program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
{
    func main<ios17>(tensor<fp32, [1, 80, 1501]> audio_signal, tensor<int32, [1]> length) {
            tensor<int32, []> var_30 = const()[name = tensor<string, []>("op_30"), val = tensor<int32, []>(-1)];
            tensor<int32, [3]> x_1_perm_0 = const()[name = tensor<string, []>("x_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> audio_signal_to_fp16_dtype_0 = const()[name = tensor<string, []>("audio_signal_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<string, []> var_86_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_86_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, []> var_87_promoted_to_fp16 = const()[name = tensor<string, []>("op_87_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
            tensor<fp16, [1]> length_to_fp16 = cast(dtype = var_86_to_fp16_dtype_0, x = length)[name = tensor<string, []>("cast_3")];
            tensor<fp16, [1]> var_88_cast_fp16 = add(x = length_to_fp16, y = var_87_promoted_to_fp16)[name = tensor<string, []>("op_88_cast_fp16")];
            tensor<fp16, []> _inversed_90_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_90_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1]> _inversed_90_cast_fp16 = mul(x = var_88_cast_fp16, y = _inversed_90_y_0_to_fp16)[name = tensor<string, []>("_inversed_90_cast_fp16")];
            tensor<fp16, []> var_91_to_fp16 = const()[name = tensor<string, []>("op_91_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> lengths_1_cast_fp16 = add(x = _inversed_90_cast_fp16, y = var_91_to_fp16)[name = tensor<string, []>("lengths_1_cast_fp16")];
            tensor<fp16, [1]> lengths_3_cast_fp16 = floor(x = lengths_1_cast_fp16)[name = tensor<string, []>("lengths_3_cast_fp16")];
            tensor<fp16, []> var_95_promoted_to_fp16 = const()[name = tensor<string, []>("op_95_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
            tensor<fp16, [1]> var_96_cast_fp16 = add(x = lengths_3_cast_fp16, y = var_95_promoted_to_fp16)[name = tensor<string, []>("op_96_cast_fp16")];
            tensor<fp16, []> _inversed_98_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_98_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1]> _inversed_98_cast_fp16 = mul(x = var_96_cast_fp16, y = _inversed_98_y_0_to_fp16)[name = tensor<string, []>("_inversed_98_cast_fp16")];
            tensor<fp16, []> var_99_to_fp16 = const()[name = tensor<string, []>("op_99_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> lengths_7_cast_fp16 = add(x = _inversed_98_cast_fp16, y = var_99_to_fp16)[name = tensor<string, []>("lengths_7_cast_fp16")];
            tensor<fp16, [1]> lengths_9_cast_fp16 = floor(x = lengths_7_cast_fp16)[name = tensor<string, []>("lengths_9_cast_fp16")];
            tensor<fp16, []> var_103_promoted_to_fp16 = const()[name = tensor<string, []>("op_103_promoted_to_fp16"), val = tensor<fp16, []>(-0x1p+0)];
            tensor<fp16, [1]> var_104_cast_fp16 = add(x = lengths_9_cast_fp16, y = var_103_promoted_to_fp16)[name = tensor<string, []>("op_104_cast_fp16")];
            tensor<fp16, []> _inversed_106_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_106_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1]> _inversed_106_cast_fp16 = mul(x = var_104_cast_fp16, y = _inversed_106_y_0_to_fp16)[name = tensor<string, []>("_inversed_106_cast_fp16")];
            tensor<fp16, []> var_107_to_fp16 = const()[name = tensor<string, []>("op_107_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
            tensor<fp16, [1]> lengths_13_cast_fp16 = add(x = _inversed_106_cast_fp16, y = var_107_to_fp16)[name = tensor<string, []>("lengths_13_cast_fp16")];
            tensor<fp16, [1]> lengths_cast_fp16 = floor(x = lengths_13_cast_fp16)[name = tensor<string, []>("lengths_cast_fp16")];
            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 80, 1501]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor<string, []>("cast_2")];
            tensor<fp16, [1, 1501, 80]> x_1_cast_fp16 = transpose(perm = x_1_perm_0, x = audio_signal_to_fp16)[name = tensor<string, []>("transpose_315")];
            tensor<fp16, [1, 1, 1501, 80]> input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = x_1_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_3_strides_0 = const()[name = tensor<string, []>("input_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = tensor<string, []>("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_3_groups_0 = const()[name = tensor<string, []>("input_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 1, 3, 3]> module_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_pre_encode_conv_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2752))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> module_pre_encode_conv_0_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3328)))];
            tensor<fp16, [1, 256, 751, 40]> input_3_cast_fp16 = conv(bias = module_pre_encode_conv_0_bias_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = module_pre_encode_conv_0_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
            tensor<fp16, [1, 256, 751, 40]> input_5_cast_fp16 = relu(x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
            tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> module_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_pre_encode_conv_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3904))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6272))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> module_pre_encode_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6848)))];
            tensor<fp16, [1, 256, 376, 20]> input_7_cast_fp16 = conv(bias = module_pre_encode_conv_2_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = module_pre_encode_conv_2_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1, 1]> module_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_pre_encode_conv_3_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7424))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73024))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> module_pre_encode_conv_3_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73600)))];
            tensor<fp16, [1, 256, 376, 20]> input_9_cast_fp16 = conv(bias = module_pre_encode_conv_3_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = module_pre_encode_conv_3_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
            tensor<fp16, [1, 256, 376, 20]> input_11_cast_fp16 = relu(x = input_9_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(256)];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> module_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_pre_encode_conv_5_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74176))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76544))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> module_pre_encode_conv_5_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77120)))];
            tensor<fp16, [1, 256, 188, 10]> input_13_cast_fp16 = conv(bias = module_pre_encode_conv_5_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = module_pre_encode_conv_5_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
            tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = tensor<string, []>("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = tensor<string, []>("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_15_groups_0 = const()[name = tensor<string, []>("input_15_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 256, 1, 1]> module_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_pre_encode_conv_6_weight_to_fp16_quantized"), quantized_data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77696))), scale = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143296))), zero_point = tensor<int8, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2432)))];
            tensor<fp16, [256]> module_pre_encode_conv_6_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143872)))];
            tensor<fp16, [1, 256, 188, 10]> input_15_cast_fp16 = conv(bias = module_pre_encode_conv_6_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = module_pre_encode_conv_6_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
            tensor<fp16, [1, 256, 188, 10]> x_3_cast_fp16 = relu(x = input_15_cast_fp16)[name = tensor<string, []>("x_3_cast_fp16")];
            tensor<int32, [4]> var_157_perm_0 = const()[name = tensor<string, []>("op_157_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_158 = const()[name = tensor<string, []>("op_158"), val = tensor<int32, [3]>([1, 188, -1])];
            tensor<fp16, [1, 188, 256, 10]> var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = x_3_cast_fp16)[name = tensor<string, []>("transpose_314")];
            tensor<fp16, [1, 188, 2560]> input_17_cast_fp16 = reshape(shape = var_158, x = var_157_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
            tensor<fp16, [1024, 2560]> module_pre_encode_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_pre_encode_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144448))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2767040))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_pre_encode_out_bias_to_fp16 = const()[name = tensor<string, []>("module_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2769152)))];
            tensor<fp16, [1, 188, 1024]> linear_0_cast_fp16 = linear(bias = module_pre_encode_out_bias_to_fp16, weight = module_pre_encode_out_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
            tensor<string, []> padding_length_dtype_0 = const()[name = tensor<string, []>("padding_length_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, [1, 188]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 188]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187]])];
            tensor<int32, [1]> var_196_axes_0 = const()[name = tensor<string, []>("op_196_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<int32, [1]> encoded_length = cast(dtype = padding_length_dtype_0, x = lengths_cast_fp16)[name = tensor<string, []>("cast_1")];
            tensor<int32, [1, 1]> var_196 = expand_dims(axes = var_196_axes_0, x = encoded_length)[name = tensor<string, []>("op_196")];
            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_0, y = var_196)[name = tensor<string, []>("pad_mask_1")];
            tensor<int32, [1]> var_198_axes_0 = const()[name = tensor<string, []>("op_198_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188]> var_198 = expand_dims(axes = var_198_axes_0, x = pad_mask_1)[name = tensor<string, []>("op_198")];
            tensor<int32, [3]> var_199 = const()[name = tensor<string, []>("op_199"), val = tensor<int32, [3]>([1, 188, 1])];
            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_199, x = var_198)[name = tensor<string, []>("pad_mask_for_att_mask_1")];
            tensor<int32, [3]> var_201_perm_0 = const()[name = tensor<string, []>("op_201_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, [1, 188, 188]> var_201 = transpose(perm = var_201_perm_0, x = pad_mask_for_att_mask_1)[name = tensor<string, []>("transpose_313")];
            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_201)[name = tensor<string, []>("pad_mask_for_att_mask")];
            tensor<bool, [1, 188, 188]> const_7 = const()[name = tensor<string, []>("const_7"), 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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], [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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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], [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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, true, true, true, true, true, true, true, true, true, true, true, true]]])];
            tensor<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_7)[name = tensor<string, []>("att_mask")];
            tensor<bool, [1, 188, 188]> mask_1 = logical_not(x = att_mask)[name = tensor<string, []>("mask_1")];
            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = tensor<string, []>("pad_mask")];
            tensor<int32, [1]> input_21_axes_0 = const()[name = tensor<string, []>("input_21_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2771264)))];
            tensor<fp16, [1024]> module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2773376)))];
            tensor<fp16, []> var_9_to_fp16 = const()[name = tensor<string, []>("op_9_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 188, 1024]> input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward1_weight_to_fp16, x = linear_0_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2775488))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6974016))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6982272)))];
            tensor<fp16, [1, 188, 4096]> linear_1_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_25_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6990528))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11184896))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11187008)))];
            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
            tensor<fp16, []> var_234_to_fp16 = const()[name = tensor<string, []>("op_234_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_235_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_234_to_fp16)[name = tensor<string, []>("op_235_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_31_cast_fp16 = add(x = linear_0_cast_fp16, y = var_235_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
            tensor<int32, [1]> query_1_axes_0 = const()[name = tensor<string, []>("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_0_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11189120)))];
            tensor<fp16, [1024]> module_layers_0_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11191232)))];
            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_self_att_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("query_1_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11193344))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12241984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12244096)))];
            tensor<fp16, [1, 188, 1024]> linear_3_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_q_bias_to_fp16, weight = module_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
            tensor<int32, [4]> var_252 = const()[name = tensor<string, []>("op_252"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_252, x = linear_3_cast_fp16)[name = tensor<string, []>("q_1_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12246208))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13294848))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13296960)))];
            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_k_bias_to_fp16, weight = module_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
            tensor<int32, [4]> var_257 = const()[name = tensor<string, []>("op_257"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_257, x = linear_4_cast_fp16)[name = tensor<string, []>("k_1_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13299072))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14347712))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14349824)))];
            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_v_bias_to_fp16, weight = module_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = tensor<string, []>("linear_5_cast_fp16")];
            tensor<int32, [4]> var_262 = const()[name = tensor<string, []>("op_262"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_262, x = linear_5_cast_fp16)[name = tensor<string, []>("v_1_cast_fp16")];
            tensor<int32, [4]> value_3_perm_0 = const()[name = tensor<string, []>("value_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_0_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14351936))), scale = tensor<fp16, [8]>([0x1.e3p-9, 0x1.d48p-8, 0x1.9b8p-9, 0x1.668p-8, 0x1.b1p-9, 0x1.9bp-8, 0x1.bf8p-9, 0x1.da8p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_274_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_274_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_0_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14353024))), scale = tensor<fp16, [8]>([0x1.03p-8, 0x1.654p-8, 0x1.56cp-9, 0x1.138p-8, 0x1.804p-10, 0x1.2fp-8, 0x1.46p-9, 0x1.834p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_276_cast_fp16 = add(x = q_1_cast_fp16, y = module_layers_0_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_276_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_7_transpose_x_0 = const()[name = tensor<string, []>("x_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_7_transpose_y_0 = const()[name = tensor<string, []>("x_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_278_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_278_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14354112))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738624))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_276_cast_fp16)[name = tensor<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_278_to_fp16_quantized)[name = tensor<string, []>("x_7_cast_fp16")];
            tensor<int32, [8]> x_9_pad_0 = const()[name = tensor<string, []>("x_9_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_9_mode_0 = const()[name = tensor<string, []>("x_9_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_14_to_fp16 = const()[name = tensor<string, []>("const_14_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_9_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_9_mode_0, pad = x_9_pad_0, x = x_7_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
            tensor<int32, [4]> var_286 = const()[name = tensor<string, []>("op_286"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_11_cast_fp16 = reshape(shape = var_286, x = x_9_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
            tensor<int32, [4]> var_290_begin_0 = const()[name = tensor<string, []>("op_290_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_290_end_0 = const()[name = tensor<string, []>("op_290_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_290_end_mask_0 = const()[name = tensor<string, []>("op_290_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_290_cast_fp16 = slice_by_index(begin = var_290_begin_0, end = var_290_end_0, end_mask = var_290_end_mask_0, x = x_11_cast_fp16)[name = tensor<string, []>("op_290_cast_fp16")];
            tensor<int32, [4]> var_291 = const()[name = tensor<string, []>("op_291"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_291, x = var_290_cast_fp16)[name = tensor<string, []>("matrix_bd_1_cast_fp16")];
            tensor<bool, []> matrix_ac_1_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_1_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_96_perm_0 = const()[name = tensor<string, []>("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_97_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_310")];
            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_274_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_1_cast_fp16")];
            tensor<int32, [4]> matrix_bd_3_begin_0 = const()[name = tensor<string, []>("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_3_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_300_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = tensor<string, []>("op_300_cast_fp16")];
            tensor<fp16, []> _inversed_scores_1_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_1_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_300_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_1_cast_fp16")];
            tensor<int32, [1]> mask_3_axes_0 = const()[name = tensor<string, []>("mask_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188, 188]> mask_3 = expand_dims(axes = mask_3_axes_0, x = mask_1)[name = tensor<string, []>("mask_3")];
            tensor<fp16, []> var_12_to_fp16 = const()[name = tensor<string, []>("op_12_to_fp16"), val = tensor<fp16, []>(-0x1.388p+13)];
            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_3_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_306_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = tensor<string, []>("op_306_cast_fp16")];
            tensor<fp16, []> var_11_to_fp16 = const()[name = tensor<string, []>("op_11_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 188]> input_33_cast_fp16 = select(a = var_11_to_fp16, b = var_306_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_33_cast_fp16")];
            tensor<bool, []> x_13_transpose_x_0 = const()[name = tensor<string, []>("x_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_13_transpose_y_0 = const()[name = tensor<string, []>("x_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = v_1_cast_fp16)[name = tensor<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_33_cast_fp16, y = value_3_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
            tensor<int32, [4]> var_310_perm_0 = const()[name = tensor<string, []>("op_310_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_311 = const()[name = tensor<string, []>("op_311"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_310_cast_fp16 = transpose(perm = var_310_perm_0, x = x_13_cast_fp16)[name = tensor<string, []>("transpose_308")];
            tensor<fp16, [1, 188, 1024]> input_35_cast_fp16 = reshape(shape = var_311, x = var_310_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14739456))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15788096))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15790208)))];
            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = module_layers_0_self_attn_linear_out_bias_to_fp16, weight = module_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_39_cast_fp16 = add(x = input_31_cast_fp16, y = linear_7_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
            tensor<int32, [1]> x_17_axes_0 = const()[name = tensor<string, []>("x_17_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_0_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15792320)))];
            tensor<fp16, [1024]> module_layers_0_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15794432)))];
            tensor<fp16, [1, 188, 1024]> x_17_cast_fp16 = layer_norm(axes = x_17_axes_0, beta = module_layers_0_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_conv_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("x_17_cast_fp16")];
            tensor<int32, [3]> input_41_perm_0 = const()[name = tensor<string, []>("input_41_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15796544))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17895872))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17900032)))];
            tensor<fp16, [1, 1024, 188]> input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = x_17_cast_fp16)[name = tensor<string, []>("transpose_307")];
            tensor<fp16, [1, 2048, 188]> input_43_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv1_bias_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
            tensor<int32, []> x_19_split_num_splits_0 = const()[name = tensor<string, []>("x_19_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_19_split_axis_0 = const()[name = tensor<string, []>("x_19_split_axis_0"), val = tensor<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_43_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_19_cast_fp16")];
            tensor<int32, [1]> var_335_axes_0 = const()[name = tensor<string, []>("op_335_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 188]> var_335 = expand_dims(axes = var_335_axes_0, x = pad_mask)[name = tensor<string, []>("op_335")];
            tensor<fp16, [1, 1024, 188]> input_45_cast_fp16 = select(a = var_11_to_fp16, b = x_19_cast_fp16, cond = var_335)[name = tensor<string, []>("input_45_cast_fp16")];
            tensor<int32, [6]> input_47_pad_0 = const()[name = tensor<string, []>("input_47_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_47_mode_0 = const()[name = tensor<string, []>("input_47_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_17_to_fp16 = const()[name = tensor<string, []>("const_17_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_47_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
            tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_49_groups_0 = const()[name = tensor<string, []>("input_49_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_49_strides_0 = const()[name = tensor<string, []>("input_49_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_49_dilations_0 = const()[name = tensor<string, []>("input_49_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_248_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_248_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17904192))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17913472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_249_to_fp16 = const()[name = tensor<string, []>("const_249_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17915584)))];
            tensor<fp16, [1, 1024, 188]> input_51_cast_fp16 = conv(bias = const_249_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_248_to_fp16_quantized, x = input_47_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_53_cast_fp16 = silu(x = input_51_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
            tensor<string, []> x_21_pad_type_0 = const()[name = tensor<string, []>("x_21_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_21_strides_0 = const()[name = tensor<string, []>("x_21_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_21_pad_0 = const()[name = tensor<string, []>("x_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_21_dilations_0 = const()[name = tensor<string, []>("x_21_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_21_groups_0 = const()[name = tensor<string, []>("x_21_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17917696))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18966336))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18968448)))];
            tensor<fp16, [1, 1024, 188]> x_21_cast_fp16 = conv(bias = module_layers_0_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_53_cast_fp16)[name = tensor<string, []>("x_21_cast_fp16")];
            tensor<int32, [3]> input_55_perm_0 = const()[name = tensor<string, []>("input_55_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_21_cast_fp16)[name = tensor<string, []>("transpose_306")];
            tensor<fp16, [1, 188, 1024]> input_57_cast_fp16 = add(x = input_39_cast_fp16, y = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
            tensor<int32, [1]> input_59_axes_0 = const()[name = tensor<string, []>("input_59_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18970560)))];
            tensor<fp16, [1024]> module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18972672)))];
            tensor<fp16, [1, 188, 1024]> input_59_cast_fp16 = layer_norm(axes = input_59_axes_0, beta = module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18974784))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23169152))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23177408)))];
            tensor<fp16, [1, 188, 4096]> linear_8_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_63_cast_fp16 = silu(x = linear_8_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23185664))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27380032))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27382144)))];
            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")];
            tensor<fp16, []> var_377_to_fp16 = const()[name = tensor<string, []>("op_377_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_378_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_377_to_fp16)[name = tensor<string, []>("op_378_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_69_cast_fp16 = add(x = input_57_cast_fp16, y = var_378_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
            tensor<int32, [1]> input_71_axes_0 = const()[name = tensor<string, []>("input_71_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_0_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27384256)))];
            tensor<fp16, [1024]> module_layers_0_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27386368)))];
            tensor<fp16, [1, 188, 1024]> input_71_cast_fp16 = layer_norm(axes = input_71_axes_0, beta = module_layers_0_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm_out_weight_to_fp16, x = input_69_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
            tensor<int32, [1]> input_73_axes_0 = const()[name = tensor<string, []>("input_73_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27388480)))];
            tensor<fp16, [1024]> module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27390592)))];
            tensor<fp16, [1, 188, 1024]> input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27392704))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31587072))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31595328)))];
            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_73_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_77_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31603584))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35797952))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35800064)))];
            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")];
            tensor<fp16, []> var_408_to_fp16 = const()[name = tensor<string, []>("op_408_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_409_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_408_to_fp16)[name = tensor<string, []>("op_409_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_83_cast_fp16 = add(x = input_71_cast_fp16, y = var_409_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
            tensor<int32, [1]> query_3_axes_0 = const()[name = tensor<string, []>("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_1_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35802176)))];
            tensor<fp16, [1024]> module_layers_1_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35804288)))];
            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_self_att_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("query_3_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35806400))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36855040))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36857152)))];
            tensor<fp16, [1, 188, 1024]> linear_12_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_q_bias_to_fp16, weight = module_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = tensor<string, []>("linear_12_cast_fp16")];
            tensor<int32, [4]> var_426 = const()[name = tensor<string, []>("op_426"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_426, x = linear_12_cast_fp16)[name = tensor<string, []>("q_7_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36859264))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37907904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37910016)))];
            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_k_bias_to_fp16, weight = module_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = tensor<string, []>("linear_13_cast_fp16")];
            tensor<int32, [4]> var_431 = const()[name = tensor<string, []>("op_431"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_431, x = linear_13_cast_fp16)[name = tensor<string, []>("k_5_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37912128))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38960768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38962880)))];
            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_v_bias_to_fp16, weight = module_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = tensor<string, []>("linear_14_cast_fp16")];
            tensor<int32, [4]> var_436 = const()[name = tensor<string, []>("op_436"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_436, x = linear_14_cast_fp16)[name = tensor<string, []>("v_3_cast_fp16")];
            tensor<int32, [4]> value_5_perm_0 = const()[name = tensor<string, []>("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_1_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38964992))), scale = tensor<fp16, [8]>([0x1.58cp-8, 0x1.cbp-8, 0x1.118p-7, 0x1.b8cp-9, 0x1.408p-7, 0x1.438p-8, 0x1.908p-8, 0x1.878p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_448_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_448_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_1_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38966080))), scale = tensor<fp16, [8]>([0x1.4ep-8, 0x1.014p-8, 0x1.5d4p-8, 0x1.a68p-10, 0x1.e54p-9, 0x1.418p-9, 0x1.504p-8, 0x1.1bcp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_450_cast_fp16 = add(x = q_7_cast_fp16, y = module_layers_1_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_450_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_29_transpose_x_0 = const()[name = tensor<string, []>("x_29_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_29_transpose_y_0 = const()[name = tensor<string, []>("x_29_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_452_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_452_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38967168))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39351232))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_450_cast_fp16)[name = tensor<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_452_to_fp16_quantized)[name = tensor<string, []>("x_29_cast_fp16")];
            tensor<int32, [8]> x_31_pad_0 = const()[name = tensor<string, []>("x_31_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_31_mode_0 = const()[name = tensor<string, []>("x_31_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_24_to_fp16 = const()[name = tensor<string, []>("const_24_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_31_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_31_mode_0, pad = x_31_pad_0, x = x_29_cast_fp16)[name = tensor<string, []>("x_31_cast_fp16")];
            tensor<int32, [4]> var_460 = const()[name = tensor<string, []>("op_460"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_33_cast_fp16 = reshape(shape = var_460, x = x_31_cast_fp16)[name = tensor<string, []>("x_33_cast_fp16")];
            tensor<int32, [4]> var_464_begin_0 = const()[name = tensor<string, []>("op_464_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_464_end_0 = const()[name = tensor<string, []>("op_464_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_464_end_mask_0 = const()[name = tensor<string, []>("op_464_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_464_cast_fp16 = slice_by_index(begin = var_464_begin_0, end = var_464_end_0, end_mask = var_464_end_mask_0, x = x_33_cast_fp16)[name = tensor<string, []>("op_464_cast_fp16")];
            tensor<int32, [4]> var_465 = const()[name = tensor<string, []>("op_465"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_465, x = var_464_cast_fp16)[name = tensor<string, []>("matrix_bd_5_cast_fp16")];
            tensor<bool, []> matrix_ac_3_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_3_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_3_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_98_perm_0 = const()[name = tensor<string, []>("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_99_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_303")];
            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_448_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_3_cast_fp16")];
