()[name = string("op_258_axes_0"), val = tensor<int32, [1]>([1])];
            string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1, 188, 16]> var_252_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_252)[name = string("cast_2")];
            tensor<fp16, [1, 1, 188, 16]> var_258_cast_fp16 = expand_dims(axes = var_258_axes_0, x = var_252_to_fp16)[name = string("op_258_cast_fp16")];
            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
            tensor<fp16, [1, 256, 188, 16]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_258_cast_fp16)[name = string("expanded_mask_13_cast_fp16")];
            tensor<fp16, [1, 256, 188, 16]> input_13_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_13_cast_fp16")];
            string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 1, 1]> encoder_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154560))))[name = string("encoder_pre_encode_conv_6_weight_to_fp16_quantized")];
            tensor<fp16, [256]> encoder_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155136)))];
            tensor<fp16, [1, 256, 188, 16]> 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_13_cast_fp16)[name = string("tensor_15_cast_fp16")];
            tensor<fp16, [1, 256, 188, 16]> input_15_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 256, 188, 16]> tensor_cast_fp16 = relu(x = input_15_cast_fp16)[name = string("tensor_cast_fp16")];
            tensor<fp16, [1, 256, 188, 16]> x_3_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_3_cast_fp16")];
            tensor<int32, [4]> var_292_perm_0 = const()[name = string("op_292_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_293 = const()[name = string("op_293"), val = tensor<int32, [3]>([1, 188, -1])];
            tensor<fp16, [1, 188, 256, 16]> var_292_cast_fp16 = transpose(perm = var_292_perm_0, x = x_3_cast_fp16)[name = string("transpose_316")];
            tensor<fp16, [1, 188, 4096]> input_17_cast_fp16 = reshape(shape = var_293, x = var_292_cast_fp16)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_pre_encode_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155712))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4350080))))[name = string("encoder_pre_encode_out_weight_to_fp16_quantized")];
            tensor<fp16, [1024]> encoder_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4352192)))];
            tensor<fp16, [1, 188, 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_17_cast_fp16)[name = string("linear_0_cast_fp16")];
            tensor<int32, [1]> input_21_axes_0 = const()[name = string("input_21_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_fv_insert_ln_weight_to_fp16 = const()[name = string("encoder_fv_insert_ln_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4354304)))];
            tensor<fp16, [1024]> encoder_fv_insert_ln_bias_to_fp16 = const()[name = string("encoder_fv_insert_ln_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4356416)))];
            fp16 var_18_to_fp16 = const()[name = string("op_18_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 188, 1024]> input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = encoder_fv_insert_ln_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_fv_insert_ln_weight_to_fp16, x = linear_0_cast_fp16)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_fv_insert_down_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4358528))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5407168))))[name = string("encoder_fv_insert_down_weight_to_fp16_quantized")];
            tensor<fp16, [1024]> encoder_fv_insert_down_bias_to_fp16 = const()[name = string("encoder_fv_insert_down_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5409280)))];
            tensor<fp16, [1, 188, 1024]> linear_1_cast_fp16 = linear(bias = encoder_fv_insert_down_bias_to_fp16, weight = encoder_fv_insert_down_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_1_cast_fp16")];
            tensor<int32, [3]> input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string var_319_pad_type_0 = const()[name = string("op_319_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_319_pad_0 = const()[name = string("op_319_pad_0"), val = tensor<int32, [2]>([7, 7])];
            int32 var_319_groups_0 = const()[name = string("op_319_groups_0"), val = int32(1024)];
            tensor<int32, [1]> var_319_strides_0 = const()[name = string("op_319_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_319_dilations_0 = const()[name = string("op_319_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 15]> encoder_fv_insert_mix_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 15]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5411392))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5426816))))[name = string("encoder_fv_insert_mix_weight_to_fp16_quantized")];
            tensor<fp16, [1024]> encoder_fv_insert_mix_bias_to_fp16 = const()[name = string("encoder_fv_insert_mix_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5428928)))];
            tensor<fp16, [1, 1024, 188]> input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = linear_1_cast_fp16)[name = string("transpose_315")];
            tensor<fp16, [1, 1024, 188]> var_319_cast_fp16 = conv(bias = encoder_fv_insert_mix_bias_to_fp16, dilations = var_319_dilations_0, groups = var_319_groups_0, pad = var_319_pad_0, pad_type = var_319_pad_type_0, strides = var_319_strides_0, weight = encoder_fv_insert_mix_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("op_319_cast_fp16")];
            string var_320_mode_0 = const()[name = string("op_320_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 1024, 188]> var_320_cast_fp16 = gelu(mode = var_320_mode_0, x = var_319_cast_fp16)[name = string("op_320_cast_fp16")];
            tensor<int32, [3]> input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1024, 1024]> encoder_fv_insert_up_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431040))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6479680))))[name = string("encoder_fv_insert_up_weight_to_fp16_quantized")];
            tensor<fp16, [1024]> encoder_fv_insert_up_bias_to_fp16 = const()[name = string("encoder_fv_insert_up_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6481792)))];
            tensor<fp16, [1, 188, 1024]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = var_320_cast_fp16)[name = string("transpose_314")];
            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = encoder_fv_insert_up_bias_to_fp16, weight = encoder_fv_insert_up_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_2_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> audio_signal_1_cast_fp16 = add(x = linear_0_cast_fp16, y = linear_2_cast_fp16)[name = string("audio_signal_1_cast_fp16")];
            string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")];
            tensor<int32, [1]> var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<int32, [1]> encoder_length = cast(dtype = padding_length_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_1")];
            tensor<int32, [1, 1]> var_356 = expand_dims(axes = var_356_axes_0, x = encoder_length)[name = string("op_356")];
            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_3, y = var_356)[name = string("pad_mask_1")];
            tensor<int32, [1]> var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188]> var_358 = expand_dims(axes = var_358_axes_0, x = pad_mask_1)[name = string("op_358")];
            tensor<int32, [3]> var_359 = const()[name = string("op_359"), val = tensor<int32, [3]>([1, 188, 1])];
            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_359, x = var_358)[name = string("pad_mask_for_att_mask_1")];
            tensor<int32, [3]> var_361_perm_0 = const()[name = string("op_361_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, [1, 188, 188]> var_361 = transpose(perm = var_361_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")];
            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_361)[name = string("pad_mask_for_att_mask")];
            tensor<bool, [1, 188, 188]> const_63 = const()[name = string("const_63"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true]]])];
            tensor<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_63)[name = string("att_mask")];
            tensor<bool, [1, 188, 188]> mask_9 = logical_not(x = att_mask)[name = string("mask_9")];
            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")];
            tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6483904)))];
            tensor<fp16, [1024]> encoder_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6486016)))];
            tensor<fp16, [1, 188, 1024]> input_29_cast_fp16 = layer_norm(axes = input_29_axes_0, beta = encoder_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_0_norm_feed_forward1_weight_to_fp16, x = audio_signal_1_cast_fp16)[name = string("input_29_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6488128))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10682496))))[name = string("encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [4096]> linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10690752)))];
            tensor<fp16, [1, 188, 4096]> linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_3_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_33_cast_fp16 = silu(x = linear_3_cast_fp16)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10699008))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14893376))))[name = string("encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1024]> linear_4_bias_0_to_fp16 = const()[name = string("linear_4_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14895488)))];
            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_4_cast_fp16")];
            fp16 var_392_to_fp16 = const()[name = string("op_392_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_393_cast_fp16 = mul(x = linear_4_cast_fp16, y = var_392_to_fp16)[name = string("op_393_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_39_cast_fp16 = add(x = audio_signal_1_cast_fp16, y = var_393_cast_fp16)[name = string("input_39_cast_fp16")];
            tensor<int32, [1]> query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14897600)))];
            tensor<fp16, [1024]> encoder_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14899712)))];
            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = encoder_layers_0_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_0_norm_self_att_weight_to_fp16, x = input_39_cast_fp16)[name = string("query_1_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14901824))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15950464))))[name = string("encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")];
            tensor<int32, [4]> var_409 = const()[name = string("op_409"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_409, x = linear_5_cast_fp16)[name = string("q_1_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15952576))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17001216))))[name = string("encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_6_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_6_cast_fp16")];
            tensor<int32, [4]> var_413 = const()[name = string("op_413"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_413, x = linear_6_cast_fp16)[name = string("k_1_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17003328))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18051968))))[name = string("encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_7_cast_fp16")];
            tensor<int32, [4]> var_417 = const()[name = string("op_417"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_417, x = linear_7_cast_fp16)[name = string("v_1_cast_fp16")];
            tensor<int32, [4]> value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18054080)))];
            tensor<fp16, [1, 188, 8, 128]> var_429_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_429_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18056192)))];
            tensor<fp16, [1, 188, 8, 128]> var_431_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_431_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)];
            bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_433_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18058304))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18442368))))[name = string("op_433_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_431_cast_fp16)[name = string("transpose_312")];
            tensor<fp16, [1, 8, 188, 375]> x_7_cast_fp16 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_433_to_fp16_quantized)[name = string("x_7_cast_fp16")];
            tensor<int32, [8]> x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_9_mode_0 = const()[name = string("x_9_mode_0"), val = string("constant")];
            fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_9_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<int32, [4]> var_441 = const()[name = string("op_441"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_11_cast_fp16 = reshape(shape = var_441, x = x_9_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> var_445_begin_0 = const()[name = string("op_445_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_445_end_0 = const()[name = string("op_445_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_445_end_mask_0 = const()[name = string("op_445_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_445_cast_fp16 = slice_by_index(begin = var_445_begin_0, end = var_445_end_0, end_mask = var_445_end_mask_0, x = x_11_cast_fp16)[name = string("op_445_cast_fp16")];
            tensor<int32, [4]> var_446 = const()[name = string("op_446"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_446, x = var_445_cast_fp16)[name = string("matrix_bd_1_cast_fp16")];
            bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)];
            bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")];
            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_429_cast_fp16)[name = string("transpose_311")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_1_cast_fp16")];
            tensor<int32, [4]> matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_3_cast_fp16 = slice_by_index(begin = matrix_bd_3_begin_0, end = matrix_bd_3_end_0, end_mask = matrix_bd_3_end_mask_0, x = matrix_bd_1_cast_fp16)[name = string("matrix_bd_3_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_455_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_455_cast_fp16")];
            fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_455_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")];
            tensor<int32, [1]> mask_11_axes_0 = const()[name = string("mask_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188, 188]> mask_11 = expand_dims(axes = mask_11_axes_0, x = mask_9)[name = string("mask_11")];
            fp16 var_10_to_fp16 = const()[name = string("op_10_to_fp16"), val = fp16(-0x1.388p+13)];
            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_11)[name = string("scores_3_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_461_cast_fp16 = softmax(axis = var_25, x = scores_3_cast_fp16)[name = string("op_461_cast_fp16")];
            fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 188]> input_41_cast_fp16 = select(a = var_9_to_fp16, b = var_461_cast_fp16, cond = mask_11)[name = string("input_41_cast_fp16")];
            bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)];
            bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")];
            tensor<fp16, [1, 8, 188, 128]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_41_cast_fp16, y = value_3_cast_fp16)[name = string("x_13_cast_fp16")];
            tensor<int32, [4]> var_465_perm_0 = const()[name = string("op_465_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_466 = const()[name = string("op_466"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_465_cast_fp16 = transpose(perm = var_465_perm_0, x = x_13_cast_fp16)[name = string("transpose_308")];
            tensor<fp16, [1, 188, 1024]> input_43_cast_fp16 = reshape(shape = var_466, x = var_465_cast_fp16)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18443200))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19491840))))[name = string("encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("linear_9_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_47_cast_fp16 = add(x = input_39_cast_fp16, y = linear_9_cast_fp16)[name = string("input_47_cast_fp16")];
            tensor<int32, [1]> x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19493952)))];
            tensor<fp16, [1024]> encoder_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19496064)))];
            tensor<fp16, [1, 188, 1024]> x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = encoder_layers_0_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_17_cast_fp16")];
            tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19498176))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21595392))))[name = string("encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_17_cast_fp16)[name = string("transpose_307")];
            tensor<fp16, [1, 2048, 188]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = encoder_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
            int32 x_19_split_num_splits_0 = const()[name = string("x_19_split_num_splits_0"), val = int32(2)];
            int32 x_19_split_axis_0 = const()[name = string("x_19_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_19_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_19_split_cast_fp16_1 = split(axis = x_19_split_axis_0, num_splits = x_19_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_19_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_19_split_1_sigmoid_cast_fp16 = sigmoid(x = x_19_split_cast_fp16_1)[name = string("x_19_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_19_cast_fp16 = mul(x = x_19_split_cast_fp16_0, y = x_19_split_1_sigmoid_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<int32, [1]> var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188]> var_488 = expand_dims(axes = var_488_axes_0, x = pad_mask)[name = string("op_488")];
            tensor<fp16, [1, 1024, 188]> input_53_cast_fp16 = select(a = var_9_to_fp16, b = x_19_cast_fp16, cond = var_488)[name = string("input_53_cast_fp16")];
            tensor<int32, [6]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("constant")];
            fp16 const_73_to_fp16 = const()[name = string("const_73_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_55_cast_fp16 = pad(constant_val = const_73_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_304_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21599552))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21608832))))[name = string("const_304_to_fp16_quantized")];
            tensor<fp16, [1024]> const_305_to_fp16 = const()[name = string("const_305_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21610944)))];
            tensor<fp16, [1, 1024, 188]> input_59_cast_fp16 = conv(bias = const_305_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_304_to_fp16_quantized, x = input_55_cast_fp16)[name = string("input_59_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_61_cast_fp16 = silu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
            string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21613056))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22661696))))[name = string("encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = encoder_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [3]> input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_21_cast_fp16)[name = string("transpose_306")];
            tensor<fp16, [1, 188, 1024]> input_65_cast_fp16 = add(x = input_47_cast_fp16, y = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
            tensor<int32, [1]> input_67_axes_0 = const()[name = string("input_67_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22663808)))];
            tensor<fp16, [1024]> encoder_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22665920)))];
            tensor<fp16, [1, 188, 1024]> input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = encoder_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_0_norm_feed_forward2_weight_to_fp16, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22668032))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26862400))))[name = string("encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_67_cast_fp16)[name = string("linear_10_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_71_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_71_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26870656))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31065024))))[name = string("encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_11_cast_fp16")];
            fp16 var_526_to_fp16 = const()[name = string("op_526_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_527_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_526_to_fp16)[name = string("op_527_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_77_cast_fp16 = add(x = input_65_cast_fp16, y = var_527_cast_fp16)[name = string("input_77_cast_fp16")];
            tensor<int32, [1]> input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31067136)))];
            tensor<fp16, [1024]> encoder_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31069248)))];
            tensor<fp16, [1, 188, 1024]> input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_layers_0_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_0_norm_out_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
            tensor<int32, [1]> input_81_axes_0 = const()[name = string("input_81_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31071360)))];
            tensor<fp16, [1024]> encoder_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31073472)))];
            tensor<fp16, [1, 188, 1024]> input_81_cast_fp16 = layer_norm(axes = input_81_axes_0, beta = encoder_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_1_norm_feed_forward1_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31075584))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35269952))))[name = string("encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_12_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_81_cast_fp16)[name = string("linear_12_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_85_cast_fp16 = silu(x = linear_12_cast_fp16)[name = string("input_85_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35278208))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39472576))))[name = string("encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_13_cast_fp16")];
            fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_556_cast_fp16 = mul(x = linear_13_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_91_cast_fp16 = add(x = input_79_cast_fp16, y = var_556_cast_fp16)[name = string("input_91_cast_fp16")];
            tensor<int32, [1]> query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39474688)))];
            tensor<fp16, [1024]> encoder_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39476800)))];
            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = encoder_layers_1_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_1_norm_self_att_weight_to_fp16, x = input_91_cast_fp16)[name = string("query_3_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39478912))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40527552))))[name = string("encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")];
            tensor<int32, [4]> var_572 = const()[name = string("op_572"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_572, x = linear_14_cast_fp16)[name = string("q_7_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40529664))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41578304))))[name = string("encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_15_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_15_cast_fp16")];
            tensor<int32, [4]> var_576 = const()[name = string("op_576"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_576, x = linear_15_cast_fp16)[name = string("k_5_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41580416))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42629056))))[name = string("encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_16_cast_fp16")];
            tensor<int32, [4]> var_580 = const()[name = string("op_580"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_580, x = linear_16_cast_fp16)[name = string("v_3_cast_fp16")];
            tensor<int32, [4]> value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42631168)))];
            tensor<fp16, [1, 188, 8, 128]> var_592_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_592_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42633280)))];
            tensor<fp16, [1, 188, 8, 128]> var_594_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_594_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_29_transpose_x_0 = const()[name = string("x_29_transpose_x_0"), val = bool(false)];
            bool x_29_transpose_y_0 = const()[name = string("x_29_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_596_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42635392))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43019456))))[name = string("op_596_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_594_cast_fp16)[name = string("transpose_305")];
            tensor<fp16, [1, 8, 188, 375]> x_29_cast_fp16 = matmul(transpose_x = x_29_transpose_x_0, transpose_y = x_29_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_596_to_fp16_quantized)[name = string("x_29_cast_fp16")];
            tensor<int32, [8]> x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_31_mode_0 = const()[name = string("x_31_mode_0"), val = string("constant")];
            fp16 const_80_to_fp16 = const()[name = string("const_80_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_31_cast_fp16 = pad(constant_val = const_80_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> var_604 = const()[name = string("op_604"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_33_cast_fp16 = reshape(shape = var_604, x = x_31_cast_fp16)[name = string("x_33_cast_fp16")];
            tensor<int32, [4]> var_608_begin_0 = const()[name = string("op_608_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_608_end_0 = const()[name = string("op_608_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_608_end_mask_0 = const()[name = string("op_608_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_608_cast_fp16 = slice_by_index(begin = var_608_begin_0, end = var_608_end_0, end_mask = var_608_end_mask_0, x = x_33_cast_fp16)[name = string("op_608_cast_fp16")];
            tensor<int32, [4]> var_609 = const()[name = string("op_609"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_609, x = var_608_cast_fp16)[name = string("matrix_bd_5_cast_fp16")];
            bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)];
            bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")];
            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_592_cast_fp16)[name = string("transpose_304")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_3_cast_fp16")];
            tensor<int32, [4]> matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_7_cast_fp16 = slice_by_index(begin = matrix_bd_7_begin_0, end = matrix_bd_7_end_0, end_mask = matrix_bd_7_end_mask_0, x = matrix_bd_5_cast_fp16)[name = string("matrix_bd_7_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_618_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_618_cast_fp16")];
            fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_11)[name = string("scores_7_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_624_cast_fp16 = softmax(axis = var_25, x = scores_7_cast_fp16)[name = string("op_624_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_93_cast_fp16 = select(a = var_9_to_fp16, b = var_624_cast_fp16, cond = mask_11)[name = string("input_93_cast_fp16")];
            bool x_35_transpose_x_0 = const()[name = string("x_35_transpose_x_0"), val = bool(false)];
            bool x_35_transpose_y_0 = const()[name = string("x_35_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")];
            tensor<fp16, [1, 8, 188, 128]> x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = input_93_cast_fp16, y = value_5_cast_fp16)[name = string("x_35_cast_fp16")];
            tensor<int32, [4]> var_628_perm_0 = const()[name = string("op_628_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_629 = const()[name = string("op_629"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_628_cast_fp16 = transpose(perm = var_628_perm_0, x = x_35_cast_fp16)[name = string("transpose_301")];
            tensor<fp16, [1, 188, 1024]> input_95_cast_fp16 = reshape(shape = var_629, x = var_628_cast_fp16)[name = string("input_95_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43020288))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44068928))))[name = string("encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_18_cast_fp16)[name = string("input_99_cast_fp16")];
            tensor<int32, [1]> x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44071040)))];
            tensor<fp16, [1024]> encoder_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44073152)))];
            tensor<fp16, [1, 188, 1024]> x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = encoder_layers_1_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44075264))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46172480))))[name = string("encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_39_cast_fp16)[name = string("transpose_300")];
            tensor<fp16, [1, 2048, 188]> input_103_cast_fp16 = conv(dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = encoder_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
            int32 x_41_split_num_splits_0 = const()[name = string("x_41_split_num_splits_0"), val = int32(2)];
            int32 x_41_split_axis_0 = const()[name = string("x_41_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_41_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_41_split_cast_fp16_1 = split(axis = x_41_split_axis_0, num_splits = x_41_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_41_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_41_split_1_sigmoid_cast_fp16 = sigmoid(x = x_41_split_cast_fp16_1)[name = string("x_41_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_41_cast_fp16 = mul(x = x_41_split_cast_fp16_0, y = x_41_split_1_sigmoid_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_105_cast_fp16 = select(a = var_9_to_fp16, b = x_41_cast_fp16, cond = var_488)[name = string("input_105_cast_fp16")];
            tensor<int32, [6]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")];
            fp16 const_83_to_fp16 = const()[name = string("const_83_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_107_cast_fp16 = pad(constant_val = const_83_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_306_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46176640))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46185920))))[name = string("const_306_to_fp16_quantized")];
            tensor<fp16, [1024]> const_307_to_fp16 = const()[name = string("const_307_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46188032)))];
            tensor<fp16, [1, 1024, 188]> input_111_cast_fp16 = conv(bias = const_307_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_306_to_fp16_quantized, x = input_107_cast_fp16)[name = string("input_111_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_113_cast_fp16 = silu(x = input_111_cast_fp16)[name = string("input_113_cast_fp16")];
            string x_43_pad_type_0 = const()[name = string("x_43_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_43_strides_0 = const()[name = string("x_43_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_43_dilations_0 = const()[name = string("x_43_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_43_groups_0 = const()[name = string("x_43_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46190144))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47238784))))[name = string("encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_43_cast_fp16 = conv(dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = encoder_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_113_cast_fp16)[name = string("x_43_cast_fp16")];
            tensor<int32, [3]> input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_43_cast_fp16)[name = string("transpose_299")];
            tensor<fp16, [1, 188, 1024]> input_117_cast_fp16 = add(x = input_99_cast_fp16, y = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
            tensor<int32, [1]> input_119_axes_0 = const()[name = string("input_119_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47240896)))];
            tensor<fp16, [1024]> encoder_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47243008)))];
            tensor<fp16, [1, 188, 1024]> input_119_cast_fp16 = layer_norm(axes = input_119_axes_0, beta = encoder_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_1_norm_feed_forward2_weight_to_fp16, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47245120))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51439488))))[name = string("encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_19_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_123_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_123_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51447744))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55642112))))[name = string("encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_123_cast_fp16)[name = string("linear_20_cast_fp16")];
            fp16 var_689_to_fp16 = const()[name = string("op_689_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_690_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_689_to_fp16)[name = string("op_690_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_129_cast_fp16 = add(x = input_117_cast_fp16, y = var_690_cast_fp16)[name = string("input_129_cast_fp16")];
            tensor<int32, [1]> input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55644224)))];
            tensor<fp16, [1024]> encoder_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55646336)))];
            tensor<fp16, [1, 188, 1024]> input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_layers_1_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_1_norm_out_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
            tensor<int32, [1]> input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55648448)))];
            tensor<fp16, [1024]> encoder_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55650560)))];
            tensor<fp16, [1, 188, 1024]> input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = encoder_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_2_norm_feed_forward1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55652672))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59847040))))[name = string("encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_21_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_133_cast_fp16)[name = string("linear_21_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_137_cast_fp16 = silu(x = linear_21_cast_fp16)[name = string("input_137_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59855296))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64049664))))[name = string("encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_137_cast_fp16)[name = string("linear_22_cast_fp16")];
            fp16 var_718_to_fp16 = const()[name = string("op_718_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_719_cast_fp16 = mul(x = linear_22_cast_fp16, y = var_718_to_fp16)[name = string("op_719_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_143_cast_fp16 = add(x = input_131_cast_fp16, y = var_719_cast_fp16)[name = string("input_143_cast_fp16")];
            tensor<int32, [1]> query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64051776)))];
            tensor<fp16, [1024]> encoder_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64053888)))];
            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = encoder_layers_2_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_2_norm_self_att_weight_to_fp16, x = input_143_cast_fp16)[name = string("query_5_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64056000))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65104640))))[name = string("encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")];
            tensor<int32, [4]> var_735 = const()[name = string("op_735"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_735, x = linear_23_cast_fp16)[name = string("q_13_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65106752))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66155392))))[name = string("encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_24_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_24_cast_fp16")];
            tensor<int32, [4]> var_739 = const()[name = string("op_739"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_739, x = linear_24_cast_fp16)[name = string("k_9_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66157504))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67206144))))[name = string("encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_25_cast_fp16")];
            tensor<int32, [4]> var_743 = const()[name = string("op_743"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_743, x = linear_25_cast_fp16)[name = string("v_5_cast_fp16")];
            tensor<int32, [4]> value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67208256)))];
            tensor<fp16, [1, 188, 8, 128]> var_755_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_755_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67210368)))];
            tensor<fp16, [1, 188, 8, 128]> var_757_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_757_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_51_transpose_x_0 = const()[name = string("x_51_transpose_x_0"), val = bool(false)];
            bool x_51_transpose_y_0 = const()[name = string("x_51_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_759_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67212480))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67596544))))[name = string("op_759_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_757_cast_fp16)[name = string("transpose_298")];
            tensor<fp16, [1, 8, 188, 375]> x_51_cast_fp16 = matmul(transpose_x = x_51_transpose_x_0, transpose_y = x_51_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_759_to_fp16_quantized)[name = string("x_51_cast_fp16")];
            tensor<int32, [8]> x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")];
            fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_53_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")];
            tensor<int32, [4]> var_767 = const()[name = string("op_767"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_55_cast_fp16 = reshape(shape = var_767, x = x_53_cast_fp16)[name = string("x_55_cast_fp16")];
            tensor<int32, [4]> var_771_begin_0 = const()[name = string("op_771_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_771_end_0 = const()[name = string("op_771_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_771_end_mask_0 = const()[name = string("op_771_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_771_cast_fp16 = slice_by_index(begin = var_771_begin_0, end = var_771_end_0, end_mask = var_771_end_mask_0, x = x_55_cast_fp16)[name = string("op_771_cast_fp16")];
            tensor<int32, [4]> var_772 = const()[name = string("op_772"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_772, x = var_771_cast_fp16)[name = string("matrix_bd_9_cast_fp16")];
            bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)];
            bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")];
            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_755_cast_fp16)[name = string("transpose_297")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_5_cast_fp16")];
            tensor<int32, [4]> matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_11_cast_fp16 = slice_by_index(begin = matrix_bd_11_begin_0, end = matrix_bd_11_end_0, end_mask = matrix_bd_11_end_mask_0, x = matrix_bd_9_cast_fp16)[name = string("matrix_bd_11_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_781_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_781_cast_fp16")];
            fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_781_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_11)[name = string("scores_11_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_787_cast_fp16 = softmax(axis = var_25, x = scores_11_cast_fp16)[name = string("op_787_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_145_cast_fp16 = select(a = var_9_to_fp16, b = var_787_cast_fp16, cond = mask_11)[name = string("input_145_cast_fp16")];
            bool x_57_transpose_x_0 = const()[name = string("x_57_transpose_x_0"), val = bool(false)];
            bool x_57_transpose_y_0 = const()[name = string("x_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")];
            tensor<fp16, [1, 8, 188, 128]> x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = input_145_cast_fp16, y = value_7_cast_fp16)[name = string("x_57_cast_fp16")];
            tensor<int32, [4]> var_791_perm_0 = const()[name = string("op_791_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_792 = const()[name = string("op_792"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_791_cast_fp16 = transpose(perm = var_791_perm_0, x = x_57_cast_fp16)[name = string("transpose_294")];
            tensor<fp16, [1, 188, 1024]> input_147_cast_fp16 = reshape(shape = var_792, x = var_791_cast_fp16)[name = string("input_147_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_2_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67597376))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68646016))))[name = string("encoder_layers_2_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_2_self_attn_linear_out_weight_to_fp16_quantized, x = input_147_cast_fp16)[name = string("linear_27_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_151_cast_fp16 = add(x = input_143_cast_fp16, y = linear_27_cast_fp16)[name = string("input_151_cast_fp16")];
            tensor<int32, [1]> x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68648128)))];
            tensor<fp16, [1024]> encoder_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68650240)))];
            tensor<fp16, [1, 188, 1024]> x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = encoder_layers_2_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [3]> input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68652352))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70749568))))[name = string("encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_61_cast_fp16)[name = string("transpose_293")];
            tensor<fp16, [1, 2048, 188]> input_155_cast_fp16 = conv(dilations = input_155_dilations_0, groups = input_155_groups_0, pad = input_155_pad_0, pad_type = input_155_pad_type_0, strides = input_155_strides_0, weight = encoder_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
            int32 x_63_split_num_splits_0 = const()[name = string("x_63_split_num_splits_0"), val = int32(2)];
            int32 x_63_split_axis_0 = const()[name = string("x_63_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_63_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_63_split_cast_fp16_1 = split(axis = x_63_split_axis_0, num_splits = x_63_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_63_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_63_split_1_sigmoid_cast_fp16 = sigmoid(x = x_63_split_cast_fp16_1)[name = string("x_63_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_63_cast_fp16 = mul(x = x_63_split_cast_fp16_0, y = x_63_split_1_sigmoid_cast_fp16)[name = string("x_63_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_157_cast_fp16 = select(a = var_9_to_fp16, b = x_63_cast_fp16, cond = var_488)[name = string("input_157_cast_fp16")];
            tensor<int32, [6]> input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("constant")];
            fp16 const_93_to_fp16 = const()[name = string("const_93_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_159_cast_fp16 = pad(constant_val = const_93_to_fp16, mode = input_159_mode_0, pad = input_159_pad_0, x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("valid")];
            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_308_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70753728))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70763008))))[name = string("const_308_to_fp16_quantized")];
            tensor<fp16, [1024]> const_309_to_fp16 = const()[name = string("const_309_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70765120)))];
            tensor<fp16, [1, 1024, 188]> input_163_cast_fp16 = conv(bias = const_309_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_308_to_fp16_quantized, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_165_cast_fp16 = silu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
            string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70767232))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71815872))))[name = string("encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_65_cast_fp16 = conv(dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = encoder_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("x_65_cast_fp16")];
            tensor<int32, [3]> input_167_perm_0 = const()[name = string("input_167_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_167_cast_fp16 = transpose(perm = input_167_perm_0, x = x_65_cast_fp16)[name = string("transpose_292")];
            tensor<fp16, [1, 188, 1024]> input_169_cast_fp16 = add(x = input_151_cast_fp16, y = input_167_cast_fp16)[name = string("input_169_cast_fp16")];
            tensor<int32, [1]> input_171_axes_0 = const()[name = string("input_171_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71817984)))];
            tensor<fp16, [1024]> encoder_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71820096)))];
            tensor<fp16, [1, 188, 1024]> input_171_cast_fp16 = layer_norm(axes = input_171_axes_0, beta = encoder_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_2_norm_feed_forward2_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_2_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71822208))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76016576))))[name = string("encoder_layers_2_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_2_feed_forward2_linear1_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_28_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_175_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_175_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_2_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76024832))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80219200))))[name = string("encoder_layers_2_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_2_feed_forward2_linear2_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_29_cast_fp16")];
            fp16 var_852_to_fp16 = const()[name = string("op_852_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_853_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_852_to_fp16)[name = string("op_853_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_181_cast_fp16 = add(x = input_169_cast_fp16, y = var_853_cast_fp16)[name = string("input_181_cast_fp16")];
            tensor<int32, [1]> input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80221312)))];
            tensor<fp16, [1024]> encoder_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80223424)))];
            tensor<fp16, [1, 188, 1024]> input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_layers_2_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_2_norm_out_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
            tensor<int32, [1]> input_185_axes_0 = const()[name = string("input_185_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80225536)))];
            tensor<fp16, [1024]> encoder_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80227648)))];
            tensor<fp16, [1, 188, 1024]> input_185_cast_fp16 = layer_norm(axes = input_185_axes_0, beta = encoder_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_3_norm_feed_forward1_weight_to_fp16, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_3_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80229760))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84424128))))[name = string("encoder_layers_3_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_30_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_3_feed_forward1_linear1_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_30_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_189_cast_fp16 = silu(x = linear_30_cast_fp16)[name = string("input_189_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_3_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84432384))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88626752))))[name = string("encoder_layers_3_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_3_feed_forward1_linear2_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_31_cast_fp16")];
            fp16 var_881_to_fp16 = const()[name = string("op_881_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_882_cast_fp16 = mul(x = linear_31_cast_fp16, y = var_881_to_fp16)[name = string("op_882_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_195_cast_fp16 = add(x = input_183_cast_fp16, y = var_882_cast_fp16)[name = string("input_195_cast_fp16")];
            tensor<int32, [1]> query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88628864)))];
            tensor<fp16, [1024]> encoder_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88630976)))];
            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = encoder_layers_3_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_3_norm_self_att_weight_to_fp16, x = input_195_cast_fp16)[name = string("query_7_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_3_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88633088))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89681728))))[name = string("encoder_layers_3_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_3_self_attn_linear_q_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")];
            tensor<int32, [4]> var_898 = const()[name = string("op_898"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_898, x = linear_32_cast_fp16)[name = string("q_19_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_3_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89683840))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90732480))))[name = string("encoder_layers_3_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_33_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_3_self_attn_linear_k_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_33_cast_fp16")];
            tensor<int32, [4]> var_902 = const()[name = string("op_902"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_902, x = linear_33_cast_fp16)[name = string("k_13_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_3_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90734592))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91783232))))[name = string("encoder_layers_3_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_3_self_attn_linear_v_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_34_cast_fp16")];
            tensor<int32, [4]> var_906 = const()[name = string("op_906"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_906, x = linear_34_cast_fp16)[name = string("v_7_cast_fp16")];
            tensor<int32, [4]> value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91785344)))];
            tensor<fp16, [1, 188, 8, 128]> var_918_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_918_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91787456)))];
            tensor<fp16, [1, 188, 8, 128]> var_920_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_920_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_73_transpose_x_0 = const()[name = string("x_73_transpose_x_0"), val = bool(false)];
            bool x_73_transpose_y_0 = const()[name = string("x_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_922_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91789568))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92173632))))[name = string("op_922_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_920_cast_fp16)[name = string("transpose_291")];
            tensor<fp16, [1, 8, 188, 375]> x_73_cast_fp16 = matmul(transpose_x = x_73_transpose_x_0, transpose_y = x_73_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_922_to_fp16_quantized)[name = string("x_73_cast_fp16")];
            tensor<int32, [8]> x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_75_mode_0 = const()[name = string("x_75_mode_0"), val = string("constant")];
            fp16 const_100_to_fp16 = const()[name = string("const_100_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_75_cast_fp16 = pad(constant_val = const_100_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")];
            tensor<int32, [4]> var_930 = const()[name = string("op_930"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_77_cast_fp16 = reshape(shape = var_930, x = x_75_cast_fp16)[name = string("x_77_cast_fp16")];
            tensor<int32, [4]> var_934_begin_0 = const()[name = string("op_934_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_934_end_0 = const()[name = string("op_934_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_934_end_mask_0 = const()[name = string("op_934_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_934_cast_fp16 = slice_by_index(begin = var_934_begin_0, end = var_934_end_0, end_mask = var_934_end_mask_0, x = x_77_cast_fp16)[name = string("op_934_cast_fp16")];
            tensor<int32, [4]> var_935 = const()[name = string("op_935"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("matrix_bd_13_cast_fp16")];
            bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)];
            bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")];
            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_918_cast_fp16)[name = string("transpose_290")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_7_cast_fp16")];
            tensor<int32, [4]> matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_15_cast_fp16 = slice_by_index(begin = matrix_bd_15_begin_0, end = matrix_bd_15_end_0, end_mask = matrix_bd_15_end_mask_0, x = matrix_bd_13_cast_fp16)[name = string("matrix_bd_15_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_944_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_944_cast_fp16")];
            fp16 _inversed_scores_13_y_0_to_fp16 = const()[name = string("_inversed_scores_13_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_944_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_11)[name = string("scores_15_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_950_cast_fp16 = softmax(axis = var_25, x = scores_15_cast_fp16)[name = string("op_950_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_197_cast_fp16 = select(a = var_9_to_fp16, b = var_950_cast_fp16, cond = mask_11)[name = string("input_197_cast_fp16")];
            bool x_79_transpose_x_0 = const()[name = string("x_79_transpose_x_0"), val = bool(false)];
            bool x_79_transpose_y_0 = const()[name = string("x_79_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")];
            tensor<fp16, [1, 8, 188, 128]> x_79_cast_fp16 = matmul(transpose_x = x_79_transpose_x_0, transpose_y = x_79_transpose_y_0, x = input_197_cast_fp16, y = value_9_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<int32, [4]> var_954_perm_0 = const()[name = string("op_954_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_955 = const()[name = string("op_955"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_954_cast_fp16 = transpose(perm = var_954_perm_0, x = x_79_cast_fp16)[name = string("transpose_287")];
            tensor<fp16, [1, 188, 1024]> input_199_cast_fp16 = reshape(shape = var_955, x = var_954_cast_fp16)[name = string("input_199_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_3_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92174464))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93223104))))[name = string("encoder_layers_3_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_3_self_attn_linear_out_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_36_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_203_cast_fp16 = add(x = input_195_cast_fp16, y = linear_36_cast_fp16)[name = string("input_203_cast_fp16")];
            tensor<int32, [1]> x_83_axes_0 = const()[name = string("x_83_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93225216)))];
            tensor<fp16, [1024]> encoder_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93227328)))];
            tensor<fp16, [1, 188, 1024]> x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = encoder_layers_3_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_83_cast_fp16")];
            tensor<int32, [3]> input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93229440))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95326656))))[name = string("encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_83_cast_fp16)[name = string("transpose_286")];
            tensor<fp16, [1, 2048, 188]> input_207_cast_fp16 = conv(dilations = input_207_dilations_0, groups = input_207_groups_0, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = input_207_strides_0, weight = encoder_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")];
            int32 x_85_split_num_splits_0 = const()[name = string("x_85_split_num_splits_0"), val = int32(2)];
            int32 x_85_split_axis_0 = const()[name = string("x_85_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_85_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_85_split_cast_fp16_1 = split(axis = x_85_split_axis_0, num_splits = x_85_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_85_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_85_split_1_sigmoid_cast_fp16 = sigmoid(x = x_85_split_cast_fp16_1)[name = string("x_85_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_85_cast_fp16 = mul(x = x_85_split_cast_fp16_0, y = x_85_split_1_sigmoid_cast_fp16)[name = string("x_85_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_209_cast_fp16 = select(a = var_9_to_fp16, b = x_85_cast_fp16, cond = var_488)[name = string("input_209_cast_fp16")];
            tensor<int32, [6]> input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_211_mode_0 = const()[name = string("input_211_mode_0"), val = string("constant")];
            fp16 const_103_to_fp16 = const()[name = string("const_103_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_211_cast_fp16 = pad(constant_val = const_103_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
            string input_213_pad_type_0 = const()[name = string("input_213_pad_type_0"), val = string("valid")];
            int32 input_213_groups_0 = const()[name = string("input_213_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_213_strides_0 = const()[name = string("input_213_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_213_dilations_0 = const()[name = string("input_213_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_310_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95330816))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95340096))))[name = string("const_310_to_fp16_quantized")];
            tensor<fp16, [1024]> const_311_to_fp16 = const()[name = string("const_311_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95342208)))];
            tensor<fp16, [1, 1024, 188]> input_215_cast_fp16 = conv(bias = const_311_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = const_310_to_fp16_quantized, x = input_211_cast_fp16)[name = string("input_215_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_217_cast_fp16 = silu(x = input_215_cast_fp16)[name = string("input_217_cast_fp16")];
            string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95344320))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96392960))))[name = string("encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_87_cast_fp16 = conv(dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = encoder_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_217_cast_fp16)[name = string("x_87_cast_fp16")];
            tensor<int32, [3]> input_219_perm_0 = const()[name = string("input_219_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_219_cast_fp16 = transpose(perm = input_219_perm_0, x = x_87_cast_fp16)[name = string("transpose_285")];
            tensor<fp16, [1, 188, 1024]> input_221_cast_fp16 = add(x = input_203_cast_fp16, y = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
            tensor<int32, [1]> input_223_axes_0 = const()[name = string("input_223_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96395072)))];
            tensor<fp16, [1024]> encoder_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96397184)))];
            tensor<fp16, [1, 188, 1024]> input_223_cast_fp16 = layer_norm(axes = input_223_axes_0, beta = encoder_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_3_norm_feed_forward2_weight_to_fp16, x = input_221_cast_fp16)[name = string("input_223_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_3_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96399296))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100593664))))[name = string("encoder_layers_3_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_3_feed_forward2_linear1_weight_to_fp16_quantized, x = input_223_cast_fp16)[name = string("linear_37_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_227_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_227_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_3_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100601920))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104796288))))[name = string("encoder_layers_3_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_3_feed_forward2_linear2_weight_to_fp16_quantized, x = input_227_cast_fp16)[name = string("linear_38_cast_fp16")];
            fp16 var_1015_to_fp16 = const()[name = string("op_1015_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1016_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1015_to_fp16)[name = string("op_1016_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_233_cast_fp16 = add(x = input_221_cast_fp16, y = var_1016_cast_fp16)[name = string("input_233_cast_fp16")];
            tensor<int32, [1]> input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104798400)))];
            tensor<fp16, [1024]> encoder_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104800512)))];
            tensor<fp16, [1, 188, 1024]> input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_layers_3_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_3_norm_out_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")];
            tensor<int32, [1]> input_237_axes_0 = const()[name = string("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104802624)))];
            tensor<fp16, [1024]> encoder_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104804736)))];
            tensor<fp16, [1, 188, 1024]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = encoder_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_4_norm_feed_forward1_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_4_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104806848))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109001216))))[name = string("encoder_layers_4_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_39_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_4_feed_forward1_linear1_weight_to_fp16_quantized, x = input_237_cast_fp16)[name = string("linear_39_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_241_cast_fp16 = silu(x = linear_39_cast_fp16)[name = string("input_241_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_4_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109009472))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113203840))))[name = string("encoder_layers_4_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_4_feed_forward1_linear2_weight_to_fp16_quantized, x = input_241_cast_fp16)[name = string("linear_40_cast_fp16")];
            fp16 var_1044_to_fp16 = const()[name = string("op_1044_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1045_cast_fp16 = mul(x = linear_40_cast_fp16, y = var_1044_to_fp16)[name = string("op_1045_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_247_cast_fp16 = add(x = input_235_cast_fp16, y = var_1045_cast_fp16)[name = string("input_247_cast_fp16")];
            tensor<int32, [1]> query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113205952)))];
            tensor<fp16, [1024]> encoder_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113208064)))];
            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = encoder_layers_4_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_4_norm_self_att_weight_to_fp16, x = input_247_cast_fp16)[name = string("query_9_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_4_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113210176))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114258816))))[name = string("encoder_layers_4_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_4_self_attn_linear_q_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")];
            tensor<int32, [4]> var_1061 = const()[name = string("op_1061"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_1061, x = linear_41_cast_fp16)[name = string("q_25_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_4_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114260928))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115309568))))[name = string("encoder_layers_4_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_42_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_4_self_attn_linear_k_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_42_cast_fp16")];
            tensor<int32, [4]> var_1065 = const()[name = string("op_1065"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_1065, x = linear_42_cast_fp16)[name = string("k_17_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_4_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115311680))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116360320))))[name = string("encoder_layers_4_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_4_self_attn_linear_v_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_43_cast_fp16")];
            tensor<int32, [4]> var_1069 = const()[name = string("op_1069"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_1069, x = linear_43_cast_fp16)[name = string("v_9_cast_fp16")];
            tensor<int32, [4]> value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116362432)))];
            tensor<fp16, [1, 188, 8, 128]> var_1081_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1081_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116364544)))];
            tensor<fp16, [1, 188, 8, 128]> var_1083_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1083_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_95_transpose_x_0 = const()[name = string("x_95_transpose_x_0"), val = bool(false)];
            bool x_95_transpose_y_0 = const()[name = string("x_95_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1085_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116366656))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116750720))))[name = string("op_1085_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1083_cast_fp16)[name = string("transpose_284")];
            tensor<fp16, [1, 8, 188, 375]> x_95_cast_fp16 = matmul(transpose_x = x_95_transpose_x_0, transpose_y = x_95_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1085_to_fp16_quantized)[name = string("x_95_cast_fp16")];
            tensor<int32, [8]> x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_97_mode_0 = const()[name = string("x_97_mode_0"), val = string("constant")];
            fp16 const_110_to_fp16 = const()[name = string("const_110_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_97_cast_fp16 = pad(constant_val = const_110_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")];
            tensor<int32, [4]> var_1093 = const()[name = string("op_1093"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_99_cast_fp16 = reshape(shape = var_1093, x = x_97_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [4]> var_1097_begin_0 = const()[name = string("op_1097_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1097_end_0 = const()[name = string("op_1097_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1097_end_mask_0 = const()[name = string("op_1097_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1097_cast_fp16 = slice_by_index(begin = var_1097_begin_0, end = var_1097_end_0, end_mask = var_1097_end_mask_0, x = x_99_cast_fp16)[name = string("op_1097_cast_fp16")];
            tensor<int32, [4]> var_1098 = const()[name = string("op_1098"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_1098, x = var_1097_cast_fp16)[name = string("matrix_bd_17_cast_fp16")];
            bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)];
            bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")];
            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1081_cast_fp16)[name = string("transpose_283")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_9_cast_fp16")];
            tensor<int32, [4]> matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_19_cast_fp16 = slice_by_index(begin = matrix_bd_19_begin_0, end = matrix_bd_19_end_0, end_mask = matrix_bd_19_end_mask_0, x = matrix_bd_17_cast_fp16)[name = string("matrix_bd_19_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1107_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1107_cast_fp16")];
            fp16 _inversed_scores_17_y_0_to_fp16 = const()[name = string("_inversed_scores_17_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_1107_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_11)[name = string("scores_19_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1113_cast_fp16 = softmax(axis = var_25, x = scores_19_cast_fp16)[name = string("op_1113_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_249_cast_fp16 = select(a = var_9_to_fp16, b = var_1113_cast_fp16, cond = mask_11)[name = string("input_249_cast_fp16")];
            bool x_101_transpose_x_0 = const()[name = string("x_101_transpose_x_0"), val = bool(false)];
            bool x_101_transpose_y_0 = const()[name = string("x_101_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")];
            tensor<fp16, [1, 8, 188, 128]> x_101_cast_fp16 = matmul(transpose_x = x_101_transpose_x_0, transpose_y = x_101_transpose_y_0, x = input_249_cast_fp16, y = value_11_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> var_1117_perm_0 = const()[name = string("op_1117_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1118 = const()[name = string("op_1118"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1117_cast_fp16 = transpose(perm = var_1117_perm_0, x = x_101_cast_fp16)[name = string("transpose_280")];
            tensor<fp16, [1, 188, 1024]> input_251_cast_fp16 = reshape(shape = var_1118, x = var_1117_cast_fp16)[name = string("input_251_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_4_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116751552))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117800192))))[name = string("encoder_layers_4_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_4_self_attn_linear_out_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_45_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_255_cast_fp16 = add(x = input_247_cast_fp16, y = linear_45_cast_fp16)[name = string("input_255_cast_fp16")];
            tensor<int32, [1]> x_105_axes_0 = const()[name = string("x_105_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117802304)))];
            tensor<fp16, [1024]> encoder_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117804416)))];
            tensor<fp16, [1, 188, 1024]> x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = encoder_layers_4_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_105_cast_fp16")];
            tensor<int32, [3]> input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117806528))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119903744))))[name = string("encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_105_cast_fp16)[name = string("transpose_279")];
            tensor<fp16, [1, 2048, 188]> input_259_cast_fp16 = conv(dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = encoder_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")];
            int32 x_107_split_num_splits_0 = const()[name = string("x_107_split_num_splits_0"), val = int32(2)];
            int32 x_107_split_axis_0 = const()[name = string("x_107_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_107_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_107_split_cast_fp16_1 = split(axis = x_107_split_axis_0, num_splits = x_107_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_107_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_107_split_1_sigmoid_cast_fp16 = sigmoid(x = x_107_split_cast_fp16_1)[name = string("x_107_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_107_cast_fp16 = mul(x = x_107_split_cast_fp16_0, y = x_107_split_1_sigmoid_cast_fp16)[name = string("x_107_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_261_cast_fp16 = select(a = var_9_to_fp16, b = x_107_cast_fp16, cond = var_488)[name = string("input_261_cast_fp16")];
            tensor<int32, [6]> input_263_pad_0 = const()[name = string("input_263_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_263_mode_0 = const()[name = string("input_263_mode_0"), val = string("constant")];
            fp16 const_113_to_fp16 = const()[name = string("const_113_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_263_cast_fp16 = pad(constant_val = const_113_to_fp16, mode = input_263_mode_0, pad = input_263_pad_0, x = input_261_cast_fp16)[name = string("input_263_cast_fp16")];
            string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("valid")];
            int32 input_265_groups_0 = const()[name = string("input_265_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_265_strides_0 = const()[name = string("input_265_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_265_dilations_0 = const()[name = string("input_265_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_312_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119907904))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119917184))))[name = string("const_312_to_fp16_quantized")];
            tensor<fp16, [1024]> const_313_to_fp16 = const()[name = string("const_313_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119919296)))];
            tensor<fp16, [1, 1024, 188]> input_267_cast_fp16 = conv(bias = const_313_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = const_312_to_fp16_quantized, x = input_263_cast_fp16)[name = string("input_267_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_269_cast_fp16 = silu(x = input_267_cast_fp16)[name = string("input_269_cast_fp16")];
            string x_109_pad_type_0 = const()[name = string("x_109_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_109_strides_0 = const()[name = string("x_109_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_109_pad_0 = const()[name = string("x_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_109_dilations_0 = const()[name = string("x_109_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_109_groups_0 = const()[name = string("x_109_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119921408))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120970048))))[name = string("encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_109_cast_fp16 = conv(dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = encoder_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_269_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [3]> input_271_perm_0 = const()[name = string("input_271_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_271_cast_fp16 = transpose(perm = input_271_perm_0, x = x_109_cast_fp16)[name = string("transpose_278")];
            tensor<fp16, [1, 188, 1024]> input_273_cast_fp16 = add(x = input_255_cast_fp16, y = input_271_cast_fp16)[name = string("input_273_cast_fp16")];
            tensor<int32, [1]> input_275_axes_0 = const()[name = string("input_275_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120972160)))];
            tensor<fp16, [1024]> encoder_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120974272)))];
            tensor<fp16, [1, 188, 1024]> input_275_cast_fp16 = layer_norm(axes = input_275_axes_0, beta = encoder_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_4_norm_feed_forward2_weight_to_fp16, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_4_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120976384))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125170752))))[name = string("encoder_layers_4_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_4_feed_forward2_linear1_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_46_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_279_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_279_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_4_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125179008))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129373376))))[name = string("encoder_layers_4_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_4_feed_forward2_linear2_weight_to_fp16_quantized, x = input_279_cast_fp16)[name = string("linear_47_cast_fp16")];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1179_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1178_to_fp16)[name = string("op_1179_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_285_cast_fp16 = add(x = input_273_cast_fp16, y = var_1179_cast_fp16)[name = string("input_285_cast_fp16")];
            tensor<int32, [1]> input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129375488)))];
            tensor<fp16, [1024]> encoder_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129377600)))];
            tensor<fp16, [1, 188, 1024]> input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_layers_4_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_4_norm_out_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")];
            tensor<int32, [1]> input_289_axes_0 = const()[name = string("input_289_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129379712)))];
            tensor<fp16, [1024]> encoder_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129381824)))];
            tensor<fp16, [1, 188, 1024]> input_289_cast_fp16 = layer_norm(axes = input_289_axes_0, beta = encoder_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_5_norm_feed_forward1_weight_to_fp16, x = input_287_cast_fp16)[name = string("input_289_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_5_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129383936))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133578304))))[name = string("encoder_layers_5_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_48_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_5_feed_forward1_linear1_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_48_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_293_cast_fp16 = silu(x = linear_48_cast_fp16)[name = string("input_293_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_5_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133586560))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137780928))))[name = string("encoder_layers_5_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_5_feed_forward1_linear2_weight_to_fp16_quantized, x = input_293_cast_fp16)[name = string("linear_49_cast_fp16")];
            fp16 var_1207_to_fp16 = const()[name = string("op_1207_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1208_cast_fp16 = mul(x = linear_49_cast_fp16, y = var_1207_to_fp16)[name = string("op_1208_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_299_cast_fp16 = add(x = input_287_cast_fp16, y = var_1208_cast_fp16)[name = string("input_299_cast_fp16")];
            tensor<int32, [1]> query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137783040)))];
            tensor<fp16, [1024]> encoder_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137785152)))];
            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = encoder_layers_5_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_5_norm_self_att_weight_to_fp16, x = input_299_cast_fp16)[name = string("query_11_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_5_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137787264))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138835904))))[name = string("encoder_layers_5_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_5_self_attn_linear_q_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")];
            tensor<int32, [4]> var_1224 = const()[name = string("op_1224"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1224, x = linear_50_cast_fp16)[name = string("q_31_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_5_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138838016))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139886656))))[name = string("encoder_layers_5_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_51_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_5_self_attn_linear_k_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_51_cast_fp16")];
            tensor<int32, [4]> var_1228 = const()[name = string("op_1228"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1228, x = linear_51_cast_fp16)[name = string("k_21_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_5_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139888768))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140937408))))[name = string("encoder_layers_5_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_5_self_attn_linear_v_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_52_cast_fp16")];
            tensor<int32, [4]> var_1232 = const()[name = string("op_1232"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1232, x = linear_52_cast_fp16)[name = string("v_11_cast_fp16")];
            tensor<int32, [4]> value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140939520)))];
            tensor<fp16, [1, 188, 8, 128]> var_1244_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1244_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140941632)))];
            tensor<fp16, [1, 188, 8, 128]> var_1246_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1246_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_117_transpose_x_0 = const()[name = string("x_117_transpose_x_0"), val = bool(false)];
            bool x_117_transpose_y_0 = const()[name = string("x_117_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1248_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140943744))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141327808))))[name = string("op_1248_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1246_cast_fp16)[name = string("transpose_277")];
            tensor<fp16, [1, 8, 188, 375]> x_117_cast_fp16 = matmul(transpose_x = x_117_transpose_x_0, transpose_y = x_117_transpose_y_0, x = q_with_bias_v_11_cast_fp16, y = op_1248_to_fp16_quantized)[name = string("x_117_cast_fp16")];
            tensor<int32, [8]> x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_119_mode_0 = const()[name = string("x_119_mode_0"), val = string("constant")];
            fp16 const_120_to_fp16 = const()[name = string("const_120_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_119_cast_fp16 = pad(constant_val = const_120_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [4]> var_1256 = const()[name = string("op_1256"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_121_cast_fp16 = reshape(shape = var_1256, x = x_119_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> var_1260_begin_0 = const()[name = string("op_1260_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1260_end_0 = const()[name = string("op_1260_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1260_end_mask_0 = const()[name = string("op_1260_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1260_cast_fp16 = slice_by_index(begin = var_1260_begin_0, end = var_1260_end_0, end_mask = var_1260_end_mask_0, x = x_121_cast_fp16)[name = string("op_1260_cast_fp16")];
            tensor<int32, [4]> var_1261 = const()[name = string("op_1261"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1261, x = var_1260_cast_fp16)[name = string("matrix_bd_21_cast_fp16")];
            bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)];
            bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")];
            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1244_cast_fp16)[name = string("transpose_276")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_11_cast_fp16")];
            tensor<int32, [4]> matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_23_cast_fp16 = slice_by_index(begin = matrix_bd_23_begin_0, end = matrix_bd_23_end_0, end_mask = matrix_bd_23_end_mask_0, x = matrix_bd_21_cast_fp16)[name = string("matrix_bd_23_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1270_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1270_cast_fp16")];
            fp16 _inversed_scores_21_y_0_to_fp16 = const()[name = string("_inversed_scores_21_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1270_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_11)[name = string("scores_23_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1276_cast_fp16 = softmax(axis = var_25, x = scores_23_cast_fp16)[name = string("op_1276_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_301_cast_fp16 = select(a = var_9_to_fp16, b = var_1276_cast_fp16, cond = mask_11)[name = string("input_301_cast_fp16")];
            bool x_123_transpose_x_0 = const()[name = string("x_123_transpose_x_0"), val = bool(false)];
            bool x_123_transpose_y_0 = const()[name = string("x_123_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")];
            tensor<fp16, [1, 8, 188, 128]> x_123_cast_fp16 = matmul(transpose_x = x_123_transpose_x_0, transpose_y = x_123_transpose_y_0, x = input_301_cast_fp16, y = value_13_cast_fp16)[name = string("x_123_cast_fp16")];
            tensor<int32, [4]> var_1280_perm_0 = const()[name = string("op_1280_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1281 = const()[name = string("op_1281"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1280_cast_fp16 = transpose(perm = var_1280_perm_0, x = x_123_cast_fp16)[name = string("transpose_273")];
            tensor<fp16, [1, 188, 1024]> input_303_cast_fp16 = reshape(shape = var_1281, x = var_1280_cast_fp16)[name = string("input_303_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_5_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141328640))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142377280))))[name = string("encoder_layers_5_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_5_self_attn_linear_out_weight_to_fp16_quantized, x = input_303_cast_fp16)[name = string("linear_54_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_307_cast_fp16 = add(x = input_299_cast_fp16, y = linear_54_cast_fp16)[name = string("input_307_cast_fp16")];
            tensor<int32, [1]> x_127_axes_0 = const()[name = string("x_127_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142379392)))];
            tensor<fp16, [1024]> encoder_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142381504)))];
            tensor<fp16, [1, 188, 1024]> x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = encoder_layers_5_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_127_cast_fp16")];
            tensor<int32, [3]> input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142383616))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144480832))))[name = string("encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_127_cast_fp16)[name = string("transpose_272")];
            tensor<fp16, [1, 2048, 188]> input_311_cast_fp16 = conv(dilations = input_311_dilations_0, groups = input_311_groups_0, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = input_311_strides_0, weight = encoder_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_309_cast_fp16)[name = string("input_311_cast_fp16")];
            int32 x_129_split_num_splits_0 = const()[name = string("x_129_split_num_splits_0"), val = int32(2)];
            int32 x_129_split_axis_0 = const()[name = string("x_129_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_129_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_129_split_cast_fp16_1 = split(axis = x_129_split_axis_0, num_splits = x_129_split_num_splits_0, x = input_311_cast_fp16)[name = string("x_129_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_129_split_1_sigmoid_cast_fp16 = sigmoid(x = x_129_split_cast_fp16_1)[name = string("x_129_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_129_cast_fp16 = mul(x = x_129_split_cast_fp16_0, y = x_129_split_1_sigmoid_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_313_cast_fp16 = select(a = var_9_to_fp16, b = x_129_cast_fp16, cond = var_488)[name = string("input_313_cast_fp16")];
            tensor<int32, [6]> input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_315_mode_0 = const()[name = string("input_315_mode_0"), val = string("constant")];
            fp16 const_123_to_fp16 = const()[name = string("const_123_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_315_cast_fp16 = pad(constant_val = const_123_to_fp16, mode = input_315_mode_0, pad = input_315_pad_0, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
            string input_317_pad_type_0 = const()[name = string("input_317_pad_type_0"), val = string("valid")];
            int32 input_317_groups_0 = const()[name = string("input_317_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_317_strides_0 = const()[name = string("input_317_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_317_pad_0 = const()[name = string("input_317_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_317_dilations_0 = const()[name = string("input_317_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_314_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144484992))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144494272))))[name = string("const_314_to_fp16_quantized")];
            tensor<fp16, [1024]> const_315_to_fp16 = const()[name = string("const_315_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144496384)))];
            tensor<fp16, [1, 1024, 188]> input_319_cast_fp16 = conv(bias = const_315_to_fp16, dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = const_314_to_fp16_quantized, x = input_315_cast_fp16)[name = string("input_319_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_321_cast_fp16 = silu(x = input_319_cast_fp16)[name = string("input_321_cast_fp16")];
            string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144498496))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145547136))))[name = string("encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_131_cast_fp16 = conv(dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = encoder_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_321_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [3]> input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = x_131_cast_fp16)[name = string("transpose_271")];
            tensor<fp16, [1, 188, 1024]> input_325_cast_fp16 = add(x = input_307_cast_fp16, y = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
            tensor<int32, [1]> input_327_axes_0 = const()[name = string("input_327_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145549248)))];
            tensor<fp16, [1024]> encoder_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145551360)))];
            tensor<fp16, [1, 188, 1024]> input_327_cast_fp16 = layer_norm(axes = input_327_axes_0, beta = encoder_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_5_norm_feed_forward2_weight_to_fp16, x = input_325_cast_fp16)[name = string("input_327_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_5_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145553472))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149747840))))[name = string("encoder_layers_5_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_5_feed_forward2_linear1_weight_to_fp16_quantized, x = input_327_cast_fp16)[name = string("linear_55_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_331_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_331_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_5_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149756096))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153950464))))[name = string("encoder_layers_5_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_5_feed_forward2_linear2_weight_to_fp16_quantized, x = input_331_cast_fp16)[name = string("linear_56_cast_fp16")];
            fp16 var_1341_to_fp16 = const()[name = string("op_1341_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1342_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1341_to_fp16)[name = string("op_1342_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_337_cast_fp16 = add(x = input_325_cast_fp16, y = var_1342_cast_fp16)[name = string("input_337_cast_fp16")];
            tensor<int32, [1]> input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153952576)))];
            tensor<fp16, [1024]> encoder_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153954688)))];
            tensor<fp16, [1, 188, 1024]> input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_layers_5_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_5_norm_out_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")];
            tensor<int32, [1]> input_341_axes_0 = const()[name = string("input_341_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153956800)))];
            tensor<fp16, [1024]> encoder_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153958912)))];
            tensor<fp16, [1, 188, 1024]> input_341_cast_fp16 = layer_norm(axes = input_341_axes_0, beta = encoder_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_6_norm_feed_forward1_weight_to_fp16, x = input_339_cast_fp16)[name = string("input_341_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_6_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153961024))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158155392))))[name = string("encoder_layers_6_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_57_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_6_feed_forward1_linear1_weight_to_fp16_quantized, x = input_341_cast_fp16)[name = string("linear_57_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_345_cast_fp16 = silu(x = linear_57_cast_fp16)[name = string("input_345_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_6_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158163648))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162358016))))[name = string("encoder_layers_6_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_6_feed_forward1_linear2_weight_to_fp16_quantized, x = input_345_cast_fp16)[name = string("linear_58_cast_fp16")];
            fp16 var_1370_to_fp16 = const()[name = string("op_1370_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1371_cast_fp16 = mul(x = linear_58_cast_fp16, y = var_1370_to_fp16)[name = string("op_1371_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_351_cast_fp16 = add(x = input_339_cast_fp16, y = var_1371_cast_fp16)[name = string("input_351_cast_fp16")];
            tensor<int32, [1]> query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162360128)))];
            tensor<fp16, [1024]> encoder_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162362240)))];
            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = encoder_layers_6_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_6_norm_self_att_weight_to_fp16, x = input_351_cast_fp16)[name = string("query_13_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_6_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162364352))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163412992))))[name = string("encoder_layers_6_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_6_self_attn_linear_q_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")];