            tensor<int32, [4]> matrix_bd_7_begin_0 = const()[name = tensor<string, []>("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_7_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_474_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = tensor<string, []>("op_474_cast_fp16")];
            tensor<fp16, []> _inversed_scores_5_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_5_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_474_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_5_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_7_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_480_cast_fp16 = softmax(axis = var_30, x = scores_7_cast_fp16)[name = tensor<string, []>("op_480_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_85_cast_fp16 = select(a = var_11_to_fp16, b = var_480_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_85_cast_fp16")];
            tensor<bool, []> x_35_transpose_x_0 = const()[name = tensor<string, []>("x_35_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_35_transpose_y_0 = const()[name = tensor<string, []>("x_35_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_3_cast_fp16)[name = tensor<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_85_cast_fp16, y = value_5_cast_fp16)[name = tensor<string, []>("x_35_cast_fp16")];
            tensor<int32, [4]> var_484_perm_0 = const()[name = tensor<string, []>("op_484_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_484_cast_fp16 = transpose(perm = var_484_perm_0, x = x_35_cast_fp16)[name = tensor<string, []>("transpose_301")];
            tensor<fp16, [1, 188, 1024]> input_87_cast_fp16 = reshape(shape = var_485, x = var_484_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39352064))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40400704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40402816)))];
            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = module_layers_1_self_attn_linear_out_bias_to_fp16, weight = module_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_87_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_16_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
            tensor<int32, [1]> x_39_axes_0 = const()[name = tensor<string, []>("x_39_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_1_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40404928)))];
            tensor<fp16, [1024]> module_layers_1_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40407040)))];
            tensor<fp16, [1, 188, 1024]> x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, beta = module_layers_1_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_conv_weight_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("x_39_cast_fp16")];
            tensor<int32, [3]> input_93_perm_0 = const()[name = tensor<string, []>("input_93_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_95_pad_type_0 = const()[name = tensor<string, []>("input_95_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_95_strides_0 = const()[name = tensor<string, []>("input_95_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_95_pad_0 = const()[name = tensor<string, []>("input_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_95_dilations_0 = const()[name = tensor<string, []>("input_95_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_95_groups_0 = const()[name = tensor<string, []>("input_95_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40409152))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42506368))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42510528)))];
            tensor<fp16, [1, 1024, 188]> input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_39_cast_fp16)[name = tensor<string, []>("transpose_300")];
            tensor<fp16, [1, 2048, 188]> input_95_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv1_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
            tensor<int32, []> x_41_split_num_splits_0 = const()[name = tensor<string, []>("x_41_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_41_split_axis_0 = const()[name = tensor<string, []>("x_41_split_axis_0"), val = tensor<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_95_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_41_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_97_cast_fp16 = select(a = var_11_to_fp16, b = x_41_cast_fp16, cond = var_335)[name = tensor<string, []>("input_97_cast_fp16")];
            tensor<int32, [6]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_99_mode_0 = const()[name = tensor<string, []>("input_99_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_27_to_fp16 = const()[name = tensor<string, []>("const_27_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_99_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_99_mode_0, pad = input_99_pad_0, x = input_97_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
            tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_101_groups_0 = const()[name = tensor<string, []>("input_101_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_101_strides_0 = const()[name = tensor<string, []>("input_101_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_101_dilations_0 = const()[name = tensor<string, []>("input_101_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_250_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_250_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42514688))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42523968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_251_to_fp16 = const()[name = tensor<string, []>("const_251_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42526080)))];
            tensor<fp16, [1, 1024, 188]> input_103_cast_fp16 = conv(bias = const_251_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_250_to_fp16_quantized, x = input_99_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_105_cast_fp16 = silu(x = input_103_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
            tensor<string, []> x_43_pad_type_0 = const()[name = tensor<string, []>("x_43_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_43_strides_0 = const()[name = tensor<string, []>("x_43_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_43_pad_0 = const()[name = tensor<string, []>("x_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_43_dilations_0 = const()[name = tensor<string, []>("x_43_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_43_groups_0 = const()[name = tensor<string, []>("x_43_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42528192))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43576832))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43578944)))];
            tensor<fp16, [1, 1024, 188]> x_43_cast_fp16 = conv(bias = module_layers_1_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = tensor<string, []>("x_43_cast_fp16")];
            tensor<int32, [3]> input_107_perm_0 = const()[name = tensor<string, []>("input_107_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_43_cast_fp16)[name = tensor<string, []>("transpose_299")];
            tensor<fp16, [1, 188, 1024]> input_109_cast_fp16 = add(x = input_91_cast_fp16, y = input_107_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
            tensor<int32, [1]> input_111_axes_0 = const()[name = tensor<string, []>("input_111_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43581056)))];
            tensor<fp16, [1024]> module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43583168)))];
            tensor<fp16, [1, 188, 1024]> input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43585280))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47779648))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47787904)))];
            tensor<fp16, [1, 188, 4096]> linear_17_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_115_cast_fp16 = silu(x = linear_17_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47796160))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51990528))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51992640)))];
            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")];
            tensor<fp16, []> var_551_to_fp16 = const()[name = tensor<string, []>("op_551_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_552_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_551_to_fp16)[name = tensor<string, []>("op_552_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_121_cast_fp16 = add(x = input_109_cast_fp16, y = var_552_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
            tensor<int32, [1]> input_123_axes_0 = const()[name = tensor<string, []>("input_123_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_1_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51994752)))];
            tensor<fp16, [1024]> module_layers_1_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51996864)))];
            tensor<fp16, [1, 188, 1024]> input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = module_layers_1_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm_out_weight_to_fp16, x = input_121_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
            tensor<int32, [1]> input_125_axes_0 = const()[name = tensor<string, []>("input_125_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(51998976)))];
            tensor<fp16, [1024]> module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52001088)))];
            tensor<fp16, [1, 188, 1024]> input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52003200))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56197568))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56205824)))];
            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = tensor<string, []>("linear_19_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_129_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56214080))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60408448))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60410560)))];
            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = tensor<string, []>("linear_20_cast_fp16")];
            tensor<fp16, []> var_582_to_fp16 = const()[name = tensor<string, []>("op_582_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_583_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_582_to_fp16)[name = tensor<string, []>("op_583_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_135_cast_fp16 = add(x = input_123_cast_fp16, y = var_583_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
            tensor<int32, [1]> query_5_axes_0 = const()[name = tensor<string, []>("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_2_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60412672)))];
            tensor<fp16, [1024]> module_layers_2_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60414784)))];
            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_self_att_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("query_5_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60416896))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61465536))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61467648)))];
            tensor<fp16, [1, 188, 1024]> linear_21_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_q_bias_to_fp16, weight = module_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")];
            tensor<int32, [4]> var_600 = const()[name = tensor<string, []>("op_600"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_600, x = linear_21_cast_fp16)[name = tensor<string, []>("q_13_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61469760))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62518400))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62520512)))];
            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_k_bias_to_fp16, weight = module_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")];
            tensor<int32, [4]> var_605 = const()[name = tensor<string, []>("op_605"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_605, x = linear_22_cast_fp16)[name = tensor<string, []>("k_9_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(62522624))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63571264))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63573376)))];
            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_v_bias_to_fp16, weight = module_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")];
            tensor<int32, [4]> var_610 = const()[name = tensor<string, []>("op_610"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_610, x = linear_23_cast_fp16)[name = tensor<string, []>("v_5_cast_fp16")];
            tensor<int32, [4]> value_7_perm_0 = const()[name = tensor<string, []>("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_2_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63575488))), scale = tensor<fp16, [8]>([0x1.39p-7, 0x1.a3p-8, 0x1.ed4p-9, 0x1.7d8p-8, 0x1.114p-7, 0x1.478p-7, 0x1.15cp-8, 0x1.63cp-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_622_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_622_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_2_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63576576))), scale = tensor<fp16, [8]>([0x1.4p-8, 0x1.0b4p-8, 0x1.508p-9, 0x1.388p-8, 0x1.214p-8, 0x1.1fp-8, 0x1.2ecp-9, 0x1.52cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_624_cast_fp16 = add(x = q_13_cast_fp16, y = module_layers_2_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_624_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_51_transpose_x_0 = const()[name = tensor<string, []>("x_51_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_51_transpose_y_0 = const()[name = tensor<string, []>("x_51_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_626_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_626_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63577664))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63961728))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_624_cast_fp16)[name = tensor<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_626_to_fp16_quantized)[name = tensor<string, []>("x_51_cast_fp16")];
            tensor<int32, [8]> x_53_pad_0 = const()[name = tensor<string, []>("x_53_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_53_mode_0 = const()[name = tensor<string, []>("x_53_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_34_to_fp16 = const()[name = tensor<string, []>("const_34_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_53_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = x_51_cast_fp16)[name = tensor<string, []>("x_53_cast_fp16")];
            tensor<int32, [4]> var_634 = const()[name = tensor<string, []>("op_634"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_55_cast_fp16 = reshape(shape = var_634, x = x_53_cast_fp16)[name = tensor<string, []>("x_55_cast_fp16")];
            tensor<int32, [4]> var_638_begin_0 = const()[name = tensor<string, []>("op_638_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_638_end_0 = const()[name = tensor<string, []>("op_638_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_638_end_mask_0 = const()[name = tensor<string, []>("op_638_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_638_cast_fp16 = slice_by_index(begin = var_638_begin_0, end = var_638_end_0, end_mask = var_638_end_mask_0, x = x_55_cast_fp16)[name = tensor<string, []>("op_638_cast_fp16")];
            tensor<int32, [4]> var_639 = const()[name = tensor<string, []>("op_639"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_639, x = var_638_cast_fp16)[name = tensor<string, []>("matrix_bd_9_cast_fp16")];
            tensor<bool, []> matrix_ac_5_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_5_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_100_perm_0 = const()[name = tensor<string, []>("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_101_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_296")];
            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_622_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_5_cast_fp16")];
            tensor<int32, [4]> matrix_bd_11_begin_0 = const()[name = tensor<string, []>("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_11_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_648_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = tensor<string, []>("op_648_cast_fp16")];
            tensor<fp16, []> _inversed_scores_9_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_9_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_648_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_9_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_11_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_654_cast_fp16 = softmax(axis = var_30, x = scores_11_cast_fp16)[name = tensor<string, []>("op_654_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_137_cast_fp16 = select(a = var_11_to_fp16, b = var_654_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_137_cast_fp16")];
            tensor<bool, []> x_57_transpose_x_0 = const()[name = tensor<string, []>("x_57_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_57_transpose_y_0 = const()[name = tensor<string, []>("x_57_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_5_cast_fp16)[name = tensor<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_137_cast_fp16, y = value_7_cast_fp16)[name = tensor<string, []>("x_57_cast_fp16")];
            tensor<int32, [4]> var_658_perm_0 = const()[name = tensor<string, []>("op_658_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_659 = const()[name = tensor<string, []>("op_659"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_658_cast_fp16 = transpose(perm = var_658_perm_0, x = x_57_cast_fp16)[name = tensor<string, []>("transpose_294")];
            tensor<fp16, [1, 188, 1024]> input_139_cast_fp16 = reshape(shape = var_659, x = var_658_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_2_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63962560))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65011200))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65013312)))];
            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = module_layers_2_self_attn_linear_out_bias_to_fp16, weight = module_layers_2_self_attn_linear_out_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = tensor<string, []>("linear_25_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_143_cast_fp16 = add(x = input_135_cast_fp16, y = linear_25_cast_fp16)[name = tensor<string, []>("input_143_cast_fp16")];
            tensor<int32, [1]> x_61_axes_0 = const()[name = tensor<string, []>("x_61_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_2_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65015424)))];
            tensor<fp16, [1024]> module_layers_2_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65017536)))];
            tensor<fp16, [1, 188, 1024]> x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, beta = module_layers_2_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_conv_weight_to_fp16, x = input_143_cast_fp16)[name = tensor<string, []>("x_61_cast_fp16")];
            tensor<int32, [3]> input_145_perm_0 = const()[name = tensor<string, []>("input_145_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_147_pad_type_0 = const()[name = tensor<string, []>("input_147_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_147_strides_0 = const()[name = tensor<string, []>("input_147_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_147_pad_0 = const()[name = tensor<string, []>("input_147_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_147_dilations_0 = const()[name = tensor<string, []>("input_147_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_147_groups_0 = const()[name = tensor<string, []>("input_147_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65019648))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67116864))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67121024)))];
            tensor<fp16, [1, 1024, 188]> input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_61_cast_fp16)[name = tensor<string, []>("transpose_293")];
            tensor<fp16, [1, 2048, 188]> input_147_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
            tensor<int32, []> x_63_split_num_splits_0 = const()[name = tensor<string, []>("x_63_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_63_split_axis_0 = const()[name = tensor<string, []>("x_63_split_axis_0"), val = tensor<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_147_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_63_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_149_cast_fp16 = select(a = var_11_to_fp16, b = x_63_cast_fp16, cond = var_335)[name = tensor<string, []>("input_149_cast_fp16")];
            tensor<int32, [6]> input_151_pad_0 = const()[name = tensor<string, []>("input_151_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_151_mode_0 = const()[name = tensor<string, []>("input_151_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_37_to_fp16 = const()[name = tensor<string, []>("const_37_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_151_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_151_mode_0, pad = input_151_pad_0, x = input_149_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
            tensor<string, []> input_153_pad_type_0 = const()[name = tensor<string, []>("input_153_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_153_groups_0 = const()[name = tensor<string, []>("input_153_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_153_strides_0 = const()[name = tensor<string, []>("input_153_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_153_pad_0 = const()[name = tensor<string, []>("input_153_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_153_dilations_0 = const()[name = tensor<string, []>("input_153_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_252_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_252_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67125184))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67134464))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_253_to_fp16 = const()[name = tensor<string, []>("const_253_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67136576)))];
            tensor<fp16, [1, 1024, 188]> input_155_cast_fp16 = conv(bias = const_253_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = const_252_to_fp16_quantized, x = input_151_cast_fp16)[name = tensor<string, []>("input_155_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_157_cast_fp16 = silu(x = input_155_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")];
            tensor<string, []> x_65_pad_type_0 = const()[name = tensor<string, []>("x_65_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_65_strides_0 = const()[name = tensor<string, []>("x_65_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_65_pad_0 = const()[name = tensor<string, []>("x_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_65_dilations_0 = const()[name = tensor<string, []>("x_65_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_65_groups_0 = const()[name = tensor<string, []>("x_65_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67138688))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68187328))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68189440)))];
            tensor<fp16, [1, 1024, 188]> x_65_cast_fp16 = conv(bias = module_layers_2_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_157_cast_fp16)[name = tensor<string, []>("x_65_cast_fp16")];
            tensor<int32, [3]> input_159_perm_0 = const()[name = tensor<string, []>("input_159_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_65_cast_fp16)[name = tensor<string, []>("transpose_292")];
            tensor<fp16, [1, 188, 1024]> input_161_cast_fp16 = add(x = input_143_cast_fp16, y = input_159_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")];
            tensor<int32, [1]> input_163_axes_0 = const()[name = tensor<string, []>("input_163_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68191552)))];
            tensor<fp16, [1024]> module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68193664)))];
            tensor<fp16, [1, 188, 1024]> input_163_cast_fp16 = layer_norm(axes = input_163_axes_0, beta = module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68195776))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72390144))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72398400)))];
            tensor<fp16, [1, 188, 4096]> linear_26_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized, x = input_163_cast_fp16)[name = tensor<string, []>("linear_26_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_167_cast_fp16 = silu(x = linear_26_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72406656))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76601024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76603136)))];
            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized, x = input_167_cast_fp16)[name = tensor<string, []>("linear_27_cast_fp16")];
            tensor<fp16, []> var_725_to_fp16 = const()[name = tensor<string, []>("op_725_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_726_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_725_to_fp16)[name = tensor<string, []>("op_726_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_173_cast_fp16 = add(x = input_161_cast_fp16, y = var_726_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")];
            tensor<int32, [1]> input_175_axes_0 = const()[name = tensor<string, []>("input_175_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_2_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76605248)))];
            tensor<fp16, [1024]> module_layers_2_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76607360)))];
            tensor<fp16, [1, 188, 1024]> input_175_cast_fp16 = layer_norm(axes = input_175_axes_0, beta = module_layers_2_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm_out_weight_to_fp16, x = input_173_cast_fp16)[name = tensor<string, []>("input_175_cast_fp16")];
            tensor<int32, [1]> input_177_axes_0 = const()[name = tensor<string, []>("input_177_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76609472)))];
            tensor<fp16, [1024]> module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76611584)))];
            tensor<fp16, [1, 188, 1024]> input_177_cast_fp16 = layer_norm(axes = input_177_axes_0, beta = module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76613696))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80808064))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80816320)))];
            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized, x = input_177_cast_fp16)[name = tensor<string, []>("linear_28_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_181_cast_fp16 = silu(x = linear_28_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80824576))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85018944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85021056)))];
            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized, x = input_181_cast_fp16)[name = tensor<string, []>("linear_29_cast_fp16")];
            tensor<fp16, []> var_756_to_fp16 = const()[name = tensor<string, []>("op_756_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_757_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_756_to_fp16)[name = tensor<string, []>("op_757_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_187_cast_fp16 = add(x = input_175_cast_fp16, y = var_757_cast_fp16)[name = tensor<string, []>("input_187_cast_fp16")];
            tensor<int32, [1]> query_7_axes_0 = const()[name = tensor<string, []>("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_3_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85023168)))];
            tensor<fp16, [1024]> module_layers_3_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85025280)))];
            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_self_att_weight_to_fp16, x = input_187_cast_fp16)[name = tensor<string, []>("query_7_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85027392))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86076032))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86078144)))];
            tensor<fp16, [1, 188, 1024]> linear_30_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_q_bias_to_fp16, weight = module_layers_3_self_attn_linear_q_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = tensor<string, []>("linear_30_cast_fp16")];
            tensor<int32, [4]> var_774 = const()[name = tensor<string, []>("op_774"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_774, x = linear_30_cast_fp16)[name = tensor<string, []>("q_19_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86080256))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87128896))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87131008)))];
            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_k_bias_to_fp16, weight = module_layers_3_self_attn_linear_k_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = tensor<string, []>("linear_31_cast_fp16")];
            tensor<int32, [4]> var_779 = const()[name = tensor<string, []>("op_779"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_779, x = linear_31_cast_fp16)[name = tensor<string, []>("k_13_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(87133120))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88181760))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88183872)))];
            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_v_bias_to_fp16, weight = module_layers_3_self_attn_linear_v_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = tensor<string, []>("linear_32_cast_fp16")];
            tensor<int32, [4]> var_784 = const()[name = tensor<string, []>("op_784"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_784, x = linear_32_cast_fp16)[name = tensor<string, []>("v_7_cast_fp16")];
            tensor<int32, [4]> value_9_perm_0 = const()[name = tensor<string, []>("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_3_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88185984))), scale = tensor<fp16, [8]>([0x1.6e8p-8, 0x1.d84p-8, 0x1.f5p-8, 0x1.83p-9, 0x1.39cp-8, 0x1.47cp-8, 0x1.4ap-8, 0x1.29cp-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_796_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_796_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_3_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88187072))), scale = tensor<fp16, [8]>([0x1.048p-8, 0x1.1b8p-9, 0x1.bf4p-9, 0x1.8f8p-9, 0x1.84cp-8, 0x1.468p-9, 0x1.8c4p-9, 0x1.72p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_798_cast_fp16 = add(x = q_19_cast_fp16, y = module_layers_3_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_798_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_73_transpose_x_0 = const()[name = tensor<string, []>("x_73_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_73_transpose_y_0 = const()[name = tensor<string, []>("x_73_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_800_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_800_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88188160))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88572224))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_798_cast_fp16)[name = tensor<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_800_to_fp16_quantized)[name = tensor<string, []>("x_73_cast_fp16")];
            tensor<int32, [8]> x_75_pad_0 = const()[name = tensor<string, []>("x_75_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_75_mode_0 = const()[name = tensor<string, []>("x_75_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_44_to_fp16 = const()[name = tensor<string, []>("const_44_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_75_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = x_73_cast_fp16)[name = tensor<string, []>("x_75_cast_fp16")];
            tensor<int32, [4]> var_808 = const()[name = tensor<string, []>("op_808"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_77_cast_fp16 = reshape(shape = var_808, x = x_75_cast_fp16)[name = tensor<string, []>("x_77_cast_fp16")];
            tensor<int32, [4]> var_812_begin_0 = const()[name = tensor<string, []>("op_812_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_812_end_0 = const()[name = tensor<string, []>("op_812_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_812_end_mask_0 = const()[name = tensor<string, []>("op_812_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_812_cast_fp16 = slice_by_index(begin = var_812_begin_0, end = var_812_end_0, end_mask = var_812_end_mask_0, x = x_77_cast_fp16)[name = tensor<string, []>("op_812_cast_fp16")];
            tensor<int32, [4]> var_813 = const()[name = tensor<string, []>("op_813"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_813, x = var_812_cast_fp16)[name = tensor<string, []>("matrix_bd_13_cast_fp16")];
            tensor<bool, []> matrix_ac_7_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_7_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_102_perm_0 = const()[name = tensor<string, []>("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_103_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_289")];
            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_796_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_7_cast_fp16")];
            tensor<int32, [4]> matrix_bd_15_begin_0 = const()[name = tensor<string, []>("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_15_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_822_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = tensor<string, []>("op_822_cast_fp16")];
            tensor<fp16, []> _inversed_scores_13_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_13_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_822_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_13_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_15_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_828_cast_fp16 = softmax(axis = var_30, x = scores_15_cast_fp16)[name = tensor<string, []>("op_828_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_189_cast_fp16 = select(a = var_11_to_fp16, b = var_828_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_189_cast_fp16")];
            tensor<bool, []> x_79_transpose_x_0 = const()[name = tensor<string, []>("x_79_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_79_transpose_y_0 = const()[name = tensor<string, []>("x_79_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_7_cast_fp16)[name = tensor<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_189_cast_fp16, y = value_9_cast_fp16)[name = tensor<string, []>("x_79_cast_fp16")];
            tensor<int32, [4]> var_832_perm_0 = const()[name = tensor<string, []>("op_832_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_833 = const()[name = tensor<string, []>("op_833"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_832_cast_fp16 = transpose(perm = var_832_perm_0, x = x_79_cast_fp16)[name = tensor<string, []>("transpose_287")];
            tensor<fp16, [1, 188, 1024]> input_191_cast_fp16 = reshape(shape = var_833, x = var_832_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_3_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88573056))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89621696))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89623808)))];
            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = module_layers_3_self_attn_linear_out_bias_to_fp16, weight = module_layers_3_self_attn_linear_out_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = tensor<string, []>("linear_34_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_34_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")];
            tensor<int32, [1]> x_83_axes_0 = const()[name = tensor<string, []>("x_83_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_3_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89625920)))];
            tensor<fp16, [1024]> module_layers_3_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89628032)))];
            tensor<fp16, [1, 188, 1024]> x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = module_layers_3_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_conv_weight_to_fp16, x = input_195_cast_fp16)[name = tensor<string, []>("x_83_cast_fp16")];
            tensor<int32, [3]> input_197_perm_0 = const()[name = tensor<string, []>("input_197_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_199_pad_type_0 = const()[name = tensor<string, []>("input_199_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_199_strides_0 = const()[name = tensor<string, []>("input_199_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_199_dilations_0 = const()[name = tensor<string, []>("input_199_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_199_groups_0 = const()[name = tensor<string, []>("input_199_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89630144))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91727360))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91731520)))];
            tensor<fp16, [1, 1024, 188]> input_197_cast_fp16 = transpose(perm = input_197_perm_0, x = x_83_cast_fp16)[name = tensor<string, []>("transpose_286")];
            tensor<fp16, [1, 2048, 188]> input_199_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv1_bias_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_197_cast_fp16)[name = tensor<string, []>("input_199_cast_fp16")];
            tensor<int32, []> x_85_split_num_splits_0 = const()[name = tensor<string, []>("x_85_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_85_split_axis_0 = const()[name = tensor<string, []>("x_85_split_axis_0"), val = tensor<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_199_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_85_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_201_cast_fp16 = select(a = var_11_to_fp16, b = x_85_cast_fp16, cond = var_335)[name = tensor<string, []>("input_201_cast_fp16")];
            tensor<int32, [6]> input_203_pad_0 = const()[name = tensor<string, []>("input_203_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_203_mode_0 = const()[name = tensor<string, []>("input_203_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_47_to_fp16 = const()[name = tensor<string, []>("const_47_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_203_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = input_203_mode_0, pad = input_203_pad_0, x = input_201_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
            tensor<string, []> input_205_pad_type_0 = const()[name = tensor<string, []>("input_205_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_205_groups_0 = const()[name = tensor<string, []>("input_205_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_205_strides_0 = const()[name = tensor<string, []>("input_205_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_205_pad_0 = const()[name = tensor<string, []>("input_205_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_205_dilations_0 = const()[name = tensor<string, []>("input_205_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_254_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_254_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91735680))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91744960))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_255_to_fp16 = const()[name = tensor<string, []>("const_255_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91747072)))];
            tensor<fp16, [1, 1024, 188]> input_207_cast_fp16 = conv(bias = const_255_to_fp16, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = const_254_to_fp16_quantized, x = input_203_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_209_cast_fp16 = silu(x = input_207_cast_fp16)[name = tensor<string, []>("input_209_cast_fp16")];
            tensor<string, []> x_87_pad_type_0 = const()[name = tensor<string, []>("x_87_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_87_strides_0 = const()[name = tensor<string, []>("x_87_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_87_pad_0 = const()[name = tensor<string, []>("x_87_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_87_dilations_0 = const()[name = tensor<string, []>("x_87_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_87_groups_0 = const()[name = tensor<string, []>("x_87_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91749184))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92797824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92799936)))];
            tensor<fp16, [1, 1024, 188]> x_87_cast_fp16 = conv(bias = module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_209_cast_fp16)[name = tensor<string, []>("x_87_cast_fp16")];
            tensor<int32, [3]> input_211_perm_0 = const()[name = tensor<string, []>("input_211_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_211_cast_fp16 = transpose(perm = input_211_perm_0, x = x_87_cast_fp16)[name = tensor<string, []>("transpose_285")];
            tensor<fp16, [1, 188, 1024]> input_213_cast_fp16 = add(x = input_195_cast_fp16, y = input_211_cast_fp16)[name = tensor<string, []>("input_213_cast_fp16")];
            tensor<int32, [1]> input_215_axes_0 = const()[name = tensor<string, []>("input_215_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92802048)))];
            tensor<fp16, [1024]> module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92804160)))];
            tensor<fp16, [1, 188, 1024]> input_215_cast_fp16 = layer_norm(axes = input_215_axes_0, beta = module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_213_cast_fp16)[name = tensor<string, []>("input_215_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92806272))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97000640))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97008896)))];
            tensor<fp16, [1, 188, 4096]> linear_35_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = tensor<string, []>("linear_35_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_219_cast_fp16 = silu(x = linear_35_cast_fp16)[name = tensor<string, []>("input_219_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97017152))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101211520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101213632)))];
            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized, x = input_219_cast_fp16)[name = tensor<string, []>("linear_36_cast_fp16")];
            tensor<fp16, []> var_899_to_fp16 = const()[name = tensor<string, []>("op_899_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_900_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_899_to_fp16)[name = tensor<string, []>("op_900_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_225_cast_fp16 = add(x = input_213_cast_fp16, y = var_900_cast_fp16)[name = tensor<string, []>("input_225_cast_fp16")];
            tensor<int32, [1]> input_227_axes_0 = const()[name = tensor<string, []>("input_227_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_3_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101215744)))];
            tensor<fp16, [1024]> module_layers_3_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101217856)))];
            tensor<fp16, [1, 188, 1024]> input_227_cast_fp16 = layer_norm(axes = input_227_axes_0, beta = module_layers_3_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm_out_weight_to_fp16, x = input_225_cast_fp16)[name = tensor<string, []>("input_227_cast_fp16")];
            tensor<int32, [1]> input_229_axes_0 = const()[name = tensor<string, []>("input_229_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101219968)))];
            tensor<fp16, [1024]> module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101222080)))];
            tensor<fp16, [1, 188, 1024]> input_229_cast_fp16 = layer_norm(axes = input_229_axes_0, beta = module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101224192))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105418560))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105426816)))];
            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized, x = input_229_cast_fp16)[name = tensor<string, []>("linear_37_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_233_cast_fp16 = silu(x = linear_37_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105435072))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109629440))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109631552)))];
            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized, x = input_233_cast_fp16)[name = tensor<string, []>("linear_38_cast_fp16")];
            tensor<fp16, []> var_930_to_fp16 = const()[name = tensor<string, []>("op_930_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_931_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_930_to_fp16)[name = tensor<string, []>("op_931_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_239_cast_fp16 = add(x = input_227_cast_fp16, y = var_931_cast_fp16)[name = tensor<string, []>("input_239_cast_fp16")];
            tensor<int32, [1]> query_9_axes_0 = const()[name = tensor<string, []>("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_4_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109633664)))];
            tensor<fp16, [1024]> module_layers_4_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109635776)))];
            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_self_att_weight_to_fp16, x = input_239_cast_fp16)[name = tensor<string, []>("query_9_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109637888))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110686528))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110688640)))];
            tensor<fp16, [1, 188, 1024]> linear_39_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_q_bias_to_fp16, weight = module_layers_4_self_attn_linear_q_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = tensor<string, []>("linear_39_cast_fp16")];
            tensor<int32, [4]> var_948 = const()[name = tensor<string, []>("op_948"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_948, x = linear_39_cast_fp16)[name = tensor<string, []>("q_25_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110690752))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111739392))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111741504)))];
            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_k_bias_to_fp16, weight = module_layers_4_self_attn_linear_k_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = tensor<string, []>("linear_40_cast_fp16")];
            tensor<int32, [4]> var_953 = const()[name = tensor<string, []>("op_953"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_953, x = linear_40_cast_fp16)[name = tensor<string, []>("k_17_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111743616))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112792256))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112794368)))];
            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_v_bias_to_fp16, weight = module_layers_4_self_attn_linear_v_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = tensor<string, []>("linear_41_cast_fp16")];
            tensor<int32, [4]> var_958 = const()[name = tensor<string, []>("op_958"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_958, x = linear_41_cast_fp16)[name = tensor<string, []>("v_9_cast_fp16")];
            tensor<int32, [4]> value_11_perm_0 = const()[name = tensor<string, []>("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_4_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112796480))), scale = tensor<fp16, [8]>([0x1.a5cp-8, 0x1.62p-8, 0x1.6b8p-8, 0x1.ff8p-8, 0x1.804p-7, 0x1.92cp-8, 0x1.5fp-8, 0x1.ea4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_970_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_970_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_4_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112797568))), scale = tensor<fp16, [8]>([0x1.15cp-8, 0x1.01cp-8, 0x1.7ep-8, 0x1.23p-9, 0x1.218p-7, 0x1.4fcp-8, 0x1.114p-9, 0x1.73cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_972_cast_fp16 = add(x = q_25_cast_fp16, y = module_layers_4_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_972_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_95_transpose_x_0 = const()[name = tensor<string, []>("x_95_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_95_transpose_y_0 = const()[name = tensor<string, []>("x_95_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_974_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_974_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112798656))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113182720))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_972_cast_fp16)[name = tensor<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_974_to_fp16_quantized)[name = tensor<string, []>("x_95_cast_fp16")];
            tensor<int32, [8]> x_97_pad_0 = const()[name = tensor<string, []>("x_97_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_97_mode_0 = const()[name = tensor<string, []>("x_97_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_54_to_fp16 = const()[name = tensor<string, []>("const_54_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_97_cast_fp16 = pad(constant_val = const_54_to_fp16, mode = x_97_mode_0, pad = x_97_pad_0, x = x_95_cast_fp16)[name = tensor<string, []>("x_97_cast_fp16")];
            tensor<int32, [4]> var_982 = const()[name = tensor<string, []>("op_982"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_99_cast_fp16 = reshape(shape = var_982, x = x_97_cast_fp16)[name = tensor<string, []>("x_99_cast_fp16")];
            tensor<int32, [4]> var_986_begin_0 = const()[name = tensor<string, []>("op_986_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_986_end_0 = const()[name = tensor<string, []>("op_986_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_986_end_mask_0 = const()[name = tensor<string, []>("op_986_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_986_cast_fp16 = slice_by_index(begin = var_986_begin_0, end = var_986_end_0, end_mask = var_986_end_mask_0, x = x_99_cast_fp16)[name = tensor<string, []>("op_986_cast_fp16")];
            tensor<int32, [4]> var_987 = const()[name = tensor<string, []>("op_987"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_987, x = var_986_cast_fp16)[name = tensor<string, []>("matrix_bd_17_cast_fp16")];
            tensor<bool, []> matrix_ac_9_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_9_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_9_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_104_perm_0 = const()[name = tensor<string, []>("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_105_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_282")];
            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_970_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_9_cast_fp16")];
            tensor<int32, [4]> matrix_bd_19_begin_0 = const()[name = tensor<string, []>("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_19_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_996_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = tensor<string, []>("op_996_cast_fp16")];
            tensor<fp16, []> _inversed_scores_17_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_17_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_996_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_17_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_19_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1002_cast_fp16 = softmax(axis = var_30, x = scores_19_cast_fp16)[name = tensor<string, []>("op_1002_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_241_cast_fp16 = select(a = var_11_to_fp16, b = var_1002_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_241_cast_fp16")];
            tensor<bool, []> x_101_transpose_x_0 = const()[name = tensor<string, []>("x_101_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_101_transpose_y_0 = const()[name = tensor<string, []>("x_101_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_9_cast_fp16)[name = tensor<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_241_cast_fp16, y = value_11_cast_fp16)[name = tensor<string, []>("x_101_cast_fp16")];
            tensor<int32, [4]> var_1006_perm_0 = const()[name = tensor<string, []>("op_1006_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1007 = const()[name = tensor<string, []>("op_1007"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1006_cast_fp16 = transpose(perm = var_1006_perm_0, x = x_101_cast_fp16)[name = tensor<string, []>("transpose_280")];
            tensor<fp16, [1, 188, 1024]> input_243_cast_fp16 = reshape(shape = var_1007, x = var_1006_cast_fp16)[name = tensor<string, []>("input_243_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_4_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113183552))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114232192))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114234304)))];
            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = module_layers_4_self_attn_linear_out_bias_to_fp16, weight = module_layers_4_self_attn_linear_out_weight_to_fp16_quantized, x = input_243_cast_fp16)[name = tensor<string, []>("linear_43_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_247_cast_fp16 = add(x = input_239_cast_fp16, y = linear_43_cast_fp16)[name = tensor<string, []>("input_247_cast_fp16")];
            tensor<int32, [1]> x_105_axes_0 = const()[name = tensor<string, []>("x_105_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_4_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114236416)))];
            tensor<fp16, [1024]> module_layers_4_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114238528)))];
            tensor<fp16, [1, 188, 1024]> x_105_cast_fp16 = layer_norm(axes = x_105_axes_0, beta = module_layers_4_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_conv_weight_to_fp16, x = input_247_cast_fp16)[name = tensor<string, []>("x_105_cast_fp16")];
            tensor<int32, [3]> input_249_perm_0 = const()[name = tensor<string, []>("input_249_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_251_pad_type_0 = const()[name = tensor<string, []>("input_251_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_251_strides_0 = const()[name = tensor<string, []>("input_251_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_251_pad_0 = const()[name = tensor<string, []>("input_251_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_251_dilations_0 = const()[name = tensor<string, []>("input_251_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_251_groups_0 = const()[name = tensor<string, []>("input_251_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114240640))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116337856))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116342016)))];
            tensor<fp16, [1, 1024, 188]> input_249_cast_fp16 = transpose(perm = input_249_perm_0, x = x_105_cast_fp16)[name = tensor<string, []>("transpose_279")];
            tensor<fp16, [1, 2048, 188]> input_251_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv1_bias_to_fp16, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_249_cast_fp16)[name = tensor<string, []>("input_251_cast_fp16")];
            tensor<int32, []> x_107_split_num_splits_0 = const()[name = tensor<string, []>("x_107_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_107_split_axis_0 = const()[name = tensor<string, []>("x_107_split_axis_0"), val = tensor<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_251_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_107_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_253_cast_fp16 = select(a = var_11_to_fp16, b = x_107_cast_fp16, cond = var_335)[name = tensor<string, []>("input_253_cast_fp16")];
            tensor<int32, [6]> input_255_pad_0 = const()[name = tensor<string, []>("input_255_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_255_mode_0 = const()[name = tensor<string, []>("input_255_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_57_to_fp16 = const()[name = tensor<string, []>("const_57_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_255_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_255_mode_0, pad = input_255_pad_0, x = input_253_cast_fp16)[name = tensor<string, []>("input_255_cast_fp16")];
            tensor<string, []> input_257_pad_type_0 = const()[name = tensor<string, []>("input_257_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_257_groups_0 = const()[name = tensor<string, []>("input_257_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_257_strides_0 = const()[name = tensor<string, []>("input_257_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_257_pad_0 = const()[name = tensor<string, []>("input_257_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_257_dilations_0 = const()[name = tensor<string, []>("input_257_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_256_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_256_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116346176))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116355456))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_257_to_fp16 = const()[name = tensor<string, []>("const_257_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116357568)))];
            tensor<fp16, [1, 1024, 188]> input_259_cast_fp16 = conv(bias = const_257_to_fp16, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = const_256_to_fp16_quantized, x = input_255_cast_fp16)[name = tensor<string, []>("input_259_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_261_cast_fp16 = silu(x = input_259_cast_fp16)[name = tensor<string, []>("input_261_cast_fp16")];
            tensor<string, []> x_109_pad_type_0 = const()[name = tensor<string, []>("x_109_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_109_strides_0 = const()[name = tensor<string, []>("x_109_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_109_pad_0 = const()[name = tensor<string, []>("x_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_109_dilations_0 = const()[name = tensor<string, []>("x_109_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_109_groups_0 = const()[name = tensor<string, []>("x_109_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(116359680))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117408320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117410432)))];
            tensor<fp16, [1, 1024, 188]> x_109_cast_fp16 = conv(bias = module_layers_4_conv_pointwise_conv2_bias_to_fp16, dilations = x_109_dilations_0, groups = x_109_groups_0, pad = x_109_pad_0, pad_type = x_109_pad_type_0, strides = x_109_strides_0, weight = module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_261_cast_fp16)[name = tensor<string, []>("x_109_cast_fp16")];
            tensor<int32, [3]> input_263_perm_0 = const()[name = tensor<string, []>("input_263_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_263_cast_fp16 = transpose(perm = input_263_perm_0, x = x_109_cast_fp16)[name = tensor<string, []>("transpose_278")];
            tensor<fp16, [1, 188, 1024]> input_265_cast_fp16 = add(x = input_247_cast_fp16, y = input_263_cast_fp16)[name = tensor<string, []>("input_265_cast_fp16")];
            tensor<int32, [1]> input_267_axes_0 = const()[name = tensor<string, []>("input_267_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117412544)))];
            tensor<fp16, [1024]> module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117414656)))];
            tensor<fp16, [1, 188, 1024]> input_267_cast_fp16 = layer_norm(axes = input_267_axes_0, beta = module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor<string, []>("input_267_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117416768))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121611136))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121619392)))];
            tensor<fp16, [1, 188, 4096]> linear_44_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = tensor<string, []>("linear_44_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_271_cast_fp16 = silu(x = linear_44_cast_fp16)[name = tensor<string, []>("input_271_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121627648))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125822016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125824128)))];
            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = tensor<string, []>("linear_45_cast_fp16")];
            tensor<fp16, []> var_1073_to_fp16 = const()[name = tensor<string, []>("op_1073_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1074_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1073_to_fp16)[name = tensor<string, []>("op_1074_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_277_cast_fp16 = add(x = input_265_cast_fp16, y = var_1074_cast_fp16)[name = tensor<string, []>("input_277_cast_fp16")];
            tensor<int32, [1]> input_279_axes_0 = const()[name = tensor<string, []>("input_279_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_4_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125826240)))];
            tensor<fp16, [1024]> module_layers_4_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125828352)))];
            tensor<fp16, [1, 188, 1024]> input_279_cast_fp16 = layer_norm(axes = input_279_axes_0, beta = module_layers_4_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm_out_weight_to_fp16, x = input_277_cast_fp16)[name = tensor<string, []>("input_279_cast_fp16")];
            tensor<int32, [1]> input_281_axes_0 = const()[name = tensor<string, []>("input_281_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125830464)))];
            tensor<fp16, [1024]> module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125832576)))];
            tensor<fp16, [1, 188, 1024]> input_281_cast_fp16 = layer_norm(axes = input_281_axes_0, beta = module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_279_cast_fp16)[name = tensor<string, []>("input_281_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125834688))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130029056))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130037312)))];
            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized, x = input_281_cast_fp16)[name = tensor<string, []>("linear_46_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_285_cast_fp16 = silu(x = linear_46_cast_fp16)[name = tensor<string, []>("input_285_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130045568))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134239936))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134242048)))];
            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = tensor<string, []>("linear_47_cast_fp16")];
            tensor<fp16, []> var_1104_to_fp16 = const()[name = tensor<string, []>("op_1104_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1105_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1104_to_fp16)[name = tensor<string, []>("op_1105_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_291_cast_fp16 = add(x = input_279_cast_fp16, y = var_1105_cast_fp16)[name = tensor<string, []>("input_291_cast_fp16")];
            tensor<int32, [1]> query_11_axes_0 = const()[name = tensor<string, []>("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_5_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134244160)))];
            tensor<fp16, [1024]> module_layers_5_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134246272)))];
            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_self_att_weight_to_fp16, x = input_291_cast_fp16)[name = tensor<string, []>("query_11_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134248384))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135297024))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135299136)))];
            tensor<fp16, [1, 188, 1024]> linear_48_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_q_bias_to_fp16, weight = module_layers_5_self_attn_linear_q_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = tensor<string, []>("linear_48_cast_fp16")];
            tensor<int32, [4]> var_1122 = const()[name = tensor<string, []>("op_1122"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1122, x = linear_48_cast_fp16)[name = tensor<string, []>("q_31_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135301248))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136349888))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136352000)))];
            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_k_bias_to_fp16, weight = module_layers_5_self_attn_linear_k_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = tensor<string, []>("linear_49_cast_fp16")];
            tensor<int32, [4]> var_1127 = const()[name = tensor<string, []>("op_1127"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1127, x = linear_49_cast_fp16)[name = tensor<string, []>("k_21_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136354112))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137402752))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137404864)))];
            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_v_bias_to_fp16, weight = module_layers_5_self_attn_linear_v_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = tensor<string, []>("linear_50_cast_fp16")];
            tensor<int32, [4]> var_1132 = const()[name = tensor<string, []>("op_1132"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1132, x = linear_50_cast_fp16)[name = tensor<string, []>("v_11_cast_fp16")];
            tensor<int32, [4]> value_13_perm_0 = const()[name = tensor<string, []>("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_5_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137406976))), scale = tensor<fp16, [8]>([0x1.a28p-8, 0x1.d9p-9, 0x1.248p-7, 0x1.168p-7, 0x1.c5p-8, 0x1.068p-7, 0x1.21cp-7, 0x1.51p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1144_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1144_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_5_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137408064))), scale = tensor<fp16, [8]>([0x1.60cp-9, 0x1.064p-9, 0x1.75p-9, 0x1.3c4p-8, 0x1.e84p-9, 0x1.ccp-9, 0x1.d58p-10, 0x1.a14p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1146_cast_fp16 = add(x = q_31_cast_fp16, y = module_layers_5_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1146_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_117_transpose_x_0 = const()[name = tensor<string, []>("x_117_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_117_transpose_y_0 = const()[name = tensor<string, []>("x_117_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1148_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1148_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137409152))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137793216))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1146_cast_fp16)[name = tensor<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_1148_to_fp16_quantized)[name = tensor<string, []>("x_117_cast_fp16")];
            tensor<int32, [8]> x_119_pad_0 = const()[name = tensor<string, []>("x_119_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_119_mode_0 = const()[name = tensor<string, []>("x_119_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_64_to_fp16 = const()[name = tensor<string, []>("const_64_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_119_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = x_117_cast_fp16)[name = tensor<string, []>("x_119_cast_fp16")];
            tensor<int32, [4]> var_1156 = const()[name = tensor<string, []>("op_1156"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_121_cast_fp16 = reshape(shape = var_1156, x = x_119_cast_fp16)[name = tensor<string, []>("x_121_cast_fp16")];
            tensor<int32, [4]> var_1160_begin_0 = const()[name = tensor<string, []>("op_1160_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1160_end_0 = const()[name = tensor<string, []>("op_1160_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1160_end_mask_0 = const()[name = tensor<string, []>("op_1160_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1160_cast_fp16 = slice_by_index(begin = var_1160_begin_0, end = var_1160_end_0, end_mask = var_1160_end_mask_0, x = x_121_cast_fp16)[name = tensor<string, []>("op_1160_cast_fp16")];
            tensor<int32, [4]> var_1161 = const()[name = tensor<string, []>("op_1161"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1161, x = var_1160_cast_fp16)[name = tensor<string, []>("matrix_bd_21_cast_fp16")];
            tensor<bool, []> matrix_ac_11_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_11_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_11_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_11_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_106_perm_0 = const()[name = tensor<string, []>("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_107_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_275")];
            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1144_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_11_cast_fp16")];
            tensor<int32, [4]> matrix_bd_23_begin_0 = const()[name = tensor<string, []>("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_23_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1170_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = tensor<string, []>("op_1170_cast_fp16")];
            tensor<fp16, []> _inversed_scores_21_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_21_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1170_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_21_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_23_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1176_cast_fp16 = softmax(axis = var_30, x = scores_23_cast_fp16)[name = tensor<string, []>("op_1176_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_293_cast_fp16 = select(a = var_11_to_fp16, b = var_1176_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_293_cast_fp16")];
            tensor<bool, []> x_123_transpose_x_0 = const()[name = tensor<string, []>("x_123_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_123_transpose_y_0 = const()[name = tensor<string, []>("x_123_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_11_cast_fp16)[name = tensor<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_293_cast_fp16, y = value_13_cast_fp16)[name = tensor<string, []>("x_123_cast_fp16")];
            tensor<int32, [4]> var_1180_perm_0 = const()[name = tensor<string, []>("op_1180_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1181 = const()[name = tensor<string, []>("op_1181"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1180_cast_fp16 = transpose(perm = var_1180_perm_0, x = x_123_cast_fp16)[name = tensor<string, []>("transpose_273")];
            tensor<fp16, [1, 188, 1024]> input_295_cast_fp16 = reshape(shape = var_1181, x = var_1180_cast_fp16)[name = tensor<string, []>("input_295_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_5_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137794048))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138842688))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138844800)))];
            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = module_layers_5_self_attn_linear_out_bias_to_fp16, weight = module_layers_5_self_attn_linear_out_weight_to_fp16_quantized, x = input_295_cast_fp16)[name = tensor<string, []>("linear_52_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_52_cast_fp16)[name = tensor<string, []>("input_299_cast_fp16")];