            tensor<int32, [4]> var_1387 = const()[name = string("op_1387"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1387, x = linear_59_cast_fp16)[name = string("q_37_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_6_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163415104))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164463744))))[name = string("encoder_layers_6_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_60_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_6_self_attn_linear_k_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_60_cast_fp16")];
            tensor<int32, [4]> var_1391 = const()[name = string("op_1391"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1391, x = linear_60_cast_fp16)[name = string("k_25_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_6_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164465856))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165514496))))[name = string("encoder_layers_6_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_6_self_attn_linear_v_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_61_cast_fp16")];
            tensor<int32, [4]> var_1395 = const()[name = string("op_1395"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1395, x = linear_61_cast_fp16)[name = string("v_13_cast_fp16")];
            tensor<int32, [4]> value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165516608)))];
            tensor<fp16, [1, 188, 8, 128]> var_1407_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1407_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165518720)))];
            tensor<fp16, [1, 188, 8, 128]> var_1409_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1409_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_139_transpose_x_0 = const()[name = string("x_139_transpose_x_0"), val = bool(false)];
            bool x_139_transpose_y_0 = const()[name = string("x_139_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1411_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165520832))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165904896))))[name = string("op_1411_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1409_cast_fp16)[name = string("transpose_270")];
            tensor<fp16, [1, 8, 188, 375]> x_139_cast_fp16 = matmul(transpose_x = x_139_transpose_x_0, transpose_y = x_139_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1411_to_fp16_quantized)[name = string("x_139_cast_fp16")];
            tensor<int32, [8]> x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_141_mode_0 = const()[name = string("x_141_mode_0"), val = string("constant")];
            fp16 const_130_to_fp16 = const()[name = string("const_130_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_141_cast_fp16 = pad(constant_val = const_130_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> var_1419 = const()[name = string("op_1419"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_143_cast_fp16 = reshape(shape = var_1419, x = x_141_cast_fp16)[name = string("x_143_cast_fp16")];
            tensor<int32, [4]> var_1423_begin_0 = const()[name = string("op_1423_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1423_end_0 = const()[name = string("op_1423_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1423_end_mask_0 = const()[name = string("op_1423_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1423_cast_fp16 = slice_by_index(begin = var_1423_begin_0, end = var_1423_end_0, end_mask = var_1423_end_mask_0, x = x_143_cast_fp16)[name = string("op_1423_cast_fp16")];
            tensor<int32, [4]> var_1424 = const()[name = string("op_1424"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1424, x = var_1423_cast_fp16)[name = string("matrix_bd_25_cast_fp16")];
            bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)];
            bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")];
            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1407_cast_fp16)[name = string("transpose_269")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_13_cast_fp16")];
            tensor<int32, [4]> matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_27_cast_fp16 = slice_by_index(begin = matrix_bd_27_begin_0, end = matrix_bd_27_end_0, end_mask = matrix_bd_27_end_mask_0, x = matrix_bd_25_cast_fp16)[name = string("matrix_bd_27_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1433_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1433_cast_fp16")];
            fp16 _inversed_scores_25_y_0_to_fp16 = const()[name = string("_inversed_scores_25_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1433_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_11)[name = string("scores_27_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1439_cast_fp16 = softmax(axis = var_25, x = scores_27_cast_fp16)[name = string("op_1439_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_353_cast_fp16 = select(a = var_9_to_fp16, b = var_1439_cast_fp16, cond = mask_11)[name = string("input_353_cast_fp16")];
            bool x_145_transpose_x_0 = const()[name = string("x_145_transpose_x_0"), val = bool(false)];
            bool x_145_transpose_y_0 = const()[name = string("x_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")];
            tensor<fp16, [1, 8, 188, 128]> x_145_cast_fp16 = matmul(transpose_x = x_145_transpose_x_0, transpose_y = x_145_transpose_y_0, x = input_353_cast_fp16, y = value_15_cast_fp16)[name = string("x_145_cast_fp16")];
            tensor<int32, [4]> var_1443_perm_0 = const()[name = string("op_1443_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1444 = const()[name = string("op_1444"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1443_cast_fp16 = transpose(perm = var_1443_perm_0, x = x_145_cast_fp16)[name = string("transpose_266")];
            tensor<fp16, [1, 188, 1024]> input_355_cast_fp16 = reshape(shape = var_1444, x = var_1443_cast_fp16)[name = string("input_355_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_6_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165905728))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166954368))))[name = string("encoder_layers_6_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_6_self_attn_linear_out_weight_to_fp16_quantized, x = input_355_cast_fp16)[name = string("linear_63_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_359_cast_fp16 = add(x = input_351_cast_fp16, y = linear_63_cast_fp16)[name = string("input_359_cast_fp16")];
            tensor<int32, [1]> x_149_axes_0 = const()[name = string("x_149_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166956480)))];
            tensor<fp16, [1024]> encoder_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166958592)))];
            tensor<fp16, [1, 188, 1024]> x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = encoder_layers_6_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [3]> input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166960704))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169057920))))[name = string("encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_149_cast_fp16)[name = string("transpose_265")];
            tensor<fp16, [1, 2048, 188]> input_363_cast_fp16 = conv(dilations = input_363_dilations_0, groups = input_363_groups_0, pad = input_363_pad_0, pad_type = input_363_pad_type_0, strides = input_363_strides_0, weight = encoder_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")];
            int32 x_151_split_num_splits_0 = const()[name = string("x_151_split_num_splits_0"), val = int32(2)];
            int32 x_151_split_axis_0 = const()[name = string("x_151_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_151_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_151_split_cast_fp16_1 = split(axis = x_151_split_axis_0, num_splits = x_151_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_151_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_151_split_1_sigmoid_cast_fp16 = sigmoid(x = x_151_split_cast_fp16_1)[name = string("x_151_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_151_cast_fp16 = mul(x = x_151_split_cast_fp16_0, y = x_151_split_1_sigmoid_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_365_cast_fp16 = select(a = var_9_to_fp16, b = x_151_cast_fp16, cond = var_488)[name = string("input_365_cast_fp16")];
            tensor<int32, [6]> input_367_pad_0 = const()[name = string("input_367_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_367_mode_0 = const()[name = string("input_367_mode_0"), val = string("constant")];
            fp16 const_133_to_fp16 = const()[name = string("const_133_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_367_cast_fp16 = pad(constant_val = const_133_to_fp16, mode = input_367_mode_0, pad = input_367_pad_0, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")];
            string input_369_pad_type_0 = const()[name = string("input_369_pad_type_0"), val = string("valid")];
            int32 input_369_groups_0 = const()[name = string("input_369_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_369_strides_0 = const()[name = string("input_369_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_369_dilations_0 = const()[name = string("input_369_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_316_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169062080))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169071360))))[name = string("const_316_to_fp16_quantized")];
            tensor<fp16, [1024]> const_317_to_fp16 = const()[name = string("const_317_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169073472)))];
            tensor<fp16, [1, 1024, 188]> input_371_cast_fp16 = conv(bias = const_317_to_fp16, dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = const_316_to_fp16_quantized, x = input_367_cast_fp16)[name = string("input_371_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_373_cast_fp16 = silu(x = input_371_cast_fp16)[name = string("input_373_cast_fp16")];
            string x_153_pad_type_0 = const()[name = string("x_153_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_153_strides_0 = const()[name = string("x_153_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_153_pad_0 = const()[name = string("x_153_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_153_dilations_0 = const()[name = string("x_153_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_153_groups_0 = const()[name = string("x_153_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169075584))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170124224))))[name = string("encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_153_cast_fp16 = conv(dilations = x_153_dilations_0, groups = x_153_groups_0, pad = x_153_pad_0, pad_type = x_153_pad_type_0, strides = x_153_strides_0, weight = encoder_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_373_cast_fp16)[name = string("x_153_cast_fp16")];
            tensor<int32, [3]> input_375_perm_0 = const()[name = string("input_375_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_375_cast_fp16 = transpose(perm = input_375_perm_0, x = x_153_cast_fp16)[name = string("transpose_264")];
            tensor<fp16, [1, 188, 1024]> input_377_cast_fp16 = add(x = input_359_cast_fp16, y = input_375_cast_fp16)[name = string("input_377_cast_fp16")];
            tensor<int32, [1]> input_379_axes_0 = const()[name = string("input_379_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170126336)))];
            tensor<fp16, [1024]> encoder_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170128448)))];
            tensor<fp16, [1, 188, 1024]> input_379_cast_fp16 = layer_norm(axes = input_379_axes_0, beta = encoder_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_6_norm_feed_forward2_weight_to_fp16, x = input_377_cast_fp16)[name = string("input_379_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_6_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170130560))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174324928))))[name = string("encoder_layers_6_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_6_feed_forward2_linear1_weight_to_fp16_quantized, x = input_379_cast_fp16)[name = string("linear_64_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_383_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_383_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_6_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174333184))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178527552))))[name = string("encoder_layers_6_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_6_feed_forward2_linear2_weight_to_fp16_quantized, x = input_383_cast_fp16)[name = string("linear_65_cast_fp16")];
            fp16 var_1504_to_fp16 = const()[name = string("op_1504_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1505_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1504_to_fp16)[name = string("op_1505_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_389_cast_fp16 = add(x = input_377_cast_fp16, y = var_1505_cast_fp16)[name = string("input_389_cast_fp16")];
            tensor<int32, [1]> input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178529664)))];
            tensor<fp16, [1024]> encoder_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178531776)))];
            tensor<fp16, [1, 188, 1024]> input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_layers_6_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_6_norm_out_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")];
            tensor<int32, [1]> input_393_axes_0 = const()[name = string("input_393_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178533888)))];
            tensor<fp16, [1024]> encoder_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178536000)))];
            tensor<fp16, [1, 188, 1024]> input_393_cast_fp16 = layer_norm(axes = input_393_axes_0, beta = encoder_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_7_norm_feed_forward1_weight_to_fp16, x = input_391_cast_fp16)[name = string("input_393_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_7_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178538112))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182732480))))[name = string("encoder_layers_7_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_66_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_7_feed_forward1_linear1_weight_to_fp16_quantized, x = input_393_cast_fp16)[name = string("linear_66_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_397_cast_fp16 = silu(x = linear_66_cast_fp16)[name = string("input_397_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_7_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182740736))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186935104))))[name = string("encoder_layers_7_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_7_feed_forward1_linear2_weight_to_fp16_quantized, x = input_397_cast_fp16)[name = string("linear_67_cast_fp16")];
            fp16 var_1533_to_fp16 = const()[name = string("op_1533_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1534_cast_fp16 = mul(x = linear_67_cast_fp16, y = var_1533_to_fp16)[name = string("op_1534_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_403_cast_fp16 = add(x = input_391_cast_fp16, y = var_1534_cast_fp16)[name = string("input_403_cast_fp16")];
            tensor<int32, [1]> query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186937216)))];
            tensor<fp16, [1024]> encoder_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186939328)))];
            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = encoder_layers_7_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_7_norm_self_att_weight_to_fp16, x = input_403_cast_fp16)[name = string("query_15_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_7_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186941440))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187990080))))[name = string("encoder_layers_7_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_7_self_attn_linear_q_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")];
            tensor<int32, [4]> var_1550 = const()[name = string("op_1550"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1550, x = linear_68_cast_fp16)[name = string("q_43_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_7_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187992192))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189040832))))[name = string("encoder_layers_7_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_69_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_7_self_attn_linear_k_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_69_cast_fp16")];
            tensor<int32, [4]> var_1554 = const()[name = string("op_1554"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1554, x = linear_69_cast_fp16)[name = string("k_29_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_7_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189042944))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190091584))))[name = string("encoder_layers_7_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_7_self_attn_linear_v_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_70_cast_fp16")];
            tensor<int32, [4]> var_1558 = const()[name = string("op_1558"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1558, x = linear_70_cast_fp16)[name = string("v_15_cast_fp16")];
            tensor<int32, [4]> value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190093696)))];
            tensor<fp16, [1, 188, 8, 128]> var_1570_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1570_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190095808)))];
            tensor<fp16, [1, 188, 8, 128]> var_1572_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1572_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_161_transpose_x_0 = const()[name = string("x_161_transpose_x_0"), val = bool(false)];
            bool x_161_transpose_y_0 = const()[name = string("x_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1574_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190097920))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190481984))))[name = string("op_1574_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1572_cast_fp16)[name = string("transpose_263")];
            tensor<fp16, [1, 8, 188, 375]> x_161_cast_fp16 = matmul(transpose_x = x_161_transpose_x_0, transpose_y = x_161_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1574_to_fp16_quantized)[name = string("x_161_cast_fp16")];
            tensor<int32, [8]> x_163_pad_0 = const()[name = string("x_163_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_163_mode_0 = const()[name = string("x_163_mode_0"), val = string("constant")];
            fp16 const_140_to_fp16 = const()[name = string("const_140_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_163_cast_fp16 = pad(constant_val = const_140_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")];
            tensor<int32, [4]> var_1582 = const()[name = string("op_1582"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_165_cast_fp16 = reshape(shape = var_1582, x = x_163_cast_fp16)[name = string("x_165_cast_fp16")];
            tensor<int32, [4]> var_1586_begin_0 = const()[name = string("op_1586_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1586_end_0 = const()[name = string("op_1586_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1586_end_mask_0 = const()[name = string("op_1586_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1586_cast_fp16 = slice_by_index(begin = var_1586_begin_0, end = var_1586_end_0, end_mask = var_1586_end_mask_0, x = x_165_cast_fp16)[name = string("op_1586_cast_fp16")];
            tensor<int32, [4]> var_1587 = const()[name = string("op_1587"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1587, x = var_1586_cast_fp16)[name = string("matrix_bd_29_cast_fp16")];
            bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)];
            bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")];
            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1570_cast_fp16)[name = string("transpose_262")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_15_cast_fp16")];
            tensor<int32, [4]> matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_31_cast_fp16 = slice_by_index(begin = matrix_bd_31_begin_0, end = matrix_bd_31_end_0, end_mask = matrix_bd_31_end_mask_0, x = matrix_bd_29_cast_fp16)[name = string("matrix_bd_31_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1596_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1596_cast_fp16")];
            fp16 _inversed_scores_29_y_0_to_fp16 = const()[name = string("_inversed_scores_29_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1596_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_11)[name = string("scores_31_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1602_cast_fp16 = softmax(axis = var_25, x = scores_31_cast_fp16)[name = string("op_1602_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_405_cast_fp16 = select(a = var_9_to_fp16, b = var_1602_cast_fp16, cond = mask_11)[name = string("input_405_cast_fp16")];
            bool x_167_transpose_x_0 = const()[name = string("x_167_transpose_x_0"), val = bool(false)];
            bool x_167_transpose_y_0 = const()[name = string("x_167_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")];
            tensor<fp16, [1, 8, 188, 128]> x_167_cast_fp16 = matmul(transpose_x = x_167_transpose_x_0, transpose_y = x_167_transpose_y_0, x = input_405_cast_fp16, y = value_17_cast_fp16)[name = string("x_167_cast_fp16")];
            tensor<int32, [4]> var_1606_perm_0 = const()[name = string("op_1606_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1607 = const()[name = string("op_1607"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1606_cast_fp16 = transpose(perm = var_1606_perm_0, x = x_167_cast_fp16)[name = string("transpose_259")];
            tensor<fp16, [1, 188, 1024]> input_407_cast_fp16 = reshape(shape = var_1607, x = var_1606_cast_fp16)[name = string("input_407_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_7_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190482816))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191531456))))[name = string("encoder_layers_7_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_7_self_attn_linear_out_weight_to_fp16_quantized, x = input_407_cast_fp16)[name = string("linear_72_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_411_cast_fp16 = add(x = input_403_cast_fp16, y = linear_72_cast_fp16)[name = string("input_411_cast_fp16")];
            tensor<int32, [1]> x_171_axes_0 = const()[name = string("x_171_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191533568)))];
            tensor<fp16, [1024]> encoder_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191535680)))];
            tensor<fp16, [1, 188, 1024]> x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = encoder_layers_7_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [3]> input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191537792))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193635008))))[name = string("encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_171_cast_fp16)[name = string("transpose_258")];
            tensor<fp16, [1, 2048, 188]> input_415_cast_fp16 = conv(dilations = input_415_dilations_0, groups = input_415_groups_0, pad = input_415_pad_0, pad_type = input_415_pad_type_0, strides = input_415_strides_0, weight = encoder_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")];
            int32 x_173_split_num_splits_0 = const()[name = string("x_173_split_num_splits_0"), val = int32(2)];
            int32 x_173_split_axis_0 = const()[name = string("x_173_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_173_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_173_split_cast_fp16_1 = split(axis = x_173_split_axis_0, num_splits = x_173_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_173_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_173_split_1_sigmoid_cast_fp16 = sigmoid(x = x_173_split_cast_fp16_1)[name = string("x_173_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_173_cast_fp16 = mul(x = x_173_split_cast_fp16_0, y = x_173_split_1_sigmoid_cast_fp16)[name = string("x_173_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_417_cast_fp16 = select(a = var_9_to_fp16, b = x_173_cast_fp16, cond = var_488)[name = string("input_417_cast_fp16")];
            tensor<int32, [6]> input_419_pad_0 = const()[name = string("input_419_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("constant")];
            fp16 const_143_to_fp16 = const()[name = string("const_143_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_419_cast_fp16 = pad(constant_val = const_143_to_fp16, mode = input_419_mode_0, pad = input_419_pad_0, x = input_417_cast_fp16)[name = string("input_419_cast_fp16")];
            string input_421_pad_type_0 = const()[name = string("input_421_pad_type_0"), val = string("valid")];
            int32 input_421_groups_0 = const()[name = string("input_421_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_421_strides_0 = const()[name = string("input_421_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_421_pad_0 = const()[name = string("input_421_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_421_dilations_0 = const()[name = string("input_421_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_318_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193639168))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193648448))))[name = string("const_318_to_fp16_quantized")];
            tensor<fp16, [1024]> const_319_to_fp16 = const()[name = string("const_319_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193650560)))];
            tensor<fp16, [1, 1024, 188]> input_423_cast_fp16 = conv(bias = const_319_to_fp16, dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = const_318_to_fp16_quantized, x = input_419_cast_fp16)[name = string("input_423_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_425_cast_fp16 = silu(x = input_423_cast_fp16)[name = string("input_425_cast_fp16")];
            string x_175_pad_type_0 = const()[name = string("x_175_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_175_strides_0 = const()[name = string("x_175_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_175_pad_0 = const()[name = string("x_175_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_175_dilations_0 = const()[name = string("x_175_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_175_groups_0 = const()[name = string("x_175_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193652672))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194701312))))[name = string("encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_175_cast_fp16 = conv(dilations = x_175_dilations_0, groups = x_175_groups_0, pad = x_175_pad_0, pad_type = x_175_pad_type_0, strides = x_175_strides_0, weight = encoder_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_425_cast_fp16)[name = string("x_175_cast_fp16")];
            tensor<int32, [3]> input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_175_cast_fp16)[name = string("transpose_257")];
            tensor<fp16, [1, 188, 1024]> input_429_cast_fp16 = add(x = input_411_cast_fp16, y = input_427_cast_fp16)[name = string("input_429_cast_fp16")];
            tensor<int32, [1]> input_431_axes_0 = const()[name = string("input_431_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194703424)))];
            tensor<fp16, [1024]> encoder_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194705536)))];
            tensor<fp16, [1, 188, 1024]> input_431_cast_fp16 = layer_norm(axes = input_431_axes_0, beta = encoder_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_7_norm_feed_forward2_weight_to_fp16, x = input_429_cast_fp16)[name = string("input_431_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_7_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194707648))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198902016))))[name = string("encoder_layers_7_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_7_feed_forward2_linear1_weight_to_fp16_quantized, x = input_431_cast_fp16)[name = string("linear_73_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_435_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_435_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_7_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(198910272))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203104640))))[name = string("encoder_layers_7_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_7_feed_forward2_linear2_weight_to_fp16_quantized, x = input_435_cast_fp16)[name = string("linear_74_cast_fp16")];
            fp16 var_1667_to_fp16 = const()[name = string("op_1667_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1668_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1667_to_fp16)[name = string("op_1668_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_441_cast_fp16 = add(x = input_429_cast_fp16, y = var_1668_cast_fp16)[name = string("input_441_cast_fp16")];
            tensor<int32, [1]> input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203106752)))];
            tensor<fp16, [1024]> encoder_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203108864)))];
            tensor<fp16, [1, 188, 1024]> input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_layers_7_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_7_norm_out_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")];
            tensor<int32, [1]> input_445_axes_0 = const()[name = string("input_445_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203110976)))];
            tensor<fp16, [1024]> encoder_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203113088)))];
            tensor<fp16, [1, 188, 1024]> input_445_cast_fp16 = layer_norm(axes = input_445_axes_0, beta = encoder_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_8_norm_feed_forward1_weight_to_fp16, x = input_443_cast_fp16)[name = string("input_445_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_8_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203115200))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207309568))))[name = string("encoder_layers_8_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_75_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_8_feed_forward1_linear1_weight_to_fp16_quantized, x = input_445_cast_fp16)[name = string("linear_75_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_449_cast_fp16 = silu(x = linear_75_cast_fp16)[name = string("input_449_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_8_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207317824))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211512192))))[name = string("encoder_layers_8_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_8_feed_forward1_linear2_weight_to_fp16_quantized, x = input_449_cast_fp16)[name = string("linear_76_cast_fp16")];
            fp16 var_1696_to_fp16 = const()[name = string("op_1696_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1697_cast_fp16 = mul(x = linear_76_cast_fp16, y = var_1696_to_fp16)[name = string("op_1697_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_455_cast_fp16 = add(x = input_443_cast_fp16, y = var_1697_cast_fp16)[name = string("input_455_cast_fp16")];
            tensor<int32, [1]> query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211514304)))];
            tensor<fp16, [1024]> encoder_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211516416)))];
            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = encoder_layers_8_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_8_norm_self_att_weight_to_fp16, x = input_455_cast_fp16)[name = string("query_17_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_8_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211518528))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212567168))))[name = string("encoder_layers_8_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_8_self_attn_linear_q_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")];
            tensor<int32, [4]> var_1713 = const()[name = string("op_1713"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1713, x = linear_77_cast_fp16)[name = string("q_49_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_8_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212569280))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213617920))))[name = string("encoder_layers_8_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_78_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_8_self_attn_linear_k_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_78_cast_fp16")];
            tensor<int32, [4]> var_1717 = const()[name = string("op_1717"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1717, x = linear_78_cast_fp16)[name = string("k_33_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_8_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213620032))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214668672))))[name = string("encoder_layers_8_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_8_self_attn_linear_v_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_79_cast_fp16")];
            tensor<int32, [4]> var_1721 = const()[name = string("op_1721"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1721, x = linear_79_cast_fp16)[name = string("v_17_cast_fp16")];
            tensor<int32, [4]> value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214670784)))];
            tensor<fp16, [1, 188, 8, 128]> var_1733_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1733_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214672896)))];
            tensor<fp16, [1, 188, 8, 128]> var_1735_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1735_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_183_transpose_x_0 = const()[name = string("x_183_transpose_x_0"), val = bool(false)];
            bool x_183_transpose_y_0 = const()[name = string("x_183_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1737_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214675008))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215059072))))[name = string("op_1737_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1735_cast_fp16)[name = string("transpose_256")];
            tensor<fp16, [1, 8, 188, 375]> x_183_cast_fp16 = matmul(transpose_x = x_183_transpose_x_0, transpose_y = x_183_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1737_to_fp16_quantized)[name = string("x_183_cast_fp16")];
            tensor<int32, [8]> x_185_pad_0 = const()[name = string("x_185_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_185_mode_0 = const()[name = string("x_185_mode_0"), val = string("constant")];
            fp16 const_150_to_fp16 = const()[name = string("const_150_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_185_cast_fp16 = pad(constant_val = const_150_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")];
            tensor<int32, [4]> var_1745 = const()[name = string("op_1745"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_187_cast_fp16 = reshape(shape = var_1745, x = x_185_cast_fp16)[name = string("x_187_cast_fp16")];
            tensor<int32, [4]> var_1749_begin_0 = const()[name = string("op_1749_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1749_end_0 = const()[name = string("op_1749_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1749_end_mask_0 = const()[name = string("op_1749_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1749_cast_fp16 = slice_by_index(begin = var_1749_begin_0, end = var_1749_end_0, end_mask = var_1749_end_mask_0, x = x_187_cast_fp16)[name = string("op_1749_cast_fp16")];
            tensor<int32, [4]> var_1750 = const()[name = string("op_1750"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1750, x = var_1749_cast_fp16)[name = string("matrix_bd_33_cast_fp16")];
            bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)];
            bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")];
            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1733_cast_fp16)[name = string("transpose_255")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_17_cast_fp16")];
            tensor<int32, [4]> matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_35_cast_fp16 = slice_by_index(begin = matrix_bd_35_begin_0, end = matrix_bd_35_end_0, end_mask = matrix_bd_35_end_mask_0, x = matrix_bd_33_cast_fp16)[name = string("matrix_bd_35_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1759_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1759_cast_fp16")];
            fp16 _inversed_scores_33_y_0_to_fp16 = const()[name = string("_inversed_scores_33_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1759_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_11)[name = string("scores_35_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1765_cast_fp16 = softmax(axis = var_25, x = scores_35_cast_fp16)[name = string("op_1765_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_457_cast_fp16 = select(a = var_9_to_fp16, b = var_1765_cast_fp16, cond = mask_11)[name = string("input_457_cast_fp16")];
            bool x_189_transpose_x_0 = const()[name = string("x_189_transpose_x_0"), val = bool(false)];
            bool x_189_transpose_y_0 = const()[name = string("x_189_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")];
            tensor<fp16, [1, 8, 188, 128]> x_189_cast_fp16 = matmul(transpose_x = x_189_transpose_x_0, transpose_y = x_189_transpose_y_0, x = input_457_cast_fp16, y = value_19_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [4]> var_1769_perm_0 = const()[name = string("op_1769_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1770 = const()[name = string("op_1770"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1769_cast_fp16 = transpose(perm = var_1769_perm_0, x = x_189_cast_fp16)[name = string("transpose_252")];
            tensor<fp16, [1, 188, 1024]> input_459_cast_fp16 = reshape(shape = var_1770, x = var_1769_cast_fp16)[name = string("input_459_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_8_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215059904))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216108544))))[name = string("encoder_layers_8_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_8_self_attn_linear_out_weight_to_fp16_quantized, x = input_459_cast_fp16)[name = string("linear_81_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_463_cast_fp16 = add(x = input_455_cast_fp16, y = linear_81_cast_fp16)[name = string("input_463_cast_fp16")];
            tensor<int32, [1]> x_193_axes_0 = const()[name = string("x_193_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216110656)))];
            tensor<fp16, [1024]> encoder_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216112768)))];
            tensor<fp16, [1, 188, 1024]> x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = encoder_layers_8_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_193_cast_fp16")];
            tensor<int32, [3]> input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216114880))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218212096))))[name = string("encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_193_cast_fp16)[name = string("transpose_251")];
            tensor<fp16, [1, 2048, 188]> input_467_cast_fp16 = conv(dilations = input_467_dilations_0, groups = input_467_groups_0, pad = input_467_pad_0, pad_type = input_467_pad_type_0, strides = input_467_strides_0, weight = encoder_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")];
            int32 x_195_split_num_splits_0 = const()[name = string("x_195_split_num_splits_0"), val = int32(2)];
            int32 x_195_split_axis_0 = const()[name = string("x_195_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_195_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_195_split_cast_fp16_1 = split(axis = x_195_split_axis_0, num_splits = x_195_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_195_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_195_split_1_sigmoid_cast_fp16 = sigmoid(x = x_195_split_cast_fp16_1)[name = string("x_195_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_195_cast_fp16 = mul(x = x_195_split_cast_fp16_0, y = x_195_split_1_sigmoid_cast_fp16)[name = string("x_195_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_469_cast_fp16 = select(a = var_9_to_fp16, b = x_195_cast_fp16, cond = var_488)[name = string("input_469_cast_fp16")];
            tensor<int32, [6]> input_471_pad_0 = const()[name = string("input_471_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_471_mode_0 = const()[name = string("input_471_mode_0"), val = string("constant")];
            fp16 const_153_to_fp16 = const()[name = string("const_153_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_471_cast_fp16 = pad(constant_val = const_153_to_fp16, mode = input_471_mode_0, pad = input_471_pad_0, x = input_469_cast_fp16)[name = string("input_471_cast_fp16")];
            string input_473_pad_type_0 = const()[name = string("input_473_pad_type_0"), val = string("valid")];
            int32 input_473_groups_0 = const()[name = string("input_473_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_473_strides_0 = const()[name = string("input_473_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_473_pad_0 = const()[name = string("input_473_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_473_dilations_0 = const()[name = string("input_473_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_320_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218216256))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218225536))))[name = string("const_320_to_fp16_quantized")];
            tensor<fp16, [1024]> const_321_to_fp16 = const()[name = string("const_321_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218227648)))];
            tensor<fp16, [1, 1024, 188]> input_475_cast_fp16 = conv(bias = const_321_to_fp16, dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = const_320_to_fp16_quantized, x = input_471_cast_fp16)[name = string("input_475_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_477_cast_fp16 = silu(x = input_475_cast_fp16)[name = string("input_477_cast_fp16")];
            string x_197_pad_type_0 = const()[name = string("x_197_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_197_strides_0 = const()[name = string("x_197_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_197_pad_0 = const()[name = string("x_197_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_197_dilations_0 = const()[name = string("x_197_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_197_groups_0 = const()[name = string("x_197_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218229760))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219278400))))[name = string("encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_197_cast_fp16 = conv(dilations = x_197_dilations_0, groups = x_197_groups_0, pad = x_197_pad_0, pad_type = x_197_pad_type_0, strides = x_197_strides_0, weight = encoder_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_477_cast_fp16)[name = string("x_197_cast_fp16")];
            tensor<int32, [3]> input_479_perm_0 = const()[name = string("input_479_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_479_cast_fp16 = transpose(perm = input_479_perm_0, x = x_197_cast_fp16)[name = string("transpose_250")];
            tensor<fp16, [1, 188, 1024]> input_481_cast_fp16 = add(x = input_463_cast_fp16, y = input_479_cast_fp16)[name = string("input_481_cast_fp16")];
            tensor<int32, [1]> input_483_axes_0 = const()[name = string("input_483_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219280512)))];
            tensor<fp16, [1024]> encoder_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219282624)))];
            tensor<fp16, [1, 188, 1024]> input_483_cast_fp16 = layer_norm(axes = input_483_axes_0, beta = encoder_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_8_norm_feed_forward2_weight_to_fp16, x = input_481_cast_fp16)[name = string("input_483_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_8_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219284736))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223479104))))[name = string("encoder_layers_8_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_8_feed_forward2_linear1_weight_to_fp16_quantized, x = input_483_cast_fp16)[name = string("linear_82_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_487_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_487_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_8_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223487360))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227681728))))[name = string("encoder_layers_8_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_8_feed_forward2_linear2_weight_to_fp16_quantized, x = input_487_cast_fp16)[name = string("linear_83_cast_fp16")];
            fp16 var_1830_to_fp16 = const()[name = string("op_1830_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1831_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1830_to_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_493_cast_fp16 = add(x = input_481_cast_fp16, y = var_1831_cast_fp16)[name = string("input_493_cast_fp16")];
            tensor<int32, [1]> input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227683840)))];
            tensor<fp16, [1024]> encoder_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227685952)))];
            tensor<fp16, [1, 188, 1024]> input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_layers_8_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_8_norm_out_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")];
            tensor<int32, [1]> input_497_axes_0 = const()[name = string("input_497_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227688064)))];
            tensor<fp16, [1024]> encoder_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227690176)))];
            tensor<fp16, [1, 188, 1024]> input_497_cast_fp16 = layer_norm(axes = input_497_axes_0, beta = encoder_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_9_norm_feed_forward1_weight_to_fp16, x = input_495_cast_fp16)[name = string("input_497_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_9_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227692288))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231886656))))[name = string("encoder_layers_9_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_84_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_9_feed_forward1_linear1_weight_to_fp16_quantized, x = input_497_cast_fp16)[name = string("linear_84_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_501_cast_fp16 = silu(x = linear_84_cast_fp16)[name = string("input_501_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_9_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231894912))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236089280))))[name = string("encoder_layers_9_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_9_feed_forward1_linear2_weight_to_fp16_quantized, x = input_501_cast_fp16)[name = string("linear_85_cast_fp16")];
            fp16 var_1859_to_fp16 = const()[name = string("op_1859_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1860_cast_fp16 = mul(x = linear_85_cast_fp16, y = var_1859_to_fp16)[name = string("op_1860_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_507_cast_fp16 = add(x = input_495_cast_fp16, y = var_1860_cast_fp16)[name = string("input_507_cast_fp16")];
            tensor<int32, [1]> query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236091392)))];
            tensor<fp16, [1024]> encoder_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236093504)))];
            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = encoder_layers_9_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_9_norm_self_att_weight_to_fp16, x = input_507_cast_fp16)[name = string("query_19_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_9_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236095616))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237144256))))[name = string("encoder_layers_9_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_9_self_attn_linear_q_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")];
            tensor<int32, [4]> var_1876 = const()[name = string("op_1876"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_1876, x = linear_86_cast_fp16)[name = string("q_55_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_9_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237146368))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238195008))))[name = string("encoder_layers_9_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_87_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_9_self_attn_linear_k_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_87_cast_fp16")];
            tensor<int32, [4]> var_1880 = const()[name = string("op_1880"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_1880, x = linear_87_cast_fp16)[name = string("k_37_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_9_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238197120))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239245760))))[name = string("encoder_layers_9_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_9_self_attn_linear_v_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_88_cast_fp16")];
            tensor<int32, [4]> var_1884 = const()[name = string("op_1884"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_1884, x = linear_88_cast_fp16)[name = string("v_19_cast_fp16")];
            tensor<int32, [4]> value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239247872)))];
            tensor<fp16, [1, 188, 8, 128]> var_1896_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_1896_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239249984)))];
            tensor<fp16, [1, 188, 8, 128]> var_1898_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_1898_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_205_transpose_x_0 = const()[name = string("x_205_transpose_x_0"), val = bool(false)];
            bool x_205_transpose_y_0 = const()[name = string("x_205_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1900_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239252096))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239636160))))[name = string("op_1900_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1898_cast_fp16)[name = string("transpose_249")];
            tensor<fp16, [1, 8, 188, 375]> x_205_cast_fp16 = matmul(transpose_x = x_205_transpose_x_0, transpose_y = x_205_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_1900_to_fp16_quantized)[name = string("x_205_cast_fp16")];
            tensor<int32, [8]> x_207_pad_0 = const()[name = string("x_207_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_207_mode_0 = const()[name = string("x_207_mode_0"), val = string("constant")];
            fp16 const_160_to_fp16 = const()[name = string("const_160_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_207_cast_fp16 = pad(constant_val = const_160_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")];
            tensor<int32, [4]> var_1908 = const()[name = string("op_1908"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_209_cast_fp16 = reshape(shape = var_1908, x = x_207_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [4]> var_1912_begin_0 = const()[name = string("op_1912_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1912_end_0 = const()[name = string("op_1912_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1912_end_mask_0 = const()[name = string("op_1912_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1912_cast_fp16 = slice_by_index(begin = var_1912_begin_0, end = var_1912_end_0, end_mask = var_1912_end_mask_0, x = x_209_cast_fp16)[name = string("op_1912_cast_fp16")];
            tensor<int32, [4]> var_1913 = const()[name = string("op_1913"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_1913, x = var_1912_cast_fp16)[name = string("matrix_bd_37_cast_fp16")];
            bool matrix_ac_19_transpose_x_0 = const()[name = string("matrix_ac_19_transpose_x_0"), val = bool(false)];
            bool matrix_ac_19_transpose_y_0 = const()[name = string("matrix_ac_19_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")];
            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1896_cast_fp16)[name = string("transpose_248")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_19_cast_fp16")];
            tensor<int32, [4]> matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_39_cast_fp16 = slice_by_index(begin = matrix_bd_39_begin_0, end = matrix_bd_39_end_0, end_mask = matrix_bd_39_end_mask_0, x = matrix_bd_37_cast_fp16)[name = string("matrix_bd_39_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1922_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_1922_cast_fp16")];
            fp16 _inversed_scores_37_y_0_to_fp16 = const()[name = string("_inversed_scores_37_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_1922_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_11)[name = string("scores_39_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1928_cast_fp16 = softmax(axis = var_25, x = scores_39_cast_fp16)[name = string("op_1928_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_509_cast_fp16 = select(a = var_9_to_fp16, b = var_1928_cast_fp16, cond = mask_11)[name = string("input_509_cast_fp16")];
            bool x_211_transpose_x_0 = const()[name = string("x_211_transpose_x_0"), val = bool(false)];
            bool x_211_transpose_y_0 = const()[name = string("x_211_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")];
            tensor<fp16, [1, 8, 188, 128]> x_211_cast_fp16 = matmul(transpose_x = x_211_transpose_x_0, transpose_y = x_211_transpose_y_0, x = input_509_cast_fp16, y = value_21_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> var_1932_perm_0 = const()[name = string("op_1932_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1933 = const()[name = string("op_1933"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1932_cast_fp16 = transpose(perm = var_1932_perm_0, x = x_211_cast_fp16)[name = string("transpose_245")];
            tensor<fp16, [1, 188, 1024]> input_511_cast_fp16 = reshape(shape = var_1933, x = var_1932_cast_fp16)[name = string("input_511_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_9_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239636992))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240685632))))[name = string("encoder_layers_9_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_9_self_attn_linear_out_weight_to_fp16_quantized, x = input_511_cast_fp16)[name = string("linear_90_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_515_cast_fp16 = add(x = input_507_cast_fp16, y = linear_90_cast_fp16)[name = string("input_515_cast_fp16")];
            tensor<int32, [1]> x_215_axes_0 = const()[name = string("x_215_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240687744)))];
            tensor<fp16, [1024]> encoder_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240689856)))];
            tensor<fp16, [1, 188, 1024]> x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = encoder_layers_9_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_215_cast_fp16")];
            tensor<int32, [3]> input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240691968))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242789184))))[name = string("encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_215_cast_fp16)[name = string("transpose_244")];
            tensor<fp16, [1, 2048, 188]> input_519_cast_fp16 = conv(dilations = input_519_dilations_0, groups = input_519_groups_0, pad = input_519_pad_0, pad_type = input_519_pad_type_0, strides = input_519_strides_0, weight = encoder_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")];
            int32 x_217_split_num_splits_0 = const()[name = string("x_217_split_num_splits_0"), val = int32(2)];
            int32 x_217_split_axis_0 = const()[name = string("x_217_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_217_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_217_split_cast_fp16_1 = split(axis = x_217_split_axis_0, num_splits = x_217_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_217_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_217_split_1_sigmoid_cast_fp16 = sigmoid(x = x_217_split_cast_fp16_1)[name = string("x_217_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_217_cast_fp16 = mul(x = x_217_split_cast_fp16_0, y = x_217_split_1_sigmoid_cast_fp16)[name = string("x_217_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_521_cast_fp16 = select(a = var_9_to_fp16, b = x_217_cast_fp16, cond = var_488)[name = string("input_521_cast_fp16")];
            tensor<int32, [6]> input_523_pad_0 = const()[name = string("input_523_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_523_mode_0 = const()[name = string("input_523_mode_0"), val = string("constant")];
            fp16 const_163_to_fp16 = const()[name = string("const_163_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_523_cast_fp16 = pad(constant_val = const_163_to_fp16, mode = input_523_mode_0, pad = input_523_pad_0, x = input_521_cast_fp16)[name = string("input_523_cast_fp16")];
            string input_525_pad_type_0 = const()[name = string("input_525_pad_type_0"), val = string("valid")];
            int32 input_525_groups_0 = const()[name = string("input_525_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_525_strides_0 = const()[name = string("input_525_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_525_pad_0 = const()[name = string("input_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_525_dilations_0 = const()[name = string("input_525_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_322_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242793344))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242802624))))[name = string("const_322_to_fp16_quantized")];
            tensor<fp16, [1024]> const_323_to_fp16 = const()[name = string("const_323_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242804736)))];
            tensor<fp16, [1, 1024, 188]> input_527_cast_fp16 = conv(bias = const_323_to_fp16, dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = const_322_to_fp16_quantized, x = input_523_cast_fp16)[name = string("input_527_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_529_cast_fp16 = silu(x = input_527_cast_fp16)[name = string("input_529_cast_fp16")];
            string x_219_pad_type_0 = const()[name = string("x_219_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_219_strides_0 = const()[name = string("x_219_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_219_pad_0 = const()[name = string("x_219_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_219_dilations_0 = const()[name = string("x_219_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_219_groups_0 = const()[name = string("x_219_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242806848))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243855488))))[name = string("encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_219_cast_fp16 = conv(dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = encoder_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_529_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [3]> input_531_perm_0 = const()[name = string("input_531_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_531_cast_fp16 = transpose(perm = input_531_perm_0, x = x_219_cast_fp16)[name = string("transpose_243")];
            tensor<fp16, [1, 188, 1024]> input_533_cast_fp16 = add(x = input_515_cast_fp16, y = input_531_cast_fp16)[name = string("input_533_cast_fp16")];
            tensor<int32, [1]> input_535_axes_0 = const()[name = string("input_535_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243857600)))];
            tensor<fp16, [1024]> encoder_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243859712)))];
            tensor<fp16, [1, 188, 1024]> input_535_cast_fp16 = layer_norm(axes = input_535_axes_0, beta = encoder_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_9_norm_feed_forward2_weight_to_fp16, x = input_533_cast_fp16)[name = string("input_535_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_9_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243861824))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248056192))))[name = string("encoder_layers_9_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_9_feed_forward2_linear1_weight_to_fp16_quantized, x = input_535_cast_fp16)[name = string("linear_91_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_539_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_539_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_9_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248064448))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252258816))))[name = string("encoder_layers_9_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_9_feed_forward2_linear2_weight_to_fp16_quantized, x = input_539_cast_fp16)[name = string("linear_92_cast_fp16")];
            fp16 var_1993_to_fp16 = const()[name = string("op_1993_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1994_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1993_to_fp16)[name = string("op_1994_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_545_cast_fp16 = add(x = input_533_cast_fp16, y = var_1994_cast_fp16)[name = string("input_545_cast_fp16")];
            tensor<int32, [1]> input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252260928)))];
            tensor<fp16, [1024]> encoder_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252263040)))];
            tensor<fp16, [1, 188, 1024]> input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_layers_9_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_9_norm_out_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")];
            tensor<int32, [1]> input_549_axes_0 = const()[name = string("input_549_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252265152)))];
            tensor<fp16, [1024]> encoder_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252267264)))];
            tensor<fp16, [1, 188, 1024]> input_549_cast_fp16 = layer_norm(axes = input_549_axes_0, beta = encoder_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_10_norm_feed_forward1_weight_to_fp16, x = input_547_cast_fp16)[name = string("input_549_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_10_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252269376))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256463744))))[name = string("encoder_layers_10_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_93_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_10_feed_forward1_linear1_weight_to_fp16_quantized, x = input_549_cast_fp16)[name = string("linear_93_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_553_cast_fp16 = silu(x = linear_93_cast_fp16)[name = string("input_553_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_10_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256472000))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260666368))))[name = string("encoder_layers_10_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_10_feed_forward1_linear2_weight_to_fp16_quantized, x = input_553_cast_fp16)[name = string("linear_94_cast_fp16")];
            fp16 var_2022_to_fp16 = const()[name = string("op_2022_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2023_cast_fp16 = mul(x = linear_94_cast_fp16, y = var_2022_to_fp16)[name = string("op_2023_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_559_cast_fp16 = add(x = input_547_cast_fp16, y = var_2023_cast_fp16)[name = string("input_559_cast_fp16")];
            tensor<int32, [1]> query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260668480)))];
            tensor<fp16, [1024]> encoder_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260670592)))];
            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = encoder_layers_10_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_10_norm_self_att_weight_to_fp16, x = input_559_cast_fp16)[name = string("query_21_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_10_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260672704))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261721344))))[name = string("encoder_layers_10_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_10_self_attn_linear_q_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")];
            tensor<int32, [4]> var_2039 = const()[name = string("op_2039"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_2039, x = linear_95_cast_fp16)[name = string("q_61_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_10_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261723456))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262772096))))[name = string("encoder_layers_10_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_96_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_10_self_attn_linear_k_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_96_cast_fp16")];
            tensor<int32, [4]> var_2043 = const()[name = string("op_2043"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_2043, x = linear_96_cast_fp16)[name = string("k_41_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_10_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262774208))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263822848))))[name = string("encoder_layers_10_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_10_self_attn_linear_v_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_97_cast_fp16")];
            tensor<int32, [4]> var_2047 = const()[name = string("op_2047"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2047, x = linear_97_cast_fp16)[name = string("v_21_cast_fp16")];
            tensor<int32, [4]> value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263824960)))];
            tensor<fp16, [1, 188, 8, 128]> var_2059_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2059_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263827072)))];
            tensor<fp16, [1, 188, 8, 128]> var_2061_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2061_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_227_transpose_x_0 = const()[name = string("x_227_transpose_x_0"), val = bool(false)];
            bool x_227_transpose_y_0 = const()[name = string("x_227_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2063_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263829184))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264213248))))[name = string("op_2063_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2061_cast_fp16)[name = string("transpose_242")];
            tensor<fp16, [1, 8, 188, 375]> x_227_cast_fp16 = matmul(transpose_x = x_227_transpose_x_0, transpose_y = x_227_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2063_to_fp16_quantized)[name = string("x_227_cast_fp16")];
            tensor<int32, [8]> x_229_pad_0 = const()[name = string("x_229_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_229_mode_0 = const()[name = string("x_229_mode_0"), val = string("constant")];
            fp16 const_170_to_fp16 = const()[name = string("const_170_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_229_cast_fp16 = pad(constant_val = const_170_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [4]> var_2071 = const()[name = string("op_2071"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_231_cast_fp16 = reshape(shape = var_2071, x = x_229_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> var_2075_begin_0 = const()[name = string("op_2075_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2075_end_0 = const()[name = string("op_2075_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2075_end_mask_0 = const()[name = string("op_2075_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2075_cast_fp16 = slice_by_index(begin = var_2075_begin_0, end = var_2075_end_0, end_mask = var_2075_end_mask_0, x = x_231_cast_fp16)[name = string("op_2075_cast_fp16")];
            tensor<int32, [4]> var_2076 = const()[name = string("op_2076"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2076, x = var_2075_cast_fp16)[name = string("matrix_bd_41_cast_fp16")];
            bool matrix_ac_21_transpose_x_0 = const()[name = string("matrix_ac_21_transpose_x_0"), val = bool(false)];
            bool matrix_ac_21_transpose_y_0 = const()[name = string("matrix_ac_21_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")];
            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2059_cast_fp16)[name = string("transpose_241")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_21_cast_fp16")];
            tensor<int32, [4]> matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_43_cast_fp16 = slice_by_index(begin = matrix_bd_43_begin_0, end = matrix_bd_43_end_0, end_mask = matrix_bd_43_end_mask_0, x = matrix_bd_41_cast_fp16)[name = string("matrix_bd_43_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2085_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2085_cast_fp16")];
            fp16 _inversed_scores_41_y_0_to_fp16 = const()[name = string("_inversed_scores_41_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2085_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_11)[name = string("scores_43_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2091_cast_fp16 = softmax(axis = var_25, x = scores_43_cast_fp16)[name = string("op_2091_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_561_cast_fp16 = select(a = var_9_to_fp16, b = var_2091_cast_fp16, cond = mask_11)[name = string("input_561_cast_fp16")];
            bool x_233_transpose_x_0 = const()[name = string("x_233_transpose_x_0"), val = bool(false)];
            bool x_233_transpose_y_0 = const()[name = string("x_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")];
            tensor<fp16, [1, 8, 188, 128]> x_233_cast_fp16 = matmul(transpose_x = x_233_transpose_x_0, transpose_y = x_233_transpose_y_0, x = input_561_cast_fp16, y = value_23_cast_fp16)[name = string("x_233_cast_fp16")];
            tensor<int32, [4]> var_2095_perm_0 = const()[name = string("op_2095_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2096 = const()[name = string("op_2096"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2095_cast_fp16 = transpose(perm = var_2095_perm_0, x = x_233_cast_fp16)[name = string("transpose_238")];
            tensor<fp16, [1, 188, 1024]> input_563_cast_fp16 = reshape(shape = var_2096, x = var_2095_cast_fp16)[name = string("input_563_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_10_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264214080))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265262720))))[name = string("encoder_layers_10_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_10_self_attn_linear_out_weight_to_fp16_quantized, x = input_563_cast_fp16)[name = string("linear_99_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_567_cast_fp16 = add(x = input_559_cast_fp16, y = linear_99_cast_fp16)[name = string("input_567_cast_fp16")];
            tensor<int32, [1]> x_237_axes_0 = const()[name = string("x_237_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265264832)))];
            tensor<fp16, [1024]> encoder_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265266944)))];
            tensor<fp16, [1, 188, 1024]> x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = encoder_layers_10_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_237_cast_fp16")];
            tensor<int32, [3]> input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265269056))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267366272))))[name = string("encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_237_cast_fp16)[name = string("transpose_237")];
            tensor<fp16, [1, 2048, 188]> input_571_cast_fp16 = conv(dilations = input_571_dilations_0, groups = input_571_groups_0, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = input_571_strides_0, weight = encoder_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")];
            int32 x_239_split_num_splits_0 = const()[name = string("x_239_split_num_splits_0"), val = int32(2)];
            int32 x_239_split_axis_0 = const()[name = string("x_239_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_239_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_239_split_cast_fp16_1 = split(axis = x_239_split_axis_0, num_splits = x_239_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_239_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_239_split_1_sigmoid_cast_fp16 = sigmoid(x = x_239_split_cast_fp16_1)[name = string("x_239_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_239_cast_fp16 = mul(x = x_239_split_cast_fp16_0, y = x_239_split_1_sigmoid_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_573_cast_fp16 = select(a = var_9_to_fp16, b = x_239_cast_fp16, cond = var_488)[name = string("input_573_cast_fp16")];
            tensor<int32, [6]> input_575_pad_0 = const()[name = string("input_575_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_575_mode_0 = const()[name = string("input_575_mode_0"), val = string("constant")];
            fp16 const_173_to_fp16 = const()[name = string("const_173_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_575_cast_fp16 = pad(constant_val = const_173_to_fp16, mode = input_575_mode_0, pad = input_575_pad_0, x = input_573_cast_fp16)[name = string("input_575_cast_fp16")];
            string input_577_pad_type_0 = const()[name = string("input_577_pad_type_0"), val = string("valid")];
            int32 input_577_groups_0 = const()[name = string("input_577_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_577_strides_0 = const()[name = string("input_577_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_577_pad_0 = const()[name = string("input_577_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_577_dilations_0 = const()[name = string("input_577_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_324_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267370432))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267379712))))[name = string("const_324_to_fp16_quantized")];
            tensor<fp16, [1024]> const_325_to_fp16 = const()[name = string("const_325_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267381824)))];
            tensor<fp16, [1, 1024, 188]> input_579_cast_fp16 = conv(bias = const_325_to_fp16, dilations = input_577_dilations_0, groups = input_577_groups_0, pad = input_577_pad_0, pad_type = input_577_pad_type_0, strides = input_577_strides_0, weight = const_324_to_fp16_quantized, x = input_575_cast_fp16)[name = string("input_579_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_581_cast_fp16 = silu(x = input_579_cast_fp16)[name = string("input_581_cast_fp16")];
            string x_241_pad_type_0 = const()[name = string("x_241_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_241_strides_0 = const()[name = string("x_241_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_241_pad_0 = const()[name = string("x_241_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_241_dilations_0 = const()[name = string("x_241_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_241_groups_0 = const()[name = string("x_241_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267383936))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268432576))))[name = string("encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_241_cast_fp16 = conv(dilations = x_241_dilations_0, groups = x_241_groups_0, pad = x_241_pad_0, pad_type = x_241_pad_type_0, strides = x_241_strides_0, weight = encoder_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_581_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [3]> input_583_perm_0 = const()[name = string("input_583_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_583_cast_fp16 = transpose(perm = input_583_perm_0, x = x_241_cast_fp16)[name = string("transpose_236")];
            tensor<fp16, [1, 188, 1024]> input_585_cast_fp16 = add(x = input_567_cast_fp16, y = input_583_cast_fp16)[name = string("input_585_cast_fp16")];
            tensor<int32, [1]> input_587_axes_0 = const()[name = string("input_587_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268434688)))];
            tensor<fp16, [1024]> encoder_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268436800)))];
            tensor<fp16, [1, 188, 1024]> input_587_cast_fp16 = layer_norm(axes = input_587_axes_0, beta = encoder_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_10_norm_feed_forward2_weight_to_fp16, x = input_585_cast_fp16)[name = string("input_587_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_10_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268438912))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272633280))))[name = string("encoder_layers_10_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_10_feed_forward2_linear1_weight_to_fp16_quantized, x = input_587_cast_fp16)[name = string("linear_100_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_591_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_591_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_10_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272641536))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276835904))))[name = string("encoder_layers_10_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_10_feed_forward2_linear2_weight_to_fp16_quantized, x = input_591_cast_fp16)[name = string("linear_101_cast_fp16")];
            fp16 var_2156_to_fp16 = const()[name = string("op_2156_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2157_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2156_to_fp16)[name = string("op_2157_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_597_cast_fp16 = add(x = input_585_cast_fp16, y = var_2157_cast_fp16)[name = string("input_597_cast_fp16")];
            tensor<int32, [1]> input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276838016)))];
            tensor<fp16, [1024]> encoder_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276840128)))];
            tensor<fp16, [1, 188, 1024]> input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_layers_10_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_10_norm_out_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")];
            tensor<int32, [1]> input_601_axes_0 = const()[name = string("input_601_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276842240)))];
            tensor<fp16, [1024]> encoder_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276844352)))];
            tensor<fp16, [1, 188, 1024]> input_601_cast_fp16 = layer_norm(axes = input_601_axes_0, beta = encoder_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_11_norm_feed_forward1_weight_to_fp16, x = input_599_cast_fp16)[name = string("input_601_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_11_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276846464))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281040832))))[name = string("encoder_layers_11_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_102_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_11_feed_forward1_linear1_weight_to_fp16_quantized, x = input_601_cast_fp16)[name = string("linear_102_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_605_cast_fp16 = silu(x = linear_102_cast_fp16)[name = string("input_605_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_11_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281049088))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285243456))))[name = string("encoder_layers_11_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_11_feed_forward1_linear2_weight_to_fp16_quantized, x = input_605_cast_fp16)[name = string("linear_103_cast_fp16")];
            fp16 var_2185_to_fp16 = const()[name = string("op_2185_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2186_cast_fp16 = mul(x = linear_103_cast_fp16, y = var_2185_to_fp16)[name = string("op_2186_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_611_cast_fp16 = add(x = input_599_cast_fp16, y = var_2186_cast_fp16)[name = string("input_611_cast_fp16")];
            tensor<int32, [1]> query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285245568)))];
            tensor<fp16, [1024]> encoder_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285247680)))];
            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = encoder_layers_11_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_11_norm_self_att_weight_to_fp16, x = input_611_cast_fp16)[name = string("query_23_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_11_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285249792))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286298432))))[name = string("encoder_layers_11_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_11_self_attn_linear_q_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")];
            tensor<int32, [4]> var_2202 = const()[name = string("op_2202"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2202, x = linear_104_cast_fp16)[name = string("q_67_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_11_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286300544))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287349184))))[name = string("encoder_layers_11_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_105_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_11_self_attn_linear_k_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_105_cast_fp16")];
            tensor<int32, [4]> var_2206 = const()[name = string("op_2206"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2206, x = linear_105_cast_fp16)[name = string("k_45_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_11_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287351296))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288399936))))[name = string("encoder_layers_11_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_11_self_attn_linear_v_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_106_cast_fp16")];
            tensor<int32, [4]> var_2210 = const()[name = string("op_2210"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2210, x = linear_106_cast_fp16)[name = string("v_23_cast_fp16")];
            tensor<int32, [4]> value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288402048)))];
            tensor<fp16, [1, 188, 8, 128]> var_2222_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2222_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288404160)))];
            tensor<fp16, [1, 188, 8, 128]> var_2224_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2224_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_249_transpose_x_0 = const()[name = string("x_249_transpose_x_0"), val = bool(false)];
            bool x_249_transpose_y_0 = const()[name = string("x_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2226_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288406272))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288790336))))[name = string("op_2226_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2224_cast_fp16)[name = string("transpose_235")];
            tensor<fp16, [1, 8, 188, 375]> x_249_cast_fp16 = matmul(transpose_x = x_249_transpose_x_0, transpose_y = x_249_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2226_to_fp16_quantized)[name = string("x_249_cast_fp16")];
            tensor<int32, [8]> x_251_pad_0 = const()[name = string("x_251_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_251_mode_0 = const()[name = string("x_251_mode_0"), val = string("constant")];
            fp16 const_180_to_fp16 = const()[name = string("const_180_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_251_cast_fp16 = pad(constant_val = const_180_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> var_2234 = const()[name = string("op_2234"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_253_cast_fp16 = reshape(shape = var_2234, x = x_251_cast_fp16)[name = string("x_253_cast_fp16")];
            tensor<int32, [4]> var_2238_begin_0 = const()[name = string("op_2238_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2238_end_0 = const()[name = string("op_2238_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2238_end_mask_0 = const()[name = string("op_2238_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2238_cast_fp16 = slice_by_index(begin = var_2238_begin_0, end = var_2238_end_0, end_mask = var_2238_end_mask_0, x = x_253_cast_fp16)[name = string("op_2238_cast_fp16")];
            tensor<int32, [4]> var_2239 = const()[name = string("op_2239"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2239, x = var_2238_cast_fp16)[name = string("matrix_bd_45_cast_fp16")];
            bool matrix_ac_23_transpose_x_0 = const()[name = string("matrix_ac_23_transpose_x_0"), val = bool(false)];
            bool matrix_ac_23_transpose_y_0 = const()[name = string("matrix_ac_23_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")];
            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2222_cast_fp16)[name = string("transpose_234")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_23_cast_fp16")];
            tensor<int32, [4]> matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_47_cast_fp16 = slice_by_index(begin = matrix_bd_47_begin_0, end = matrix_bd_47_end_0, end_mask = matrix_bd_47_end_mask_0, x = matrix_bd_45_cast_fp16)[name = string("matrix_bd_47_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2248_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2248_cast_fp16")];
            fp16 _inversed_scores_45_y_0_to_fp16 = const()[name = string("_inversed_scores_45_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2248_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_11)[name = string("scores_47_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2254_cast_fp16 = softmax(axis = var_25, x = scores_47_cast_fp16)[name = string("op_2254_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_613_cast_fp16 = select(a = var_9_to_fp16, b = var_2254_cast_fp16, cond = mask_11)[name = string("input_613_cast_fp16")];
            bool x_255_transpose_x_0 = const()[name = string("x_255_transpose_x_0"), val = bool(false)];
            bool x_255_transpose_y_0 = const()[name = string("x_255_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")];
            tensor<fp16, [1, 8, 188, 128]> x_255_cast_fp16 = matmul(transpose_x = x_255_transpose_x_0, transpose_y = x_255_transpose_y_0, x = input_613_cast_fp16, y = value_25_cast_fp16)[name = string("x_255_cast_fp16")];
            tensor<int32, [4]> var_2258_perm_0 = const()[name = string("op_2258_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2259 = const()[name = string("op_2259"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2258_cast_fp16 = transpose(perm = var_2258_perm_0, x = x_255_cast_fp16)[name = string("transpose_231")];
            tensor<fp16, [1, 188, 1024]> input_615_cast_fp16 = reshape(shape = var_2259, x = var_2258_cast_fp16)[name = string("input_615_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_11_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288791168))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289839808))))[name = string("encoder_layers_11_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_11_self_attn_linear_out_weight_to_fp16_quantized, x = input_615_cast_fp16)[name = string("linear_108_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_619_cast_fp16 = add(x = input_611_cast_fp16, y = linear_108_cast_fp16)[name = string("input_619_cast_fp16")];
            tensor<int32, [1]> x_259_axes_0 = const()[name = string("x_259_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289841920)))];
            tensor<fp16, [1024]> encoder_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289844032)))];
            tensor<fp16, [1, 188, 1024]> x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = encoder_layers_11_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<int32, [3]> input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289846144))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291943360))))[name = string("encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_259_cast_fp16)[name = string("transpose_230")];
            tensor<fp16, [1, 2048, 188]> input_623_cast_fp16 = conv(dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = encoder_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")];
            int32 x_261_split_num_splits_0 = const()[name = string("x_261_split_num_splits_0"), val = int32(2)];
            int32 x_261_split_axis_0 = const()[name = string("x_261_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_261_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_261_split_cast_fp16_1 = split(axis = x_261_split_axis_0, num_splits = x_261_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_261_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_261_split_1_sigmoid_cast_fp16 = sigmoid(x = x_261_split_cast_fp16_1)[name = string("x_261_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_261_cast_fp16 = mul(x = x_261_split_cast_fp16_0, y = x_261_split_1_sigmoid_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_625_cast_fp16 = select(a = var_9_to_fp16, b = x_261_cast_fp16, cond = var_488)[name = string("input_625_cast_fp16")];
            tensor<int32, [6]> input_627_pad_0 = const()[name = string("input_627_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_627_mode_0 = const()[name = string("input_627_mode_0"), val = string("constant")];
            fp16 const_183_to_fp16 = const()[name = string("const_183_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_627_cast_fp16 = pad(constant_val = const_183_to_fp16, mode = input_627_mode_0, pad = input_627_pad_0, x = input_625_cast_fp16)[name = string("input_627_cast_fp16")];
            string input_629_pad_type_0 = const()[name = string("input_629_pad_type_0"), val = string("valid")];
            int32 input_629_groups_0 = const()[name = string("input_629_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_629_strides_0 = const()[name = string("input_629_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_629_pad_0 = const()[name = string("input_629_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_629_dilations_0 = const()[name = string("input_629_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_326_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291947520))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291956800))))[name = string("const_326_to_fp16_quantized")];
            tensor<fp16, [1024]> const_327_to_fp16 = const()[name = string("const_327_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291958912)))];
            tensor<fp16, [1, 1024, 188]> input_631_cast_fp16 = conv(bias = const_327_to_fp16, dilations = input_629_dilations_0, groups = input_629_groups_0, pad = input_629_pad_0, pad_type = input_629_pad_type_0, strides = input_629_strides_0, weight = const_326_to_fp16_quantized, x = input_627_cast_fp16)[name = string("input_631_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_633_cast_fp16 = silu(x = input_631_cast_fp16)[name = string("input_633_cast_fp16")];
            string x_263_pad_type_0 = const()[name = string("x_263_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_263_strides_0 = const()[name = string("x_263_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_263_pad_0 = const()[name = string("x_263_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_263_dilations_0 = const()[name = string("x_263_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_263_groups_0 = const()[name = string("x_263_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291961024))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293009664))))[name = string("encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_263_cast_fp16 = conv(dilations = x_263_dilations_0, groups = x_263_groups_0, pad = x_263_pad_0, pad_type = x_263_pad_type_0, strides = x_263_strides_0, weight = encoder_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_633_cast_fp16)[name = string("x_263_cast_fp16")];
            tensor<int32, [3]> input_635_perm_0 = const()[name = string("input_635_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_635_cast_fp16 = transpose(perm = input_635_perm_0, x = x_263_cast_fp16)[name = string("transpose_229")];
            tensor<fp16, [1, 188, 1024]> input_637_cast_fp16 = add(x = input_619_cast_fp16, y = input_635_cast_fp16)[name = string("input_637_cast_fp16")];
            tensor<int32, [1]> input_639_axes_0 = const()[name = string("input_639_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293011776)))];
            tensor<fp16, [1024]> encoder_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293013888)))];
            tensor<fp16, [1, 188, 1024]> input_639_cast_fp16 = layer_norm(axes = input_639_axes_0, beta = encoder_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_11_norm_feed_forward2_weight_to_fp16, x = input_637_cast_fp16)[name = string("input_639_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_11_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293016000))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297210368))))[name = string("encoder_layers_11_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_11_feed_forward2_linear1_weight_to_fp16_quantized, x = input_639_cast_fp16)[name = string("linear_109_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_643_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_643_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_11_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297218624))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301412992))))[name = string("encoder_layers_11_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_11_feed_forward2_linear2_weight_to_fp16_quantized, x = input_643_cast_fp16)[name = string("linear_110_cast_fp16")];
            fp16 var_2319_to_fp16 = const()[name = string("op_2319_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2320_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2319_to_fp16)[name = string("op_2320_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_649_cast_fp16 = add(x = input_637_cast_fp16, y = var_2320_cast_fp16)[name = string("input_649_cast_fp16")];
            tensor<int32, [1]> input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301415104)))];
            tensor<fp16, [1024]> encoder_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301417216)))];
            tensor<fp16, [1, 188, 1024]> input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_layers_11_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_11_norm_out_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")];
            tensor<int32, [1]> input_653_axes_0 = const()[name = string("input_653_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301419328)))];
            tensor<fp16, [1024]> encoder_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301421440)))];
            tensor<fp16, [1, 188, 1024]> input_653_cast_fp16 = layer_norm(axes = input_653_axes_0, beta = encoder_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_12_norm_feed_forward1_weight_to_fp16, x = input_651_cast_fp16)[name = string("input_653_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_12_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301423552))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305617920))))[name = string("encoder_layers_12_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_111_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_12_feed_forward1_linear1_weight_to_fp16_quantized, x = input_653_cast_fp16)[name = string("linear_111_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_657_cast_fp16 = silu(x = linear_111_cast_fp16)[name = string("input_657_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_12_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305626176))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309820544))))[name = string("encoder_layers_12_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_12_feed_forward1_linear2_weight_to_fp16_quantized, x = input_657_cast_fp16)[name = string("linear_112_cast_fp16")];
            fp16 var_2348_to_fp16 = const()[name = string("op_2348_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2349_cast_fp16 = mul(x = linear_112_cast_fp16, y = var_2348_to_fp16)[name = string("op_2349_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_663_cast_fp16 = add(x = input_651_cast_fp16, y = var_2349_cast_fp16)[name = string("input_663_cast_fp16")];
            tensor<int32, [1]> query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309822656)))];
            tensor<fp16, [1024]> encoder_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309824768)))];