            tensor<int32, [1]> x_127_axes_0 = const()[name = tensor<string, []>("x_127_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_5_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138846912)))];
            tensor<fp16, [1024]> module_layers_5_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138849024)))];
            tensor<fp16, [1, 188, 1024]> x_127_cast_fp16 = layer_norm(axes = x_127_axes_0, beta = module_layers_5_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_conv_weight_to_fp16, x = input_299_cast_fp16)[name = tensor<string, []>("x_127_cast_fp16")];
            tensor<int32, [3]> input_301_perm_0 = const()[name = tensor<string, []>("input_301_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_303_pad_type_0 = const()[name = tensor<string, []>("input_303_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_303_strides_0 = const()[name = tensor<string, []>("input_303_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_303_pad_0 = const()[name = tensor<string, []>("input_303_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_303_dilations_0 = const()[name = tensor<string, []>("input_303_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_303_groups_0 = const()[name = tensor<string, []>("input_303_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138851136))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140948352))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140952512)))];
            tensor<fp16, [1, 1024, 188]> input_301_cast_fp16 = transpose(perm = input_301_perm_0, x = x_127_cast_fp16)[name = tensor<string, []>("transpose_272")];
            tensor<fp16, [1, 2048, 188]> input_303_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv1_bias_to_fp16, dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = module_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_301_cast_fp16)[name = tensor<string, []>("input_303_cast_fp16")];
            tensor<int32, []> x_129_split_num_splits_0 = const()[name = tensor<string, []>("x_129_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_129_split_axis_0 = const()[name = tensor<string, []>("x_129_split_axis_0"), val = tensor<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_303_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_129_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_305_cast_fp16 = select(a = var_11_to_fp16, b = x_129_cast_fp16, cond = var_335)[name = tensor<string, []>("input_305_cast_fp16")];
            tensor<int32, [6]> input_307_pad_0 = const()[name = tensor<string, []>("input_307_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_307_mode_0 = const()[name = tensor<string, []>("input_307_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_67_to_fp16 = const()[name = tensor<string, []>("const_67_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_307_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = tensor<string, []>("input_307_cast_fp16")];
            tensor<string, []> input_309_pad_type_0 = const()[name = tensor<string, []>("input_309_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_309_groups_0 = const()[name = tensor<string, []>("input_309_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_309_strides_0 = const()[name = tensor<string, []>("input_309_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_309_pad_0 = const()[name = tensor<string, []>("input_309_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_309_dilations_0 = const()[name = tensor<string, []>("input_309_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_258_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_258_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140956672))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140965952))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_259_to_fp16 = const()[name = tensor<string, []>("const_259_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140968064)))];
            tensor<fp16, [1, 1024, 188]> input_311_cast_fp16 = conv(bias = const_259_to_fp16, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = const_258_to_fp16_quantized, x = input_307_cast_fp16)[name = tensor<string, []>("input_311_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_313_cast_fp16 = silu(x = input_311_cast_fp16)[name = tensor<string, []>("input_313_cast_fp16")];
            tensor<string, []> x_131_pad_type_0 = const()[name = tensor<string, []>("x_131_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_131_strides_0 = const()[name = tensor<string, []>("x_131_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_131_pad_0 = const()[name = tensor<string, []>("x_131_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_131_dilations_0 = const()[name = tensor<string, []>("x_131_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_131_groups_0 = const()[name = tensor<string, []>("x_131_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140970176))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142018816))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142020928)))];
            tensor<fp16, [1, 1024, 188]> x_131_cast_fp16 = conv(bias = module_layers_5_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_313_cast_fp16)[name = tensor<string, []>("x_131_cast_fp16")];
            tensor<int32, [3]> input_315_perm_0 = const()[name = tensor<string, []>("input_315_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_315_cast_fp16 = transpose(perm = input_315_perm_0, x = x_131_cast_fp16)[name = tensor<string, []>("transpose_271")];
            tensor<fp16, [1, 188, 1024]> input_317_cast_fp16 = add(x = input_299_cast_fp16, y = input_315_cast_fp16)[name = tensor<string, []>("input_317_cast_fp16")];
            tensor<int32, [1]> input_319_axes_0 = const()[name = tensor<string, []>("input_319_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142023040)))];
            tensor<fp16, [1024]> module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142025152)))];
            tensor<fp16, [1, 188, 1024]> input_319_cast_fp16 = layer_norm(axes = input_319_axes_0, beta = module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor<string, []>("input_319_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142027264))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146221632))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146229888)))];
            tensor<fp16, [1, 188, 4096]> linear_53_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = tensor<string, []>("linear_53_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_323_cast_fp16 = silu(x = linear_53_cast_fp16)[name = tensor<string, []>("input_323_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146238144))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150432512))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150434624)))];
            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized, x = input_323_cast_fp16)[name = tensor<string, []>("linear_54_cast_fp16")];
            tensor<fp16, []> var_1247_to_fp16 = const()[name = tensor<string, []>("op_1247_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1248_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1247_to_fp16)[name = tensor<string, []>("op_1248_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_329_cast_fp16 = add(x = input_317_cast_fp16, y = var_1248_cast_fp16)[name = tensor<string, []>("input_329_cast_fp16")];
            tensor<int32, [1]> input_331_axes_0 = const()[name = tensor<string, []>("input_331_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_5_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150436736)))];
            tensor<fp16, [1024]> module_layers_5_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150438848)))];
            tensor<fp16, [1, 188, 1024]> input_331_cast_fp16 = layer_norm(axes = input_331_axes_0, beta = module_layers_5_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm_out_weight_to_fp16, x = input_329_cast_fp16)[name = tensor<string, []>("input_331_cast_fp16")];
            tensor<int32, [1]> input_333_axes_0 = const()[name = tensor<string, []>("input_333_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150440960)))];
            tensor<fp16, [1024]> module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150443072)))];
            tensor<fp16, [1, 188, 1024]> input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_331_cast_fp16)[name = tensor<string, []>("input_333_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150445184))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154639552))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154647808)))];
            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized, x = input_333_cast_fp16)[name = tensor<string, []>("linear_55_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_337_cast_fp16 = silu(x = linear_55_cast_fp16)[name = tensor<string, []>("input_337_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154656064))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158850432))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158852544)))];
            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized, x = input_337_cast_fp16)[name = tensor<string, []>("linear_56_cast_fp16")];
            tensor<fp16, []> var_1278_to_fp16 = const()[name = tensor<string, []>("op_1278_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1279_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1278_to_fp16)[name = tensor<string, []>("op_1279_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_343_cast_fp16 = add(x = input_331_cast_fp16, y = var_1279_cast_fp16)[name = tensor<string, []>("input_343_cast_fp16")];
            tensor<int32, [1]> query_13_axes_0 = const()[name = tensor<string, []>("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_6_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158854656)))];
            tensor<fp16, [1024]> module_layers_6_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158856768)))];
            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_self_att_weight_to_fp16, x = input_343_cast_fp16)[name = tensor<string, []>("query_13_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158858880))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159907520))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159909632)))];
            tensor<fp16, [1, 188, 1024]> linear_57_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_q_bias_to_fp16, weight = module_layers_6_self_attn_linear_q_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = tensor<string, []>("linear_57_cast_fp16")];
            tensor<int32, [4]> var_1296 = const()[name = tensor<string, []>("op_1296"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1296, x = linear_57_cast_fp16)[name = tensor<string, []>("q_37_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159911744))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160960384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160962496)))];
            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_k_bias_to_fp16, weight = module_layers_6_self_attn_linear_k_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = tensor<string, []>("linear_58_cast_fp16")];
            tensor<int32, [4]> var_1301 = const()[name = tensor<string, []>("op_1301"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1301, x = linear_58_cast_fp16)[name = tensor<string, []>("k_25_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160964608))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162013248))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162015360)))];
            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_v_bias_to_fp16, weight = module_layers_6_self_attn_linear_v_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = tensor<string, []>("linear_59_cast_fp16")];
            tensor<int32, [4]> var_1306 = const()[name = tensor<string, []>("op_1306"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1306, x = linear_59_cast_fp16)[name = tensor<string, []>("v_13_cast_fp16")];
            tensor<int32, [4]> value_15_perm_0 = const()[name = tensor<string, []>("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_6_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162017472))), scale = tensor<fp16, [8]>([0x1.654p-9, 0x1.0cp-7, 0x1.3e8p-9, 0x1.388p-8, 0x1.c9cp-8, 0x1.bfp-8, 0x1.3fcp-8, 0x1.2p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1318_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1318_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_6_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162018560))), scale = tensor<fp16, [8]>([0x1.8e4p-9, 0x1.5fcp-8, 0x1.7bp-9, 0x1.558p-9, 0x1.94p-9, 0x1.004p-8, 0x1.4c4p-8, 0x1.d7p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1320_cast_fp16 = add(x = q_37_cast_fp16, y = module_layers_6_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1320_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_139_transpose_x_0 = const()[name = tensor<string, []>("x_139_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_139_transpose_y_0 = const()[name = tensor<string, []>("x_139_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1322_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1322_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162019648))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162403712))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1320_cast_fp16)[name = tensor<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_1322_to_fp16_quantized)[name = tensor<string, []>("x_139_cast_fp16")];
            tensor<int32, [8]> x_141_pad_0 = const()[name = tensor<string, []>("x_141_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_141_mode_0 = const()[name = tensor<string, []>("x_141_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_74_to_fp16 = const()[name = tensor<string, []>("const_74_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_141_cast_fp16 = pad(constant_val = const_74_to_fp16, mode = x_141_mode_0, pad = x_141_pad_0, x = x_139_cast_fp16)[name = tensor<string, []>("x_141_cast_fp16")];
            tensor<int32, [4]> var_1330 = const()[name = tensor<string, []>("op_1330"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_143_cast_fp16 = reshape(shape = var_1330, x = x_141_cast_fp16)[name = tensor<string, []>("x_143_cast_fp16")];
            tensor<int32, [4]> var_1334_begin_0 = const()[name = tensor<string, []>("op_1334_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1334_end_0 = const()[name = tensor<string, []>("op_1334_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1334_end_mask_0 = const()[name = tensor<string, []>("op_1334_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1334_cast_fp16 = slice_by_index(begin = var_1334_begin_0, end = var_1334_end_0, end_mask = var_1334_end_mask_0, x = x_143_cast_fp16)[name = tensor<string, []>("op_1334_cast_fp16")];
            tensor<int32, [4]> var_1335 = const()[name = tensor<string, []>("op_1335"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1335, x = var_1334_cast_fp16)[name = tensor<string, []>("matrix_bd_25_cast_fp16")];
            tensor<bool, []> matrix_ac_13_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_13_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_108_perm_0 = const()[name = tensor<string, []>("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_109_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_268")];
            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1318_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_13_cast_fp16")];
            tensor<int32, [4]> matrix_bd_27_begin_0 = const()[name = tensor<string, []>("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_27_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1344_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = tensor<string, []>("op_1344_cast_fp16")];
            tensor<fp16, []> _inversed_scores_25_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_25_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1344_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_25_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_27_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1350_cast_fp16 = softmax(axis = var_30, x = scores_27_cast_fp16)[name = tensor<string, []>("op_1350_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_345_cast_fp16 = select(a = var_11_to_fp16, b = var_1350_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_345_cast_fp16")];
            tensor<bool, []> x_145_transpose_x_0 = const()[name = tensor<string, []>("x_145_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_145_transpose_y_0 = const()[name = tensor<string, []>("x_145_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_13_cast_fp16)[name = tensor<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_345_cast_fp16, y = value_15_cast_fp16)[name = tensor<string, []>("x_145_cast_fp16")];
            tensor<int32, [4]> var_1354_perm_0 = const()[name = tensor<string, []>("op_1354_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1355 = const()[name = tensor<string, []>("op_1355"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1354_cast_fp16 = transpose(perm = var_1354_perm_0, x = x_145_cast_fp16)[name = tensor<string, []>("transpose_266")];
            tensor<fp16, [1, 188, 1024]> input_347_cast_fp16 = reshape(shape = var_1355, x = var_1354_cast_fp16)[name = tensor<string, []>("input_347_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_6_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162404544))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163453184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163455296)))];
            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = module_layers_6_self_attn_linear_out_bias_to_fp16, weight = module_layers_6_self_attn_linear_out_weight_to_fp16_quantized, x = input_347_cast_fp16)[name = tensor<string, []>("linear_61_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_351_cast_fp16 = add(x = input_343_cast_fp16, y = linear_61_cast_fp16)[name = tensor<string, []>("input_351_cast_fp16")];
            tensor<int32, [1]> x_149_axes_0 = const()[name = tensor<string, []>("x_149_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_6_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163457408)))];
            tensor<fp16, [1024]> module_layers_6_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163459520)))];
            tensor<fp16, [1, 188, 1024]> x_149_cast_fp16 = layer_norm(axes = x_149_axes_0, beta = module_layers_6_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_conv_weight_to_fp16, x = input_351_cast_fp16)[name = tensor<string, []>("x_149_cast_fp16")];
            tensor<int32, [3]> input_353_perm_0 = const()[name = tensor<string, []>("input_353_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_355_pad_type_0 = const()[name = tensor<string, []>("input_355_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_355_strides_0 = const()[name = tensor<string, []>("input_355_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_355_pad_0 = const()[name = tensor<string, []>("input_355_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_355_dilations_0 = const()[name = tensor<string, []>("input_355_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_355_groups_0 = const()[name = tensor<string, []>("input_355_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163461632))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165558848))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165563008)))];
            tensor<fp16, [1, 1024, 188]> input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_149_cast_fp16)[name = tensor<string, []>("transpose_265")];
            tensor<fp16, [1, 2048, 188]> input_355_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv1_bias_to_fp16, dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_353_cast_fp16)[name = tensor<string, []>("input_355_cast_fp16")];
            tensor<int32, []> x_151_split_num_splits_0 = const()[name = tensor<string, []>("x_151_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_151_split_axis_0 = const()[name = tensor<string, []>("x_151_split_axis_0"), val = tensor<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_355_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_151_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_357_cast_fp16 = select(a = var_11_to_fp16, b = x_151_cast_fp16, cond = var_335)[name = tensor<string, []>("input_357_cast_fp16")];
            tensor<int32, [6]> input_359_pad_0 = const()[name = tensor<string, []>("input_359_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_359_mode_0 = const()[name = tensor<string, []>("input_359_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_77_to_fp16 = const()[name = tensor<string, []>("const_77_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_359_cast_fp16 = pad(constant_val = const_77_to_fp16, mode = input_359_mode_0, pad = input_359_pad_0, x = input_357_cast_fp16)[name = tensor<string, []>("input_359_cast_fp16")];
            tensor<string, []> input_361_pad_type_0 = const()[name = tensor<string, []>("input_361_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_361_groups_0 = const()[name = tensor<string, []>("input_361_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_361_strides_0 = const()[name = tensor<string, []>("input_361_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_361_pad_0 = const()[name = tensor<string, []>("input_361_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_361_dilations_0 = const()[name = tensor<string, []>("input_361_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_260_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_260_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165567168))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165576448))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_261_to_fp16 = const()[name = tensor<string, []>("const_261_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165578560)))];
            tensor<fp16, [1, 1024, 188]> input_363_cast_fp16 = conv(bias = const_261_to_fp16, dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = const_260_to_fp16_quantized, x = input_359_cast_fp16)[name = tensor<string, []>("input_363_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_365_cast_fp16 = silu(x = input_363_cast_fp16)[name = tensor<string, []>("input_365_cast_fp16")];
            tensor<string, []> x_153_pad_type_0 = const()[name = tensor<string, []>("x_153_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_153_strides_0 = const()[name = tensor<string, []>("x_153_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_153_pad_0 = const()[name = tensor<string, []>("x_153_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_153_dilations_0 = const()[name = tensor<string, []>("x_153_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_153_groups_0 = const()[name = tensor<string, []>("x_153_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165580672))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166629312))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166631424)))];
            tensor<fp16, [1, 1024, 188]> x_153_cast_fp16 = conv(bias = module_layers_6_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_365_cast_fp16)[name = tensor<string, []>("x_153_cast_fp16")];
            tensor<int32, [3]> input_367_perm_0 = const()[name = tensor<string, []>("input_367_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_367_cast_fp16 = transpose(perm = input_367_perm_0, x = x_153_cast_fp16)[name = tensor<string, []>("transpose_264")];
            tensor<fp16, [1, 188, 1024]> input_369_cast_fp16 = add(x = input_351_cast_fp16, y = input_367_cast_fp16)[name = tensor<string, []>("input_369_cast_fp16")];
            tensor<int32, [1]> input_371_axes_0 = const()[name = tensor<string, []>("input_371_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166633536)))];
            tensor<fp16, [1024]> module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166635648)))];
            tensor<fp16, [1, 188, 1024]> input_371_cast_fp16 = layer_norm(axes = input_371_axes_0, beta = module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor<string, []>("input_371_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166637760))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170832128))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170840384)))];
            tensor<fp16, [1, 188, 4096]> linear_62_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized, x = input_371_cast_fp16)[name = tensor<string, []>("linear_62_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_375_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor<string, []>("input_375_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170848640))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175043008))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175045120)))];
            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized, x = input_375_cast_fp16)[name = tensor<string, []>("linear_63_cast_fp16")];
            tensor<fp16, []> var_1421_to_fp16 = const()[name = tensor<string, []>("op_1421_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1422_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1421_to_fp16)[name = tensor<string, []>("op_1422_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_381_cast_fp16 = add(x = input_369_cast_fp16, y = var_1422_cast_fp16)[name = tensor<string, []>("input_381_cast_fp16")];
            tensor<int32, [1]> input_383_axes_0 = const()[name = tensor<string, []>("input_383_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_6_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175047232)))];
            tensor<fp16, [1024]> module_layers_6_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175049344)))];
            tensor<fp16, [1, 188, 1024]> input_383_cast_fp16 = layer_norm(axes = input_383_axes_0, beta = module_layers_6_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm_out_weight_to_fp16, x = input_381_cast_fp16)[name = tensor<string, []>("input_383_cast_fp16")];
            tensor<int32, [1]> input_385_axes_0 = const()[name = tensor<string, []>("input_385_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175051456)))];
            tensor<fp16, [1024]> module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175053568)))];
            tensor<fp16, [1, 188, 1024]> input_385_cast_fp16 = layer_norm(axes = input_385_axes_0, beta = module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_383_cast_fp16)[name = tensor<string, []>("input_385_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175055680))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179250048))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179258304)))];
            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized, x = input_385_cast_fp16)[name = tensor<string, []>("linear_64_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_389_cast_fp16 = silu(x = linear_64_cast_fp16)[name = tensor<string, []>("input_389_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(179266560))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183460928))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183463040)))];
            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized, x = input_389_cast_fp16)[name = tensor<string, []>("linear_65_cast_fp16")];
            tensor<fp16, []> var_1452_to_fp16 = const()[name = tensor<string, []>("op_1452_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1453_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1452_to_fp16)[name = tensor<string, []>("op_1453_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_395_cast_fp16 = add(x = input_383_cast_fp16, y = var_1453_cast_fp16)[name = tensor<string, []>("input_395_cast_fp16")];
            tensor<int32, [1]> query_15_axes_0 = const()[name = tensor<string, []>("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_7_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183465152)))];
            tensor<fp16, [1024]> module_layers_7_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183467264)))];
            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_self_att_weight_to_fp16, x = input_395_cast_fp16)[name = tensor<string, []>("query_15_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(183469376))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184518016))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184520128)))];
            tensor<fp16, [1, 188, 1024]> linear_66_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_q_bias_to_fp16, weight = module_layers_7_self_attn_linear_q_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = tensor<string, []>("linear_66_cast_fp16")];
            tensor<int32, [4]> var_1470 = const()[name = tensor<string, []>("op_1470"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1470, x = linear_66_cast_fp16)[name = tensor<string, []>("q_43_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184522240))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185570880))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185572992)))];
            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_k_bias_to_fp16, weight = module_layers_7_self_attn_linear_k_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = tensor<string, []>("linear_67_cast_fp16")];
            tensor<int32, [4]> var_1475 = const()[name = tensor<string, []>("op_1475"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1475, x = linear_67_cast_fp16)[name = tensor<string, []>("k_29_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185575104))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186623744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186625856)))];
            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_v_bias_to_fp16, weight = module_layers_7_self_attn_linear_v_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = tensor<string, []>("linear_68_cast_fp16")];
            tensor<int32, [4]> var_1480 = const()[name = tensor<string, []>("op_1480"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1480, x = linear_68_cast_fp16)[name = tensor<string, []>("v_15_cast_fp16")];
            tensor<int32, [4]> value_17_perm_0 = const()[name = tensor<string, []>("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_7_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186627968))), scale = tensor<fp16, [8]>([0x1.568p-8, 0x1.9b8p-8, 0x1.d88p-9, 0x1.c3p-8, 0x1.6e4p-8, 0x1.88p-8, 0x1.704p-8, 0x1.988p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1492_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1492_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_7_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186629056))), scale = tensor<fp16, [8]>([0x1.46cp-8, 0x1.a2p-9, 0x1.5f8p-8, 0x1.eacp-9, 0x1.abp-9, 0x1.cc4p-9, 0x1.e98p-9, 0x1.268p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1494_cast_fp16 = add(x = q_43_cast_fp16, y = module_layers_7_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1494_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_161_transpose_x_0 = const()[name = tensor<string, []>("x_161_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_161_transpose_y_0 = const()[name = tensor<string, []>("x_161_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1496_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1496_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186630144))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187014208))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1494_cast_fp16)[name = tensor<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_1496_to_fp16_quantized)[name = tensor<string, []>("x_161_cast_fp16")];
            tensor<int32, [8]> x_163_pad_0 = const()[name = tensor<string, []>("x_163_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_163_mode_0 = const()[name = tensor<string, []>("x_163_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_84_to_fp16 = const()[name = tensor<string, []>("const_84_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_163_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = x_163_mode_0, pad = x_163_pad_0, x = x_161_cast_fp16)[name = tensor<string, []>("x_163_cast_fp16")];
            tensor<int32, [4]> var_1504 = const()[name = tensor<string, []>("op_1504"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_165_cast_fp16 = reshape(shape = var_1504, x = x_163_cast_fp16)[name = tensor<string, []>("x_165_cast_fp16")];
            tensor<int32, [4]> var_1508_begin_0 = const()[name = tensor<string, []>("op_1508_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1508_end_0 = const()[name = tensor<string, []>("op_1508_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1508_end_mask_0 = const()[name = tensor<string, []>("op_1508_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1508_cast_fp16 = slice_by_index(begin = var_1508_begin_0, end = var_1508_end_0, end_mask = var_1508_end_mask_0, x = x_165_cast_fp16)[name = tensor<string, []>("op_1508_cast_fp16")];
            tensor<int32, [4]> var_1509 = const()[name = tensor<string, []>("op_1509"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1509, x = var_1508_cast_fp16)[name = tensor<string, []>("matrix_bd_29_cast_fp16")];
            tensor<bool, []> matrix_ac_15_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_15_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_15_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_15_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_110_perm_0 = const()[name = tensor<string, []>("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_111_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_261")];
            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1492_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_15_cast_fp16")];
            tensor<int32, [4]> matrix_bd_31_begin_0 = const()[name = tensor<string, []>("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_31_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1518_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = tensor<string, []>("op_1518_cast_fp16")];
            tensor<fp16, []> _inversed_scores_29_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_29_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1518_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_29_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_31_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1524_cast_fp16 = softmax(axis = var_30, x = scores_31_cast_fp16)[name = tensor<string, []>("op_1524_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_397_cast_fp16 = select(a = var_11_to_fp16, b = var_1524_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_397_cast_fp16")];
            tensor<bool, []> x_167_transpose_x_0 = const()[name = tensor<string, []>("x_167_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_167_transpose_y_0 = const()[name = tensor<string, []>("x_167_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_15_cast_fp16)[name = tensor<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_397_cast_fp16, y = value_17_cast_fp16)[name = tensor<string, []>("x_167_cast_fp16")];
            tensor<int32, [4]> var_1528_perm_0 = const()[name = tensor<string, []>("op_1528_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1529 = const()[name = tensor<string, []>("op_1529"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1528_cast_fp16 = transpose(perm = var_1528_perm_0, x = x_167_cast_fp16)[name = tensor<string, []>("transpose_259")];
            tensor<fp16, [1, 188, 1024]> input_399_cast_fp16 = reshape(shape = var_1529, x = var_1528_cast_fp16)[name = tensor<string, []>("input_399_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_7_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187015040))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188063680))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188065792)))];