            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = encoder_layers_12_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_12_norm_self_att_weight_to_fp16, x = input_663_cast_fp16)[name = string("query_25_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_12_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309826880))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310875520))))[name = string("encoder_layers_12_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_12_self_attn_linear_q_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")];
            tensor<int32, [4]> var_2365 = const()[name = string("op_2365"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2365, x = linear_113_cast_fp16)[name = string("q_73_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_12_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310877632))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311926272))))[name = string("encoder_layers_12_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_114_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_12_self_attn_linear_k_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_114_cast_fp16")];
            tensor<int32, [4]> var_2369 = const()[name = string("op_2369"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2369, x = linear_114_cast_fp16)[name = string("k_49_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_12_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311928384))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312977024))))[name = string("encoder_layers_12_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_12_self_attn_linear_v_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_115_cast_fp16")];
            tensor<int32, [4]> var_2373 = const()[name = string("op_2373"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2373, x = linear_115_cast_fp16)[name = string("v_25_cast_fp16")];
            tensor<int32, [4]> value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312979136)))];
            tensor<fp16, [1, 188, 8, 128]> var_2385_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2385_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312981248)))];
            tensor<fp16, [1, 188, 8, 128]> var_2387_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2387_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_271_transpose_x_0 = const()[name = string("x_271_transpose_x_0"), val = bool(false)];
            bool x_271_transpose_y_0 = const()[name = string("x_271_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2389_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312983360))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313367424))))[name = string("op_2389_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2387_cast_fp16)[name = string("transpose_228")];
            tensor<fp16, [1, 8, 188, 375]> x_271_cast_fp16 = matmul(transpose_x = x_271_transpose_x_0, transpose_y = x_271_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2389_to_fp16_quantized)[name = string("x_271_cast_fp16")];
            tensor<int32, [8]> x_273_pad_0 = const()[name = string("x_273_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_273_mode_0 = const()[name = string("x_273_mode_0"), val = string("constant")];
            fp16 const_190_to_fp16 = const()[name = string("const_190_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_273_cast_fp16 = pad(constant_val = const_190_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")];
            tensor<int32, [4]> var_2397 = const()[name = string("op_2397"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_275_cast_fp16 = reshape(shape = var_2397, x = x_273_cast_fp16)[name = string("x_275_cast_fp16")];
            tensor<int32, [4]> var_2401_begin_0 = const()[name = string("op_2401_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2401_end_0 = const()[name = string("op_2401_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2401_end_mask_0 = const()[name = string("op_2401_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2401_cast_fp16 = slice_by_index(begin = var_2401_begin_0, end = var_2401_end_0, end_mask = var_2401_end_mask_0, x = x_275_cast_fp16)[name = string("op_2401_cast_fp16")];
            tensor<int32, [4]> var_2402 = const()[name = string("op_2402"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2402, x = var_2401_cast_fp16)[name = string("matrix_bd_49_cast_fp16")];
            bool matrix_ac_25_transpose_x_0 = const()[name = string("matrix_ac_25_transpose_x_0"), val = bool(false)];
            bool matrix_ac_25_transpose_y_0 = const()[name = string("matrix_ac_25_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")];
            tensor<fp16, [1, 8, 188, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2385_cast_fp16)[name = string("transpose_227")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_120, y = transpose_121)[name = string("matrix_ac_25_cast_fp16")];
            tensor<int32, [4]> matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_51_cast_fp16 = slice_by_index(begin = matrix_bd_51_begin_0, end = matrix_bd_51_end_0, end_mask = matrix_bd_51_end_mask_0, x = matrix_bd_49_cast_fp16)[name = string("matrix_bd_51_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2411_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2411_cast_fp16")];
            fp16 _inversed_scores_49_y_0_to_fp16 = const()[name = string("_inversed_scores_49_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2411_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_11)[name = string("scores_51_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2417_cast_fp16 = softmax(axis = var_25, x = scores_51_cast_fp16)[name = string("op_2417_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_665_cast_fp16 = select(a = var_9_to_fp16, b = var_2417_cast_fp16, cond = mask_11)[name = string("input_665_cast_fp16")];
            bool x_277_transpose_x_0 = const()[name = string("x_277_transpose_x_0"), val = bool(false)];
            bool x_277_transpose_y_0 = const()[name = string("x_277_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")];
            tensor<fp16, [1, 8, 188, 128]> x_277_cast_fp16 = matmul(transpose_x = x_277_transpose_x_0, transpose_y = x_277_transpose_y_0, x = input_665_cast_fp16, y = value_27_cast_fp16)[name = string("x_277_cast_fp16")];
            tensor<int32, [4]> var_2421_perm_0 = const()[name = string("op_2421_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2422 = const()[name = string("op_2422"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2421_cast_fp16 = transpose(perm = var_2421_perm_0, x = x_277_cast_fp16)[name = string("transpose_224")];
            tensor<fp16, [1, 188, 1024]> input_667_cast_fp16 = reshape(shape = var_2422, x = var_2421_cast_fp16)[name = string("input_667_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_12_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313368256))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314416896))))[name = string("encoder_layers_12_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_12_self_attn_linear_out_weight_to_fp16_quantized, x = input_667_cast_fp16)[name = string("linear_117_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_671_cast_fp16 = add(x = input_663_cast_fp16, y = linear_117_cast_fp16)[name = string("input_671_cast_fp16")];
            tensor<int32, [1]> x_281_axes_0 = const()[name = string("x_281_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314419008)))];
            tensor<fp16, [1024]> encoder_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314421120)))];
            tensor<fp16, [1, 188, 1024]> x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = encoder_layers_12_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_281_cast_fp16")];
            tensor<int32, [3]> input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314423232))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316520448))))[name = string("encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_281_cast_fp16)[name = string("transpose_223")];
            tensor<fp16, [1, 2048, 188]> input_675_cast_fp16 = conv(dilations = input_675_dilations_0, groups = input_675_groups_0, pad = input_675_pad_0, pad_type = input_675_pad_type_0, strides = input_675_strides_0, weight = encoder_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")];
            int32 x_283_split_num_splits_0 = const()[name = string("x_283_split_num_splits_0"), val = int32(2)];
            int32 x_283_split_axis_0 = const()[name = string("x_283_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_283_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_283_split_cast_fp16_1 = split(axis = x_283_split_axis_0, num_splits = x_283_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_283_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_283_split_1_sigmoid_cast_fp16 = sigmoid(x = x_283_split_cast_fp16_1)[name = string("x_283_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_283_cast_fp16 = mul(x = x_283_split_cast_fp16_0, y = x_283_split_1_sigmoid_cast_fp16)[name = string("x_283_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_677_cast_fp16 = select(a = var_9_to_fp16, b = x_283_cast_fp16, cond = var_488)[name = string("input_677_cast_fp16")];
            tensor<int32, [6]> input_679_pad_0 = const()[name = string("input_679_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_679_mode_0 = const()[name = string("input_679_mode_0"), val = string("constant")];
            fp16 const_193_to_fp16 = const()[name = string("const_193_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_679_cast_fp16 = pad(constant_val = const_193_to_fp16, mode = input_679_mode_0, pad = input_679_pad_0, x = input_677_cast_fp16)[name = string("input_679_cast_fp16")];
            string input_681_pad_type_0 = const()[name = string("input_681_pad_type_0"), val = string("valid")];
            int32 input_681_groups_0 = const()[name = string("input_681_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_681_strides_0 = const()[name = string("input_681_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_681_pad_0 = const()[name = string("input_681_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_681_dilations_0 = const()[name = string("input_681_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_328_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316524608))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316533888))))[name = string("const_328_to_fp16_quantized")];
            tensor<fp16, [1024]> const_329_to_fp16 = const()[name = string("const_329_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316536000)))];
            tensor<fp16, [1, 1024, 188]> input_683_cast_fp16 = conv(bias = const_329_to_fp16, dilations = input_681_dilations_0, groups = input_681_groups_0, pad = input_681_pad_0, pad_type = input_681_pad_type_0, strides = input_681_strides_0, weight = const_328_to_fp16_quantized, x = input_679_cast_fp16)[name = string("input_683_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_685_cast_fp16 = silu(x = input_683_cast_fp16)[name = string("input_685_cast_fp16")];
            string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316538112))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317586752))))[name = string("encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_285_cast_fp16 = conv(dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = encoder_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_685_cast_fp16)[name = string("x_285_cast_fp16")];
            tensor<int32, [3]> input_687_perm_0 = const()[name = string("input_687_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_687_cast_fp16 = transpose(perm = input_687_perm_0, x = x_285_cast_fp16)[name = string("transpose_222")];
            tensor<fp16, [1, 188, 1024]> input_689_cast_fp16 = add(x = input_671_cast_fp16, y = input_687_cast_fp16)[name = string("input_689_cast_fp16")];
            tensor<int32, [1]> input_691_axes_0 = const()[name = string("input_691_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317588864)))];
            tensor<fp16, [1024]> encoder_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317590976)))];
            tensor<fp16, [1, 188, 1024]> input_691_cast_fp16 = layer_norm(axes = input_691_axes_0, beta = encoder_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_12_norm_feed_forward2_weight_to_fp16, x = input_689_cast_fp16)[name = string("input_691_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_12_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317593088))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321787456))))[name = string("encoder_layers_12_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_12_feed_forward2_linear1_weight_to_fp16_quantized, x = input_691_cast_fp16)[name = string("linear_118_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_695_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_695_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_12_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321795712))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325990080))))[name = string("encoder_layers_12_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_12_feed_forward2_linear2_weight_to_fp16_quantized, x = input_695_cast_fp16)[name = string("linear_119_cast_fp16")];
            fp16 var_2482_to_fp16 = const()[name = string("op_2482_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2483_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2482_to_fp16)[name = string("op_2483_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_701_cast_fp16 = add(x = input_689_cast_fp16, y = var_2483_cast_fp16)[name = string("input_701_cast_fp16")];
            tensor<int32, [1]> input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325992192)))];
            tensor<fp16, [1024]> encoder_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325994304)))];
            tensor<fp16, [1, 188, 1024]> input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_layers_12_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_12_norm_out_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")];
            tensor<int32, [1]> input_705_axes_0 = const()[name = string("input_705_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325996416)))];
            tensor<fp16, [1024]> encoder_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325998528)))];
            tensor<fp16, [1, 188, 1024]> input_705_cast_fp16 = layer_norm(axes = input_705_axes_0, beta = encoder_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_13_norm_feed_forward1_weight_to_fp16, x = input_703_cast_fp16)[name = string("input_705_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_13_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326000640))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330195008))))[name = string("encoder_layers_13_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_120_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_13_feed_forward1_linear1_weight_to_fp16_quantized, x = input_705_cast_fp16)[name = string("linear_120_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_709_cast_fp16 = silu(x = linear_120_cast_fp16)[name = string("input_709_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_13_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330203264))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334397632))))[name = string("encoder_layers_13_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_13_feed_forward1_linear2_weight_to_fp16_quantized, x = input_709_cast_fp16)[name = string("linear_121_cast_fp16")];
            fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2512_cast_fp16 = mul(x = linear_121_cast_fp16, y = var_2511_to_fp16)[name = string("op_2512_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_715_cast_fp16 = add(x = input_703_cast_fp16, y = var_2512_cast_fp16)[name = string("input_715_cast_fp16")];
            tensor<int32, [1]> query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334399744)))];
            tensor<fp16, [1024]> encoder_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334401856)))];
            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = encoder_layers_13_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_13_norm_self_att_weight_to_fp16, x = input_715_cast_fp16)[name = string("query_27_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_13_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334403968))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335452608))))[name = string("encoder_layers_13_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_13_self_attn_linear_q_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")];
            tensor<int32, [4]> var_2528 = const()[name = string("op_2528"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2528, x = linear_122_cast_fp16)[name = string("q_79_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_13_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335454720))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336503360))))[name = string("encoder_layers_13_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_123_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_13_self_attn_linear_k_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_123_cast_fp16")];
            tensor<int32, [4]> var_2532 = const()[name = string("op_2532"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2532, x = linear_123_cast_fp16)[name = string("k_53_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_13_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336505472))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337554112))))[name = string("encoder_layers_13_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_13_self_attn_linear_v_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_124_cast_fp16")];
            tensor<int32, [4]> var_2536 = const()[name = string("op_2536"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2536, x = linear_124_cast_fp16)[name = string("v_27_cast_fp16")];
            tensor<int32, [4]> value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337556224)))];
            tensor<fp16, [1, 188, 8, 128]> var_2548_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2548_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337558336)))];
            tensor<fp16, [1, 188, 8, 128]> var_2550_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2550_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_293_transpose_x_0 = const()[name = string("x_293_transpose_x_0"), val = bool(false)];
            bool x_293_transpose_y_0 = const()[name = string("x_293_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2552_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337560448))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337944512))))[name = string("op_2552_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2550_cast_fp16)[name = string("transpose_221")];
            tensor<fp16, [1, 8, 188, 375]> x_293_cast_fp16 = matmul(transpose_x = x_293_transpose_x_0, transpose_y = x_293_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2552_to_fp16_quantized)[name = string("x_293_cast_fp16")];
            tensor<int32, [8]> x_295_pad_0 = const()[name = string("x_295_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_295_mode_0 = const()[name = string("x_295_mode_0"), val = string("constant")];
            fp16 const_200_to_fp16 = const()[name = string("const_200_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_295_cast_fp16 = pad(constant_val = const_200_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")];
            tensor<int32, [4]> var_2560 = const()[name = string("op_2560"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_297_cast_fp16 = reshape(shape = var_2560, x = x_295_cast_fp16)[name = string("x_297_cast_fp16")];
            tensor<int32, [4]> var_2564_begin_0 = const()[name = string("op_2564_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2564_end_0 = const()[name = string("op_2564_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2564_end_mask_0 = const()[name = string("op_2564_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2564_cast_fp16 = slice_by_index(begin = var_2564_begin_0, end = var_2564_end_0, end_mask = var_2564_end_mask_0, x = x_297_cast_fp16)[name = string("op_2564_cast_fp16")];
            tensor<int32, [4]> var_2565 = const()[name = string("op_2565"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2565, x = var_2564_cast_fp16)[name = string("matrix_bd_53_cast_fp16")];
            bool matrix_ac_27_transpose_x_0 = const()[name = string("matrix_ac_27_transpose_x_0"), val = bool(false)];
            bool matrix_ac_27_transpose_y_0 = const()[name = string("matrix_ac_27_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")];
            tensor<fp16, [1, 8, 188, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2548_cast_fp16)[name = string("transpose_220")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_122, y = transpose_123)[name = string("matrix_ac_27_cast_fp16")];
            tensor<int32, [4]> matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_55_cast_fp16 = slice_by_index(begin = matrix_bd_55_begin_0, end = matrix_bd_55_end_0, end_mask = matrix_bd_55_end_mask_0, x = matrix_bd_53_cast_fp16)[name = string("matrix_bd_55_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2574_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2574_cast_fp16")];
            fp16 _inversed_scores_53_y_0_to_fp16 = const()[name = string("_inversed_scores_53_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2574_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_11)[name = string("scores_55_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2580_cast_fp16 = softmax(axis = var_25, x = scores_55_cast_fp16)[name = string("op_2580_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_717_cast_fp16 = select(a = var_9_to_fp16, b = var_2580_cast_fp16, cond = mask_11)[name = string("input_717_cast_fp16")];
            bool x_299_transpose_x_0 = const()[name = string("x_299_transpose_x_0"), val = bool(false)];
            bool x_299_transpose_y_0 = const()[name = string("x_299_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")];
            tensor<fp16, [1, 8, 188, 128]> x_299_cast_fp16 = matmul(transpose_x = x_299_transpose_x_0, transpose_y = x_299_transpose_y_0, x = input_717_cast_fp16, y = value_29_cast_fp16)[name = string("x_299_cast_fp16")];
            tensor<int32, [4]> var_2584_perm_0 = const()[name = string("op_2584_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2585 = const()[name = string("op_2585"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2584_cast_fp16 = transpose(perm = var_2584_perm_0, x = x_299_cast_fp16)[name = string("transpose_217")];
            tensor<fp16, [1, 188, 1024]> input_719_cast_fp16 = reshape(shape = var_2585, x = var_2584_cast_fp16)[name = string("input_719_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_13_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337945344))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338993984))))[name = string("encoder_layers_13_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_13_self_attn_linear_out_weight_to_fp16_quantized, x = input_719_cast_fp16)[name = string("linear_126_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_723_cast_fp16 = add(x = input_715_cast_fp16, y = linear_126_cast_fp16)[name = string("input_723_cast_fp16")];
            tensor<int32, [1]> x_303_axes_0 = const()[name = string("x_303_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338996096)))];
            tensor<fp16, [1024]> encoder_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338998208)))];
            tensor<fp16, [1, 188, 1024]> x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = encoder_layers_13_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_303_cast_fp16")];
            tensor<int32, [3]> input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339000320))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341097536))))[name = string("encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_303_cast_fp16)[name = string("transpose_216")];
            tensor<fp16, [1, 2048, 188]> input_727_cast_fp16 = conv(dilations = input_727_dilations_0, groups = input_727_groups_0, pad = input_727_pad_0, pad_type = input_727_pad_type_0, strides = input_727_strides_0, weight = encoder_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")];
            int32 x_305_split_num_splits_0 = const()[name = string("x_305_split_num_splits_0"), val = int32(2)];
            int32 x_305_split_axis_0 = const()[name = string("x_305_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_305_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_305_split_cast_fp16_1 = split(axis = x_305_split_axis_0, num_splits = x_305_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_305_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_305_split_1_sigmoid_cast_fp16 = sigmoid(x = x_305_split_cast_fp16_1)[name = string("x_305_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_305_cast_fp16 = mul(x = x_305_split_cast_fp16_0, y = x_305_split_1_sigmoid_cast_fp16)[name = string("x_305_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_729_cast_fp16 = select(a = var_9_to_fp16, b = x_305_cast_fp16, cond = var_488)[name = string("input_729_cast_fp16")];
            tensor<int32, [6]> input_731_pad_0 = const()[name = string("input_731_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_731_mode_0 = const()[name = string("input_731_mode_0"), val = string("constant")];
            fp16 const_203_to_fp16 = const()[name = string("const_203_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_731_cast_fp16 = pad(constant_val = const_203_to_fp16, mode = input_731_mode_0, pad = input_731_pad_0, x = input_729_cast_fp16)[name = string("input_731_cast_fp16")];
            string input_733_pad_type_0 = const()[name = string("input_733_pad_type_0"), val = string("valid")];
            int32 input_733_groups_0 = const()[name = string("input_733_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_733_strides_0 = const()[name = string("input_733_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_733_pad_0 = const()[name = string("input_733_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_733_dilations_0 = const()[name = string("input_733_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_330_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341101696))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341110976))))[name = string("const_330_to_fp16_quantized")];
            tensor<fp16, [1024]> const_331_to_fp16 = const()[name = string("const_331_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341113088)))];
            tensor<fp16, [1, 1024, 188]> input_735_cast_fp16 = conv(bias = const_331_to_fp16, dilations = input_733_dilations_0, groups = input_733_groups_0, pad = input_733_pad_0, pad_type = input_733_pad_type_0, strides = input_733_strides_0, weight = const_330_to_fp16_quantized, x = input_731_cast_fp16)[name = string("input_735_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_737_cast_fp16 = silu(x = input_735_cast_fp16)[name = string("input_737_cast_fp16")];
            string x_307_pad_type_0 = const()[name = string("x_307_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_307_strides_0 = const()[name = string("x_307_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_307_pad_0 = const()[name = string("x_307_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_307_dilations_0 = const()[name = string("x_307_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_307_groups_0 = const()[name = string("x_307_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341115200))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342163840))))[name = string("encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_307_cast_fp16 = conv(dilations = x_307_dilations_0, groups = x_307_groups_0, pad = x_307_pad_0, pad_type = x_307_pad_type_0, strides = x_307_strides_0, weight = encoder_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_737_cast_fp16)[name = string("x_307_cast_fp16")];
            tensor<int32, [3]> input_739_perm_0 = const()[name = string("input_739_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_739_cast_fp16 = transpose(perm = input_739_perm_0, x = x_307_cast_fp16)[name = string("transpose_215")];
            tensor<fp16, [1, 188, 1024]> input_741_cast_fp16 = add(x = input_723_cast_fp16, y = input_739_cast_fp16)[name = string("input_741_cast_fp16")];
            tensor<int32, [1]> input_743_axes_0 = const()[name = string("input_743_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342165952)))];
            tensor<fp16, [1024]> encoder_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342168064)))];
            tensor<fp16, [1, 188, 1024]> input_743_cast_fp16 = layer_norm(axes = input_743_axes_0, beta = encoder_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_13_norm_feed_forward2_weight_to_fp16, x = input_741_cast_fp16)[name = string("input_743_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_13_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342170176))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346364544))))[name = string("encoder_layers_13_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_13_feed_forward2_linear1_weight_to_fp16_quantized, x = input_743_cast_fp16)[name = string("linear_127_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_747_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_747_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_13_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346372800))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350567168))))[name = string("encoder_layers_13_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_13_feed_forward2_linear2_weight_to_fp16_quantized, x = input_747_cast_fp16)[name = string("linear_128_cast_fp16")];
            fp16 var_2645_to_fp16 = const()[name = string("op_2645_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2646_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2645_to_fp16)[name = string("op_2646_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_753_cast_fp16 = add(x = input_741_cast_fp16, y = var_2646_cast_fp16)[name = string("input_753_cast_fp16")];
            tensor<int32, [1]> input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350569280)))];
            tensor<fp16, [1024]> encoder_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350571392)))];
            tensor<fp16, [1, 188, 1024]> input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_layers_13_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_13_norm_out_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")];
            tensor<int32, [1]> input_757_axes_0 = const()[name = string("input_757_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350573504)))];
            tensor<fp16, [1024]> encoder_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350575616)))];
            tensor<fp16, [1, 188, 1024]> input_757_cast_fp16 = layer_norm(axes = input_757_axes_0, beta = encoder_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_14_norm_feed_forward1_weight_to_fp16, x = input_755_cast_fp16)[name = string("input_757_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_14_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350577728))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354772096))))[name = string("encoder_layers_14_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_129_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_14_feed_forward1_linear1_weight_to_fp16_quantized, x = input_757_cast_fp16)[name = string("linear_129_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_761_cast_fp16 = silu(x = linear_129_cast_fp16)[name = string("input_761_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_14_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354780352))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358974720))))[name = string("encoder_layers_14_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_14_feed_forward1_linear2_weight_to_fp16_quantized, x = input_761_cast_fp16)[name = string("linear_130_cast_fp16")];
            fp16 var_2674_to_fp16 = const()[name = string("op_2674_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2675_cast_fp16 = mul(x = linear_130_cast_fp16, y = var_2674_to_fp16)[name = string("op_2675_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_767_cast_fp16 = add(x = input_755_cast_fp16, y = var_2675_cast_fp16)[name = string("input_767_cast_fp16")];
            tensor<int32, [1]> query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358976832)))];
            tensor<fp16, [1024]> encoder_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358978944)))];
            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = encoder_layers_14_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_14_norm_self_att_weight_to_fp16, x = input_767_cast_fp16)[name = string("query_29_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_14_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358981056))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360029696))))[name = string("encoder_layers_14_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_14_self_attn_linear_q_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")];
            tensor<int32, [4]> var_2691 = const()[name = string("op_2691"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2691, x = linear_131_cast_fp16)[name = string("q_85_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_14_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360031808))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361080448))))[name = string("encoder_layers_14_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_132_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_14_self_attn_linear_k_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_132_cast_fp16")];
            tensor<int32, [4]> var_2695 = const()[name = string("op_2695"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2695, x = linear_132_cast_fp16)[name = string("k_57_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_14_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361082560))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362131200))))[name = string("encoder_layers_14_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_14_self_attn_linear_v_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_133_cast_fp16")];
            tensor<int32, [4]> var_2699 = const()[name = string("op_2699"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2699, x = linear_133_cast_fp16)[name = string("v_29_cast_fp16")];
            tensor<int32, [4]> value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362133312)))];
            tensor<fp16, [1, 188, 8, 128]> var_2711_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2711_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362135424)))];
            tensor<fp16, [1, 188, 8, 128]> var_2713_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2713_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_315_transpose_x_0 = const()[name = string("x_315_transpose_x_0"), val = bool(false)];
            bool x_315_transpose_y_0 = const()[name = string("x_315_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2715_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362137536))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362521600))))[name = string("op_2715_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2713_cast_fp16)[name = string("transpose_214")];
            tensor<fp16, [1, 8, 188, 375]> x_315_cast_fp16 = matmul(transpose_x = x_315_transpose_x_0, transpose_y = x_315_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2715_to_fp16_quantized)[name = string("x_315_cast_fp16")];
            tensor<int32, [8]> x_317_pad_0 = const()[name = string("x_317_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_317_mode_0 = const()[name = string("x_317_mode_0"), val = string("constant")];
            fp16 const_210_to_fp16 = const()[name = string("const_210_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_317_cast_fp16 = pad(constant_val = const_210_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")];
            tensor<int32, [4]> var_2723 = const()[name = string("op_2723"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_319_cast_fp16 = reshape(shape = var_2723, x = x_317_cast_fp16)[name = string("x_319_cast_fp16")];
            tensor<int32, [4]> var_2727_begin_0 = const()[name = string("op_2727_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2727_end_0 = const()[name = string("op_2727_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2727_end_mask_0 = const()[name = string("op_2727_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2727_cast_fp16 = slice_by_index(begin = var_2727_begin_0, end = var_2727_end_0, end_mask = var_2727_end_mask_0, x = x_319_cast_fp16)[name = string("op_2727_cast_fp16")];
            tensor<int32, [4]> var_2728 = const()[name = string("op_2728"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_2728, x = var_2727_cast_fp16)[name = string("matrix_bd_57_cast_fp16")];
            bool matrix_ac_29_transpose_x_0 = const()[name = string("matrix_ac_29_transpose_x_0"), val = bool(false)];
            bool matrix_ac_29_transpose_y_0 = const()[name = string("matrix_ac_29_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")];
            tensor<fp16, [1, 8, 188, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2711_cast_fp16)[name = string("transpose_213")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_124, y = transpose_125)[name = string("matrix_ac_29_cast_fp16")];
            tensor<int32, [4]> matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_59_cast_fp16 = slice_by_index(begin = matrix_bd_59_begin_0, end = matrix_bd_59_end_0, end_mask = matrix_bd_59_end_mask_0, x = matrix_bd_57_cast_fp16)[name = string("matrix_bd_59_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2737_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_2737_cast_fp16")];
            fp16 _inversed_scores_57_y_0_to_fp16 = const()[name = string("_inversed_scores_57_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_2737_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_11)[name = string("scores_59_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2743_cast_fp16 = softmax(axis = var_25, x = scores_59_cast_fp16)[name = string("op_2743_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_769_cast_fp16 = select(a = var_9_to_fp16, b = var_2743_cast_fp16, cond = mask_11)[name = string("input_769_cast_fp16")];
            bool x_321_transpose_x_0 = const()[name = string("x_321_transpose_x_0"), val = bool(false)];
            bool x_321_transpose_y_0 = const()[name = string("x_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")];
            tensor<fp16, [1, 8, 188, 128]> x_321_cast_fp16 = matmul(transpose_x = x_321_transpose_x_0, transpose_y = x_321_transpose_y_0, x = input_769_cast_fp16, y = value_31_cast_fp16)[name = string("x_321_cast_fp16")];
            tensor<int32, [4]> var_2747_perm_0 = const()[name = string("op_2747_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2748 = const()[name = string("op_2748"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2747_cast_fp16 = transpose(perm = var_2747_perm_0, x = x_321_cast_fp16)[name = string("transpose_210")];
            tensor<fp16, [1, 188, 1024]> input_771_cast_fp16 = reshape(shape = var_2748, x = var_2747_cast_fp16)[name = string("input_771_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_14_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362522432))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363571072))))[name = string("encoder_layers_14_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_14_self_attn_linear_out_weight_to_fp16_quantized, x = input_771_cast_fp16)[name = string("linear_135_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_775_cast_fp16 = add(x = input_767_cast_fp16, y = linear_135_cast_fp16)[name = string("input_775_cast_fp16")];
            tensor<int32, [1]> x_325_axes_0 = const()[name = string("x_325_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363573184)))];
            tensor<fp16, [1024]> encoder_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363575296)))];
            tensor<fp16, [1, 188, 1024]> x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = encoder_layers_14_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_325_cast_fp16")];
            tensor<int32, [3]> input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363577408))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365674624))))[name = string("encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_325_cast_fp16)[name = string("transpose_209")];
            tensor<fp16, [1, 2048, 188]> input_779_cast_fp16 = conv(dilations = input_779_dilations_0, groups = input_779_groups_0, pad = input_779_pad_0, pad_type = input_779_pad_type_0, strides = input_779_strides_0, weight = encoder_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")];
            int32 x_327_split_num_splits_0 = const()[name = string("x_327_split_num_splits_0"), val = int32(2)];
            int32 x_327_split_axis_0 = const()[name = string("x_327_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_327_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_327_split_cast_fp16_1 = split(axis = x_327_split_axis_0, num_splits = x_327_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_327_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_327_split_1_sigmoid_cast_fp16 = sigmoid(x = x_327_split_cast_fp16_1)[name = string("x_327_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_327_cast_fp16 = mul(x = x_327_split_cast_fp16_0, y = x_327_split_1_sigmoid_cast_fp16)[name = string("x_327_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_781_cast_fp16 = select(a = var_9_to_fp16, b = x_327_cast_fp16, cond = var_488)[name = string("input_781_cast_fp16")];
            tensor<int32, [6]> input_783_pad_0 = const()[name = string("input_783_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_783_mode_0 = const()[name = string("input_783_mode_0"), val = string("constant")];
            fp16 const_213_to_fp16 = const()[name = string("const_213_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_783_cast_fp16 = pad(constant_val = const_213_to_fp16, mode = input_783_mode_0, pad = input_783_pad_0, x = input_781_cast_fp16)[name = string("input_783_cast_fp16")];
            string input_785_pad_type_0 = const()[name = string("input_785_pad_type_0"), val = string("valid")];
            int32 input_785_groups_0 = const()[name = string("input_785_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_785_strides_0 = const()[name = string("input_785_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_785_pad_0 = const()[name = string("input_785_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_785_dilations_0 = const()[name = string("input_785_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_332_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365678784))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365688064))))[name = string("const_332_to_fp16_quantized")];
            tensor<fp16, [1024]> const_333_to_fp16 = const()[name = string("const_333_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365690176)))];
            tensor<fp16, [1, 1024, 188]> input_787_cast_fp16 = conv(bias = const_333_to_fp16, dilations = input_785_dilations_0, groups = input_785_groups_0, pad = input_785_pad_0, pad_type = input_785_pad_type_0, strides = input_785_strides_0, weight = const_332_to_fp16_quantized, x = input_783_cast_fp16)[name = string("input_787_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_789_cast_fp16 = silu(x = input_787_cast_fp16)[name = string("input_789_cast_fp16")];
            string x_329_pad_type_0 = const()[name = string("x_329_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_329_strides_0 = const()[name = string("x_329_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_329_pad_0 = const()[name = string("x_329_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_329_dilations_0 = const()[name = string("x_329_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_329_groups_0 = const()[name = string("x_329_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365692288))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366740928))))[name = string("encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_329_cast_fp16 = conv(dilations = x_329_dilations_0, groups = x_329_groups_0, pad = x_329_pad_0, pad_type = x_329_pad_type_0, strides = x_329_strides_0, weight = encoder_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_789_cast_fp16)[name = string("x_329_cast_fp16")];
            tensor<int32, [3]> input_791_perm_0 = const()[name = string("input_791_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_791_cast_fp16 = transpose(perm = input_791_perm_0, x = x_329_cast_fp16)[name = string("transpose_208")];
            tensor<fp16, [1, 188, 1024]> input_793_cast_fp16 = add(x = input_775_cast_fp16, y = input_791_cast_fp16)[name = string("input_793_cast_fp16")];
            tensor<int32, [1]> input_795_axes_0 = const()[name = string("input_795_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366743040)))];
            tensor<fp16, [1024]> encoder_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366745152)))];
            tensor<fp16, [1, 188, 1024]> input_795_cast_fp16 = layer_norm(axes = input_795_axes_0, beta = encoder_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_14_norm_feed_forward2_weight_to_fp16, x = input_793_cast_fp16)[name = string("input_795_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_14_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366747264))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370941632))))[name = string("encoder_layers_14_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_14_feed_forward2_linear1_weight_to_fp16_quantized, x = input_795_cast_fp16)[name = string("linear_136_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_799_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_799_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_14_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(370949888))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375144256))))[name = string("encoder_layers_14_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_14_feed_forward2_linear2_weight_to_fp16_quantized, x = input_799_cast_fp16)[name = string("linear_137_cast_fp16")];
            fp16 var_2808_to_fp16 = const()[name = string("op_2808_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2809_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2808_to_fp16)[name = string("op_2809_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_805_cast_fp16 = add(x = input_793_cast_fp16, y = var_2809_cast_fp16)[name = string("input_805_cast_fp16")];
            tensor<int32, [1]> input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375146368)))];
            tensor<fp16, [1024]> encoder_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375148480)))];
            tensor<fp16, [1, 188, 1024]> input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_layers_14_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_14_norm_out_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")];
            tensor<int32, [1]> input_809_axes_0 = const()[name = string("input_809_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375150592)))];
            tensor<fp16, [1024]> encoder_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375152704)))];
            tensor<fp16, [1, 188, 1024]> input_809_cast_fp16 = layer_norm(axes = input_809_axes_0, beta = encoder_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_15_norm_feed_forward1_weight_to_fp16, x = input_807_cast_fp16)[name = string("input_809_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_15_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375154816))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379349184))))[name = string("encoder_layers_15_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_138_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_15_feed_forward1_linear1_weight_to_fp16_quantized, x = input_809_cast_fp16)[name = string("linear_138_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_813_cast_fp16 = silu(x = linear_138_cast_fp16)[name = string("input_813_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_15_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379357440))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383551808))))[name = string("encoder_layers_15_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_15_feed_forward1_linear2_weight_to_fp16_quantized, x = input_813_cast_fp16)[name = string("linear_139_cast_fp16")];