            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = module_layers_7_self_attn_linear_out_bias_to_fp16, weight = module_layers_7_self_attn_linear_out_weight_to_fp16_quantized, x = input_399_cast_fp16)[name = tensor<string, []>("linear_70_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_403_cast_fp16 = add(x = input_395_cast_fp16, y = linear_70_cast_fp16)[name = tensor<string, []>("input_403_cast_fp16")];
            tensor<int32, [1]> x_171_axes_0 = const()[name = tensor<string, []>("x_171_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_7_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188067904)))];
            tensor<fp16, [1024]> module_layers_7_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188070016)))];
            tensor<fp16, [1, 188, 1024]> x_171_cast_fp16 = layer_norm(axes = x_171_axes_0, beta = module_layers_7_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_conv_weight_to_fp16, x = input_403_cast_fp16)[name = tensor<string, []>("x_171_cast_fp16")];
            tensor<int32, [3]> input_405_perm_0 = const()[name = tensor<string, []>("input_405_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_407_pad_type_0 = const()[name = tensor<string, []>("input_407_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_407_strides_0 = const()[name = tensor<string, []>("input_407_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_407_pad_0 = const()[name = tensor<string, []>("input_407_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_407_dilations_0 = const()[name = tensor<string, []>("input_407_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_407_groups_0 = const()[name = tensor<string, []>("input_407_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188072128))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190169344))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190173504)))];
            tensor<fp16, [1, 1024, 188]> input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = x_171_cast_fp16)[name = tensor<string, []>("transpose_258")];
            tensor<fp16, [1, 2048, 188]> input_407_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv1_bias_to_fp16, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_405_cast_fp16)[name = tensor<string, []>("input_407_cast_fp16")];
            tensor<int32, []> x_173_split_num_splits_0 = const()[name = tensor<string, []>("x_173_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_173_split_axis_0 = const()[name = tensor<string, []>("x_173_split_axis_0"), val = tensor<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_407_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_173_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_409_cast_fp16 = select(a = var_11_to_fp16, b = x_173_cast_fp16, cond = var_335)[name = tensor<string, []>("input_409_cast_fp16")];
            tensor<int32, [6]> input_411_pad_0 = const()[name = tensor<string, []>("input_411_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_411_mode_0 = const()[name = tensor<string, []>("input_411_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_87_to_fp16 = const()[name = tensor<string, []>("const_87_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_411_cast_fp16 = pad(constant_val = const_87_to_fp16, mode = input_411_mode_0, pad = input_411_pad_0, x = input_409_cast_fp16)[name = tensor<string, []>("input_411_cast_fp16")];
            tensor<string, []> input_413_pad_type_0 = const()[name = tensor<string, []>("input_413_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_413_groups_0 = const()[name = tensor<string, []>("input_413_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_413_strides_0 = const()[name = tensor<string, []>("input_413_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_413_pad_0 = const()[name = tensor<string, []>("input_413_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_413_dilations_0 = const()[name = tensor<string, []>("input_413_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_262_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_262_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190177664))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190186944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_263_to_fp16 = const()[name = tensor<string, []>("const_263_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190189056)))];
            tensor<fp16, [1, 1024, 188]> input_415_cast_fp16 = conv(bias = const_263_to_fp16, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = const_262_to_fp16_quantized, x = input_411_cast_fp16)[name = tensor<string, []>("input_415_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_417_cast_fp16 = silu(x = input_415_cast_fp16)[name = tensor<string, []>("input_417_cast_fp16")];
            tensor<string, []> x_175_pad_type_0 = const()[name = tensor<string, []>("x_175_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_175_strides_0 = const()[name = tensor<string, []>("x_175_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_175_pad_0 = const()[name = tensor<string, []>("x_175_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_175_dilations_0 = const()[name = tensor<string, []>("x_175_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_175_groups_0 = const()[name = tensor<string, []>("x_175_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(190191168))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191239808))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191241920)))];
            tensor<fp16, [1, 1024, 188]> x_175_cast_fp16 = conv(bias = module_layers_7_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_417_cast_fp16)[name = tensor<string, []>("x_175_cast_fp16")];
            tensor<int32, [3]> input_419_perm_0 = const()[name = tensor<string, []>("input_419_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_419_cast_fp16 = transpose(perm = input_419_perm_0, x = x_175_cast_fp16)[name = tensor<string, []>("transpose_257")];
            tensor<fp16, [1, 188, 1024]> input_421_cast_fp16 = add(x = input_403_cast_fp16, y = input_419_cast_fp16)[name = tensor<string, []>("input_421_cast_fp16")];
            tensor<int32, [1]> input_423_axes_0 = const()[name = tensor<string, []>("input_423_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191244032)))];
            tensor<fp16, [1024]> module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191246144)))];
            tensor<fp16, [1, 188, 1024]> input_423_cast_fp16 = layer_norm(axes = input_423_axes_0, beta = module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_421_cast_fp16)[name = tensor<string, []>("input_423_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191248256))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195442624))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195450880)))];
            tensor<fp16, [1, 188, 4096]> linear_71_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized, x = input_423_cast_fp16)[name = tensor<string, []>("linear_71_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_427_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor<string, []>("input_427_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(195459136))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199653504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199655616)))];
            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized, x = input_427_cast_fp16)[name = tensor<string, []>("linear_72_cast_fp16")];
            tensor<fp16, []> var_1595_to_fp16 = const()[name = tensor<string, []>("op_1595_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1596_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1595_to_fp16)[name = tensor<string, []>("op_1596_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_433_cast_fp16 = add(x = input_421_cast_fp16, y = var_1596_cast_fp16)[name = tensor<string, []>("input_433_cast_fp16")];
            tensor<int32, [1]> input_435_axes_0 = const()[name = tensor<string, []>("input_435_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_7_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199657728)))];
            tensor<fp16, [1024]> module_layers_7_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199659840)))];
            tensor<fp16, [1, 188, 1024]> input_435_cast_fp16 = layer_norm(axes = input_435_axes_0, beta = module_layers_7_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm_out_weight_to_fp16, x = input_433_cast_fp16)[name = tensor<string, []>("input_435_cast_fp16")];
            tensor<int32, [1]> input_437_axes_0 = const()[name = tensor<string, []>("input_437_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199661952)))];
            tensor<fp16, [1024]> module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199664064)))];
            tensor<fp16, [1, 188, 1024]> input_437_cast_fp16 = layer_norm(axes = input_437_axes_0, beta = module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_435_cast_fp16)[name = tensor<string, []>("input_437_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199666176))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203860544))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203868800)))];
            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized, x = input_437_cast_fp16)[name = tensor<string, []>("linear_73_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_441_cast_fp16 = silu(x = linear_73_cast_fp16)[name = tensor<string, []>("input_441_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203877056))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208071424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208073536)))];
            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized, x = input_441_cast_fp16)[name = tensor<string, []>("linear_74_cast_fp16")];
            tensor<fp16, []> var_1626_to_fp16 = const()[name = tensor<string, []>("op_1626_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1627_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1626_to_fp16)[name = tensor<string, []>("op_1627_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_447_cast_fp16 = add(x = input_435_cast_fp16, y = var_1627_cast_fp16)[name = tensor<string, []>("input_447_cast_fp16")];
            tensor<int32, [1]> query_17_axes_0 = const()[name = tensor<string, []>("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_8_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208075648)))];
            tensor<fp16, [1024]> module_layers_8_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208077760)))];
            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_self_att_weight_to_fp16, x = input_447_cast_fp16)[name = tensor<string, []>("query_17_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208079872))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209128512))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209130624)))];
            tensor<fp16, [1, 188, 1024]> linear_75_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_q_bias_to_fp16, weight = module_layers_8_self_attn_linear_q_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = tensor<string, []>("linear_75_cast_fp16")];
            tensor<int32, [4]> var_1644 = const()[name = tensor<string, []>("op_1644"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1644, x = linear_75_cast_fp16)[name = tensor<string, []>("q_49_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209132736))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210181376))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210183488)))];
            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_k_bias_to_fp16, weight = module_layers_8_self_attn_linear_k_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = tensor<string, []>("linear_76_cast_fp16")];
            tensor<int32, [4]> var_1649 = const()[name = tensor<string, []>("op_1649"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1649, x = linear_76_cast_fp16)[name = tensor<string, []>("k_33_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210185600))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211234240))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211236352)))];
            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_v_bias_to_fp16, weight = module_layers_8_self_attn_linear_v_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = tensor<string, []>("linear_77_cast_fp16")];
            tensor<int32, [4]> var_1654 = const()[name = tensor<string, []>("op_1654"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1654, x = linear_77_cast_fp16)[name = tensor<string, []>("v_17_cast_fp16")];
            tensor<int32, [4]> value_19_perm_0 = const()[name = tensor<string, []>("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_8_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211238464))), scale = tensor<fp16, [8]>([0x1.88p-8, 0x1.2b8p-8, 0x1.76p-8, 0x1.2e4p-7, 0x1.fdp-9, 0x1.0d4p-7, 0x1.f2p-8, 0x1.a9cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1666_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1666_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_8_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211239552))), scale = tensor<fp16, [8]>([0x1.074p-8, 0x1.30cp-9, 0x1.06p-9, 0x1.4cp-8, 0x1.454p-8, 0x1.53p-9, 0x1.354p-8, 0x1.678p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1668_cast_fp16 = add(x = q_49_cast_fp16, y = module_layers_8_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1668_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_183_transpose_x_0 = const()[name = tensor<string, []>("x_183_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_183_transpose_y_0 = const()[name = tensor<string, []>("x_183_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1670_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1670_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211240640))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211624704))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1668_cast_fp16)[name = tensor<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_1670_to_fp16_quantized)[name = tensor<string, []>("x_183_cast_fp16")];
            tensor<int32, [8]> x_185_pad_0 = const()[name = tensor<string, []>("x_185_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_185_mode_0 = const()[name = tensor<string, []>("x_185_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_94_to_fp16 = const()[name = tensor<string, []>("const_94_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_185_cast_fp16 = pad(constant_val = const_94_to_fp16, mode = x_185_mode_0, pad = x_185_pad_0, x = x_183_cast_fp16)[name = tensor<string, []>("x_185_cast_fp16")];
            tensor<int32, [4]> var_1678 = const()[name = tensor<string, []>("op_1678"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_187_cast_fp16 = reshape(shape = var_1678, x = x_185_cast_fp16)[name = tensor<string, []>("x_187_cast_fp16")];
            tensor<int32, [4]> var_1682_begin_0 = const()[name = tensor<string, []>("op_1682_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1682_end_0 = const()[name = tensor<string, []>("op_1682_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1682_end_mask_0 = const()[name = tensor<string, []>("op_1682_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1682_cast_fp16 = slice_by_index(begin = var_1682_begin_0, end = var_1682_end_0, end_mask = var_1682_end_mask_0, x = x_187_cast_fp16)[name = tensor<string, []>("op_1682_cast_fp16")];
            tensor<int32, [4]> var_1683 = const()[name = tensor<string, []>("op_1683"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1683, x = var_1682_cast_fp16)[name = tensor<string, []>("matrix_bd_33_cast_fp16")];
            tensor<bool, []> matrix_ac_17_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_17_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_17_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_17_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_112_perm_0 = const()[name = tensor<string, []>("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_113_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_254")];
            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1666_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_17_cast_fp16")];
            tensor<int32, [4]> matrix_bd_35_begin_0 = const()[name = tensor<string, []>("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_35_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1692_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = tensor<string, []>("op_1692_cast_fp16")];
            tensor<fp16, []> _inversed_scores_33_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_33_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1692_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_33_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_35_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1698_cast_fp16 = softmax(axis = var_30, x = scores_35_cast_fp16)[name = tensor<string, []>("op_1698_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_449_cast_fp16 = select(a = var_11_to_fp16, b = var_1698_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_449_cast_fp16")];
            tensor<bool, []> x_189_transpose_x_0 = const()[name = tensor<string, []>("x_189_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_189_transpose_y_0 = const()[name = tensor<string, []>("x_189_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_17_cast_fp16)[name = tensor<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_449_cast_fp16, y = value_19_cast_fp16)[name = tensor<string, []>("x_189_cast_fp16")];
            tensor<int32, [4]> var_1702_perm_0 = const()[name = tensor<string, []>("op_1702_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1703 = const()[name = tensor<string, []>("op_1703"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1702_cast_fp16 = transpose(perm = var_1702_perm_0, x = x_189_cast_fp16)[name = tensor<string, []>("transpose_252")];
            tensor<fp16, [1, 188, 1024]> input_451_cast_fp16 = reshape(shape = var_1703, x = var_1702_cast_fp16)[name = tensor<string, []>("input_451_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_8_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211625536))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212674176))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212676288)))];
            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = module_layers_8_self_attn_linear_out_bias_to_fp16, weight = module_layers_8_self_attn_linear_out_weight_to_fp16_quantized, x = input_451_cast_fp16)[name = tensor<string, []>("linear_79_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_455_cast_fp16 = add(x = input_447_cast_fp16, y = linear_79_cast_fp16)[name = tensor<string, []>("input_455_cast_fp16")];
            tensor<int32, [1]> x_193_axes_0 = const()[name = tensor<string, []>("x_193_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_8_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212678400)))];
            tensor<fp16, [1024]> module_layers_8_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212680512)))];
            tensor<fp16, [1, 188, 1024]> x_193_cast_fp16 = layer_norm(axes = x_193_axes_0, beta = module_layers_8_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_conv_weight_to_fp16, x = input_455_cast_fp16)[name = tensor<string, []>("x_193_cast_fp16")];
            tensor<int32, [3]> input_457_perm_0 = const()[name = tensor<string, []>("input_457_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_459_pad_type_0 = const()[name = tensor<string, []>("input_459_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_459_strides_0 = const()[name = tensor<string, []>("input_459_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_459_pad_0 = const()[name = tensor<string, []>("input_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_459_dilations_0 = const()[name = tensor<string, []>("input_459_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_459_groups_0 = const()[name = tensor<string, []>("input_459_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212682624))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214779840))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214784000)))];
            tensor<fp16, [1, 1024, 188]> input_457_cast_fp16 = transpose(perm = input_457_perm_0, x = x_193_cast_fp16)[name = tensor<string, []>("transpose_251")];
            tensor<fp16, [1, 2048, 188]> input_459_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv1_bias_to_fp16, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_457_cast_fp16)[name = tensor<string, []>("input_459_cast_fp16")];
            tensor<int32, []> x_195_split_num_splits_0 = const()[name = tensor<string, []>("x_195_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_195_split_axis_0 = const()[name = tensor<string, []>("x_195_split_axis_0"), val = tensor<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_459_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_195_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_461_cast_fp16 = select(a = var_11_to_fp16, b = x_195_cast_fp16, cond = var_335)[name = tensor<string, []>("input_461_cast_fp16")];
            tensor<int32, [6]> input_463_pad_0 = const()[name = tensor<string, []>("input_463_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_463_mode_0 = const()[name = tensor<string, []>("input_463_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_97_to_fp16 = const()[name = tensor<string, []>("const_97_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_463_cast_fp16 = pad(constant_val = const_97_to_fp16, mode = input_463_mode_0, pad = input_463_pad_0, x = input_461_cast_fp16)[name = tensor<string, []>("input_463_cast_fp16")];
            tensor<string, []> input_465_pad_type_0 = const()[name = tensor<string, []>("input_465_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_465_groups_0 = const()[name = tensor<string, []>("input_465_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_465_strides_0 = const()[name = tensor<string, []>("input_465_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_465_pad_0 = const()[name = tensor<string, []>("input_465_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_465_dilations_0 = const()[name = tensor<string, []>("input_465_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_264_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_264_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214788160))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214797440))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_265_to_fp16 = const()[name = tensor<string, []>("const_265_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214799552)))];
            tensor<fp16, [1, 1024, 188]> input_467_cast_fp16 = conv(bias = const_265_to_fp16, dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = const_264_to_fp16_quantized, x = input_463_cast_fp16)[name = tensor<string, []>("input_467_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_469_cast_fp16 = silu(x = input_467_cast_fp16)[name = tensor<string, []>("input_469_cast_fp16")];
            tensor<string, []> x_197_pad_type_0 = const()[name = tensor<string, []>("x_197_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_197_strides_0 = const()[name = tensor<string, []>("x_197_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_197_pad_0 = const()[name = tensor<string, []>("x_197_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_197_dilations_0 = const()[name = tensor<string, []>("x_197_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_197_groups_0 = const()[name = tensor<string, []>("x_197_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214801664))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215850304))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215852416)))];
            tensor<fp16, [1, 1024, 188]> x_197_cast_fp16 = conv(bias = module_layers_8_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_469_cast_fp16)[name = tensor<string, []>("x_197_cast_fp16")];
            tensor<int32, [3]> input_471_perm_0 = const()[name = tensor<string, []>("input_471_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_471_cast_fp16 = transpose(perm = input_471_perm_0, x = x_197_cast_fp16)[name = tensor<string, []>("transpose_250")];
            tensor<fp16, [1, 188, 1024]> input_473_cast_fp16 = add(x = input_455_cast_fp16, y = input_471_cast_fp16)[name = tensor<string, []>("input_473_cast_fp16")];
            tensor<int32, [1]> input_475_axes_0 = const()[name = tensor<string, []>("input_475_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215854528)))];
            tensor<fp16, [1024]> module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215856640)))];
            tensor<fp16, [1, 188, 1024]> input_475_cast_fp16 = layer_norm(axes = input_475_axes_0, beta = module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_473_cast_fp16)[name = tensor<string, []>("input_475_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215858752))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220053120))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220061376)))];
            tensor<fp16, [1, 188, 4096]> linear_80_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized, x = input_475_cast_fp16)[name = tensor<string, []>("linear_80_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_479_cast_fp16 = silu(x = linear_80_cast_fp16)[name = tensor<string, []>("input_479_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220069632))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224264000))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224266112)))];
            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized, x = input_479_cast_fp16)[name = tensor<string, []>("linear_81_cast_fp16")];
            tensor<fp16, []> var_1769_to_fp16 = const()[name = tensor<string, []>("op_1769_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1770_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_1769_to_fp16)[name = tensor<string, []>("op_1770_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_485_cast_fp16 = add(x = input_473_cast_fp16, y = var_1770_cast_fp16)[name = tensor<string, []>("input_485_cast_fp16")];
            tensor<int32, [1]> input_487_axes_0 = const()[name = tensor<string, []>("input_487_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_8_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224268224)))];
            tensor<fp16, [1024]> module_layers_8_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224270336)))];
            tensor<fp16, [1, 188, 1024]> input_487_cast_fp16 = layer_norm(axes = input_487_axes_0, beta = module_layers_8_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_8_norm_out_weight_to_fp16, x = input_485_cast_fp16)[name = tensor<string, []>("input_487_cast_fp16")];
            tensor<int32, [1]> input_489_axes_0 = const()[name = tensor<string, []>("input_489_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224272448)))];
            tensor<fp16, [1024]> module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224274560)))];
            tensor<fp16, [1, 188, 1024]> input_489_cast_fp16 = layer_norm(axes = input_489_axes_0, beta = module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_487_cast_fp16)[name = tensor<string, []>("input_489_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224276672))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228471040))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228479296)))];
            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized, x = input_489_cast_fp16)[name = tensor<string, []>("linear_82_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_493_cast_fp16 = silu(x = linear_82_cast_fp16)[name = tensor<string, []>("input_493_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228487552))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232681920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232684032)))];
            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized, x = input_493_cast_fp16)[name = tensor<string, []>("linear_83_cast_fp16")];
            tensor<fp16, []> var_1800_to_fp16 = const()[name = tensor<string, []>("op_1800_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1801_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_1800_to_fp16)[name = tensor<string, []>("op_1801_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_499_cast_fp16 = add(x = input_487_cast_fp16, y = var_1801_cast_fp16)[name = tensor<string, []>("input_499_cast_fp16")];
            tensor<int32, [1]> query_19_axes_0 = const()[name = tensor<string, []>("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_9_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232686144)))];
            tensor<fp16, [1024]> module_layers_9_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232688256)))];
            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_self_att_weight_to_fp16, x = input_499_cast_fp16)[name = tensor<string, []>("query_19_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(232690368))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233739008))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233741120)))];
            tensor<fp16, [1, 188, 1024]> linear_84_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_q_bias_to_fp16, weight = module_layers_9_self_attn_linear_q_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = tensor<string, []>("linear_84_cast_fp16")];
            tensor<int32, [4]> var_1818 = const()[name = tensor<string, []>("op_1818"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_1818, x = linear_84_cast_fp16)[name = tensor<string, []>("q_55_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233743232))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234791872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234793984)))];
            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_k_bias_to_fp16, weight = module_layers_9_self_attn_linear_k_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = tensor<string, []>("linear_85_cast_fp16")];
            tensor<int32, [4]> var_1823 = const()[name = tensor<string, []>("op_1823"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_1823, x = linear_85_cast_fp16)[name = tensor<string, []>("k_37_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(234796096))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235844736))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235846848)))];
            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_v_bias_to_fp16, weight = module_layers_9_self_attn_linear_v_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = tensor<string, []>("linear_86_cast_fp16")];
            tensor<int32, [4]> var_1828 = const()[name = tensor<string, []>("op_1828"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_1828, x = linear_86_cast_fp16)[name = tensor<string, []>("v_19_cast_fp16")];
            tensor<int32, [4]> value_21_perm_0 = const()[name = tensor<string, []>("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_9_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235848960))), scale = tensor<fp16, [8]>([0x1.95cp-8, 0x1.788p-8, 0x1.91p-8, 0x1.9d8p-7, 0x1.15p-8, 0x1.808p-8, 0x1.484p-8, 0x1.b64p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1840_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_1840_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_9_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235850048))), scale = tensor<fp16, [8]>([0x1.038p-8, 0x1.c2cp-8, 0x1.27cp-8, 0x1.e6p-8, 0x1.14p-9, 0x1.6b8p-8, 0x1.77cp-8, 0x1.0d4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_1842_cast_fp16 = add(x = q_55_cast_fp16, y = module_layers_9_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_1842_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_205_transpose_x_0 = const()[name = tensor<string, []>("x_205_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_205_transpose_y_0 = const()[name = tensor<string, []>("x_205_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_1844_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_1844_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(235851136))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236235200))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_1842_cast_fp16)[name = tensor<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_1844_to_fp16_quantized)[name = tensor<string, []>("x_205_cast_fp16")];
            tensor<int32, [8]> x_207_pad_0 = const()[name = tensor<string, []>("x_207_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_207_mode_0 = const()[name = tensor<string, []>("x_207_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_104_to_fp16 = const()[name = tensor<string, []>("const_104_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_207_cast_fp16 = pad(constant_val = const_104_to_fp16, mode = x_207_mode_0, pad = x_207_pad_0, x = x_205_cast_fp16)[name = tensor<string, []>("x_207_cast_fp16")];
            tensor<int32, [4]> var_1852 = const()[name = tensor<string, []>("op_1852"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_209_cast_fp16 = reshape(shape = var_1852, x = x_207_cast_fp16)[name = tensor<string, []>("x_209_cast_fp16")];
            tensor<int32, [4]> var_1856_begin_0 = const()[name = tensor<string, []>("op_1856_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_1856_end_0 = const()[name = tensor<string, []>("op_1856_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_1856_end_mask_0 = const()[name = tensor<string, []>("op_1856_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_1856_cast_fp16 = slice_by_index(begin = var_1856_begin_0, end = var_1856_end_0, end_mask = var_1856_end_mask_0, x = x_209_cast_fp16)[name = tensor<string, []>("op_1856_cast_fp16")];
            tensor<int32, [4]> var_1857 = const()[name = tensor<string, []>("op_1857"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_1857, x = var_1856_cast_fp16)[name = tensor<string, []>("matrix_bd_37_cast_fp16")];
            tensor<bool, []> matrix_ac_19_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_19_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_19_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_114_perm_0 = const()[name = tensor<string, []>("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_115_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_247")];
            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_1840_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_19_cast_fp16")];
            tensor<int32, [4]> matrix_bd_39_begin_0 = const()[name = tensor<string, []>("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_39_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1866_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = tensor<string, []>("op_1866_cast_fp16")];
            tensor<fp16, []> _inversed_scores_37_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_37_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_1866_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_37_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_39_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_1872_cast_fp16 = softmax(axis = var_30, x = scores_39_cast_fp16)[name = tensor<string, []>("op_1872_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_501_cast_fp16 = select(a = var_11_to_fp16, b = var_1872_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_501_cast_fp16")];
            tensor<bool, []> x_211_transpose_x_0 = const()[name = tensor<string, []>("x_211_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_211_transpose_y_0 = const()[name = tensor<string, []>("x_211_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_19_cast_fp16)[name = tensor<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_501_cast_fp16, y = value_21_cast_fp16)[name = tensor<string, []>("x_211_cast_fp16")];
            tensor<int32, [4]> var_1876_perm_0 = const()[name = tensor<string, []>("op_1876_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1877 = const()[name = tensor<string, []>("op_1877"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_1876_cast_fp16 = transpose(perm = var_1876_perm_0, x = x_211_cast_fp16)[name = tensor<string, []>("transpose_245")];