            fp16 var_2837_to_fp16 = const()[name = string("op_2837_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2838_cast_fp16 = mul(x = linear_139_cast_fp16, y = var_2837_to_fp16)[name = string("op_2838_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_819_cast_fp16 = add(x = input_807_cast_fp16, y = var_2838_cast_fp16)[name = string("input_819_cast_fp16")];
            tensor<int32, [1]> query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383553920)))];
            tensor<fp16, [1024]> encoder_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383556032)))];
            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = encoder_layers_15_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_15_norm_self_att_weight_to_fp16, x = input_819_cast_fp16)[name = string("query_31_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_15_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383558144))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384606784))))[name = string("encoder_layers_15_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_15_self_attn_linear_q_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")];
            tensor<int32, [4]> var_2854 = const()[name = string("op_2854"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_2854, x = linear_140_cast_fp16)[name = string("q_91_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_15_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384608896))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385657536))))[name = string("encoder_layers_15_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_141_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_15_self_attn_linear_k_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_141_cast_fp16")];
            tensor<int32, [4]> var_2858 = const()[name = string("op_2858"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_2858, x = linear_141_cast_fp16)[name = string("k_61_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_15_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385659648))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386708288))))[name = string("encoder_layers_15_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_15_self_attn_linear_v_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_142_cast_fp16")];
            tensor<int32, [4]> var_2862 = const()[name = string("op_2862"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_2862, x = linear_142_cast_fp16)[name = string("v_31_cast_fp16")];
            tensor<int32, [4]> value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386710400)))];
            tensor<fp16, [1, 188, 8, 128]> var_2874_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_2874_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386712512)))];
            tensor<fp16, [1, 188, 8, 128]> var_2876_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_2876_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_337_transpose_x_0 = const()[name = string("x_337_transpose_x_0"), val = bool(false)];
            bool x_337_transpose_y_0 = const()[name = string("x_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2878_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386714624))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387098688))))[name = string("op_2878_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2876_cast_fp16)[name = string("transpose_207")];
            tensor<fp16, [1, 8, 188, 375]> x_337_cast_fp16 = matmul(transpose_x = x_337_transpose_x_0, transpose_y = x_337_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_2878_to_fp16_quantized)[name = string("x_337_cast_fp16")];
            tensor<int32, [8]> x_339_pad_0 = const()[name = string("x_339_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_339_mode_0 = const()[name = string("x_339_mode_0"), val = string("constant")];
            fp16 const_220_to_fp16 = const()[name = string("const_220_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_339_cast_fp16 = pad(constant_val = const_220_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")];
            tensor<int32, [4]> var_2886 = const()[name = string("op_2886"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_341_cast_fp16 = reshape(shape = var_2886, x = x_339_cast_fp16)[name = string("x_341_cast_fp16")];
            tensor<int32, [4]> var_2890_begin_0 = const()[name = string("op_2890_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2890_end_0 = const()[name = string("op_2890_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2890_end_mask_0 = const()[name = string("op_2890_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2890_cast_fp16 = slice_by_index(begin = var_2890_begin_0, end = var_2890_end_0, end_mask = var_2890_end_mask_0, x = x_341_cast_fp16)[name = string("op_2890_cast_fp16")];
            tensor<int32, [4]> var_2891 = const()[name = string("op_2891"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_2891, x = var_2890_cast_fp16)[name = string("matrix_bd_61_cast_fp16")];
            bool matrix_ac_31_transpose_x_0 = const()[name = string("matrix_ac_31_transpose_x_0"), val = bool(false)];
            bool matrix_ac_31_transpose_y_0 = const()[name = string("matrix_ac_31_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")];
            tensor<fp16, [1, 8, 188, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2874_cast_fp16)[name = string("transpose_206")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_126, y = transpose_127)[name = string("matrix_ac_31_cast_fp16")];
            tensor<int32, [4]> matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_63_cast_fp16 = slice_by_index(begin = matrix_bd_63_begin_0, end = matrix_bd_63_end_0, end_mask = matrix_bd_63_end_mask_0, x = matrix_bd_61_cast_fp16)[name = string("matrix_bd_63_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2900_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_2900_cast_fp16")];
            fp16 _inversed_scores_61_y_0_to_fp16 = const()[name = string("_inversed_scores_61_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_2900_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_11)[name = string("scores_63_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2906_cast_fp16 = softmax(axis = var_25, x = scores_63_cast_fp16)[name = string("op_2906_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_821_cast_fp16 = select(a = var_9_to_fp16, b = var_2906_cast_fp16, cond = mask_11)[name = string("input_821_cast_fp16")];
            bool x_343_transpose_x_0 = const()[name = string("x_343_transpose_x_0"), val = bool(false)];
            bool x_343_transpose_y_0 = const()[name = string("x_343_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")];
            tensor<fp16, [1, 8, 188, 128]> x_343_cast_fp16 = matmul(transpose_x = x_343_transpose_x_0, transpose_y = x_343_transpose_y_0, x = input_821_cast_fp16, y = value_33_cast_fp16)[name = string("x_343_cast_fp16")];
            tensor<int32, [4]> var_2910_perm_0 = const()[name = string("op_2910_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2911 = const()[name = string("op_2911"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2910_cast_fp16 = transpose(perm = var_2910_perm_0, x = x_343_cast_fp16)[name = string("transpose_203")];
            tensor<fp16, [1, 188, 1024]> input_823_cast_fp16 = reshape(shape = var_2911, x = var_2910_cast_fp16)[name = string("input_823_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_15_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387099520))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388148160))))[name = string("encoder_layers_15_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_15_self_attn_linear_out_weight_to_fp16_quantized, x = input_823_cast_fp16)[name = string("linear_144_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_827_cast_fp16 = add(x = input_819_cast_fp16, y = linear_144_cast_fp16)[name = string("input_827_cast_fp16")];
            tensor<int32, [1]> x_347_axes_0 = const()[name = string("x_347_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388150272)))];
            tensor<fp16, [1024]> encoder_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388152384)))];
            tensor<fp16, [1, 188, 1024]> x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = encoder_layers_15_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_347_cast_fp16")];
            tensor<int32, [3]> input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388154496))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390251712))))[name = string("encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_347_cast_fp16)[name = string("transpose_202")];
            tensor<fp16, [1, 2048, 188]> input_831_cast_fp16 = conv(dilations = input_831_dilations_0, groups = input_831_groups_0, pad = input_831_pad_0, pad_type = input_831_pad_type_0, strides = input_831_strides_0, weight = encoder_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")];
            int32 x_349_split_num_splits_0 = const()[name = string("x_349_split_num_splits_0"), val = int32(2)];
            int32 x_349_split_axis_0 = const()[name = string("x_349_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_349_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_349_split_cast_fp16_1 = split(axis = x_349_split_axis_0, num_splits = x_349_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_349_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_349_split_1_sigmoid_cast_fp16 = sigmoid(x = x_349_split_cast_fp16_1)[name = string("x_349_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_349_cast_fp16 = mul(x = x_349_split_cast_fp16_0, y = x_349_split_1_sigmoid_cast_fp16)[name = string("x_349_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_833_cast_fp16 = select(a = var_9_to_fp16, b = x_349_cast_fp16, cond = var_488)[name = string("input_833_cast_fp16")];
            tensor<int32, [6]> input_835_pad_0 = const()[name = string("input_835_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_835_mode_0 = const()[name = string("input_835_mode_0"), val = string("constant")];
            fp16 const_223_to_fp16 = const()[name = string("const_223_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_835_cast_fp16 = pad(constant_val = const_223_to_fp16, mode = input_835_mode_0, pad = input_835_pad_0, x = input_833_cast_fp16)[name = string("input_835_cast_fp16")];
            string input_837_pad_type_0 = const()[name = string("input_837_pad_type_0"), val = string("valid")];
            int32 input_837_groups_0 = const()[name = string("input_837_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_837_strides_0 = const()[name = string("input_837_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_837_pad_0 = const()[name = string("input_837_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_837_dilations_0 = const()[name = string("input_837_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_334_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390255872))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390265152))))[name = string("const_334_to_fp16_quantized")];
            tensor<fp16, [1024]> const_335_to_fp16 = const()[name = string("const_335_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390267264)))];
            tensor<fp16, [1, 1024, 188]> input_839_cast_fp16 = conv(bias = const_335_to_fp16, dilations = input_837_dilations_0, groups = input_837_groups_0, pad = input_837_pad_0, pad_type = input_837_pad_type_0, strides = input_837_strides_0, weight = const_334_to_fp16_quantized, x = input_835_cast_fp16)[name = string("input_839_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_841_cast_fp16 = silu(x = input_839_cast_fp16)[name = string("input_841_cast_fp16")];
            string x_351_pad_type_0 = const()[name = string("x_351_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_351_strides_0 = const()[name = string("x_351_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_351_pad_0 = const()[name = string("x_351_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_351_dilations_0 = const()[name = string("x_351_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_351_groups_0 = const()[name = string("x_351_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390269376))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391318016))))[name = string("encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_351_cast_fp16 = conv(dilations = x_351_dilations_0, groups = x_351_groups_0, pad = x_351_pad_0, pad_type = x_351_pad_type_0, strides = x_351_strides_0, weight = encoder_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_841_cast_fp16)[name = string("x_351_cast_fp16")];
            tensor<int32, [3]> input_843_perm_0 = const()[name = string("input_843_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_843_cast_fp16 = transpose(perm = input_843_perm_0, x = x_351_cast_fp16)[name = string("transpose_201")];
            tensor<fp16, [1, 188, 1024]> input_845_cast_fp16 = add(x = input_827_cast_fp16, y = input_843_cast_fp16)[name = string("input_845_cast_fp16")];
            tensor<int32, [1]> input_847_axes_0 = const()[name = string("input_847_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391320128)))];
            tensor<fp16, [1024]> encoder_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391322240)))];
            tensor<fp16, [1, 188, 1024]> input_847_cast_fp16 = layer_norm(axes = input_847_axes_0, beta = encoder_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_15_norm_feed_forward2_weight_to_fp16, x = input_845_cast_fp16)[name = string("input_847_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_15_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391324352))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395518720))))[name = string("encoder_layers_15_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_15_feed_forward2_linear1_weight_to_fp16_quantized, x = input_847_cast_fp16)[name = string("linear_145_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_851_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_851_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_15_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395526976))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399721344))))[name = string("encoder_layers_15_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_15_feed_forward2_linear2_weight_to_fp16_quantized, x = input_851_cast_fp16)[name = string("linear_146_cast_fp16")];
            fp16 var_2971_to_fp16 = const()[name = string("op_2971_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2972_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_2971_to_fp16)[name = string("op_2972_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_857_cast_fp16 = add(x = input_845_cast_fp16, y = var_2972_cast_fp16)[name = string("input_857_cast_fp16")];
            tensor<int32, [1]> input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399723456)))];
            tensor<fp16, [1024]> encoder_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399725568)))];
            tensor<fp16, [1, 188, 1024]> input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_layers_15_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_15_norm_out_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")];
            tensor<int32, [1]> input_861_axes_0 = const()[name = string("input_861_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399727680)))];
            tensor<fp16, [1024]> encoder_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399729792)))];
            tensor<fp16, [1, 188, 1024]> input_861_cast_fp16 = layer_norm(axes = input_861_axes_0, beta = encoder_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_16_norm_feed_forward1_weight_to_fp16, x = input_859_cast_fp16)[name = string("input_861_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_16_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399731904))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403926272))))[name = string("encoder_layers_16_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_147_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_16_feed_forward1_linear1_weight_to_fp16_quantized, x = input_861_cast_fp16)[name = string("linear_147_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_865_cast_fp16 = silu(x = linear_147_cast_fp16)[name = string("input_865_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_16_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403934528))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408128896))))[name = string("encoder_layers_16_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_16_feed_forward1_linear2_weight_to_fp16_quantized, x = input_865_cast_fp16)[name = string("linear_148_cast_fp16")];
            fp16 var_3000_to_fp16 = const()[name = string("op_3000_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3001_cast_fp16 = mul(x = linear_148_cast_fp16, y = var_3000_to_fp16)[name = string("op_3001_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_871_cast_fp16 = add(x = input_859_cast_fp16, y = var_3001_cast_fp16)[name = string("input_871_cast_fp16")];
            tensor<int32, [1]> query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408131008)))];
            tensor<fp16, [1024]> encoder_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408133120)))];
            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = encoder_layers_16_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_16_norm_self_att_weight_to_fp16, x = input_871_cast_fp16)[name = string("query_33_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_16_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408135232))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409183872))))[name = string("encoder_layers_16_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_16_self_attn_linear_q_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")];
            tensor<int32, [4]> var_3017 = const()[name = string("op_3017"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3017, x = linear_149_cast_fp16)[name = string("q_97_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_16_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409185984))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410234624))))[name = string("encoder_layers_16_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_150_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_16_self_attn_linear_k_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_150_cast_fp16")];
            tensor<int32, [4]> var_3021 = const()[name = string("op_3021"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3021, x = linear_150_cast_fp16)[name = string("k_65_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_16_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410236736))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411285376))))[name = string("encoder_layers_16_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_16_self_attn_linear_v_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_151_cast_fp16")];
            tensor<int32, [4]> var_3025 = const()[name = string("op_3025"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3025, x = linear_151_cast_fp16)[name = string("v_33_cast_fp16")];
            tensor<int32, [4]> value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411287488)))];
            tensor<fp16, [1, 188, 8, 128]> var_3037_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3037_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411289600)))];
            tensor<fp16, [1, 188, 8, 128]> var_3039_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3039_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_359_transpose_x_0 = const()[name = string("x_359_transpose_x_0"), val = bool(false)];
            bool x_359_transpose_y_0 = const()[name = string("x_359_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3041_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411291712))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411675776))))[name = string("op_3041_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3039_cast_fp16)[name = string("transpose_200")];
            tensor<fp16, [1, 8, 188, 375]> x_359_cast_fp16 = matmul(transpose_x = x_359_transpose_x_0, transpose_y = x_359_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3041_to_fp16_quantized)[name = string("x_359_cast_fp16")];
            tensor<int32, [8]> x_361_pad_0 = const()[name = string("x_361_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_361_mode_0 = const()[name = string("x_361_mode_0"), val = string("constant")];
            fp16 const_230_to_fp16 = const()[name = string("const_230_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_361_cast_fp16 = pad(constant_val = const_230_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")];
            tensor<int32, [4]> var_3049 = const()[name = string("op_3049"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_363_cast_fp16 = reshape(shape = var_3049, x = x_361_cast_fp16)[name = string("x_363_cast_fp16")];
            tensor<int32, [4]> var_3053_begin_0 = const()[name = string("op_3053_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3053_end_0 = const()[name = string("op_3053_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3053_end_mask_0 = const()[name = string("op_3053_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3053_cast_fp16 = slice_by_index(begin = var_3053_begin_0, end = var_3053_end_0, end_mask = var_3053_end_mask_0, x = x_363_cast_fp16)[name = string("op_3053_cast_fp16")];
            tensor<int32, [4]> var_3054 = const()[name = string("op_3054"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3054, x = var_3053_cast_fp16)[name = string("matrix_bd_65_cast_fp16")];
            bool matrix_ac_33_transpose_x_0 = const()[name = string("matrix_ac_33_transpose_x_0"), val = bool(false)];
            bool matrix_ac_33_transpose_y_0 = const()[name = string("matrix_ac_33_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")];
            tensor<fp16, [1, 8, 188, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3037_cast_fp16)[name = string("transpose_199")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_128, y = transpose_129)[name = string("matrix_ac_33_cast_fp16")];
            tensor<int32, [4]> matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_67_cast_fp16 = slice_by_index(begin = matrix_bd_67_begin_0, end = matrix_bd_67_end_0, end_mask = matrix_bd_67_end_mask_0, x = matrix_bd_65_cast_fp16)[name = string("matrix_bd_67_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3063_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3063_cast_fp16")];
            fp16 _inversed_scores_65_y_0_to_fp16 = const()[name = string("_inversed_scores_65_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3063_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_11)[name = string("scores_67_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3069_cast_fp16 = softmax(axis = var_25, x = scores_67_cast_fp16)[name = string("op_3069_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_873_cast_fp16 = select(a = var_9_to_fp16, b = var_3069_cast_fp16, cond = mask_11)[name = string("input_873_cast_fp16")];
            bool x_365_transpose_x_0 = const()[name = string("x_365_transpose_x_0"), val = bool(false)];
            bool x_365_transpose_y_0 = const()[name = string("x_365_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")];
            tensor<fp16, [1, 8, 188, 128]> x_365_cast_fp16 = matmul(transpose_x = x_365_transpose_x_0, transpose_y = x_365_transpose_y_0, x = input_873_cast_fp16, y = value_35_cast_fp16)[name = string("x_365_cast_fp16")];
            tensor<int32, [4]> var_3073_perm_0 = const()[name = string("op_3073_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3074 = const()[name = string("op_3074"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3073_cast_fp16 = transpose(perm = var_3073_perm_0, x = x_365_cast_fp16)[name = string("transpose_196")];
            tensor<fp16, [1, 188, 1024]> input_875_cast_fp16 = reshape(shape = var_3074, x = var_3073_cast_fp16)[name = string("input_875_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_16_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411676608))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412725248))))[name = string("encoder_layers_16_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_16_self_attn_linear_out_weight_to_fp16_quantized, x = input_875_cast_fp16)[name = string("linear_153_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_879_cast_fp16 = add(x = input_871_cast_fp16, y = linear_153_cast_fp16)[name = string("input_879_cast_fp16")];
            tensor<int32, [1]> x_369_axes_0 = const()[name = string("x_369_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412727360)))];
            tensor<fp16, [1024]> encoder_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412729472)))];
            tensor<fp16, [1, 188, 1024]> x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = encoder_layers_16_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_369_cast_fp16")];
            tensor<int32, [3]> input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412731584))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414828800))))[name = string("encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_369_cast_fp16)[name = string("transpose_195")];
            tensor<fp16, [1, 2048, 188]> input_883_cast_fp16 = conv(dilations = input_883_dilations_0, groups = input_883_groups_0, pad = input_883_pad_0, pad_type = input_883_pad_type_0, strides = input_883_strides_0, weight = encoder_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")];
            int32 x_371_split_num_splits_0 = const()[name = string("x_371_split_num_splits_0"), val = int32(2)];
            int32 x_371_split_axis_0 = const()[name = string("x_371_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_371_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_371_split_cast_fp16_1 = split(axis = x_371_split_axis_0, num_splits = x_371_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_371_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_371_split_1_sigmoid_cast_fp16 = sigmoid(x = x_371_split_cast_fp16_1)[name = string("x_371_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_371_cast_fp16 = mul(x = x_371_split_cast_fp16_0, y = x_371_split_1_sigmoid_cast_fp16)[name = string("x_371_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_885_cast_fp16 = select(a = var_9_to_fp16, b = x_371_cast_fp16, cond = var_488)[name = string("input_885_cast_fp16")];
            tensor<int32, [6]> input_887_pad_0 = const()[name = string("input_887_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_887_mode_0 = const()[name = string("input_887_mode_0"), val = string("constant")];
            fp16 const_233_to_fp16 = const()[name = string("const_233_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_887_cast_fp16 = pad(constant_val = const_233_to_fp16, mode = input_887_mode_0, pad = input_887_pad_0, x = input_885_cast_fp16)[name = string("input_887_cast_fp16")];
            string input_889_pad_type_0 = const()[name = string("input_889_pad_type_0"), val = string("valid")];
            int32 input_889_groups_0 = const()[name = string("input_889_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_889_strides_0 = const()[name = string("input_889_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_889_pad_0 = const()[name = string("input_889_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_889_dilations_0 = const()[name = string("input_889_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_336_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414832960))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414842240))))[name = string("const_336_to_fp16_quantized")];
            tensor<fp16, [1024]> const_337_to_fp16 = const()[name = string("const_337_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414844352)))];
            tensor<fp16, [1, 1024, 188]> input_891_cast_fp16 = conv(bias = const_337_to_fp16, dilations = input_889_dilations_0, groups = input_889_groups_0, pad = input_889_pad_0, pad_type = input_889_pad_type_0, strides = input_889_strides_0, weight = const_336_to_fp16_quantized, x = input_887_cast_fp16)[name = string("input_891_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_893_cast_fp16 = silu(x = input_891_cast_fp16)[name = string("input_893_cast_fp16")];
            string x_373_pad_type_0 = const()[name = string("x_373_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_373_strides_0 = const()[name = string("x_373_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_373_pad_0 = const()[name = string("x_373_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_373_dilations_0 = const()[name = string("x_373_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_373_groups_0 = const()[name = string("x_373_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414846464))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415895104))))[name = string("encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_373_cast_fp16 = conv(dilations = x_373_dilations_0, groups = x_373_groups_0, pad = x_373_pad_0, pad_type = x_373_pad_type_0, strides = x_373_strides_0, weight = encoder_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_893_cast_fp16)[name = string("x_373_cast_fp16")];
            tensor<int32, [3]> input_895_perm_0 = const()[name = string("input_895_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_895_cast_fp16 = transpose(perm = input_895_perm_0, x = x_373_cast_fp16)[name = string("transpose_194")];
            tensor<fp16, [1, 188, 1024]> input_897_cast_fp16 = add(x = input_879_cast_fp16, y = input_895_cast_fp16)[name = string("input_897_cast_fp16")];
            tensor<int32, [1]> input_899_axes_0 = const()[name = string("input_899_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415897216)))];
            tensor<fp16, [1024]> encoder_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415899328)))];
            tensor<fp16, [1, 188, 1024]> input_899_cast_fp16 = layer_norm(axes = input_899_axes_0, beta = encoder_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_16_norm_feed_forward2_weight_to_fp16, x = input_897_cast_fp16)[name = string("input_899_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_16_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415901440))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420095808))))[name = string("encoder_layers_16_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_16_feed_forward2_linear1_weight_to_fp16_quantized, x = input_899_cast_fp16)[name = string("linear_154_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_903_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_903_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_16_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420104064))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424298432))))[name = string("encoder_layers_16_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_16_feed_forward2_linear2_weight_to_fp16_quantized, x = input_903_cast_fp16)[name = string("linear_155_cast_fp16")];
            fp16 var_3134_to_fp16 = const()[name = string("op_3134_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3135_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3134_to_fp16)[name = string("op_3135_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_909_cast_fp16 = add(x = input_897_cast_fp16, y = var_3135_cast_fp16)[name = string("input_909_cast_fp16")];
            tensor<int32, [1]> input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424300544)))];
            tensor<fp16, [1024]> encoder_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424302656)))];
            tensor<fp16, [1, 188, 1024]> input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_layers_16_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_16_norm_out_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")];
            tensor<int32, [1]> input_913_axes_0 = const()[name = string("input_913_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424304768)))];
            tensor<fp16, [1024]> encoder_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424306880)))];
            tensor<fp16, [1, 188, 1024]> input_913_cast_fp16 = layer_norm(axes = input_913_axes_0, beta = encoder_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_17_norm_feed_forward1_weight_to_fp16, x = input_911_cast_fp16)[name = string("input_913_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_17_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424308992))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(428503360))))[name = string("encoder_layers_17_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_156_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_17_feed_forward1_linear1_weight_to_fp16_quantized, x = input_913_cast_fp16)[name = string("linear_156_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_917_cast_fp16 = silu(x = linear_156_cast_fp16)[name = string("input_917_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_17_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(428511616))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432705984))))[name = string("encoder_layers_17_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_17_feed_forward1_linear2_weight_to_fp16_quantized, x = input_917_cast_fp16)[name = string("linear_157_cast_fp16")];
            fp16 var_3163_to_fp16 = const()[name = string("op_3163_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3164_cast_fp16 = mul(x = linear_157_cast_fp16, y = var_3163_to_fp16)[name = string("op_3164_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_923_cast_fp16 = add(x = input_911_cast_fp16, y = var_3164_cast_fp16)[name = string("input_923_cast_fp16")];
            tensor<int32, [1]> query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432708096)))];
            tensor<fp16, [1024]> encoder_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432710208)))];
            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = encoder_layers_17_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_17_norm_self_att_weight_to_fp16, x = input_923_cast_fp16)[name = string("query_35_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_17_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432712320))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433760960))))[name = string("encoder_layers_17_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_17_self_attn_linear_q_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")];
            tensor<int32, [4]> var_3180 = const()[name = string("op_3180"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3180, x = linear_158_cast_fp16)[name = string("q_103_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_17_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433763072))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434811712))))[name = string("encoder_layers_17_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_159_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_17_self_attn_linear_k_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_159_cast_fp16")];
            tensor<int32, [4]> var_3184 = const()[name = string("op_3184"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3184, x = linear_159_cast_fp16)[name = string("k_69_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_17_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434813824))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435862464))))[name = string("encoder_layers_17_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_17_self_attn_linear_v_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_160_cast_fp16")];
            tensor<int32, [4]> var_3188 = const()[name = string("op_3188"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3188, x = linear_160_cast_fp16)[name = string("v_35_cast_fp16")];
            tensor<int32, [4]> value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435864576)))];
            tensor<fp16, [1, 188, 8, 128]> var_3200_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3200_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435866688)))];
            tensor<fp16, [1, 188, 8, 128]> var_3202_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3202_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_381_transpose_x_0 = const()[name = string("x_381_transpose_x_0"), val = bool(false)];
            bool x_381_transpose_y_0 = const()[name = string("x_381_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3204_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435868800))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436252864))))[name = string("op_3204_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3202_cast_fp16)[name = string("transpose_193")];
            tensor<fp16, [1, 8, 188, 375]> x_381_cast_fp16 = matmul(transpose_x = x_381_transpose_x_0, transpose_y = x_381_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3204_to_fp16_quantized)[name = string("x_381_cast_fp16")];
            tensor<int32, [8]> x_383_pad_0 = const()[name = string("x_383_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_383_mode_0 = const()[name = string("x_383_mode_0"), val = string("constant")];
            fp16 const_240_to_fp16 = const()[name = string("const_240_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_383_cast_fp16 = pad(constant_val = const_240_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")];
            tensor<int32, [4]> var_3212 = const()[name = string("op_3212"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_385_cast_fp16 = reshape(shape = var_3212, x = x_383_cast_fp16)[name = string("x_385_cast_fp16")];
            tensor<int32, [4]> var_3216_begin_0 = const()[name = string("op_3216_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3216_end_0 = const()[name = string("op_3216_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3216_end_mask_0 = const()[name = string("op_3216_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3216_cast_fp16 = slice_by_index(begin = var_3216_begin_0, end = var_3216_end_0, end_mask = var_3216_end_mask_0, x = x_385_cast_fp16)[name = string("op_3216_cast_fp16")];
            tensor<int32, [4]> var_3217 = const()[name = string("op_3217"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3217, x = var_3216_cast_fp16)[name = string("matrix_bd_69_cast_fp16")];
            bool matrix_ac_35_transpose_x_0 = const()[name = string("matrix_ac_35_transpose_x_0"), val = bool(false)];
            bool matrix_ac_35_transpose_y_0 = const()[name = string("matrix_ac_35_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")];
            tensor<fp16, [1, 8, 188, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3200_cast_fp16)[name = string("transpose_192")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_130, y = transpose_131)[name = string("matrix_ac_35_cast_fp16")];
            tensor<int32, [4]> matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_71_cast_fp16 = slice_by_index(begin = matrix_bd_71_begin_0, end = matrix_bd_71_end_0, end_mask = matrix_bd_71_end_mask_0, x = matrix_bd_69_cast_fp16)[name = string("matrix_bd_71_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3226_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3226_cast_fp16")];
            fp16 _inversed_scores_69_y_0_to_fp16 = const()[name = string("_inversed_scores_69_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3226_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_11)[name = string("scores_71_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3232_cast_fp16 = softmax(axis = var_25, x = scores_71_cast_fp16)[name = string("op_3232_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_925_cast_fp16 = select(a = var_9_to_fp16, b = var_3232_cast_fp16, cond = mask_11)[name = string("input_925_cast_fp16")];
            bool x_387_transpose_x_0 = const()[name = string("x_387_transpose_x_0"), val = bool(false)];
            bool x_387_transpose_y_0 = const()[name = string("x_387_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")];
            tensor<fp16, [1, 8, 188, 128]> x_387_cast_fp16 = matmul(transpose_x = x_387_transpose_x_0, transpose_y = x_387_transpose_y_0, x = input_925_cast_fp16, y = value_37_cast_fp16)[name = string("x_387_cast_fp16")];
            tensor<int32, [4]> var_3236_perm_0 = const()[name = string("op_3236_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3237 = const()[name = string("op_3237"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3236_cast_fp16 = transpose(perm = var_3236_perm_0, x = x_387_cast_fp16)[name = string("transpose_189")];
            tensor<fp16, [1, 188, 1024]> input_927_cast_fp16 = reshape(shape = var_3237, x = var_3236_cast_fp16)[name = string("input_927_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_17_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436253696))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437302336))))[name = string("encoder_layers_17_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_17_self_attn_linear_out_weight_to_fp16_quantized, x = input_927_cast_fp16)[name = string("linear_162_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_931_cast_fp16 = add(x = input_923_cast_fp16, y = linear_162_cast_fp16)[name = string("input_931_cast_fp16")];
            tensor<int32, [1]> x_391_axes_0 = const()[name = string("x_391_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437304448)))];
            tensor<fp16, [1024]> encoder_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437306560)))];
            tensor<fp16, [1, 188, 1024]> x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = encoder_layers_17_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_391_cast_fp16")];
            tensor<int32, [3]> input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437308672))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439405888))))[name = string("encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_391_cast_fp16)[name = string("transpose_188")];
            tensor<fp16, [1, 2048, 188]> input_935_cast_fp16 = conv(dilations = input_935_dilations_0, groups = input_935_groups_0, pad = input_935_pad_0, pad_type = input_935_pad_type_0, strides = input_935_strides_0, weight = encoder_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")];
            int32 x_393_split_num_splits_0 = const()[name = string("x_393_split_num_splits_0"), val = int32(2)];
            int32 x_393_split_axis_0 = const()[name = string("x_393_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_393_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_393_split_cast_fp16_1 = split(axis = x_393_split_axis_0, num_splits = x_393_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_393_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_393_split_1_sigmoid_cast_fp16 = sigmoid(x = x_393_split_cast_fp16_1)[name = string("x_393_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_393_cast_fp16 = mul(x = x_393_split_cast_fp16_0, y = x_393_split_1_sigmoid_cast_fp16)[name = string("x_393_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_937_cast_fp16 = select(a = var_9_to_fp16, b = x_393_cast_fp16, cond = var_488)[name = string("input_937_cast_fp16")];
            tensor<int32, [6]> input_939_pad_0 = const()[name = string("input_939_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_939_mode_0 = const()[name = string("input_939_mode_0"), val = string("constant")];
            fp16 const_243_to_fp16 = const()[name = string("const_243_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_939_cast_fp16 = pad(constant_val = const_243_to_fp16, mode = input_939_mode_0, pad = input_939_pad_0, x = input_937_cast_fp16)[name = string("input_939_cast_fp16")];
            string input_941_pad_type_0 = const()[name = string("input_941_pad_type_0"), val = string("valid")];
            int32 input_941_groups_0 = const()[name = string("input_941_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_941_strides_0 = const()[name = string("input_941_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_941_pad_0 = const()[name = string("input_941_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_941_dilations_0 = const()[name = string("input_941_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_338_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439410048))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439419328))))[name = string("const_338_to_fp16_quantized")];
            tensor<fp16, [1024]> const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439421440)))];
            tensor<fp16, [1, 1024, 188]> input_943_cast_fp16 = conv(bias = const_339_to_fp16, dilations = input_941_dilations_0, groups = input_941_groups_0, pad = input_941_pad_0, pad_type = input_941_pad_type_0, strides = input_941_strides_0, weight = const_338_to_fp16_quantized, x = input_939_cast_fp16)[name = string("input_943_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_945_cast_fp16 = silu(x = input_943_cast_fp16)[name = string("input_945_cast_fp16")];
            string x_395_pad_type_0 = const()[name = string("x_395_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_395_strides_0 = const()[name = string("x_395_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_395_pad_0 = const()[name = string("x_395_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_395_dilations_0 = const()[name = string("x_395_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_395_groups_0 = const()[name = string("x_395_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439423552))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440472192))))[name = string("encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_395_cast_fp16 = conv(dilations = x_395_dilations_0, groups = x_395_groups_0, pad = x_395_pad_0, pad_type = x_395_pad_type_0, strides = x_395_strides_0, weight = encoder_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_945_cast_fp16)[name = string("x_395_cast_fp16")];
            tensor<int32, [3]> input_947_perm_0 = const()[name = string("input_947_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_947_cast_fp16 = transpose(perm = input_947_perm_0, x = x_395_cast_fp16)[name = string("transpose_187")];
            tensor<fp16, [1, 188, 1024]> input_949_cast_fp16 = add(x = input_931_cast_fp16, y = input_947_cast_fp16)[name = string("input_949_cast_fp16")];
            tensor<int32, [1]> input_951_axes_0 = const()[name = string("input_951_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440474304)))];
            tensor<fp16, [1024]> encoder_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440476416)))];
            tensor<fp16, [1, 188, 1024]> input_951_cast_fp16 = layer_norm(axes = input_951_axes_0, beta = encoder_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_17_norm_feed_forward2_weight_to_fp16, x = input_949_cast_fp16)[name = string("input_951_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_17_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440478528))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444672896))))[name = string("encoder_layers_17_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_17_feed_forward2_linear1_weight_to_fp16_quantized, x = input_951_cast_fp16)[name = string("linear_163_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_955_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_955_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_17_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444681152))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448875520))))[name = string("encoder_layers_17_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_17_feed_forward2_linear2_weight_to_fp16_quantized, x = input_955_cast_fp16)[name = string("linear_164_cast_fp16")];
            fp16 var_3297_to_fp16 = const()[name = string("op_3297_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3298_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3297_to_fp16)[name = string("op_3298_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_961_cast_fp16 = add(x = input_949_cast_fp16, y = var_3298_cast_fp16)[name = string("input_961_cast_fp16")];
            tensor<int32, [1]> input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448877632)))];
            tensor<fp16, [1024]> encoder_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448879744)))];
            tensor<fp16, [1, 188, 1024]> input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_layers_17_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_17_norm_out_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")];
            tensor<int32, [1]> input_965_axes_0 = const()[name = string("input_965_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448881856)))];
            tensor<fp16, [1024]> encoder_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448883968)))];
            tensor<fp16, [1, 188, 1024]> input_965_cast_fp16 = layer_norm(axes = input_965_axes_0, beta = encoder_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_18_norm_feed_forward1_weight_to_fp16, x = input_963_cast_fp16)[name = string("input_965_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_18_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448886080))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(453080448))))[name = string("encoder_layers_18_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_165_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_18_feed_forward1_linear1_weight_to_fp16_quantized, x = input_965_cast_fp16)[name = string("linear_165_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_969_cast_fp16 = silu(x = linear_165_cast_fp16)[name = string("input_969_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_18_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(453088704))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457283072))))[name = string("encoder_layers_18_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_18_feed_forward1_linear2_weight_to_fp16_quantized, x = input_969_cast_fp16)[name = string("linear_166_cast_fp16")];
            fp16 var_3326_to_fp16 = const()[name = string("op_3326_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3327_cast_fp16 = mul(x = linear_166_cast_fp16, y = var_3326_to_fp16)[name = string("op_3327_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_975_cast_fp16 = add(x = input_963_cast_fp16, y = var_3327_cast_fp16)[name = string("input_975_cast_fp16")];
            tensor<int32, [1]> query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457285184)))];
            tensor<fp16, [1024]> encoder_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457287296)))];
            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = encoder_layers_18_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_18_norm_self_att_weight_to_fp16, x = input_975_cast_fp16)[name = string("query_37_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_18_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457289408))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458338048))))[name = string("encoder_layers_18_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_18_self_attn_linear_q_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")];
            tensor<int32, [4]> var_3343 = const()[name = string("op_3343"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3343, x = linear_167_cast_fp16)[name = string("q_109_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_18_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458340160))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459388800))))[name = string("encoder_layers_18_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_168_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_18_self_attn_linear_k_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_168_cast_fp16")];
            tensor<int32, [4]> var_3347 = const()[name = string("op_3347"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3347, x = linear_168_cast_fp16)[name = string("k_73_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_18_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459390912))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460439552))))[name = string("encoder_layers_18_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_18_self_attn_linear_v_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_169_cast_fp16")];
            tensor<int32, [4]> var_3351 = const()[name = string("op_3351"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3351, x = linear_169_cast_fp16)[name = string("v_37_cast_fp16")];
            tensor<int32, [4]> value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460441664)))];
            tensor<fp16, [1, 188, 8, 128]> var_3363_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3363_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460443776)))];
            tensor<fp16, [1, 188, 8, 128]> var_3365_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3365_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_403_transpose_x_0 = const()[name = string("x_403_transpose_x_0"), val = bool(false)];
            bool x_403_transpose_y_0 = const()[name = string("x_403_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3367_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460445888))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460829952))))[name = string("op_3367_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3365_cast_fp16)[name = string("transpose_186")];
            tensor<fp16, [1, 8, 188, 375]> x_403_cast_fp16 = matmul(transpose_x = x_403_transpose_x_0, transpose_y = x_403_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3367_to_fp16_quantized)[name = string("x_403_cast_fp16")];
            tensor<int32, [8]> x_405_pad_0 = const()[name = string("x_405_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_405_mode_0 = const()[name = string("x_405_mode_0"), val = string("constant")];
            fp16 const_250_to_fp16 = const()[name = string("const_250_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_405_cast_fp16 = pad(constant_val = const_250_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")];
            tensor<int32, [4]> var_3375 = const()[name = string("op_3375"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_407_cast_fp16 = reshape(shape = var_3375, x = x_405_cast_fp16)[name = string("x_407_cast_fp16")];
            tensor<int32, [4]> var_3379_begin_0 = const()[name = string("op_3379_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3379_end_0 = const()[name = string("op_3379_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3379_end_mask_0 = const()[name = string("op_3379_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3379_cast_fp16 = slice_by_index(begin = var_3379_begin_0, end = var_3379_end_0, end_mask = var_3379_end_mask_0, x = x_407_cast_fp16)[name = string("op_3379_cast_fp16")];
            tensor<int32, [4]> var_3380 = const()[name = string("op_3380"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3380, x = var_3379_cast_fp16)[name = string("matrix_bd_73_cast_fp16")];
            bool matrix_ac_37_transpose_x_0 = const()[name = string("matrix_ac_37_transpose_x_0"), val = bool(false)];
            bool matrix_ac_37_transpose_y_0 = const()[name = string("matrix_ac_37_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")];
            tensor<fp16, [1, 8, 188, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3363_cast_fp16)[name = string("transpose_185")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_132, y = transpose_133)[name = string("matrix_ac_37_cast_fp16")];
            tensor<int32, [4]> matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_75_cast_fp16 = slice_by_index(begin = matrix_bd_75_begin_0, end = matrix_bd_75_end_0, end_mask = matrix_bd_75_end_mask_0, x = matrix_bd_73_cast_fp16)[name = string("matrix_bd_75_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3389_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3389_cast_fp16")];
            fp16 _inversed_scores_73_y_0_to_fp16 = const()[name = string("_inversed_scores_73_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3389_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_11)[name = string("scores_75_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3395_cast_fp16 = softmax(axis = var_25, x = scores_75_cast_fp16)[name = string("op_3395_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_977_cast_fp16 = select(a = var_9_to_fp16, b = var_3395_cast_fp16, cond = mask_11)[name = string("input_977_cast_fp16")];
            bool x_409_transpose_x_0 = const()[name = string("x_409_transpose_x_0"), val = bool(false)];
            bool x_409_transpose_y_0 = const()[name = string("x_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")];
            tensor<fp16, [1, 8, 188, 128]> x_409_cast_fp16 = matmul(transpose_x = x_409_transpose_x_0, transpose_y = x_409_transpose_y_0, x = input_977_cast_fp16, y = value_39_cast_fp16)[name = string("x_409_cast_fp16")];
            tensor<int32, [4]> var_3399_perm_0 = const()[name = string("op_3399_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3400 = const()[name = string("op_3400"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3399_cast_fp16 = transpose(perm = var_3399_perm_0, x = x_409_cast_fp16)[name = string("transpose_182")];
            tensor<fp16, [1, 188, 1024]> input_979_cast_fp16 = reshape(shape = var_3400, x = var_3399_cast_fp16)[name = string("input_979_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460830784))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(461879424))))[name = string("encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_979_cast_fp16)[name = string("linear_171_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_983_cast_fp16 = add(x = input_975_cast_fp16, y = linear_171_cast_fp16)[name = string("input_983_cast_fp16")];
            tensor<int32, [1]> x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(461881536)))];
            tensor<fp16, [1024]> encoder_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(461883648)))];
            tensor<fp16, [1, 188, 1024]> x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = encoder_layers_18_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_413_cast_fp16")];
            tensor<int32, [3]> input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(461885760))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463982976))))[name = string("encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_413_cast_fp16)[name = string("transpose_181")];
            tensor<fp16, [1, 2048, 188]> input_987_cast_fp16 = conv(dilations = input_987_dilations_0, groups = input_987_groups_0, pad = input_987_pad_0, pad_type = input_987_pad_type_0, strides = input_987_strides_0, weight = encoder_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_985_cast_fp16)[name = string("input_987_cast_fp16")];
            int32 x_415_split_num_splits_0 = const()[name = string("x_415_split_num_splits_0"), val = int32(2)];
            int32 x_415_split_axis_0 = const()[name = string("x_415_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_415_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_415_split_cast_fp16_1 = split(axis = x_415_split_axis_0, num_splits = x_415_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_415_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_415_split_1_sigmoid_cast_fp16 = sigmoid(x = x_415_split_cast_fp16_1)[name = string("x_415_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_415_cast_fp16 = mul(x = x_415_split_cast_fp16_0, y = x_415_split_1_sigmoid_cast_fp16)[name = string("x_415_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_989_cast_fp16 = select(a = var_9_to_fp16, b = x_415_cast_fp16, cond = var_488)[name = string("input_989_cast_fp16")];
            tensor<int32, [6]> input_991_pad_0 = const()[name = string("input_991_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_991_mode_0 = const()[name = string("input_991_mode_0"), val = string("constant")];
            fp16 const_253_to_fp16 = const()[name = string("const_253_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_991_cast_fp16 = pad(constant_val = const_253_to_fp16, mode = input_991_mode_0, pad = input_991_pad_0, x = input_989_cast_fp16)[name = string("input_991_cast_fp16")];
            string input_993_pad_type_0 = const()[name = string("input_993_pad_type_0"), val = string("valid")];
            int32 input_993_groups_0 = const()[name = string("input_993_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_993_strides_0 = const()[name = string("input_993_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_993_pad_0 = const()[name = string("input_993_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_993_dilations_0 = const()[name = string("input_993_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_340_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463987136))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463996416))))[name = string("const_340_to_fp16_quantized")];
            tensor<fp16, [1024]> const_341_to_fp16 = const()[name = string("const_341_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463998528)))];
            tensor<fp16, [1, 1024, 188]> input_995_cast_fp16 = conv(bias = const_341_to_fp16, dilations = input_993_dilations_0, groups = input_993_groups_0, pad = input_993_pad_0, pad_type = input_993_pad_type_0, strides = input_993_strides_0, weight = const_340_to_fp16_quantized, x = input_991_cast_fp16)[name = string("input_995_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_997_cast_fp16 = silu(x = input_995_cast_fp16)[name = string("input_997_cast_fp16")];
            string x_417_pad_type_0 = const()[name = string("x_417_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_417_strides_0 = const()[name = string("x_417_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_417_pad_0 = const()[name = string("x_417_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_417_dilations_0 = const()[name = string("x_417_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_417_groups_0 = const()[name = string("x_417_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464000640))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465049280))))[name = string("encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_417_cast_fp16 = conv(dilations = x_417_dilations_0, groups = x_417_groups_0, pad = x_417_pad_0, pad_type = x_417_pad_type_0, strides = x_417_strides_0, weight = encoder_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_997_cast_fp16)[name = string("x_417_cast_fp16")];
            tensor<int32, [3]> input_999_perm_0 = const()[name = string("input_999_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_999_cast_fp16 = transpose(perm = input_999_perm_0, x = x_417_cast_fp16)[name = string("transpose_180")];
            tensor<fp16, [1, 188, 1024]> input_1001_cast_fp16 = add(x = input_983_cast_fp16, y = input_999_cast_fp16)[name = string("input_1001_cast_fp16")];
            tensor<int32, [1]> input_1003_axes_0 = const()[name = string("input_1003_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465051392)))];
            tensor<fp16, [1024]> encoder_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465053504)))];
            tensor<fp16, [1, 188, 1024]> input_1003_cast_fp16 = layer_norm(axes = input_1003_axes_0, beta = encoder_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_18_norm_feed_forward2_weight_to_fp16, x = input_1001_cast_fp16)[name = string("input_1003_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465055616))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(469249984))))[name = string("encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1003_cast_fp16)[name = string("linear_172_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1007_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1007_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(469258240))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473452608))))[name = string("encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1007_cast_fp16)[name = string("linear_173_cast_fp16")];
            fp16 var_3460_to_fp16 = const()[name = string("op_3460_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3461_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3460_to_fp16)[name = string("op_3461_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1013_cast_fp16 = add(x = input_1001_cast_fp16, y = var_3461_cast_fp16)[name = string("input_1013_cast_fp16")];
            tensor<int32, [1]> input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473454720)))];
            tensor<fp16, [1024]> encoder_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473456832)))];
            tensor<fp16, [1, 188, 1024]> input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_layers_18_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_18_norm_out_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")];
            tensor<int32, [1]> input_1017_axes_0 = const()[name = string("input_1017_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473458944)))];
            tensor<fp16, [1024]> encoder_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473461056)))];
            tensor<fp16, [1, 188, 1024]> input_1017_cast_fp16 = layer_norm(axes = input_1017_axes_0, beta = encoder_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1015_cast_fp16)[name = string("input_1017_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473463168))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(477657536))))[name = string("encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_174_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1017_cast_fp16)[name = string("linear_174_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1021_cast_fp16 = silu(x = linear_174_cast_fp16)[name = string("input_1021_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(477665792))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481860160))))[name = string("encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1021_cast_fp16)[name = string("linear_175_cast_fp16")];
            fp16 var_3489_to_fp16 = const()[name = string("op_3489_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3490_cast_fp16 = mul(x = linear_175_cast_fp16, y = var_3489_to_fp16)[name = string("op_3490_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1027_cast_fp16 = add(x = input_1015_cast_fp16, y = var_3490_cast_fp16)[name = string("input_1027_cast_fp16")];
            tensor<int32, [1]> query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481862272)))];
            tensor<fp16, [1024]> encoder_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481864384)))];
            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = encoder_layers_19_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_19_norm_self_att_weight_to_fp16, x = input_1027_cast_fp16)[name = string("query_39_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481866496))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482915136))))[name = string("encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")];
            tensor<int32, [4]> var_3506 = const()[name = string("op_3506"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3506, x = linear_176_cast_fp16)[name = string("q_115_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482917248))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483965888))))[name = string("encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_177_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_177_cast_fp16")];
            tensor<int32, [4]> var_3510 = const()[name = string("op_3510"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3510, x = linear_177_cast_fp16)[name = string("k_77_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483968000))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485016640))))[name = string("encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_178_cast_fp16")];
            tensor<int32, [4]> var_3514 = const()[name = string("op_3514"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3514, x = linear_178_cast_fp16)[name = string("v_39_cast_fp16")];
            tensor<int32, [4]> value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485018752)))];
            tensor<fp16, [1, 188, 8, 128]> var_3526_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3526_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485020864)))];
            tensor<fp16, [1, 188, 8, 128]> var_3528_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3528_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_425_transpose_x_0 = const()[name = string("x_425_transpose_x_0"), val = bool(false)];
            bool x_425_transpose_y_0 = const()[name = string("x_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3530_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485022976))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485407040))))[name = string("op_3530_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3528_cast_fp16)[name = string("transpose_179")];
            tensor<fp16, [1, 8, 188, 375]> x_425_cast_fp16 = matmul(transpose_x = x_425_transpose_x_0, transpose_y = x_425_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3530_to_fp16_quantized)[name = string("x_425_cast_fp16")];
            tensor<int32, [8]> x_427_pad_0 = const()[name = string("x_427_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_427_mode_0 = const()[name = string("x_427_mode_0"), val = string("constant")];
            fp16 const_260_to_fp16 = const()[name = string("const_260_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_427_cast_fp16 = pad(constant_val = const_260_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")];
            tensor<int32, [4]> var_3538 = const()[name = string("op_3538"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_429_cast_fp16 = reshape(shape = var_3538, x = x_427_cast_fp16)[name = string("x_429_cast_fp16")];
            tensor<int32, [4]> var_3542_begin_0 = const()[name = string("op_3542_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3542_end_0 = const()[name = string("op_3542_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3542_end_mask_0 = const()[name = string("op_3542_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3542_cast_fp16 = slice_by_index(begin = var_3542_begin_0, end = var_3542_end_0, end_mask = var_3542_end_mask_0, x = x_429_cast_fp16)[name = string("op_3542_cast_fp16")];
            tensor<int32, [4]> var_3543 = const()[name = string("op_3543"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3543, x = var_3542_cast_fp16)[name = string("matrix_bd_77_cast_fp16")];
            bool matrix_ac_39_transpose_x_0 = const()[name = string("matrix_ac_39_transpose_x_0"), val = bool(false)];
            bool matrix_ac_39_transpose_y_0 = const()[name = string("matrix_ac_39_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")];
            tensor<fp16, [1, 8, 188, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3526_cast_fp16)[name = string("transpose_178")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_134, y = transpose_135)[name = string("matrix_ac_39_cast_fp16")];
            tensor<int32, [4]> matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_79_cast_fp16 = slice_by_index(begin = matrix_bd_79_begin_0, end = matrix_bd_79_end_0, end_mask = matrix_bd_79_end_mask_0, x = matrix_bd_77_cast_fp16)[name = string("matrix_bd_79_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3552_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3552_cast_fp16")];
            fp16 _inversed_scores_77_y_0_to_fp16 = const()[name = string("_inversed_scores_77_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3552_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_11)[name = string("scores_79_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3558_cast_fp16 = softmax(axis = var_25, x = scores_79_cast_fp16)[name = string("op_3558_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1029_cast_fp16 = select(a = var_9_to_fp16, b = var_3558_cast_fp16, cond = mask_11)[name = string("input_1029_cast_fp16")];
            bool x_431_transpose_x_0 = const()[name = string("x_431_transpose_x_0"), val = bool(false)];
            bool x_431_transpose_y_0 = const()[name = string("x_431_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")];
            tensor<fp16, [1, 8, 188, 128]> x_431_cast_fp16 = matmul(transpose_x = x_431_transpose_x_0, transpose_y = x_431_transpose_y_0, x = input_1029_cast_fp16, y = value_41_cast_fp16)[name = string("x_431_cast_fp16")];
            tensor<int32, [4]> var_3562_perm_0 = const()[name = string("op_3562_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3563 = const()[name = string("op_3563"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3562_cast_fp16 = transpose(perm = var_3562_perm_0, x = x_431_cast_fp16)[name = string("transpose_175")];
            tensor<fp16, [1, 188, 1024]> input_1031_cast_fp16 = reshape(shape = var_3563, x = var_3562_cast_fp16)[name = string("input_1031_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485407872))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486456512))))[name = string("encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1031_cast_fp16)[name = string("linear_180_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1035_cast_fp16 = add(x = input_1027_cast_fp16, y = linear_180_cast_fp16)[name = string("input_1035_cast_fp16")];
            tensor<int32, [1]> x_435_axes_0 = const()[name = string("x_435_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486458624)))];
            tensor<fp16, [1024]> encoder_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486460736)))];
            tensor<fp16, [1, 188, 1024]> x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = encoder_layers_19_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_435_cast_fp16")];
            tensor<int32, [3]> input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(486462848))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488560064))))[name = string("encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_435_cast_fp16)[name = string("transpose_174")];
            tensor<fp16, [1, 2048, 188]> input_1039_cast_fp16 = conv(dilations = input_1039_dilations_0, groups = input_1039_groups_0, pad = input_1039_pad_0, pad_type = input_1039_pad_type_0, strides = input_1039_strides_0, weight = encoder_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")];
            int32 x_437_split_num_splits_0 = const()[name = string("x_437_split_num_splits_0"), val = int32(2)];
            int32 x_437_split_axis_0 = const()[name = string("x_437_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_437_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_437_split_cast_fp16_1 = split(axis = x_437_split_axis_0, num_splits = x_437_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_437_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_437_split_1_sigmoid_cast_fp16 = sigmoid(x = x_437_split_cast_fp16_1)[name = string("x_437_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_437_cast_fp16 = mul(x = x_437_split_cast_fp16_0, y = x_437_split_1_sigmoid_cast_fp16)[name = string("x_437_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1041_cast_fp16 = select(a = var_9_to_fp16, b = x_437_cast_fp16, cond = var_488)[name = string("input_1041_cast_fp16")];
            tensor<int32, [6]> input_1043_pad_0 = const()[name = string("input_1043_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_1043_mode_0 = const()[name = string("input_1043_mode_0"), val = string("constant")];
            fp16 const_263_to_fp16 = const()[name = string("const_263_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1043_cast_fp16 = pad(constant_val = const_263_to_fp16, mode = input_1043_mode_0, pad = input_1043_pad_0, x = input_1041_cast_fp16)[name = string("input_1043_cast_fp16")];
            string input_1045_pad_type_0 = const()[name = string("input_1045_pad_type_0"), val = string("valid")];
            int32 input_1045_groups_0 = const()[name = string("input_1045_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_1045_strides_0 = const()[name = string("input_1045_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1045_pad_0 = const()[name = string("input_1045_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1045_dilations_0 = const()[name = string("input_1045_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_342_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488564224))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488573504))))[name = string("const_342_to_fp16_quantized")];
            tensor<fp16, [1024]> const_343_to_fp16 = const()[name = string("const_343_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488575616)))];
            tensor<fp16, [1, 1024, 188]> input_1047_cast_fp16 = conv(bias = const_343_to_fp16, dilations = input_1045_dilations_0, groups = input_1045_groups_0, pad = input_1045_pad_0, pad_type = input_1045_pad_type_0, strides = input_1045_strides_0, weight = const_342_to_fp16_quantized, x = input_1043_cast_fp16)[name = string("input_1047_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1049_cast_fp16 = silu(x = input_1047_cast_fp16)[name = string("input_1049_cast_fp16")];
            string x_439_pad_type_0 = const()[name = string("x_439_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_439_strides_0 = const()[name = string("x_439_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_439_pad_0 = const()[name = string("x_439_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_439_dilations_0 = const()[name = string("x_439_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_439_groups_0 = const()[name = string("x_439_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488577728))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489626368))))[name = string("encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_439_cast_fp16 = conv(dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = encoder_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1049_cast_fp16)[name = string("x_439_cast_fp16")];
            tensor<int32, [3]> input_1051_perm_0 = const()[name = string("input_1051_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1051_cast_fp16 = transpose(perm = input_1051_perm_0, x = x_439_cast_fp16)[name = string("transpose_173")];
            tensor<fp16, [1, 188, 1024]> input_1053_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")];
            tensor<int32, [1]> input_1055_axes_0 = const()[name = string("input_1055_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489628480)))];
            tensor<fp16, [1024]> encoder_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489630592)))];
            tensor<fp16, [1, 188, 1024]> input_1055_cast_fp16 = layer_norm(axes = input_1055_axes_0, beta = encoder_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1053_cast_fp16)[name = string("input_1055_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489632704))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493827072))))[name = string("encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1055_cast_fp16)[name = string("linear_181_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1059_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1059_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(493835328))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498029696))))[name = string("encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1059_cast_fp16)[name = string("linear_182_cast_fp16")];
            fp16 var_3623_to_fp16 = const()[name = string("op_3623_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3624_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3623_to_fp16)[name = string("op_3624_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1065_cast_fp16 = add(x = input_1053_cast_fp16, y = var_3624_cast_fp16)[name = string("input_1065_cast_fp16")];
            tensor<int32, [1]> input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498031808)))];
            tensor<fp16, [1024]> encoder_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498033920)))];
            tensor<fp16, [1, 188, 1024]> input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_layers_19_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_19_norm_out_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")];
            tensor<int32, [1]> input_1069_axes_0 = const()[name = string("input_1069_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498036032)))];
            tensor<fp16, [1024]> encoder_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498038144)))];
            tensor<fp16, [1, 188, 1024]> input_1069_cast_fp16 = layer_norm(axes = input_1069_axes_0, beta = encoder_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1067_cast_fp16)[name = string("input_1069_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498040256))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(502234624))))[name = string("encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_183_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1069_cast_fp16)[name = string("linear_183_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1073_cast_fp16 = silu(x = linear_183_cast_fp16)[name = string("input_1073_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(502242880))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506437248))))[name = string("encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1073_cast_fp16)[name = string("linear_184_cast_fp16")];
            fp16 var_3652_to_fp16 = const()[name = string("op_3652_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3653_cast_fp16 = mul(x = linear_184_cast_fp16, y = var_3652_to_fp16)[name = string("op_3653_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1079_cast_fp16 = add(x = input_1067_cast_fp16, y = var_3653_cast_fp16)[name = string("input_1079_cast_fp16")];
            tensor<int32, [1]> query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506439360)))];
            tensor<fp16, [1024]> encoder_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506441472)))];
            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = encoder_layers_20_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_20_norm_self_att_weight_to_fp16, x = input_1079_cast_fp16)[name = string("query_41_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506443584))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(507492224))))[name = string("encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")];
            tensor<int32, [4]> var_3669 = const()[name = string("op_3669"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_3669, x = linear_185_cast_fp16)[name = string("q_121_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(507494336))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508542976))))[name = string("encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_186_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_186_cast_fp16")];
            tensor<int32, [4]> var_3673 = const()[name = string("op_3673"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_3673, x = linear_186_cast_fp16)[name = string("k_81_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508545088))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509593728))))[name = string("encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_187_cast_fp16")];
            tensor<int32, [4]> var_3677 = const()[name = string("op_3677"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_3677, x = linear_187_cast_fp16)[name = string("v_41_cast_fp16")];
            tensor<int32, [4]> value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509595840)))];
            tensor<fp16, [1, 188, 8, 128]> var_3689_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_3689_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509597952)))];
            tensor<fp16, [1, 188, 8, 128]> var_3691_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_3691_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_447_transpose_x_0 = const()[name = string("x_447_transpose_x_0"), val = bool(false)];
            bool x_447_transpose_y_0 = const()[name = string("x_447_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3693_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509600064))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509984128))))[name = string("op_3693_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3691_cast_fp16)[name = string("transpose_172")];
            tensor<fp16, [1, 8, 188, 375]> x_447_cast_fp16 = matmul(transpose_x = x_447_transpose_x_0, transpose_y = x_447_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_3693_to_fp16_quantized)[name = string("x_447_cast_fp16")];
            tensor<int32, [8]> x_449_pad_0 = const()[name = string("x_449_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_449_mode_0 = const()[name = string("x_449_mode_0"), val = string("constant")];
            fp16 const_270_to_fp16 = const()[name = string("const_270_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_449_cast_fp16 = pad(constant_val = const_270_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")];
            tensor<int32, [4]> var_3701 = const()[name = string("op_3701"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_451_cast_fp16 = reshape(shape = var_3701, x = x_449_cast_fp16)[name = string("x_451_cast_fp16")];
            tensor<int32, [4]> var_3705_begin_0 = const()[name = string("op_3705_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3705_end_0 = const()[name = string("op_3705_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3705_end_mask_0 = const()[name = string("op_3705_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3705_cast_fp16 = slice_by_index(begin = var_3705_begin_0, end = var_3705_end_0, end_mask = var_3705_end_mask_0, x = x_451_cast_fp16)[name = string("op_3705_cast_fp16")];
            tensor<int32, [4]> var_3706 = const()[name = string("op_3706"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_3706, x = var_3705_cast_fp16)[name = string("matrix_bd_81_cast_fp16")];
            bool matrix_ac_41_transpose_x_0 = const()[name = string("matrix_ac_41_transpose_x_0"), val = bool(false)];
            bool matrix_ac_41_transpose_y_0 = const()[name = string("matrix_ac_41_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")];
            tensor<fp16, [1, 8, 188, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3689_cast_fp16)[name = string("transpose_171")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_136, y = transpose_137)[name = string("matrix_ac_41_cast_fp16")];
            tensor<int32, [4]> matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_83_cast_fp16 = slice_by_index(begin = matrix_bd_83_begin_0, end = matrix_bd_83_end_0, end_mask = matrix_bd_83_end_mask_0, x = matrix_bd_81_cast_fp16)[name = string("matrix_bd_83_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3715_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_3715_cast_fp16")];
            fp16 _inversed_scores_81_y_0_to_fp16 = const()[name = string("_inversed_scores_81_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_3715_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_11)[name = string("scores_83_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3721_cast_fp16 = softmax(axis = var_25, x = scores_83_cast_fp16)[name = string("op_3721_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1081_cast_fp16 = select(a = var_9_to_fp16, b = var_3721_cast_fp16, cond = mask_11)[name = string("input_1081_cast_fp16")];
            bool x_453_transpose_x_0 = const()[name = string("x_453_transpose_x_0"), val = bool(false)];
            bool x_453_transpose_y_0 = const()[name = string("x_453_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")];
            tensor<fp16, [1, 8, 188, 128]> x_453_cast_fp16 = matmul(transpose_x = x_453_transpose_x_0, transpose_y = x_453_transpose_y_0, x = input_1081_cast_fp16, y = value_43_cast_fp16)[name = string("x_453_cast_fp16")];
            tensor<int32, [4]> var_3725_perm_0 = const()[name = string("op_3725_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3726 = const()[name = string("op_3726"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3725_cast_fp16 = transpose(perm = var_3725_perm_0, x = x_453_cast_fp16)[name = string("transpose_168")];
            tensor<fp16, [1, 188, 1024]> input_1083_cast_fp16 = reshape(shape = var_3726, x = var_3725_cast_fp16)[name = string("input_1083_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509984960))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511033600))))[name = string("encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1083_cast_fp16)[name = string("linear_189_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1087_cast_fp16 = add(x = input_1079_cast_fp16, y = linear_189_cast_fp16)[name = string("input_1087_cast_fp16")];
            tensor<int32, [1]> x_457_axes_0 = const()[name = string("x_457_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511035712)))];
            tensor<fp16, [1024]> encoder_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511037824)))];
            tensor<fp16, [1, 188, 1024]> x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = encoder_layers_20_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_457_cast_fp16")];
            tensor<int32, [3]> input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511039936))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513137152))))[name = string("encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_457_cast_fp16)[name = string("transpose_167")];
            tensor<fp16, [1, 2048, 188]> input_1091_cast_fp16 = conv(dilations = input_1091_dilations_0, groups = input_1091_groups_0, pad = input_1091_pad_0, pad_type = input_1091_pad_type_0, strides = input_1091_strides_0, weight = encoder_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")];
            int32 x_459_split_num_splits_0 = const()[name = string("x_459_split_num_splits_0"), val = int32(2)];
            int32 x_459_split_axis_0 = const()[name = string("x_459_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_459_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_459_split_cast_fp16_1 = split(axis = x_459_split_axis_0, num_splits = x_459_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_459_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_459_split_1_sigmoid_cast_fp16 = sigmoid(x = x_459_split_cast_fp16_1)[name = string("x_459_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_459_cast_fp16 = mul(x = x_459_split_cast_fp16_0, y = x_459_split_1_sigmoid_cast_fp16)[name = string("x_459_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1093_cast_fp16 = select(a = var_9_to_fp16, b = x_459_cast_fp16, cond = var_488)[name = string("input_1093_cast_fp16")];
            tensor<int32, [6]> input_1095_pad_0 = const()[name = string("input_1095_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_1095_mode_0 = const()[name = string("input_1095_mode_0"), val = string("constant")];
            fp16 const_273_to_fp16 = const()[name = string("const_273_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1095_cast_fp16 = pad(constant_val = const_273_to_fp16, mode = input_1095_mode_0, pad = input_1095_pad_0, x = input_1093_cast_fp16)[name = string("input_1095_cast_fp16")];
            string input_1097_pad_type_0 = const()[name = string("input_1097_pad_type_0"), val = string("valid")];
            int32 input_1097_groups_0 = const()[name = string("input_1097_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_1097_strides_0 = const()[name = string("input_1097_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1097_pad_0 = const()[name = string("input_1097_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1097_dilations_0 = const()[name = string("input_1097_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_344_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513141312))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513150592))))[name = string("const_344_to_fp16_quantized")];
            tensor<fp16, [1024]> const_345_to_fp16 = const()[name = string("const_345_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513152704)))];
            tensor<fp16, [1, 1024, 188]> input_1099_cast_fp16 = conv(bias = const_345_to_fp16, dilations = input_1097_dilations_0, groups = input_1097_groups_0, pad = input_1097_pad_0, pad_type = input_1097_pad_type_0, strides = input_1097_strides_0, weight = const_344_to_fp16_quantized, x = input_1095_cast_fp16)[name = string("input_1099_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1101_cast_fp16 = silu(x = input_1099_cast_fp16)[name = string("input_1101_cast_fp16")];
            string x_461_pad_type_0 = const()[name = string("x_461_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_461_strides_0 = const()[name = string("x_461_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_461_pad_0 = const()[name = string("x_461_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_461_dilations_0 = const()[name = string("x_461_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_461_groups_0 = const()[name = string("x_461_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513154816))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514203456))))[name = string("encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_461_cast_fp16 = conv(dilations = x_461_dilations_0, groups = x_461_groups_0, pad = x_461_pad_0, pad_type = x_461_pad_type_0, strides = x_461_strides_0, weight = encoder_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1101_cast_fp16)[name = string("x_461_cast_fp16")];
            tensor<int32, [3]> input_1103_perm_0 = const()[name = string("input_1103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1103_cast_fp16 = transpose(perm = input_1103_perm_0, x = x_461_cast_fp16)[name = string("transpose_166")];
            tensor<fp16, [1, 188, 1024]> input_1105_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")];
            tensor<int32, [1]> input_1107_axes_0 = const()[name = string("input_1107_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514205568)))];
            tensor<fp16, [1024]> encoder_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514207680)))];
            tensor<fp16, [1, 188, 1024]> input_1107_cast_fp16 = layer_norm(axes = input_1107_axes_0, beta = encoder_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1105_cast_fp16)[name = string("input_1107_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514209792))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518404160))))[name = string("encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1107_cast_fp16)[name = string("linear_190_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1111_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1111_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518412416))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522606784))))[name = string("encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1111_cast_fp16)[name = string("linear_191_cast_fp16")];
            fp16 var_3786_to_fp16 = const()[name = string("op_3786_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3787_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3786_to_fp16)[name = string("op_3787_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1117_cast_fp16 = add(x = input_1105_cast_fp16, y = var_3787_cast_fp16)[name = string("input_1117_cast_fp16")];
            tensor<int32, [1]> input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522608896)))];
            tensor<fp16, [1024]> encoder_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522611008)))];
            tensor<fp16, [1, 188, 1024]> input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_layers_20_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_20_norm_out_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")];
            tensor<int32, [1]> input_1121_axes_0 = const()[name = string("input_1121_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522613120)))];
            tensor<fp16, [1024]> encoder_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522615232)))];
            tensor<fp16, [1, 188, 1024]> input_1121_cast_fp16 = layer_norm(axes = input_1121_axes_0, beta = encoder_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1119_cast_fp16)[name = string("input_1121_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522617344))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526811712))))[name = string("encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_192_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1121_cast_fp16)[name = string("linear_192_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1125_cast_fp16 = silu(x = linear_192_cast_fp16)[name = string("input_1125_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526819968))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531014336))))[name = string("encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1125_cast_fp16)[name = string("linear_193_cast_fp16")];
            fp16 var_3815_to_fp16 = const()[name = string("op_3815_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3816_cast_fp16 = mul(x = linear_193_cast_fp16, y = var_3815_to_fp16)[name = string("op_3816_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1131_cast_fp16 = add(x = input_1119_cast_fp16, y = var_3816_cast_fp16)[name = string("input_1131_cast_fp16")];
            tensor<int32, [1]> query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531016448)))];