            tensor<fp16, [1, 188, 1024]> input_503_cast_fp16 = reshape(shape = var_1877, x = var_1876_cast_fp16)[name = tensor<string, []>("input_503_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_9_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236236032))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237284672))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237286784)))];
            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = module_layers_9_self_attn_linear_out_bias_to_fp16, weight = module_layers_9_self_attn_linear_out_weight_to_fp16_quantized, x = input_503_cast_fp16)[name = tensor<string, []>("linear_88_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_507_cast_fp16 = add(x = input_499_cast_fp16, y = linear_88_cast_fp16)[name = tensor<string, []>("input_507_cast_fp16")];
            tensor<int32, [1]> x_215_axes_0 = const()[name = tensor<string, []>("x_215_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_9_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237288896)))];
            tensor<fp16, [1024]> module_layers_9_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237291008)))];
            tensor<fp16, [1, 188, 1024]> x_215_cast_fp16 = layer_norm(axes = x_215_axes_0, beta = module_layers_9_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_conv_weight_to_fp16, x = input_507_cast_fp16)[name = tensor<string, []>("x_215_cast_fp16")];
            tensor<int32, [3]> input_509_perm_0 = const()[name = tensor<string, []>("input_509_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_511_pad_type_0 = const()[name = tensor<string, []>("input_511_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_511_strides_0 = const()[name = tensor<string, []>("input_511_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_511_pad_0 = const()[name = tensor<string, []>("input_511_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_511_dilations_0 = const()[name = tensor<string, []>("input_511_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_511_groups_0 = const()[name = tensor<string, []>("input_511_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(237293120))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239390336))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239394496)))];
            tensor<fp16, [1, 1024, 188]> input_509_cast_fp16 = transpose(perm = input_509_perm_0, x = x_215_cast_fp16)[name = tensor<string, []>("transpose_244")];
            tensor<fp16, [1, 2048, 188]> input_511_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv1_bias_to_fp16, dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_509_cast_fp16)[name = tensor<string, []>("input_511_cast_fp16")];
            tensor<int32, []> x_217_split_num_splits_0 = const()[name = tensor<string, []>("x_217_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_217_split_axis_0 = const()[name = tensor<string, []>("x_217_split_axis_0"), val = tensor<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_511_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_217_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_513_cast_fp16 = select(a = var_11_to_fp16, b = x_217_cast_fp16, cond = var_335)[name = tensor<string, []>("input_513_cast_fp16")];
            tensor<int32, [6]> input_515_pad_0 = const()[name = tensor<string, []>("input_515_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_515_mode_0 = const()[name = tensor<string, []>("input_515_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_107_to_fp16 = const()[name = tensor<string, []>("const_107_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_515_cast_fp16 = pad(constant_val = const_107_to_fp16, mode = input_515_mode_0, pad = input_515_pad_0, x = input_513_cast_fp16)[name = tensor<string, []>("input_515_cast_fp16")];
            tensor<string, []> input_517_pad_type_0 = const()[name = tensor<string, []>("input_517_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_517_groups_0 = const()[name = tensor<string, []>("input_517_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_517_strides_0 = const()[name = tensor<string, []>("input_517_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_517_pad_0 = const()[name = tensor<string, []>("input_517_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_517_dilations_0 = const()[name = tensor<string, []>("input_517_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_266_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_266_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239398656))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239407936))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_267_to_fp16 = const()[name = tensor<string, []>("const_267_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239410048)))];
            tensor<fp16, [1, 1024, 188]> input_519_cast_fp16 = conv(bias = const_267_to_fp16, dilations = input_517_dilations_0, groups = input_517_groups_0, pad = input_517_pad_0, pad_type = input_517_pad_type_0, strides = input_517_strides_0, weight = const_266_to_fp16_quantized, x = input_515_cast_fp16)[name = tensor<string, []>("input_519_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_521_cast_fp16 = silu(x = input_519_cast_fp16)[name = tensor<string, []>("input_521_cast_fp16")];
            tensor<string, []> x_219_pad_type_0 = const()[name = tensor<string, []>("x_219_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_219_strides_0 = const()[name = tensor<string, []>("x_219_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_219_pad_0 = const()[name = tensor<string, []>("x_219_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_219_dilations_0 = const()[name = tensor<string, []>("x_219_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_219_groups_0 = const()[name = tensor<string, []>("x_219_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239412160))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240460800))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240462912)))];
            tensor<fp16, [1, 1024, 188]> x_219_cast_fp16 = conv(bias = module_layers_9_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_521_cast_fp16)[name = tensor<string, []>("x_219_cast_fp16")];
            tensor<int32, [3]> input_523_perm_0 = const()[name = tensor<string, []>("input_523_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_523_cast_fp16 = transpose(perm = input_523_perm_0, x = x_219_cast_fp16)[name = tensor<string, []>("transpose_243")];
            tensor<fp16, [1, 188, 1024]> input_525_cast_fp16 = add(x = input_507_cast_fp16, y = input_523_cast_fp16)[name = tensor<string, []>("input_525_cast_fp16")];
            tensor<int32, [1]> input_527_axes_0 = const()[name = tensor<string, []>("input_527_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240465024)))];
            tensor<fp16, [1024]> module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240467136)))];
            tensor<fp16, [1, 188, 1024]> input_527_cast_fp16 = layer_norm(axes = input_527_axes_0, beta = module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_525_cast_fp16)[name = tensor<string, []>("input_527_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(240469248))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244663616))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244671872)))];
            tensor<fp16, [1, 188, 4096]> linear_89_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized, x = input_527_cast_fp16)[name = tensor<string, []>("linear_89_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_531_cast_fp16 = silu(x = linear_89_cast_fp16)[name = tensor<string, []>("input_531_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(244680128))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248874496))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248876608)))];
            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized, x = input_531_cast_fp16)[name = tensor<string, []>("linear_90_cast_fp16")];
            tensor<fp16, []> var_1943_to_fp16 = const()[name = tensor<string, []>("op_1943_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1944_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1943_to_fp16)[name = tensor<string, []>("op_1944_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_537_cast_fp16 = add(x = input_525_cast_fp16, y = var_1944_cast_fp16)[name = tensor<string, []>("input_537_cast_fp16")];
            tensor<int32, [1]> input_539_axes_0 = const()[name = tensor<string, []>("input_539_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_9_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248878720)))];
            tensor<fp16, [1024]> module_layers_9_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248880832)))];
            tensor<fp16, [1, 188, 1024]> input_539_cast_fp16 = layer_norm(axes = input_539_axes_0, beta = module_layers_9_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_9_norm_out_weight_to_fp16, x = input_537_cast_fp16)[name = tensor<string, []>("input_539_cast_fp16")];
            tensor<int32, [1]> input_541_axes_0 = const()[name = tensor<string, []>("input_541_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248882944)))];
            tensor<fp16, [1024]> module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248885056)))];
            tensor<fp16, [1, 188, 1024]> input_541_cast_fp16 = layer_norm(axes = input_541_axes_0, beta = module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_539_cast_fp16)[name = tensor<string, []>("input_541_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248887168))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253081536))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253089792)))];
            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized, x = input_541_cast_fp16)[name = tensor<string, []>("linear_91_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_545_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor<string, []>("input_545_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253098048))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257292416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257294528)))];
            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized, x = input_545_cast_fp16)[name = tensor<string, []>("linear_92_cast_fp16")];
            tensor<fp16, []> var_1974_to_fp16 = const()[name = tensor<string, []>("op_1974_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_1975_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_1974_to_fp16)[name = tensor<string, []>("op_1975_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_551_cast_fp16 = add(x = input_539_cast_fp16, y = var_1975_cast_fp16)[name = tensor<string, []>("input_551_cast_fp16")];
            tensor<int32, [1]> query_21_axes_0 = const()[name = tensor<string, []>("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_10_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257296640)))];
            tensor<fp16, [1024]> module_layers_10_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257298752)))];
            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_self_att_weight_to_fp16, x = input_551_cast_fp16)[name = tensor<string, []>("query_21_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257300864))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258349504))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258351616)))];
            tensor<fp16, [1, 188, 1024]> linear_93_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_q_bias_to_fp16, weight = module_layers_10_self_attn_linear_q_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = tensor<string, []>("linear_93_cast_fp16")];
            tensor<int32, [4]> var_1992 = const()[name = tensor<string, []>("op_1992"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_1992, x = linear_93_cast_fp16)[name = tensor<string, []>("q_61_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258353728))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259402368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259404480)))];
            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_k_bias_to_fp16, weight = module_layers_10_self_attn_linear_k_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = tensor<string, []>("linear_94_cast_fp16")];
            tensor<int32, [4]> var_1997 = const()[name = tensor<string, []>("op_1997"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_1997, x = linear_94_cast_fp16)[name = tensor<string, []>("k_41_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259406592))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260455232))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260457344)))];
            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_v_bias_to_fp16, weight = module_layers_10_self_attn_linear_v_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = tensor<string, []>("linear_95_cast_fp16")];
            tensor<int32, [4]> var_2002 = const()[name = tensor<string, []>("op_2002"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2002, x = linear_95_cast_fp16)[name = tensor<string, []>("v_21_cast_fp16")];
            tensor<int32, [4]> value_23_perm_0 = const()[name = tensor<string, []>("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_10_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260459456))), scale = tensor<fp16, [8]>([0x1.afcp-8, 0x1.f5cp-8, 0x1.41p-7, 0x1.0d8p-8, 0x1.6c8p-8, 0x1.014p-7, 0x1.9e8p-8, 0x1.4d8p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2014_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2014_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_10_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260460544))), scale = tensor<fp16, [8]>([0x1.99cp-8, 0x1.80cp-9, 0x1.c6p-9, 0x1.12cp-9, 0x1.0e4p-8, 0x1.0fp-8, 0x1.868p-8, 0x1.b48p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2016_cast_fp16 = add(x = q_61_cast_fp16, y = module_layers_10_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2016_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_227_transpose_x_0 = const()[name = tensor<string, []>("x_227_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_227_transpose_y_0 = const()[name = tensor<string, []>("x_227_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2018_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2018_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260461632))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260845696))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2016_cast_fp16)[name = tensor<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_2018_to_fp16_quantized)[name = tensor<string, []>("x_227_cast_fp16")];
            tensor<int32, [8]> x_229_pad_0 = const()[name = tensor<string, []>("x_229_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_229_mode_0 = const()[name = tensor<string, []>("x_229_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_114_to_fp16 = const()[name = tensor<string, []>("const_114_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_229_cast_fp16 = pad(constant_val = const_114_to_fp16, mode = x_229_mode_0, pad = x_229_pad_0, x = x_227_cast_fp16)[name = tensor<string, []>("x_229_cast_fp16")];
            tensor<int32, [4]> var_2026 = const()[name = tensor<string, []>("op_2026"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_231_cast_fp16 = reshape(shape = var_2026, x = x_229_cast_fp16)[name = tensor<string, []>("x_231_cast_fp16")];
            tensor<int32, [4]> var_2030_begin_0 = const()[name = tensor<string, []>("op_2030_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2030_end_0 = const()[name = tensor<string, []>("op_2030_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2030_end_mask_0 = const()[name = tensor<string, []>("op_2030_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2030_cast_fp16 = slice_by_index(begin = var_2030_begin_0, end = var_2030_end_0, end_mask = var_2030_end_mask_0, x = x_231_cast_fp16)[name = tensor<string, []>("op_2030_cast_fp16")];
            tensor<int32, [4]> var_2031 = const()[name = tensor<string, []>("op_2031"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2031, x = var_2030_cast_fp16)[name = tensor<string, []>("matrix_bd_41_cast_fp16")];
            tensor<bool, []> matrix_ac_21_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_21_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_21_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_21_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_116_perm_0 = const()[name = tensor<string, []>("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_117_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_240")];
            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2014_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_21_cast_fp16")];
            tensor<int32, [4]> matrix_bd_43_begin_0 = const()[name = tensor<string, []>("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_43_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2040_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = tensor<string, []>("op_2040_cast_fp16")];
            tensor<fp16, []> _inversed_scores_41_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_41_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2040_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_41_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_43_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2046_cast_fp16 = softmax(axis = var_30, x = scores_43_cast_fp16)[name = tensor<string, []>("op_2046_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_553_cast_fp16 = select(a = var_11_to_fp16, b = var_2046_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_553_cast_fp16")];
            tensor<bool, []> x_233_transpose_x_0 = const()[name = tensor<string, []>("x_233_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_233_transpose_y_0 = const()[name = tensor<string, []>("x_233_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_21_cast_fp16)[name = tensor<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_553_cast_fp16, y = value_23_cast_fp16)[name = tensor<string, []>("x_233_cast_fp16")];
            tensor<int32, [4]> var_2050_perm_0 = const()[name = tensor<string, []>("op_2050_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2051 = const()[name = tensor<string, []>("op_2051"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2050_cast_fp16 = transpose(perm = var_2050_perm_0, x = x_233_cast_fp16)[name = tensor<string, []>("transpose_238")];
            tensor<fp16, [1, 188, 1024]> input_555_cast_fp16 = reshape(shape = var_2051, x = var_2050_cast_fp16)[name = tensor<string, []>("input_555_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_10_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(260846528))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261895168))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261897280)))];
            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = module_layers_10_self_attn_linear_out_bias_to_fp16, weight = module_layers_10_self_attn_linear_out_weight_to_fp16_quantized, x = input_555_cast_fp16)[name = tensor<string, []>("linear_97_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_559_cast_fp16 = add(x = input_551_cast_fp16, y = linear_97_cast_fp16)[name = tensor<string, []>("input_559_cast_fp16")];
            tensor<int32, [1]> x_237_axes_0 = const()[name = tensor<string, []>("x_237_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_10_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261899392)))];
            tensor<fp16, [1024]> module_layers_10_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261901504)))];
            tensor<fp16, [1, 188, 1024]> x_237_cast_fp16 = layer_norm(axes = x_237_axes_0, beta = module_layers_10_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_conv_weight_to_fp16, x = input_559_cast_fp16)[name = tensor<string, []>("x_237_cast_fp16")];
            tensor<int32, [3]> input_561_perm_0 = const()[name = tensor<string, []>("input_561_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_563_pad_type_0 = const()[name = tensor<string, []>("input_563_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_563_strides_0 = const()[name = tensor<string, []>("input_563_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_563_pad_0 = const()[name = tensor<string, []>("input_563_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_563_dilations_0 = const()[name = tensor<string, []>("input_563_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_563_groups_0 = const()[name = tensor<string, []>("input_563_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261903616))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264000832))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264004992)))];
            tensor<fp16, [1, 1024, 188]> input_561_cast_fp16 = transpose(perm = input_561_perm_0, x = x_237_cast_fp16)[name = tensor<string, []>("transpose_237")];
            tensor<fp16, [1, 2048, 188]> input_563_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv1_bias_to_fp16, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_561_cast_fp16)[name = tensor<string, []>("input_563_cast_fp16")];
            tensor<int32, []> x_239_split_num_splits_0 = const()[name = tensor<string, []>("x_239_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_239_split_axis_0 = const()[name = tensor<string, []>("x_239_split_axis_0"), val = tensor<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_563_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_239_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_565_cast_fp16 = select(a = var_11_to_fp16, b = x_239_cast_fp16, cond = var_335)[name = tensor<string, []>("input_565_cast_fp16")];
            tensor<int32, [6]> input_567_pad_0 = const()[name = tensor<string, []>("input_567_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_567_mode_0 = const()[name = tensor<string, []>("input_567_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_117_to_fp16 = const()[name = tensor<string, []>("const_117_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_567_cast_fp16 = pad(constant_val = const_117_to_fp16, mode = input_567_mode_0, pad = input_567_pad_0, x = input_565_cast_fp16)[name = tensor<string, []>("input_567_cast_fp16")];
            tensor<string, []> input_569_pad_type_0 = const()[name = tensor<string, []>("input_569_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_569_groups_0 = const()[name = tensor<string, []>("input_569_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_569_strides_0 = const()[name = tensor<string, []>("input_569_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_569_pad_0 = const()[name = tensor<string, []>("input_569_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_569_dilations_0 = const()[name = tensor<string, []>("input_569_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_268_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_268_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264009152))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264018432))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_269_to_fp16 = const()[name = tensor<string, []>("const_269_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264020544)))];
            tensor<fp16, [1, 1024, 188]> input_571_cast_fp16 = conv(bias = const_269_to_fp16, dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = const_268_to_fp16_quantized, x = input_567_cast_fp16)[name = tensor<string, []>("input_571_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_573_cast_fp16 = silu(x = input_571_cast_fp16)[name = tensor<string, []>("input_573_cast_fp16")];
            tensor<string, []> x_241_pad_type_0 = const()[name = tensor<string, []>("x_241_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_241_strides_0 = const()[name = tensor<string, []>("x_241_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_241_pad_0 = const()[name = tensor<string, []>("x_241_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_241_dilations_0 = const()[name = tensor<string, []>("x_241_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_241_groups_0 = const()[name = tensor<string, []>("x_241_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264022656))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265071296))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265073408)))];
            tensor<fp16, [1, 1024, 188]> x_241_cast_fp16 = conv(bias = module_layers_10_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_573_cast_fp16)[name = tensor<string, []>("x_241_cast_fp16")];
            tensor<int32, [3]> input_575_perm_0 = const()[name = tensor<string, []>("input_575_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_575_cast_fp16 = transpose(perm = input_575_perm_0, x = x_241_cast_fp16)[name = tensor<string, []>("transpose_236")];
            tensor<fp16, [1, 188, 1024]> input_577_cast_fp16 = add(x = input_559_cast_fp16, y = input_575_cast_fp16)[name = tensor<string, []>("input_577_cast_fp16")];
            tensor<int32, [1]> input_579_axes_0 = const()[name = tensor<string, []>("input_579_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265075520)))];
            tensor<fp16, [1024]> module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265077632)))];
            tensor<fp16, [1, 188, 1024]> input_579_cast_fp16 = layer_norm(axes = input_579_axes_0, beta = module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_577_cast_fp16)[name = tensor<string, []>("input_579_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265079744))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269274112))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269282368)))];
            tensor<fp16, [1, 188, 4096]> linear_98_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized, x = input_579_cast_fp16)[name = tensor<string, []>("linear_98_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_583_cast_fp16 = silu(x = linear_98_cast_fp16)[name = tensor<string, []>("input_583_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269290624))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273484992))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273487104)))];
            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized, x = input_583_cast_fp16)[name = tensor<string, []>("linear_99_cast_fp16")];
            tensor<fp16, []> var_2117_to_fp16 = const()[name = tensor<string, []>("op_2117_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2118_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2117_to_fp16)[name = tensor<string, []>("op_2118_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_589_cast_fp16 = add(x = input_577_cast_fp16, y = var_2118_cast_fp16)[name = tensor<string, []>("input_589_cast_fp16")];
            tensor<int32, [1]> input_591_axes_0 = const()[name = tensor<string, []>("input_591_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_10_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273489216)))];
            tensor<fp16, [1024]> module_layers_10_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273491328)))];
            tensor<fp16, [1, 188, 1024]> input_591_cast_fp16 = layer_norm(axes = input_591_axes_0, beta = module_layers_10_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_10_norm_out_weight_to_fp16, x = input_589_cast_fp16)[name = tensor<string, []>("input_591_cast_fp16")];
            tensor<int32, [1]> input_593_axes_0 = const()[name = tensor<string, []>("input_593_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273493440)))];
            tensor<fp16, [1024]> module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273495552)))];
            tensor<fp16, [1, 188, 1024]> input_593_cast_fp16 = layer_norm(axes = input_593_axes_0, beta = module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_591_cast_fp16)[name = tensor<string, []>("input_593_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(273497664))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277692032))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277700288)))];
            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized, x = input_593_cast_fp16)[name = tensor<string, []>("linear_100_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_597_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor<string, []>("input_597_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277708544))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281902912))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281905024)))];
            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized, x = input_597_cast_fp16)[name = tensor<string, []>("linear_101_cast_fp16")];
            tensor<fp16, []> var_2148_to_fp16 = const()[name = tensor<string, []>("op_2148_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2149_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2148_to_fp16)[name = tensor<string, []>("op_2149_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_603_cast_fp16 = add(x = input_591_cast_fp16, y = var_2149_cast_fp16)[name = tensor<string, []>("input_603_cast_fp16")];
            tensor<int32, [1]> query_23_axes_0 = const()[name = tensor<string, []>("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_11_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281907136)))];
            tensor<fp16, [1024]> module_layers_11_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281909248)))];
            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_self_att_weight_to_fp16, x = input_603_cast_fp16)[name = tensor<string, []>("query_23_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281911360))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282960000))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282962112)))];
            tensor<fp16, [1, 188, 1024]> linear_102_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_q_bias_to_fp16, weight = module_layers_11_self_attn_linear_q_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = tensor<string, []>("linear_102_cast_fp16")];
            tensor<int32, [4]> var_2166 = const()[name = tensor<string, []>("op_2166"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2166, x = linear_102_cast_fp16)[name = tensor<string, []>("q_67_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(282964224))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284012864))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284014976)))];
            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_k_bias_to_fp16, weight = module_layers_11_self_attn_linear_k_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = tensor<string, []>("linear_103_cast_fp16")];
            tensor<int32, [4]> var_2171 = const()[name = tensor<string, []>("op_2171"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2171, x = linear_103_cast_fp16)[name = tensor<string, []>("k_45_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(284017088))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285065728))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285067840)))];
            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_v_bias_to_fp16, weight = module_layers_11_self_attn_linear_v_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = tensor<string, []>("linear_104_cast_fp16")];
            tensor<int32, [4]> var_2176 = const()[name = tensor<string, []>("op_2176"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2176, x = linear_104_cast_fp16)[name = tensor<string, []>("v_23_cast_fp16")];
            tensor<int32, [4]> value_25_perm_0 = const()[name = tensor<string, []>("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_11_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285069952))), scale = tensor<fp16, [8]>([0x1.898p-8, 0x1.78p-8, 0x1.ee8p-8, 0x1.a48p-8, 0x1.0ap-7, 0x1.964p-8, 0x1.e04p-8, 0x1.a68p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2188_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2188_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_11_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285071040))), scale = tensor<fp16, [8]>([0x1.b48p-9, 0x1.628p-8, 0x1.57cp-8, 0x1.fb8p-9, 0x1.7d8p-9, 0x1.4acp-9, 0x1.3b4p-8, 0x1.3e4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2190_cast_fp16 = add(x = q_67_cast_fp16, y = module_layers_11_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2190_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_249_transpose_x_0 = const()[name = tensor<string, []>("x_249_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_249_transpose_y_0 = const()[name = tensor<string, []>("x_249_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2192_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2192_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285072128))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285456192))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2190_cast_fp16)[name = tensor<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_2192_to_fp16_quantized)[name = tensor<string, []>("x_249_cast_fp16")];
            tensor<int32, [8]> x_251_pad_0 = const()[name = tensor<string, []>("x_251_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_251_mode_0 = const()[name = tensor<string, []>("x_251_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_124_to_fp16 = const()[name = tensor<string, []>("const_124_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_251_cast_fp16 = pad(constant_val = const_124_to_fp16, mode = x_251_mode_0, pad = x_251_pad_0, x = x_249_cast_fp16)[name = tensor<string, []>("x_251_cast_fp16")];
            tensor<int32, [4]> var_2200 = const()[name = tensor<string, []>("op_2200"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_253_cast_fp16 = reshape(shape = var_2200, x = x_251_cast_fp16)[name = tensor<string, []>("x_253_cast_fp16")];
            tensor<int32, [4]> var_2204_begin_0 = const()[name = tensor<string, []>("op_2204_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2204_end_0 = const()[name = tensor<string, []>("op_2204_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2204_end_mask_0 = const()[name = tensor<string, []>("op_2204_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2204_cast_fp16 = slice_by_index(begin = var_2204_begin_0, end = var_2204_end_0, end_mask = var_2204_end_mask_0, x = x_253_cast_fp16)[name = tensor<string, []>("op_2204_cast_fp16")];
            tensor<int32, [4]> var_2205 = const()[name = tensor<string, []>("op_2205"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2205, x = var_2204_cast_fp16)[name = tensor<string, []>("matrix_bd_45_cast_fp16")];
            tensor<bool, []> matrix_ac_23_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_23_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_23_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_23_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_118_perm_0 = const()[name = tensor<string, []>("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_119_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_233")];
            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2188_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_23_cast_fp16")];
            tensor<int32, [4]> matrix_bd_47_begin_0 = const()[name = tensor<string, []>("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_47_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2214_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = tensor<string, []>("op_2214_cast_fp16")];
            tensor<fp16, []> _inversed_scores_45_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_45_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2214_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_45_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_47_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2220_cast_fp16 = softmax(axis = var_30, x = scores_47_cast_fp16)[name = tensor<string, []>("op_2220_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_605_cast_fp16 = select(a = var_11_to_fp16, b = var_2220_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_605_cast_fp16")];