            tensor<fp16, [1024]> encoder_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531018560)))];
            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = encoder_layers_21_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_21_norm_self_att_weight_to_fp16, x = input_1131_cast_fp16)[name = string("query_43_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531020672))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(532069312))))[name = string("encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")];
            tensor<int32, [4]> var_3832 = const()[name = string("op_3832"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_3832, x = linear_194_cast_fp16)[name = string("q_127_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(532071424))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533120064))))[name = string("encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_195_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_195_cast_fp16")];
            tensor<int32, [4]> var_3836 = const()[name = string("op_3836"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_3836, x = linear_195_cast_fp16)[name = string("k_85_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533122176))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534170816))))[name = string("encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_196_cast_fp16")];
            tensor<int32, [4]> var_3840 = const()[name = string("op_3840"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_3840, x = linear_196_cast_fp16)[name = string("v_43_cast_fp16")];
            tensor<int32, [4]> value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534172928)))];
            tensor<fp16, [1, 188, 8, 128]> var_3852_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_3852_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534175040)))];
            tensor<fp16, [1, 188, 8, 128]> var_3854_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_3854_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_469_transpose_x_0 = const()[name = string("x_469_transpose_x_0"), val = bool(false)];
            bool x_469_transpose_y_0 = const()[name = string("x_469_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3856_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534177152))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534561216))))[name = string("op_3856_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3854_cast_fp16)[name = string("transpose_165")];
            tensor<fp16, [1, 8, 188, 375]> x_469_cast_fp16 = matmul(transpose_x = x_469_transpose_x_0, transpose_y = x_469_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_3856_to_fp16_quantized)[name = string("x_469_cast_fp16")];
            tensor<int32, [8]> x_471_pad_0 = const()[name = string("x_471_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_471_mode_0 = const()[name = string("x_471_mode_0"), val = string("constant")];
            fp16 const_280_to_fp16 = const()[name = string("const_280_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_471_cast_fp16 = pad(constant_val = const_280_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")];
            tensor<int32, [4]> var_3864 = const()[name = string("op_3864"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_473_cast_fp16 = reshape(shape = var_3864, x = x_471_cast_fp16)[name = string("x_473_cast_fp16")];
            tensor<int32, [4]> var_3868_begin_0 = const()[name = string("op_3868_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3868_end_0 = const()[name = string("op_3868_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3868_end_mask_0 = const()[name = string("op_3868_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3868_cast_fp16 = slice_by_index(begin = var_3868_begin_0, end = var_3868_end_0, end_mask = var_3868_end_mask_0, x = x_473_cast_fp16)[name = string("op_3868_cast_fp16")];
            tensor<int32, [4]> var_3869 = const()[name = string("op_3869"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_3869, x = var_3868_cast_fp16)[name = string("matrix_bd_85_cast_fp16")];
            bool matrix_ac_43_transpose_x_0 = const()[name = string("matrix_ac_43_transpose_x_0"), val = bool(false)];
            bool matrix_ac_43_transpose_y_0 = const()[name = string("matrix_ac_43_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")];
            tensor<fp16, [1, 8, 188, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3852_cast_fp16)[name = string("transpose_164")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_138, y = transpose_139)[name = string("matrix_ac_43_cast_fp16")];
            tensor<int32, [4]> matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_87_cast_fp16 = slice_by_index(begin = matrix_bd_87_begin_0, end = matrix_bd_87_end_0, end_mask = matrix_bd_87_end_mask_0, x = matrix_bd_85_cast_fp16)[name = string("matrix_bd_87_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3878_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_3878_cast_fp16")];
            fp16 _inversed_scores_85_y_0_to_fp16 = const()[name = string("_inversed_scores_85_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_3878_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_11)[name = string("scores_87_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3884_cast_fp16 = softmax(axis = var_25, x = scores_87_cast_fp16)[name = string("op_3884_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1133_cast_fp16 = select(a = var_9_to_fp16, b = var_3884_cast_fp16, cond = mask_11)[name = string("input_1133_cast_fp16")];
            bool x_475_transpose_x_0 = const()[name = string("x_475_transpose_x_0"), val = bool(false)];
            bool x_475_transpose_y_0 = const()[name = string("x_475_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")];
            tensor<fp16, [1, 8, 188, 128]> x_475_cast_fp16 = matmul(transpose_x = x_475_transpose_x_0, transpose_y = x_475_transpose_y_0, x = input_1133_cast_fp16, y = value_45_cast_fp16)[name = string("x_475_cast_fp16")];
            tensor<int32, [4]> var_3888_perm_0 = const()[name = string("op_3888_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3889 = const()[name = string("op_3889"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3888_cast_fp16 = transpose(perm = var_3888_perm_0, x = x_475_cast_fp16)[name = string("transpose_161")];
            tensor<fp16, [1, 188, 1024]> input_1135_cast_fp16 = reshape(shape = var_3889, x = var_3888_cast_fp16)[name = string("input_1135_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_21_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534562048))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535610688))))[name = string("encoder_layers_21_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_21_self_attn_linear_out_weight_to_fp16_quantized, x = input_1135_cast_fp16)[name = string("linear_198_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1139_cast_fp16 = add(x = input_1131_cast_fp16, y = linear_198_cast_fp16)[name = string("input_1139_cast_fp16")];
            tensor<int32, [1]> x_479_axes_0 = const()[name = string("x_479_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535612800)))];
            tensor<fp16, [1024]> encoder_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535614912)))];
            tensor<fp16, [1, 188, 1024]> x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = encoder_layers_21_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_479_cast_fp16")];
            tensor<int32, [3]> input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535617024))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537714240))))[name = string("encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_479_cast_fp16)[name = string("transpose_160")];
            tensor<fp16, [1, 2048, 188]> input_1143_cast_fp16 = conv(dilations = input_1143_dilations_0, groups = input_1143_groups_0, pad = input_1143_pad_0, pad_type = input_1143_pad_type_0, strides = input_1143_strides_0, weight = encoder_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")];
            int32 x_481_split_num_splits_0 = const()[name = string("x_481_split_num_splits_0"), val = int32(2)];
            int32 x_481_split_axis_0 = const()[name = string("x_481_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_481_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_481_split_cast_fp16_1 = split(axis = x_481_split_axis_0, num_splits = x_481_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_481_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_481_split_1_sigmoid_cast_fp16 = sigmoid(x = x_481_split_cast_fp16_1)[name = string("x_481_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_481_cast_fp16 = mul(x = x_481_split_cast_fp16_0, y = x_481_split_1_sigmoid_cast_fp16)[name = string("x_481_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1145_cast_fp16 = select(a = var_9_to_fp16, b = x_481_cast_fp16, cond = var_488)[name = string("input_1145_cast_fp16")];
            tensor<int32, [6]> input_1147_pad_0 = const()[name = string("input_1147_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_1147_mode_0 = const()[name = string("input_1147_mode_0"), val = string("constant")];
            fp16 const_283_to_fp16 = const()[name = string("const_283_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1147_cast_fp16 = pad(constant_val = const_283_to_fp16, mode = input_1147_mode_0, pad = input_1147_pad_0, x = input_1145_cast_fp16)[name = string("input_1147_cast_fp16")];
            string input_1149_pad_type_0 = const()[name = string("input_1149_pad_type_0"), val = string("valid")];
            int32 input_1149_groups_0 = const()[name = string("input_1149_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_1149_strides_0 = const()[name = string("input_1149_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1149_pad_0 = const()[name = string("input_1149_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1149_dilations_0 = const()[name = string("input_1149_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_346_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537718400))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537727680))))[name = string("const_346_to_fp16_quantized")];
            tensor<fp16, [1024]> const_347_to_fp16 = const()[name = string("const_347_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537729792)))];
            tensor<fp16, [1, 1024, 188]> input_1151_cast_fp16 = conv(bias = const_347_to_fp16, dilations = input_1149_dilations_0, groups = input_1149_groups_0, pad = input_1149_pad_0, pad_type = input_1149_pad_type_0, strides = input_1149_strides_0, weight = const_346_to_fp16_quantized, x = input_1147_cast_fp16)[name = string("input_1151_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1153_cast_fp16 = silu(x = input_1151_cast_fp16)[name = string("input_1153_cast_fp16")];
            string x_483_pad_type_0 = const()[name = string("x_483_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_483_strides_0 = const()[name = string("x_483_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_483_pad_0 = const()[name = string("x_483_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_483_dilations_0 = const()[name = string("x_483_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_483_groups_0 = const()[name = string("x_483_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537731904))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538780544))))[name = string("encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_483_cast_fp16 = conv(dilations = x_483_dilations_0, groups = x_483_groups_0, pad = x_483_pad_0, pad_type = x_483_pad_type_0, strides = x_483_strides_0, weight = encoder_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1153_cast_fp16)[name = string("x_483_cast_fp16")];
            tensor<int32, [3]> input_1155_perm_0 = const()[name = string("input_1155_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1155_cast_fp16 = transpose(perm = input_1155_perm_0, x = x_483_cast_fp16)[name = string("transpose_159")];
            tensor<fp16, [1, 188, 1024]> input_1157_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")];
            tensor<int32, [1]> input_1159_axes_0 = const()[name = string("input_1159_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538782656)))];
            tensor<fp16, [1024]> encoder_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538784768)))];
            tensor<fp16, [1, 188, 1024]> input_1159_cast_fp16 = layer_norm(axes = input_1159_axes_0, beta = encoder_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1157_cast_fp16)[name = string("input_1159_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_21_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538786880))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(542981248))))[name = string("encoder_layers_21_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_21_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1159_cast_fp16)[name = string("linear_199_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1163_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1163_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_21_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(542989504))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547183872))))[name = string("encoder_layers_21_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_21_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1163_cast_fp16)[name = string("linear_200_cast_fp16")];
            fp16 var_3949_to_fp16 = const()[name = string("op_3949_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3950_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_3949_to_fp16)[name = string("op_3950_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1169_cast_fp16 = add(x = input_1157_cast_fp16, y = var_3950_cast_fp16)[name = string("input_1169_cast_fp16")];
            tensor<int32, [1]> input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547185984)))];
            tensor<fp16, [1024]> encoder_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547188096)))];
            tensor<fp16, [1, 188, 1024]> input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_layers_21_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_21_norm_out_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")];
            tensor<int32, [1]> input_1173_axes_0 = const()[name = string("input_1173_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547190208)))];
            tensor<fp16, [1024]> encoder_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547192320)))];
            tensor<fp16, [1, 188, 1024]> input_1173_cast_fp16 = layer_norm(axes = input_1173_axes_0, beta = encoder_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("input_1173_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_22_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547194432))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(551388800))))[name = string("encoder_layers_22_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_201_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_22_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1173_cast_fp16)[name = string("linear_201_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1177_cast_fp16 = silu(x = linear_201_cast_fp16)[name = string("input_1177_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_22_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(551397056))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555591424))))[name = string("encoder_layers_22_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_22_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1177_cast_fp16)[name = string("linear_202_cast_fp16")];
            fp16 var_3978_to_fp16 = const()[name = string("op_3978_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3979_cast_fp16 = mul(x = linear_202_cast_fp16, y = var_3978_to_fp16)[name = string("op_3979_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1183_cast_fp16 = add(x = input_1171_cast_fp16, y = var_3979_cast_fp16)[name = string("input_1183_cast_fp16")];
            tensor<int32, [1]> query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555593536)))];
            tensor<fp16, [1024]> encoder_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555595648)))];
            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = encoder_layers_22_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_22_norm_self_att_weight_to_fp16, x = input_1183_cast_fp16)[name = string("query_45_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_22_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555597760))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556646400))))[name = string("encoder_layers_22_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_22_self_attn_linear_q_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")];
            tensor<int32, [4]> var_3995 = const()[name = string("op_3995"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_3995, x = linear_203_cast_fp16)[name = string("q_133_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_22_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556648512))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557697152))))[name = string("encoder_layers_22_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_204_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_22_self_attn_linear_k_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_204_cast_fp16")];
            tensor<int32, [4]> var_3999 = const()[name = string("op_3999"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_3999, x = linear_204_cast_fp16)[name = string("k_89_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_22_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557699264))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(558747904))))[name = string("encoder_layers_22_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_22_self_attn_linear_v_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_205_cast_fp16")];
            tensor<int32, [4]> var_4003 = const()[name = string("op_4003"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4003, x = linear_205_cast_fp16)[name = string("v_45_cast_fp16")];
            tensor<int32, [4]> value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(558750016)))];
            tensor<fp16, [1, 188, 8, 128]> var_4015_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_4015_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(558752128)))];
            tensor<fp16, [1, 188, 8, 128]> var_4017_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_4017_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_491_transpose_x_0 = const()[name = string("x_491_transpose_x_0"), val = bool(false)];
            bool x_491_transpose_y_0 = const()[name = string("x_491_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_4019_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(558754240))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559138304))))[name = string("op_4019_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4017_cast_fp16)[name = string("transpose_158")];
            tensor<fp16, [1, 8, 188, 375]> x_491_cast_fp16 = matmul(transpose_x = x_491_transpose_x_0, transpose_y = x_491_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_4019_to_fp16_quantized)[name = string("x_491_cast_fp16")];
            tensor<int32, [8]> x_493_pad_0 = const()[name = string("x_493_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_493_mode_0 = const()[name = string("x_493_mode_0"), val = string("constant")];
            fp16 const_290_to_fp16 = const()[name = string("const_290_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_493_cast_fp16 = pad(constant_val = const_290_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")];
            tensor<int32, [4]> var_4027 = const()[name = string("op_4027"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_495_cast_fp16 = reshape(shape = var_4027, x = x_493_cast_fp16)[name = string("x_495_cast_fp16")];
            tensor<int32, [4]> var_4031_begin_0 = const()[name = string("op_4031_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_4031_end_0 = const()[name = string("op_4031_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_4031_end_mask_0 = const()[name = string("op_4031_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_4031_cast_fp16 = slice_by_index(begin = var_4031_begin_0, end = var_4031_end_0, end_mask = var_4031_end_mask_0, x = x_495_cast_fp16)[name = string("op_4031_cast_fp16")];
            tensor<int32, [4]> var_4032 = const()[name = string("op_4032"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4032, x = var_4031_cast_fp16)[name = string("matrix_bd_89_cast_fp16")];
            bool matrix_ac_45_transpose_x_0 = const()[name = string("matrix_ac_45_transpose_x_0"), val = bool(false)];
            bool matrix_ac_45_transpose_y_0 = const()[name = string("matrix_ac_45_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")];
            tensor<fp16, [1, 8, 188, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_4015_cast_fp16)[name = string("transpose_157")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_140, y = transpose_141)[name = string("matrix_ac_45_cast_fp16")];
            tensor<int32, [4]> matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_91_cast_fp16 = slice_by_index(begin = matrix_bd_91_begin_0, end = matrix_bd_91_end_0, end_mask = matrix_bd_91_end_mask_0, x = matrix_bd_89_cast_fp16)[name = string("matrix_bd_91_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4041_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_4041_cast_fp16")];
            fp16 _inversed_scores_89_y_0_to_fp16 = const()[name = string("_inversed_scores_89_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4041_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_11)[name = string("scores_91_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4047_cast_fp16 = softmax(axis = var_25, x = scores_91_cast_fp16)[name = string("op_4047_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1185_cast_fp16 = select(a = var_9_to_fp16, b = var_4047_cast_fp16, cond = mask_11)[name = string("input_1185_cast_fp16")];
            bool x_497_transpose_x_0 = const()[name = string("x_497_transpose_x_0"), val = bool(false)];
            bool x_497_transpose_y_0 = const()[name = string("x_497_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")];
            tensor<fp16, [1, 8, 188, 128]> x_497_cast_fp16 = matmul(transpose_x = x_497_transpose_x_0, transpose_y = x_497_transpose_y_0, x = input_1185_cast_fp16, y = value_47_cast_fp16)[name = string("x_497_cast_fp16")];
            tensor<int32, [4]> var_4051_perm_0 = const()[name = string("op_4051_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_4052 = const()[name = string("op_4052"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_4051_cast_fp16 = transpose(perm = var_4051_perm_0, x = x_497_cast_fp16)[name = string("transpose_154")];
            tensor<fp16, [1, 188, 1024]> input_1187_cast_fp16 = reshape(shape = var_4052, x = var_4051_cast_fp16)[name = string("input_1187_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_22_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559139136))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560187776))))[name = string("encoder_layers_22_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_22_self_attn_linear_out_weight_to_fp16_quantized, x = input_1187_cast_fp16)[name = string("linear_207_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1191_cast_fp16 = add(x = input_1183_cast_fp16, y = linear_207_cast_fp16)[name = string("input_1191_cast_fp16")];
            tensor<int32, [1]> x_501_axes_0 = const()[name = string("x_501_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560189888)))];
            tensor<fp16, [1024]> encoder_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560192000)))];
            tensor<fp16, [1, 188, 1024]> x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = encoder_layers_22_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_501_cast_fp16")];
            tensor<int32, [3]> input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(560194112))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562291328))))[name = string("encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_501_cast_fp16)[name = string("transpose_153")];
            tensor<fp16, [1, 2048, 188]> input_1195_cast_fp16 = conv(dilations = input_1195_dilations_0, groups = input_1195_groups_0, pad = input_1195_pad_0, pad_type = input_1195_pad_type_0, strides = input_1195_strides_0, weight = encoder_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")];
            int32 x_503_split_num_splits_0 = const()[name = string("x_503_split_num_splits_0"), val = int32(2)];
            int32 x_503_split_axis_0 = const()[name = string("x_503_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_503_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_503_split_cast_fp16_1 = split(axis = x_503_split_axis_0, num_splits = x_503_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_503_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_503_split_1_sigmoid_cast_fp16 = sigmoid(x = x_503_split_cast_fp16_1)[name = string("x_503_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_503_cast_fp16 = mul(x = x_503_split_cast_fp16_0, y = x_503_split_1_sigmoid_cast_fp16)[name = string("x_503_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1197_cast_fp16 = select(a = var_9_to_fp16, b = x_503_cast_fp16, cond = var_488)[name = string("input_1197_cast_fp16")];
            tensor<int32, [6]> input_1199_pad_0 = const()[name = string("input_1199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_1199_mode_0 = const()[name = string("input_1199_mode_0"), val = string("constant")];
            fp16 const_293_to_fp16 = const()[name = string("const_293_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1199_cast_fp16 = pad(constant_val = const_293_to_fp16, mode = input_1199_mode_0, pad = input_1199_pad_0, x = input_1197_cast_fp16)[name = string("input_1199_cast_fp16")];
            string input_1201_pad_type_0 = const()[name = string("input_1201_pad_type_0"), val = string("valid")];
            int32 input_1201_groups_0 = const()[name = string("input_1201_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_1201_strides_0 = const()[name = string("input_1201_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1201_pad_0 = const()[name = string("input_1201_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1201_dilations_0 = const()[name = string("input_1201_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_348_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562295488))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562304768))))[name = string("const_348_to_fp16_quantized")];
            tensor<fp16, [1024]> const_349_to_fp16 = const()[name = string("const_349_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562306880)))];
            tensor<fp16, [1, 1024, 188]> input_1203_cast_fp16 = conv(bias = const_349_to_fp16, dilations = input_1201_dilations_0, groups = input_1201_groups_0, pad = input_1201_pad_0, pad_type = input_1201_pad_type_0, strides = input_1201_strides_0, weight = const_348_to_fp16_quantized, x = input_1199_cast_fp16)[name = string("input_1203_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1205_cast_fp16 = silu(x = input_1203_cast_fp16)[name = string("input_1205_cast_fp16")];
            string x_505_pad_type_0 = const()[name = string("x_505_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_505_strides_0 = const()[name = string("x_505_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_505_pad_0 = const()[name = string("x_505_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_505_dilations_0 = const()[name = string("x_505_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_505_groups_0 = const()[name = string("x_505_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562308992))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(563357632))))[name = string("encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_505_cast_fp16 = conv(dilations = x_505_dilations_0, groups = x_505_groups_0, pad = x_505_pad_0, pad_type = x_505_pad_type_0, strides = x_505_strides_0, weight = encoder_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1205_cast_fp16)[name = string("x_505_cast_fp16")];
            tensor<int32, [3]> input_1207_perm_0 = const()[name = string("input_1207_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1207_cast_fp16 = transpose(perm = input_1207_perm_0, x = x_505_cast_fp16)[name = string("transpose_152")];
            tensor<fp16, [1, 188, 1024]> input_1209_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")];
            tensor<int32, [1]> input_1211_axes_0 = const()[name = string("input_1211_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(563359744)))];
            tensor<fp16, [1024]> encoder_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(563361856)))];
            tensor<fp16, [1, 188, 1024]> input_1211_cast_fp16 = layer_norm(axes = input_1211_axes_0, beta = encoder_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1209_cast_fp16)[name = string("input_1211_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_22_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(563363968))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567558336))))[name = string("encoder_layers_22_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_22_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1211_cast_fp16)[name = string("linear_208_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1215_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1215_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_22_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567566592))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571760960))))[name = string("encoder_layers_22_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_22_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1215_cast_fp16)[name = string("linear_209_cast_fp16")];
            fp16 var_4112_to_fp16 = const()[name = string("op_4112_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4113_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4112_to_fp16)[name = string("op_4113_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1221_cast_fp16 = add(x = input_1209_cast_fp16, y = var_4113_cast_fp16)[name = string("input_1221_cast_fp16")];
            tensor<int32, [1]> input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571763072)))];
            tensor<fp16, [1024]> encoder_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571765184)))];
            tensor<fp16, [1, 188, 1024]> input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_layers_22_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_22_norm_out_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")];
            tensor<int32, [1]> input_1225_axes_0 = const()[name = string("input_1225_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571767296)))];
            tensor<fp16, [1024]> encoder_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571769408)))];
            tensor<fp16, [1, 188, 1024]> input_1225_cast_fp16 = layer_norm(axes = input_1225_axes_0, beta = encoder_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("input_1225_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_23_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571771520))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(575965888))))[name = string("encoder_layers_23_feed_forward1_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_210_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_23_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1225_cast_fp16)[name = string("linear_210_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1229_cast_fp16 = silu(x = linear_210_cast_fp16)[name = string("input_1229_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_23_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(575974144))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580168512))))[name = string("encoder_layers_23_feed_forward1_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_23_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1229_cast_fp16)[name = string("linear_211_cast_fp16")];
            fp16 var_4141_to_fp16 = const()[name = string("op_4141_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4142_cast_fp16 = mul(x = linear_211_cast_fp16, y = var_4141_to_fp16)[name = string("op_4142_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1235_cast_fp16 = add(x = input_1223_cast_fp16, y = var_4142_cast_fp16)[name = string("input_1235_cast_fp16")];
            tensor<int32, [1]> query_axes_0 = const()[name = string("query_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580170624)))];
            tensor<fp16, [1024]> encoder_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580172736)))];
            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = encoder_layers_23_norm_self_att_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_23_norm_self_att_weight_to_fp16, x = input_1235_cast_fp16)[name = string("query_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_23_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580174848))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(581223488))))[name = string("encoder_layers_23_self_attn_linear_q_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_23_self_attn_linear_q_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_212_cast_fp16")];
            tensor<int32, [4]> var_4158 = const()[name = string("op_4158"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4158, x = linear_212_cast_fp16)[name = string("q_139_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_23_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(581225600))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582274240))))[name = string("encoder_layers_23_self_attn_linear_k_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_213_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_23_self_attn_linear_k_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_213_cast_fp16")];
            tensor<int32, [4]> var_4162 = const()[name = string("op_4162"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4162, x = linear_213_cast_fp16)[name = string("k_93_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_23_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582276352))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583324992))))[name = string("encoder_layers_23_self_attn_linear_v_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_23_self_attn_linear_v_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_214_cast_fp16")];
            tensor<int32, [4]> var_4166 = const()[name = string("op_4166"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4166, x = linear_214_cast_fp16)[name = string("v_cast_fp16")];
            tensor<int32, [4]> value_perm_0 = const()[name = string("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> encoder_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583327104)))];
            tensor<fp16, [1, 188, 8, 128]> var_4178_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4178_cast_fp16")];
            tensor<fp16, [8, 128]> encoder_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583329216)))];
            tensor<fp16, [1, 188, 8, 128]> var_4180_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4180_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            bool x_513_transpose_x_0 = const()[name = string("x_513_transpose_x_0"), val = bool(false)];
            bool x_513_transpose_y_0 = const()[name = string("x_513_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 375]> op_4182_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583331328))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583715392))))[name = string("op_4182_to_fp16_quantized")];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4180_cast_fp16)[name = string("transpose_151")];
            tensor<fp16, [1, 8, 188, 375]> x_513_cast_fp16 = matmul(transpose_x = x_513_transpose_x_0, transpose_y = x_513_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4182_to_fp16_quantized)[name = string("x_513_cast_fp16")];
            tensor<int32, [8]> x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            string x_515_mode_0 = const()[name = string("x_515_mode_0"), val = string("constant")];
            fp16 const_300_to_fp16 = const()[name = string("const_300_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_515_cast_fp16 = pad(constant_val = const_300_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")];
            tensor<int32, [4]> var_4190 = const()[name = string("op_4190"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_517_cast_fp16 = reshape(shape = var_4190, x = x_515_cast_fp16)[name = string("x_517_cast_fp16")];
            tensor<int32, [4]> var_4194_begin_0 = const()[name = string("op_4194_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_4194_end_0 = const()[name = string("op_4194_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_4194_end_mask_0 = const()[name = string("op_4194_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_4194_cast_fp16 = slice_by_index(begin = var_4194_begin_0, end = var_4194_end_0, end_mask = var_4194_end_mask_0, x = x_517_cast_fp16)[name = string("op_4194_cast_fp16")];
            tensor<int32, [4]> var_4195 = const()[name = string("op_4195"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4195, x = var_4194_cast_fp16)[name = string("matrix_bd_93_cast_fp16")];
            bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)];
            bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 8, 128, 188]> transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")];
            tensor<fp16, [1, 8, 188, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4178_cast_fp16)[name = string("transpose_150")];
            tensor<fp16, [1, 8, 188, 188]> matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_142, y = transpose_143)[name = string("matrix_ac_cast_fp16")];
            tensor<int32, [4]> matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
            tensor<fp16, [1, 8, 188, 188]> matrix_bd_cast_fp16 = slice_by_index(begin = matrix_bd_begin_0, end = matrix_bd_end_0, end_mask = matrix_bd_end_mask_0, x = matrix_bd_93_cast_fp16)[name = string("matrix_bd_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4204_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4204_cast_fp16")];
            fp16 _inversed_scores_93_y_0_to_fp16 = const()[name = string("_inversed_scores_93_y_0_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4204_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_10_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_11)[name = string("scores_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4210_cast_fp16 = softmax(axis = var_25, x = scores_cast_fp16)[name = string("op_4210_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1237_cast_fp16 = select(a = var_9_to_fp16, b = var_4210_cast_fp16, cond = mask_11)[name = string("input_1237_cast_fp16")];
            bool x_519_transpose_x_0 = const()[name = string("x_519_transpose_x_0"), val = bool(false)];
            bool x_519_transpose_y_0 = const()[name = string("x_519_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")];
            tensor<fp16, [1, 8, 188, 128]> x_519_cast_fp16 = matmul(transpose_x = x_519_transpose_x_0, transpose_y = x_519_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_519_cast_fp16")];
            tensor<int32, [4]> var_4214_perm_0 = const()[name = string("op_4214_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_4215 = const()[name = string("op_4215"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_4214_cast_fp16 = transpose(perm = var_4214_perm_0, x = x_519_cast_fp16)[name = string("transpose_147")];
            tensor<fp16, [1, 188, 1024]> input_1239_cast_fp16 = reshape(shape = var_4215, x = var_4214_cast_fp16)[name = string("input_1239_cast_fp16")];
            tensor<fp16, [1024, 1024]> encoder_layers_23_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583716224))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584764864))))[name = string("encoder_layers_23_self_attn_linear_out_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_23_self_attn_linear_out_weight_to_fp16_quantized, x = input_1239_cast_fp16)[name = string("linear_216_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1243_cast_fp16 = add(x = input_1235_cast_fp16, y = linear_216_cast_fp16)[name = string("input_1243_cast_fp16")];
            tensor<int32, [1]> x_523_axes_0 = const()[name = string("x_523_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584766976)))];
            tensor<fp16, [1024]> encoder_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584769088)))];
            tensor<fp16, [1, 188, 1024]> x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = encoder_layers_23_norm_conv_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_523_cast_fp16")];
            tensor<int32, [3]> input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 1024, 1]> encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(584771200))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586868416))))[name = string("encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_523_cast_fp16)[name = string("transpose_146")];
            tensor<fp16, [1, 2048, 188]> input_1247_cast_fp16 = conv(dilations = input_1247_dilations_0, groups = input_1247_groups_0, pad = input_1247_pad_0, pad_type = input_1247_pad_type_0, strides = input_1247_strides_0, weight = encoder_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")];
            int32 x_525_split_num_splits_0 = const()[name = string("x_525_split_num_splits_0"), val = int32(2)];
            int32 x_525_split_axis_0 = const()[name = string("x_525_split_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 188]> x_525_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_525_split_cast_fp16_1 = split(axis = x_525_split_axis_0, num_splits = x_525_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_525_split_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_525_split_1_sigmoid_cast_fp16 = sigmoid(x = x_525_split_cast_fp16_1)[name = string("x_525_split_1_sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> x_525_cast_fp16 = mul(x = x_525_split_cast_fp16_0, y = x_525_split_1_sigmoid_cast_fp16)[name = string("x_525_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1249_cast_fp16 = select(a = var_9_to_fp16, b = x_525_cast_fp16, cond = var_488)[name = string("input_1249_cast_fp16")];
            tensor<int32, [6]> input_1251_pad_0 = const()[name = string("input_1251_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_1251_mode_0 = const()[name = string("input_1251_mode_0"), val = string("constant")];
            fp16 const_303_to_fp16 = const()[name = string("const_303_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1251_cast_fp16 = pad(constant_val = const_303_to_fp16, mode = input_1251_mode_0, pad = input_1251_pad_0, x = input_1249_cast_fp16)[name = string("input_1251_cast_fp16")];
            string input_1253_pad_type_0 = const()[name = string("input_1253_pad_type_0"), val = string("valid")];
            int32 input_1253_groups_0 = const()[name = string("input_1253_groups_0"), val = int32(1024)];
            tensor<int32, [1]> input_1253_strides_0 = const()[name = string("input_1253_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1253_pad_0 = const()[name = string("input_1253_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1253_dilations_0 = const()[name = string("input_1253_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_350_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586872576))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586881856))))[name = string("const_350_to_fp16_quantized")];
            tensor<fp16, [1024]> const_351_to_fp16 = const()[name = string("const_351_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586883968)))];
            tensor<fp16, [1, 1024, 188]> input_1255_cast_fp16 = conv(bias = const_351_to_fp16, dilations = input_1253_dilations_0, groups = input_1253_groups_0, pad = input_1253_pad_0, pad_type = input_1253_pad_type_0, strides = input_1253_strides_0, weight = const_350_to_fp16_quantized, x = input_1251_cast_fp16)[name = string("input_1255_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1257_cast_fp16 = silu(x = input_1255_cast_fp16)[name = string("input_1257_cast_fp16")];
            string x_527_pad_type_0 = const()[name = string("x_527_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_527_strides_0 = const()[name = string("x_527_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_527_pad_0 = const()[name = string("x_527_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_527_dilations_0 = const()[name = string("x_527_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_527_groups_0 = const()[name = string("x_527_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 1024, 1]> encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586886080))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(587934720))))[name = string("encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 1024, 188]> x_527_cast_fp16 = conv(dilations = x_527_dilations_0, groups = x_527_groups_0, pad = x_527_pad_0, pad_type = x_527_pad_type_0, strides = x_527_strides_0, weight = encoder_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1257_cast_fp16)[name = string("x_527_cast_fp16")];
            tensor<int32, [3]> input_1259_perm_0 = const()[name = string("input_1259_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1259_cast_fp16 = transpose(perm = input_1259_perm_0, x = x_527_cast_fp16)[name = string("transpose_145")];
            tensor<fp16, [1, 188, 1024]> input_1261_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")];
            tensor<int32, [1]> input_1263_axes_0 = const()[name = string("input_1263_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(587936832)))];
            tensor<fp16, [1024]> encoder_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(587938944)))];
            tensor<fp16, [1, 188, 1024]> input_1263_cast_fp16 = layer_norm(axes = input_1263_axes_0, beta = encoder_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1261_cast_fp16)[name = string("input_1263_cast_fp16")];
            tensor<fp16, [4096, 1024]> encoder_layers_23_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(587941056))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(592135424))))[name = string("encoder_layers_23_feed_forward2_linear1_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 4096]> linear_217_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = encoder_layers_23_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1263_cast_fp16)[name = string("linear_217_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1267_cast_fp16 = silu(x = linear_217_cast_fp16)[name = string("input_1267_cast_fp16")];
            tensor<fp16, [1024, 4096]> encoder_layers_23_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(592143680))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596338048))))[name = string("encoder_layers_23_feed_forward2_linear2_weight_to_fp16_quantized")];
            tensor<fp16, [1, 188, 1024]> linear_218_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = encoder_layers_23_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1267_cast_fp16)[name = string("linear_218_cast_fp16")];
            fp16 var_4275_to_fp16 = const()[name = string("op_4275_to_fp16"), val = fp16(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4276_cast_fp16 = mul(x = linear_218_cast_fp16, y = var_4275_to_fp16)[name = string("op_4276_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1261_cast_fp16, y = var_4276_cast_fp16)[name = string("input_cast_fp16")];
            tensor<int32, [1]> audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> encoder_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596340160)))];
            tensor<fp16, [1024]> encoder_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596342272)))];
            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_layers_23_norm_out_bias_to_fp16, epsilon = var_18_to_fp16, gamma = encoder_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")];
            tensor<int32, [3]> obj_1_perm_0 = const()[name = string("obj_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp16, [1, 1024, 188]> obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")];
            tensor<fp32, [1, 1024, 188]> encoder = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = string("cast_0")];
        } -> (encoder, encoder_length);
}