            tensor<bool, []> x_255_transpose_x_0 = const()[name = tensor<string, []>("x_255_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_255_transpose_y_0 = const()[name = tensor<string, []>("x_255_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_23_cast_fp16)[name = tensor<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_605_cast_fp16, y = value_25_cast_fp16)[name = tensor<string, []>("x_255_cast_fp16")];
            tensor<int32, [4]> var_2224_perm_0 = const()[name = tensor<string, []>("op_2224_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2225 = const()[name = tensor<string, []>("op_2225"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2224_cast_fp16 = transpose(perm = var_2224_perm_0, x = x_255_cast_fp16)[name = tensor<string, []>("transpose_231")];
            tensor<fp16, [1, 188, 1024]> input_607_cast_fp16 = reshape(shape = var_2225, x = var_2224_cast_fp16)[name = tensor<string, []>("input_607_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_11_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285457024))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286505664))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286507776)))];
            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = module_layers_11_self_attn_linear_out_bias_to_fp16, weight = module_layers_11_self_attn_linear_out_weight_to_fp16_quantized, x = input_607_cast_fp16)[name = tensor<string, []>("linear_106_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_611_cast_fp16 = add(x = input_603_cast_fp16, y = linear_106_cast_fp16)[name = tensor<string, []>("input_611_cast_fp16")];
            tensor<int32, [1]> x_259_axes_0 = const()[name = tensor<string, []>("x_259_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_11_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286509888)))];
            tensor<fp16, [1024]> module_layers_11_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286512000)))];
            tensor<fp16, [1, 188, 1024]> x_259_cast_fp16 = layer_norm(axes = x_259_axes_0, beta = module_layers_11_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_conv_weight_to_fp16, x = input_611_cast_fp16)[name = tensor<string, []>("x_259_cast_fp16")];
            tensor<int32, [3]> input_613_perm_0 = const()[name = tensor<string, []>("input_613_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_615_pad_type_0 = const()[name = tensor<string, []>("input_615_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_615_strides_0 = const()[name = tensor<string, []>("input_615_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_615_pad_0 = const()[name = tensor<string, []>("input_615_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_615_dilations_0 = const()[name = tensor<string, []>("input_615_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_615_groups_0 = const()[name = tensor<string, []>("input_615_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286514112))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288611328))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288615488)))];
            tensor<fp16, [1, 1024, 188]> input_613_cast_fp16 = transpose(perm = input_613_perm_0, x = x_259_cast_fp16)[name = tensor<string, []>("transpose_230")];
            tensor<fp16, [1, 2048, 188]> input_615_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv1_bias_to_fp16, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_613_cast_fp16)[name = tensor<string, []>("input_615_cast_fp16")];
            tensor<int32, []> x_261_split_num_splits_0 = const()[name = tensor<string, []>("x_261_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_261_split_axis_0 = const()[name = tensor<string, []>("x_261_split_axis_0"), val = tensor<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_615_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_261_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_617_cast_fp16 = select(a = var_11_to_fp16, b = x_261_cast_fp16, cond = var_335)[name = tensor<string, []>("input_617_cast_fp16")];
            tensor<int32, [6]> input_619_pad_0 = const()[name = tensor<string, []>("input_619_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_619_mode_0 = const()[name = tensor<string, []>("input_619_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_127_to_fp16 = const()[name = tensor<string, []>("const_127_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_619_cast_fp16 = pad(constant_val = const_127_to_fp16, mode = input_619_mode_0, pad = input_619_pad_0, x = input_617_cast_fp16)[name = tensor<string, []>("input_619_cast_fp16")];
            tensor<string, []> input_621_pad_type_0 = const()[name = tensor<string, []>("input_621_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_621_groups_0 = const()[name = tensor<string, []>("input_621_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_621_strides_0 = const()[name = tensor<string, []>("input_621_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_621_pad_0 = const()[name = tensor<string, []>("input_621_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_621_dilations_0 = const()[name = tensor<string, []>("input_621_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_270_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_270_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288619648))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288628928))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_271_to_fp16 = const()[name = tensor<string, []>("const_271_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288631040)))];
            tensor<fp16, [1, 1024, 188]> input_623_cast_fp16 = conv(bias = const_271_to_fp16, dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = const_270_to_fp16_quantized, x = input_619_cast_fp16)[name = tensor<string, []>("input_623_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_625_cast_fp16 = silu(x = input_623_cast_fp16)[name = tensor<string, []>("input_625_cast_fp16")];
            tensor<string, []> x_263_pad_type_0 = const()[name = tensor<string, []>("x_263_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_263_strides_0 = const()[name = tensor<string, []>("x_263_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_263_pad_0 = const()[name = tensor<string, []>("x_263_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_263_dilations_0 = const()[name = tensor<string, []>("x_263_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_263_groups_0 = const()[name = tensor<string, []>("x_263_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288633152))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289681792))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289683904)))];
            tensor<fp16, [1, 1024, 188]> x_263_cast_fp16 = conv(bias = module_layers_11_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_625_cast_fp16)[name = tensor<string, []>("x_263_cast_fp16")];
            tensor<int32, [3]> input_627_perm_0 = const()[name = tensor<string, []>("input_627_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_627_cast_fp16 = transpose(perm = input_627_perm_0, x = x_263_cast_fp16)[name = tensor<string, []>("transpose_229")];
            tensor<fp16, [1, 188, 1024]> input_629_cast_fp16 = add(x = input_611_cast_fp16, y = input_627_cast_fp16)[name = tensor<string, []>("input_629_cast_fp16")];
            tensor<int32, [1]> input_631_axes_0 = const()[name = tensor<string, []>("input_631_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289686016)))];
            tensor<fp16, [1024]> module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289688128)))];
            tensor<fp16, [1, 188, 1024]> input_631_cast_fp16 = layer_norm(axes = input_631_axes_0, beta = module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_629_cast_fp16)[name = tensor<string, []>("input_631_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289690240))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293884608))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293892864)))];
            tensor<fp16, [1, 188, 4096]> linear_107_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized, x = input_631_cast_fp16)[name = tensor<string, []>("linear_107_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_635_cast_fp16 = silu(x = linear_107_cast_fp16)[name = tensor<string, []>("input_635_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(293901120))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298095488))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298097600)))];
            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized, x = input_635_cast_fp16)[name = tensor<string, []>("linear_108_cast_fp16")];
            tensor<fp16, []> var_2291_to_fp16 = const()[name = tensor<string, []>("op_2291_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2292_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2291_to_fp16)[name = tensor<string, []>("op_2292_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_641_cast_fp16 = add(x = input_629_cast_fp16, y = var_2292_cast_fp16)[name = tensor<string, []>("input_641_cast_fp16")];
            tensor<int32, [1]> input_643_axes_0 = const()[name = tensor<string, []>("input_643_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_11_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298099712)))];
            tensor<fp16, [1024]> module_layers_11_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298101824)))];
            tensor<fp16, [1, 188, 1024]> input_643_cast_fp16 = layer_norm(axes = input_643_axes_0, beta = module_layers_11_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_11_norm_out_weight_to_fp16, x = input_641_cast_fp16)[name = tensor<string, []>("input_643_cast_fp16")];
            tensor<int32, [1]> input_645_axes_0 = const()[name = tensor<string, []>("input_645_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298103936)))];
            tensor<fp16, [1024]> module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298106048)))];
            tensor<fp16, [1, 188, 1024]> input_645_cast_fp16 = layer_norm(axes = input_645_axes_0, beta = module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_643_cast_fp16)[name = tensor<string, []>("input_645_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298108160))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302302528))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302310784)))];
            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized, x = input_645_cast_fp16)[name = tensor<string, []>("linear_109_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_649_cast_fp16 = silu(x = linear_109_cast_fp16)[name = tensor<string, []>("input_649_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302319040))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306513408))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306515520)))];
            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized, x = input_649_cast_fp16)[name = tensor<string, []>("linear_110_cast_fp16")];
            tensor<fp16, []> var_2322_to_fp16 = const()[name = tensor<string, []>("op_2322_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2323_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2322_to_fp16)[name = tensor<string, []>("op_2323_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_655_cast_fp16 = add(x = input_643_cast_fp16, y = var_2323_cast_fp16)[name = tensor<string, []>("input_655_cast_fp16")];
            tensor<int32, [1]> query_25_axes_0 = const()[name = tensor<string, []>("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_12_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306517632)))];
            tensor<fp16, [1024]> module_layers_12_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306519744)))];
            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_self_att_weight_to_fp16, x = input_655_cast_fp16)[name = tensor<string, []>("query_25_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306521856))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307570496))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307572608)))];
            tensor<fp16, [1, 188, 1024]> linear_111_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_q_bias_to_fp16, weight = module_layers_12_self_attn_linear_q_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = tensor<string, []>("linear_111_cast_fp16")];
            tensor<int32, [4]> var_2340 = const()[name = tensor<string, []>("op_2340"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2340, x = linear_111_cast_fp16)[name = tensor<string, []>("q_73_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307574720))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308623360))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308625472)))];
            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_k_bias_to_fp16, weight = module_layers_12_self_attn_linear_k_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = tensor<string, []>("linear_112_cast_fp16")];
            tensor<int32, [4]> var_2345 = const()[name = tensor<string, []>("op_2345"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2345, x = linear_112_cast_fp16)[name = tensor<string, []>("k_49_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308627584))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309676224))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309678336)))];
            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_v_bias_to_fp16, weight = module_layers_12_self_attn_linear_v_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = tensor<string, []>("linear_113_cast_fp16")];
            tensor<int32, [4]> var_2350 = const()[name = tensor<string, []>("op_2350"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2350, x = linear_113_cast_fp16)[name = tensor<string, []>("v_25_cast_fp16")];
            tensor<int32, [4]> value_27_perm_0 = const()[name = tensor<string, []>("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_12_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309680448))), scale = tensor<fp16, [8]>([0x1.c04p-8, 0x1.a4p-8, 0x1.a68p-8, 0x1.c6cp-8, 0x1.bb8p-8, 0x1.098p-7, 0x1.264p-7, 0x1.26cp-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2362_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2362_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_12_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309681536))), scale = tensor<fp16, [8]>([0x1.568p-9, 0x1.8ecp-8, 0x1.768p-9, 0x1.05cp-8, 0x1.098p-8, 0x1.034p-8, 0x1.424p-8, 0x1.e2cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2364_cast_fp16 = add(x = q_73_cast_fp16, y = module_layers_12_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2364_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_271_transpose_x_0 = const()[name = tensor<string, []>("x_271_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_271_transpose_y_0 = const()[name = tensor<string, []>("x_271_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2366_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2366_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(309682624))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310066688))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2364_cast_fp16)[name = tensor<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_2366_to_fp16_quantized)[name = tensor<string, []>("x_271_cast_fp16")];
            tensor<int32, [8]> x_273_pad_0 = const()[name = tensor<string, []>("x_273_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_273_mode_0 = const()[name = tensor<string, []>("x_273_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_134_to_fp16 = const()[name = tensor<string, []>("const_134_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_273_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = x_273_mode_0, pad = x_273_pad_0, x = x_271_cast_fp16)[name = tensor<string, []>("x_273_cast_fp16")];
            tensor<int32, [4]> var_2374 = const()[name = tensor<string, []>("op_2374"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_275_cast_fp16 = reshape(shape = var_2374, x = x_273_cast_fp16)[name = tensor<string, []>("x_275_cast_fp16")];
            tensor<int32, [4]> var_2378_begin_0 = const()[name = tensor<string, []>("op_2378_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2378_end_0 = const()[name = tensor<string, []>("op_2378_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2378_end_mask_0 = const()[name = tensor<string, []>("op_2378_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2378_cast_fp16 = slice_by_index(begin = var_2378_begin_0, end = var_2378_end_0, end_mask = var_2378_end_mask_0, x = x_275_cast_fp16)[name = tensor<string, []>("op_2378_cast_fp16")];
            tensor<int32, [4]> var_2379 = const()[name = tensor<string, []>("op_2379"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2379, x = var_2378_cast_fp16)[name = tensor<string, []>("matrix_bd_49_cast_fp16")];
            tensor<bool, []> matrix_ac_25_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_25_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_120_perm_0 = const()[name = tensor<string, []>("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_121_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_226")];
            tensor<fp16, [1, 8, 188, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2362_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_25_cast_fp16")];
            tensor<int32, [4]> matrix_bd_51_begin_0 = const()[name = tensor<string, []>("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_51_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2388_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = tensor<string, []>("op_2388_cast_fp16")];
            tensor<fp16, []> _inversed_scores_49_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_49_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2388_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_49_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_51_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2394_cast_fp16 = softmax(axis = var_30, x = scores_51_cast_fp16)[name = tensor<string, []>("op_2394_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_657_cast_fp16 = select(a = var_11_to_fp16, b = var_2394_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_657_cast_fp16")];
            tensor<bool, []> x_277_transpose_x_0 = const()[name = tensor<string, []>("x_277_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_277_transpose_y_0 = const()[name = tensor<string, []>("x_277_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_25_cast_fp16)[name = tensor<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_657_cast_fp16, y = value_27_cast_fp16)[name = tensor<string, []>("x_277_cast_fp16")];
            tensor<int32, [4]> var_2398_perm_0 = const()[name = tensor<string, []>("op_2398_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2399 = const()[name = tensor<string, []>("op_2399"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2398_cast_fp16 = transpose(perm = var_2398_perm_0, x = x_277_cast_fp16)[name = tensor<string, []>("transpose_224")];
            tensor<fp16, [1, 188, 1024]> input_659_cast_fp16 = reshape(shape = var_2399, x = var_2398_cast_fp16)[name = tensor<string, []>("input_659_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_12_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310067520))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311116160))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311118272)))];
            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = module_layers_12_self_attn_linear_out_bias_to_fp16, weight = module_layers_12_self_attn_linear_out_weight_to_fp16_quantized, x = input_659_cast_fp16)[name = tensor<string, []>("linear_115_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_663_cast_fp16 = add(x = input_655_cast_fp16, y = linear_115_cast_fp16)[name = tensor<string, []>("input_663_cast_fp16")];
            tensor<int32, [1]> x_281_axes_0 = const()[name = tensor<string, []>("x_281_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_12_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311120384)))];
            tensor<fp16, [1024]> module_layers_12_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311122496)))];
            tensor<fp16, [1, 188, 1024]> x_281_cast_fp16 = layer_norm(axes = x_281_axes_0, beta = module_layers_12_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_conv_weight_to_fp16, x = input_663_cast_fp16)[name = tensor<string, []>("x_281_cast_fp16")];
            tensor<int32, [3]> input_665_perm_0 = const()[name = tensor<string, []>("input_665_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_667_pad_type_0 = const()[name = tensor<string, []>("input_667_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_667_strides_0 = const()[name = tensor<string, []>("input_667_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_667_pad_0 = const()[name = tensor<string, []>("input_667_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_667_dilations_0 = const()[name = tensor<string, []>("input_667_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_667_groups_0 = const()[name = tensor<string, []>("input_667_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311124608))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313221824))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313225984)))];
            tensor<fp16, [1, 1024, 188]> input_665_cast_fp16 = transpose(perm = input_665_perm_0, x = x_281_cast_fp16)[name = tensor<string, []>("transpose_223")];
            tensor<fp16, [1, 2048, 188]> input_667_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv1_bias_to_fp16, dilations = input_667_dilations_0, groups = input_667_groups_0, pad = input_667_pad_0, pad_type = input_667_pad_type_0, strides = input_667_strides_0, weight = module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_665_cast_fp16)[name = tensor<string, []>("input_667_cast_fp16")];
            tensor<int32, []> x_283_split_num_splits_0 = const()[name = tensor<string, []>("x_283_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_283_split_axis_0 = const()[name = tensor<string, []>("x_283_split_axis_0"), val = tensor<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_667_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_283_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_669_cast_fp16 = select(a = var_11_to_fp16, b = x_283_cast_fp16, cond = var_335)[name = tensor<string, []>("input_669_cast_fp16")];
            tensor<int32, [6]> input_671_pad_0 = const()[name = tensor<string, []>("input_671_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_671_mode_0 = const()[name = tensor<string, []>("input_671_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_137_to_fp16 = const()[name = tensor<string, []>("const_137_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_671_cast_fp16 = pad(constant_val = const_137_to_fp16, mode = input_671_mode_0, pad = input_671_pad_0, x = input_669_cast_fp16)[name = tensor<string, []>("input_671_cast_fp16")];
            tensor<string, []> input_673_pad_type_0 = const()[name = tensor<string, []>("input_673_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_673_groups_0 = const()[name = tensor<string, []>("input_673_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_673_strides_0 = const()[name = tensor<string, []>("input_673_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_673_pad_0 = const()[name = tensor<string, []>("input_673_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_673_dilations_0 = const()[name = tensor<string, []>("input_673_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_272_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_272_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313230144))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313239424))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_273_to_fp16 = const()[name = tensor<string, []>("const_273_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313241536)))];
            tensor<fp16, [1, 1024, 188]> input_675_cast_fp16 = conv(bias = const_273_to_fp16, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = const_272_to_fp16_quantized, x = input_671_cast_fp16)[name = tensor<string, []>("input_675_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_677_cast_fp16 = silu(x = input_675_cast_fp16)[name = tensor<string, []>("input_677_cast_fp16")];
            tensor<string, []> x_285_pad_type_0 = const()[name = tensor<string, []>("x_285_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_285_strides_0 = const()[name = tensor<string, []>("x_285_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_285_pad_0 = const()[name = tensor<string, []>("x_285_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_285_dilations_0 = const()[name = tensor<string, []>("x_285_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_285_groups_0 = const()[name = tensor<string, []>("x_285_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313243648))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314292288))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314294400)))];
            tensor<fp16, [1, 1024, 188]> x_285_cast_fp16 = conv(bias = module_layers_12_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_677_cast_fp16)[name = tensor<string, []>("x_285_cast_fp16")];
            tensor<int32, [3]> input_679_perm_0 = const()[name = tensor<string, []>("input_679_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_679_cast_fp16 = transpose(perm = input_679_perm_0, x = x_285_cast_fp16)[name = tensor<string, []>("transpose_222")];
            tensor<fp16, [1, 188, 1024]> input_681_cast_fp16 = add(x = input_663_cast_fp16, y = input_679_cast_fp16)[name = tensor<string, []>("input_681_cast_fp16")];
            tensor<int32, [1]> input_683_axes_0 = const()[name = tensor<string, []>("input_683_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314296512)))];
            tensor<fp16, [1024]> module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314298624)))];
            tensor<fp16, [1, 188, 1024]> input_683_cast_fp16 = layer_norm(axes = input_683_axes_0, beta = module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_681_cast_fp16)[name = tensor<string, []>("input_683_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314300736))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318495104))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318503360)))];
            tensor<fp16, [1, 188, 4096]> linear_116_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized, x = input_683_cast_fp16)[name = tensor<string, []>("linear_116_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_687_cast_fp16 = silu(x = linear_116_cast_fp16)[name = tensor<string, []>("input_687_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318511616))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322705984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322708096)))];
            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized, x = input_687_cast_fp16)[name = tensor<string, []>("linear_117_cast_fp16")];
            tensor<fp16, []> var_2465_to_fp16 = const()[name = tensor<string, []>("op_2465_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2466_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2465_to_fp16)[name = tensor<string, []>("op_2466_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_693_cast_fp16 = add(x = input_681_cast_fp16, y = var_2466_cast_fp16)[name = tensor<string, []>("input_693_cast_fp16")];
            tensor<int32, [1]> input_695_axes_0 = const()[name = tensor<string, []>("input_695_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_12_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322710208)))];
            tensor<fp16, [1024]> module_layers_12_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322712320)))];
            tensor<fp16, [1, 188, 1024]> input_695_cast_fp16 = layer_norm(axes = input_695_axes_0, beta = module_layers_12_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_12_norm_out_weight_to_fp16, x = input_693_cast_fp16)[name = tensor<string, []>("input_695_cast_fp16")];
            tensor<int32, [1]> input_697_axes_0 = const()[name = tensor<string, []>("input_697_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322714432)))];
            tensor<fp16, [1024]> module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322716544)))];
            tensor<fp16, [1, 188, 1024]> input_697_cast_fp16 = layer_norm(axes = input_697_axes_0, beta = module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_695_cast_fp16)[name = tensor<string, []>("input_697_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322718656))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326913024))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326921280)))];
            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized, x = input_697_cast_fp16)[name = tensor<string, []>("linear_118_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_701_cast_fp16 = silu(x = linear_118_cast_fp16)[name = tensor<string, []>("input_701_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326929536))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331123904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331126016)))];
            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized, x = input_701_cast_fp16)[name = tensor<string, []>("linear_119_cast_fp16")];
            tensor<fp16, []> var_2496_to_fp16 = const()[name = tensor<string, []>("op_2496_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2497_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2496_to_fp16)[name = tensor<string, []>("op_2497_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_707_cast_fp16 = add(x = input_695_cast_fp16, y = var_2497_cast_fp16)[name = tensor<string, []>("input_707_cast_fp16")];
            tensor<int32, [1]> query_27_axes_0 = const()[name = tensor<string, []>("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_13_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331128128)))];
            tensor<fp16, [1024]> module_layers_13_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331130240)))];
            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_self_att_weight_to_fp16, x = input_707_cast_fp16)[name = tensor<string, []>("query_27_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(331132352))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332180992))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332183104)))];
            tensor<fp16, [1, 188, 1024]> linear_120_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_q_bias_to_fp16, weight = module_layers_13_self_attn_linear_q_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = tensor<string, []>("linear_120_cast_fp16")];
            tensor<int32, [4]> var_2514 = const()[name = tensor<string, []>("op_2514"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2514, x = linear_120_cast_fp16)[name = tensor<string, []>("q_79_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(332185216))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333233856))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333235968)))];
            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_k_bias_to_fp16, weight = module_layers_13_self_attn_linear_k_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = tensor<string, []>("linear_121_cast_fp16")];
            tensor<int32, [4]> var_2519 = const()[name = tensor<string, []>("op_2519"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2519, x = linear_121_cast_fp16)[name = tensor<string, []>("k_53_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333238080))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334286720))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334288832)))];
            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_v_bias_to_fp16, weight = module_layers_13_self_attn_linear_v_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = tensor<string, []>("linear_122_cast_fp16")];
            tensor<int32, [4]> var_2524 = const()[name = tensor<string, []>("op_2524"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2524, x = linear_122_cast_fp16)[name = tensor<string, []>("v_27_cast_fp16")];
            tensor<int32, [4]> value_29_perm_0 = const()[name = tensor<string, []>("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_13_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334290944))), scale = tensor<fp16, [8]>([0x1.7cp-7, 0x1.e2cp-8, 0x1.b2cp-8, 0x1.93p-7, 0x1.a44p-7, 0x1.468p-7, 0x1.e1p-8, 0x1.1a8p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2536_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2536_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_13_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334292032))), scale = tensor<fp16, [8]>([0x1.13cp-8, 0x1.598p-8, 0x1.bb8p-9, 0x1.d5p-9, 0x1.7e8p-9, 0x1.34cp-8, 0x1.dc4p-9, 0x1.214p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2538_cast_fp16 = add(x = q_79_cast_fp16, y = module_layers_13_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2538_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_293_transpose_x_0 = const()[name = tensor<string, []>("x_293_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_293_transpose_y_0 = const()[name = tensor<string, []>("x_293_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2540_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2540_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334293120))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334677184))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2538_cast_fp16)[name = tensor<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_2540_to_fp16_quantized)[name = tensor<string, []>("x_293_cast_fp16")];
            tensor<int32, [8]> x_295_pad_0 = const()[name = tensor<string, []>("x_295_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_295_mode_0 = const()[name = tensor<string, []>("x_295_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_144_to_fp16 = const()[name = tensor<string, []>("const_144_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_295_cast_fp16 = pad(constant_val = const_144_to_fp16, mode = x_295_mode_0, pad = x_295_pad_0, x = x_293_cast_fp16)[name = tensor<string, []>("x_295_cast_fp16")];
            tensor<int32, [4]> var_2548 = const()[name = tensor<string, []>("op_2548"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_297_cast_fp16 = reshape(shape = var_2548, x = x_295_cast_fp16)[name = tensor<string, []>("x_297_cast_fp16")];
            tensor<int32, [4]> var_2552_begin_0 = const()[name = tensor<string, []>("op_2552_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2552_end_0 = const()[name = tensor<string, []>("op_2552_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2552_end_mask_0 = const()[name = tensor<string, []>("op_2552_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2552_cast_fp16 = slice_by_index(begin = var_2552_begin_0, end = var_2552_end_0, end_mask = var_2552_end_mask_0, x = x_297_cast_fp16)[name = tensor<string, []>("op_2552_cast_fp16")];
            tensor<int32, [4]> var_2553 = const()[name = tensor<string, []>("op_2553"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2553, x = var_2552_cast_fp16)[name = tensor<string, []>("matrix_bd_53_cast_fp16")];
            tensor<bool, []> matrix_ac_27_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_27_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_27_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_27_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_122_perm_0 = const()[name = tensor<string, []>("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_123_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_219")];
            tensor<fp16, [1, 8, 188, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2536_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_27_cast_fp16")];
            tensor<int32, [4]> matrix_bd_55_begin_0 = const()[name = tensor<string, []>("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_55_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2562_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = tensor<string, []>("op_2562_cast_fp16")];
            tensor<fp16, []> _inversed_scores_53_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_53_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2562_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_53_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_55_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2568_cast_fp16 = softmax(axis = var_30, x = scores_55_cast_fp16)[name = tensor<string, []>("op_2568_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_709_cast_fp16 = select(a = var_11_to_fp16, b = var_2568_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_709_cast_fp16")];
            tensor<bool, []> x_299_transpose_x_0 = const()[name = tensor<string, []>("x_299_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_299_transpose_y_0 = const()[name = tensor<string, []>("x_299_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_27_cast_fp16)[name = tensor<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_709_cast_fp16, y = value_29_cast_fp16)[name = tensor<string, []>("x_299_cast_fp16")];
            tensor<int32, [4]> var_2572_perm_0 = const()[name = tensor<string, []>("op_2572_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2573 = const()[name = tensor<string, []>("op_2573"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2572_cast_fp16 = transpose(perm = var_2572_perm_0, x = x_299_cast_fp16)[name = tensor<string, []>("transpose_217")];
            tensor<fp16, [1, 188, 1024]> input_711_cast_fp16 = reshape(shape = var_2573, x = var_2572_cast_fp16)[name = tensor<string, []>("input_711_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_13_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(334678016))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335726656))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335728768)))];
            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = module_layers_13_self_attn_linear_out_bias_to_fp16, weight = module_layers_13_self_attn_linear_out_weight_to_fp16_quantized, x = input_711_cast_fp16)[name = tensor<string, []>("linear_124_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_715_cast_fp16 = add(x = input_707_cast_fp16, y = linear_124_cast_fp16)[name = tensor<string, []>("input_715_cast_fp16")];
            tensor<int32, [1]> x_303_axes_0 = const()[name = tensor<string, []>("x_303_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_13_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335730880)))];
            tensor<fp16, [1024]> module_layers_13_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335732992)))];
            tensor<fp16, [1, 188, 1024]> x_303_cast_fp16 = layer_norm(axes = x_303_axes_0, beta = module_layers_13_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_conv_weight_to_fp16, x = input_715_cast_fp16)[name = tensor<string, []>("x_303_cast_fp16")];
            tensor<int32, [3]> input_717_perm_0 = const()[name = tensor<string, []>("input_717_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_719_pad_type_0 = const()[name = tensor<string, []>("input_719_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_719_strides_0 = const()[name = tensor<string, []>("input_719_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_719_pad_0 = const()[name = tensor<string, []>("input_719_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_719_dilations_0 = const()[name = tensor<string, []>("input_719_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_719_groups_0 = const()[name = tensor<string, []>("input_719_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335735104))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337832320))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337836480)))];
            tensor<fp16, [1, 1024, 188]> input_717_cast_fp16 = transpose(perm = input_717_perm_0, x = x_303_cast_fp16)[name = tensor<string, []>("transpose_216")];
            tensor<fp16, [1, 2048, 188]> input_719_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv1_bias_to_fp16, dilations = input_719_dilations_0, groups = input_719_groups_0, pad = input_719_pad_0, pad_type = input_719_pad_type_0, strides = input_719_strides_0, weight = module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_717_cast_fp16)[name = tensor<string, []>("input_719_cast_fp16")];
            tensor<int32, []> x_305_split_num_splits_0 = const()[name = tensor<string, []>("x_305_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_305_split_axis_0 = const()[name = tensor<string, []>("x_305_split_axis_0"), val = tensor<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_719_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_305_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_721_cast_fp16 = select(a = var_11_to_fp16, b = x_305_cast_fp16, cond = var_335)[name = tensor<string, []>("input_721_cast_fp16")];
            tensor<int32, [6]> input_723_pad_0 = const()[name = tensor<string, []>("input_723_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_723_mode_0 = const()[name = tensor<string, []>("input_723_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_147_to_fp16 = const()[name = tensor<string, []>("const_147_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_723_cast_fp16 = pad(constant_val = const_147_to_fp16, mode = input_723_mode_0, pad = input_723_pad_0, x = input_721_cast_fp16)[name = tensor<string, []>("input_723_cast_fp16")];
            tensor<string, []> input_725_pad_type_0 = const()[name = tensor<string, []>("input_725_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_725_groups_0 = const()[name = tensor<string, []>("input_725_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_725_strides_0 = const()[name = tensor<string, []>("input_725_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_725_pad_0 = const()[name = tensor<string, []>("input_725_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_725_dilations_0 = const()[name = tensor<string, []>("input_725_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_274_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_274_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337840640))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337849920))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_275_to_fp16 = const()[name = tensor<string, []>("const_275_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337852032)))];
            tensor<fp16, [1, 1024, 188]> input_727_cast_fp16 = conv(bias = const_275_to_fp16, dilations = input_725_dilations_0, groups = input_725_groups_0, pad = input_725_pad_0, pad_type = input_725_pad_type_0, strides = input_725_strides_0, weight = const_274_to_fp16_quantized, x = input_723_cast_fp16)[name = tensor<string, []>("input_727_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_729_cast_fp16 = silu(x = input_727_cast_fp16)[name = tensor<string, []>("input_729_cast_fp16")];
            tensor<string, []> x_307_pad_type_0 = const()[name = tensor<string, []>("x_307_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_307_strides_0 = const()[name = tensor<string, []>("x_307_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_307_pad_0 = const()[name = tensor<string, []>("x_307_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_307_dilations_0 = const()[name = tensor<string, []>("x_307_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_307_groups_0 = const()[name = tensor<string, []>("x_307_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337854144))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338902784))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338904896)))];
            tensor<fp16, [1, 1024, 188]> x_307_cast_fp16 = conv(bias = module_layers_13_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_729_cast_fp16)[name = tensor<string, []>("x_307_cast_fp16")];
            tensor<int32, [3]> input_731_perm_0 = const()[name = tensor<string, []>("input_731_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_731_cast_fp16 = transpose(perm = input_731_perm_0, x = x_307_cast_fp16)[name = tensor<string, []>("transpose_215")];
            tensor<fp16, [1, 188, 1024]> input_733_cast_fp16 = add(x = input_715_cast_fp16, y = input_731_cast_fp16)[name = tensor<string, []>("input_733_cast_fp16")];
            tensor<int32, [1]> input_735_axes_0 = const()[name = tensor<string, []>("input_735_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338907008)))];
            tensor<fp16, [1024]> module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338909120)))];
            tensor<fp16, [1, 188, 1024]> input_735_cast_fp16 = layer_norm(axes = input_735_axes_0, beta = module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_733_cast_fp16)[name = tensor<string, []>("input_735_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(338911232))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343105600))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343113856)))];
            tensor<fp16, [1, 188, 4096]> linear_125_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized, x = input_735_cast_fp16)[name = tensor<string, []>("linear_125_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_739_cast_fp16 = silu(x = linear_125_cast_fp16)[name = tensor<string, []>("input_739_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343122112))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347316480))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347318592)))];
            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized, x = input_739_cast_fp16)[name = tensor<string, []>("linear_126_cast_fp16")];
            tensor<fp16, []> var_2639_to_fp16 = const()[name = tensor<string, []>("op_2639_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2640_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2639_to_fp16)[name = tensor<string, []>("op_2640_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_745_cast_fp16 = add(x = input_733_cast_fp16, y = var_2640_cast_fp16)[name = tensor<string, []>("input_745_cast_fp16")];
            tensor<int32, [1]> input_747_axes_0 = const()[name = tensor<string, []>("input_747_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_13_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347320704)))];
            tensor<fp16, [1024]> module_layers_13_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347322816)))];
            tensor<fp16, [1, 188, 1024]> input_747_cast_fp16 = layer_norm(axes = input_747_axes_0, beta = module_layers_13_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_13_norm_out_weight_to_fp16, x = input_745_cast_fp16)[name = tensor<string, []>("input_747_cast_fp16")];
            tensor<int32, [1]> input_749_axes_0 = const()[name = tensor<string, []>("input_749_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347324928)))];
            tensor<fp16, [1024]> module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347327040)))];
            tensor<fp16, [1, 188, 1024]> input_749_cast_fp16 = layer_norm(axes = input_749_axes_0, beta = module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_747_cast_fp16)[name = tensor<string, []>("input_749_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347329152))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(351523520))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(351531776)))];
            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized, x = input_749_cast_fp16)[name = tensor<string, []>("linear_127_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_753_cast_fp16 = silu(x = linear_127_cast_fp16)[name = tensor<string, []>("input_753_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(351540032))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355734400))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355736512)))];
            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized, x = input_753_cast_fp16)[name = tensor<string, []>("linear_128_cast_fp16")];
            tensor<fp16, []> var_2670_to_fp16 = const()[name = tensor<string, []>("op_2670_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2671_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2670_to_fp16)[name = tensor<string, []>("op_2671_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_759_cast_fp16 = add(x = input_747_cast_fp16, y = var_2671_cast_fp16)[name = tensor<string, []>("input_759_cast_fp16")];
            tensor<int32, [1]> query_29_axes_0 = const()[name = tensor<string, []>("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_14_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355738624)))];
            tensor<fp16, [1024]> module_layers_14_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355740736)))];
            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_self_att_weight_to_fp16, x = input_759_cast_fp16)[name = tensor<string, []>("query_29_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355742848))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356791488))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356793600)))];
            tensor<fp16, [1, 188, 1024]> linear_129_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_q_bias_to_fp16, weight = module_layers_14_self_attn_linear_q_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = tensor<string, []>("linear_129_cast_fp16")];
            tensor<int32, [4]> var_2688 = const()[name = tensor<string, []>("op_2688"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2688, x = linear_129_cast_fp16)[name = tensor<string, []>("q_85_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356795712))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357844352))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357846464)))];
            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_k_bias_to_fp16, weight = module_layers_14_self_attn_linear_k_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = tensor<string, []>("linear_130_cast_fp16")];
            tensor<int32, [4]> var_2693 = const()[name = tensor<string, []>("op_2693"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2693, x = linear_130_cast_fp16)[name = tensor<string, []>("k_57_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357848576))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358897216))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358899328)))];
            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_v_bias_to_fp16, weight = module_layers_14_self_attn_linear_v_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = tensor<string, []>("linear_131_cast_fp16")];
            tensor<int32, [4]> var_2698 = const()[name = tensor<string, []>("op_2698"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2698, x = linear_131_cast_fp16)[name = tensor<string, []>("v_29_cast_fp16")];
            tensor<int32, [4]> value_31_perm_0 = const()[name = tensor<string, []>("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_14_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358901440))), scale = tensor<fp16, [8]>([0x1.f34p-8, 0x1.004p-7, 0x1.858p-7, 0x1.4cp-7, 0x1.b3cp-8, 0x1.4p-7, 0x1.01cp-7, 0x1.458p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2710_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2710_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_14_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358902528))), scale = tensor<fp16, [8]>([0x1.6bp-8, 0x1.1acp-9, 0x1.55cp-9, 0x1.6c4p-8, 0x1.67cp-8, 0x1.9bp-9, 0x1.af4p-9, 0x1.06p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2712_cast_fp16 = add(x = q_85_cast_fp16, y = module_layers_14_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2712_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_315_transpose_x_0 = const()[name = tensor<string, []>("x_315_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_315_transpose_y_0 = const()[name = tensor<string, []>("x_315_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2714_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2714_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358903616))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359287680))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2712_cast_fp16)[name = tensor<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_2714_to_fp16_quantized)[name = tensor<string, []>("x_315_cast_fp16")];
            tensor<int32, [8]> x_317_pad_0 = const()[name = tensor<string, []>("x_317_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_317_mode_0 = const()[name = tensor<string, []>("x_317_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_154_to_fp16 = const()[name = tensor<string, []>("const_154_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_317_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = x_317_mode_0, pad = x_317_pad_0, x = x_315_cast_fp16)[name = tensor<string, []>("x_317_cast_fp16")];
            tensor<int32, [4]> var_2722 = const()[name = tensor<string, []>("op_2722"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_319_cast_fp16 = reshape(shape = var_2722, x = x_317_cast_fp16)[name = tensor<string, []>("x_319_cast_fp16")];
            tensor<int32, [4]> var_2726_begin_0 = const()[name = tensor<string, []>("op_2726_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2726_end_0 = const()[name = tensor<string, []>("op_2726_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2726_end_mask_0 = const()[name = tensor<string, []>("op_2726_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2726_cast_fp16 = slice_by_index(begin = var_2726_begin_0, end = var_2726_end_0, end_mask = var_2726_end_mask_0, x = x_319_cast_fp16)[name = tensor<string, []>("op_2726_cast_fp16")];
            tensor<int32, [4]> var_2727 = const()[name = tensor<string, []>("op_2727"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_2727, x = var_2726_cast_fp16)[name = tensor<string, []>("matrix_bd_57_cast_fp16")];
            tensor<bool, []> matrix_ac_29_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_29_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_29_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_29_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_124_perm_0 = const()[name = tensor<string, []>("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_125_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_212")];
            tensor<fp16, [1, 8, 188, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2710_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_29_cast_fp16")];
            tensor<int32, [4]> matrix_bd_59_begin_0 = const()[name = tensor<string, []>("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_59_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2736_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = tensor<string, []>("op_2736_cast_fp16")];
            tensor<fp16, []> _inversed_scores_57_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_57_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_2736_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_57_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_59_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2742_cast_fp16 = softmax(axis = var_30, x = scores_59_cast_fp16)[name = tensor<string, []>("op_2742_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_761_cast_fp16 = select(a = var_11_to_fp16, b = var_2742_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_761_cast_fp16")];
            tensor<bool, []> x_321_transpose_x_0 = const()[name = tensor<string, []>("x_321_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_321_transpose_y_0 = const()[name = tensor<string, []>("x_321_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_29_cast_fp16)[name = tensor<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_761_cast_fp16, y = value_31_cast_fp16)[name = tensor<string, []>("x_321_cast_fp16")];
            tensor<int32, [4]> var_2746_perm_0 = const()[name = tensor<string, []>("op_2746_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2747 = const()[name = tensor<string, []>("op_2747"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2746_cast_fp16 = transpose(perm = var_2746_perm_0, x = x_321_cast_fp16)[name = tensor<string, []>("transpose_210")];
            tensor<fp16, [1, 188, 1024]> input_763_cast_fp16 = reshape(shape = var_2747, x = var_2746_cast_fp16)[name = tensor<string, []>("input_763_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_14_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359288512))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360337152))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360339264)))];
            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = module_layers_14_self_attn_linear_out_bias_to_fp16, weight = module_layers_14_self_attn_linear_out_weight_to_fp16_quantized, x = input_763_cast_fp16)[name = tensor<string, []>("linear_133_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_767_cast_fp16 = add(x = input_759_cast_fp16, y = linear_133_cast_fp16)[name = tensor<string, []>("input_767_cast_fp16")];
            tensor<int32, [1]> x_325_axes_0 = const()[name = tensor<string, []>("x_325_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_14_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360341376)))];
            tensor<fp16, [1024]> module_layers_14_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360343488)))];
            tensor<fp16, [1, 188, 1024]> x_325_cast_fp16 = layer_norm(axes = x_325_axes_0, beta = module_layers_14_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_conv_weight_to_fp16, x = input_767_cast_fp16)[name = tensor<string, []>("x_325_cast_fp16")];
            tensor<int32, [3]> input_769_perm_0 = const()[name = tensor<string, []>("input_769_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_771_pad_type_0 = const()[name = tensor<string, []>("input_771_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_771_strides_0 = const()[name = tensor<string, []>("input_771_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_771_pad_0 = const()[name = tensor<string, []>("input_771_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_771_dilations_0 = const()[name = tensor<string, []>("input_771_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_771_groups_0 = const()[name = tensor<string, []>("input_771_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360345600))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362442816))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362446976)))];
            tensor<fp16, [1, 1024, 188]> input_769_cast_fp16 = transpose(perm = input_769_perm_0, x = x_325_cast_fp16)[name = tensor<string, []>("transpose_209")];
            tensor<fp16, [1, 2048, 188]> input_771_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv1_bias_to_fp16, dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_769_cast_fp16)[name = tensor<string, []>("input_771_cast_fp16")];
            tensor<int32, []> x_327_split_num_splits_0 = const()[name = tensor<string, []>("x_327_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_327_split_axis_0 = const()[name = tensor<string, []>("x_327_split_axis_0"), val = tensor<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_771_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_327_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_773_cast_fp16 = select(a = var_11_to_fp16, b = x_327_cast_fp16, cond = var_335)[name = tensor<string, []>("input_773_cast_fp16")];
            tensor<int32, [6]> input_775_pad_0 = const()[name = tensor<string, []>("input_775_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_775_mode_0 = const()[name = tensor<string, []>("input_775_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_157_to_fp16 = const()[name = tensor<string, []>("const_157_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_775_cast_fp16 = pad(constant_val = const_157_to_fp16, mode = input_775_mode_0, pad = input_775_pad_0, x = input_773_cast_fp16)[name = tensor<string, []>("input_775_cast_fp16")];
            tensor<string, []> input_777_pad_type_0 = const()[name = tensor<string, []>("input_777_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_777_groups_0 = const()[name = tensor<string, []>("input_777_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_777_strides_0 = const()[name = tensor<string, []>("input_777_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_777_pad_0 = const()[name = tensor<string, []>("input_777_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_777_dilations_0 = const()[name = tensor<string, []>("input_777_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_276_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_276_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362451136))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362460416))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_277_to_fp16 = const()[name = tensor<string, []>("const_277_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362462528)))];
            tensor<fp16, [1, 1024, 188]> input_779_cast_fp16 = conv(bias = const_277_to_fp16, dilations = input_777_dilations_0, groups = input_777_groups_0, pad = input_777_pad_0, pad_type = input_777_pad_type_0, strides = input_777_strides_0, weight = const_276_to_fp16_quantized, x = input_775_cast_fp16)[name = tensor<string, []>("input_779_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_781_cast_fp16 = silu(x = input_779_cast_fp16)[name = tensor<string, []>("input_781_cast_fp16")];
            tensor<string, []> x_329_pad_type_0 = const()[name = tensor<string, []>("x_329_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_329_strides_0 = const()[name = tensor<string, []>("x_329_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_329_pad_0 = const()[name = tensor<string, []>("x_329_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_329_dilations_0 = const()[name = tensor<string, []>("x_329_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_329_groups_0 = const()[name = tensor<string, []>("x_329_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362464640))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363513280))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363515392)))];
            tensor<fp16, [1, 1024, 188]> x_329_cast_fp16 = conv(bias = module_layers_14_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_781_cast_fp16)[name = tensor<string, []>("x_329_cast_fp16")];
            tensor<int32, [3]> input_783_perm_0 = const()[name = tensor<string, []>("input_783_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_783_cast_fp16 = transpose(perm = input_783_perm_0, x = x_329_cast_fp16)[name = tensor<string, []>("transpose_208")];
            tensor<fp16, [1, 188, 1024]> input_785_cast_fp16 = add(x = input_767_cast_fp16, y = input_783_cast_fp16)[name = tensor<string, []>("input_785_cast_fp16")];
            tensor<int32, [1]> input_787_axes_0 = const()[name = tensor<string, []>("input_787_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363517504)))];
            tensor<fp16, [1024]> module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363519616)))];
            tensor<fp16, [1, 188, 1024]> input_787_cast_fp16 = layer_norm(axes = input_787_axes_0, beta = module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_785_cast_fp16)[name = tensor<string, []>("input_787_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363521728))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367716096))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367724352)))];
            tensor<fp16, [1, 188, 4096]> linear_134_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized, x = input_787_cast_fp16)[name = tensor<string, []>("linear_134_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_791_cast_fp16 = silu(x = linear_134_cast_fp16)[name = tensor<string, []>("input_791_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(367732608))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371926976))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371929088)))];
            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized, x = input_791_cast_fp16)[name = tensor<string, []>("linear_135_cast_fp16")];
            tensor<fp16, []> var_2813_to_fp16 = const()[name = tensor<string, []>("op_2813_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2814_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_2813_to_fp16)[name = tensor<string, []>("op_2814_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_797_cast_fp16 = add(x = input_785_cast_fp16, y = var_2814_cast_fp16)[name = tensor<string, []>("input_797_cast_fp16")];
            tensor<int32, [1]> input_799_axes_0 = const()[name = tensor<string, []>("input_799_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_14_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371931200)))];
            tensor<fp16, [1024]> module_layers_14_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371933312)))];
            tensor<fp16, [1, 188, 1024]> input_799_cast_fp16 = layer_norm(axes = input_799_axes_0, beta = module_layers_14_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_14_norm_out_weight_to_fp16, x = input_797_cast_fp16)[name = tensor<string, []>("input_799_cast_fp16")];
            tensor<int32, [1]> input_801_axes_0 = const()[name = tensor<string, []>("input_801_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371935424)))];
            tensor<fp16, [1024]> module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371937536)))];
            tensor<fp16, [1, 188, 1024]> input_801_cast_fp16 = layer_norm(axes = input_801_axes_0, beta = module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_799_cast_fp16)[name = tensor<string, []>("input_801_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(371939648))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376134016))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376142272)))];
            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized, x = input_801_cast_fp16)[name = tensor<string, []>("linear_136_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_805_cast_fp16 = silu(x = linear_136_cast_fp16)[name = tensor<string, []>("input_805_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376150528))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380344896))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380347008)))];
            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized, x = input_805_cast_fp16)[name = tensor<string, []>("linear_137_cast_fp16")];
            tensor<fp16, []> var_2844_to_fp16 = const()[name = tensor<string, []>("op_2844_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2845_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_2844_to_fp16)[name = tensor<string, []>("op_2845_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_811_cast_fp16 = add(x = input_799_cast_fp16, y = var_2845_cast_fp16)[name = tensor<string, []>("input_811_cast_fp16")];
            tensor<int32, [1]> query_31_axes_0 = const()[name = tensor<string, []>("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_15_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380349120)))];
            tensor<fp16, [1024]> module_layers_15_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380351232)))];
            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_self_att_weight_to_fp16, x = input_811_cast_fp16)[name = tensor<string, []>("query_31_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(380353344))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381401984))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381404096)))];
            tensor<fp16, [1, 188, 1024]> linear_138_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_q_bias_to_fp16, weight = module_layers_15_self_attn_linear_q_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = tensor<string, []>("linear_138_cast_fp16")];
            tensor<int32, [4]> var_2862 = const()[name = tensor<string, []>("op_2862"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_2862, x = linear_138_cast_fp16)[name = tensor<string, []>("q_91_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381406208))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382454848))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382456960)))];
            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_k_bias_to_fp16, weight = module_layers_15_self_attn_linear_k_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = tensor<string, []>("linear_139_cast_fp16")];
            tensor<int32, [4]> var_2867 = const()[name = tensor<string, []>("op_2867"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_2867, x = linear_139_cast_fp16)[name = tensor<string, []>("k_61_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382459072))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383507712))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383509824)))];
            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_v_bias_to_fp16, weight = module_layers_15_self_attn_linear_v_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = tensor<string, []>("linear_140_cast_fp16")];
            tensor<int32, [4]> var_2872 = const()[name = tensor<string, []>("op_2872"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_2872, x = linear_140_cast_fp16)[name = tensor<string, []>("v_31_cast_fp16")];
            tensor<int32, [4]> value_33_perm_0 = const()[name = tensor<string, []>("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_15_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383511936))), scale = tensor<fp16, [8]>([0x1.838p-8, 0x1.8bp-8, 0x1.4cp-8, 0x1.5fp-8, 0x1.0fp-8, 0x1.d68p-8, 0x1.d14p-8, 0x1.a84p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2884_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_2884_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_15_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383513024))), scale = tensor<fp16, [8]>([0x1.9bp-9, 0x1.2p-8, 0x1.e94p-9, 0x1.4p-8, 0x1.038p-8, 0x1.2c8p-8, 0x1.57cp-9, 0x1.248p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_2886_cast_fp16 = add(x = q_91_cast_fp16, y = module_layers_15_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_2886_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_337_transpose_x_0 = const()[name = tensor<string, []>("x_337_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_337_transpose_y_0 = const()[name = tensor<string, []>("x_337_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_2888_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_2888_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383514112))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383898176))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_2886_cast_fp16)[name = tensor<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_2888_to_fp16_quantized)[name = tensor<string, []>("x_337_cast_fp16")];
            tensor<int32, [8]> x_339_pad_0 = const()[name = tensor<string, []>("x_339_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_339_mode_0 = const()[name = tensor<string, []>("x_339_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_164_to_fp16 = const()[name = tensor<string, []>("const_164_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_339_cast_fp16 = pad(constant_val = const_164_to_fp16, mode = x_339_mode_0, pad = x_339_pad_0, x = x_337_cast_fp16)[name = tensor<string, []>("x_339_cast_fp16")];
            tensor<int32, [4]> var_2896 = const()[name = tensor<string, []>("op_2896"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_341_cast_fp16 = reshape(shape = var_2896, x = x_339_cast_fp16)[name = tensor<string, []>("x_341_cast_fp16")];
            tensor<int32, [4]> var_2900_begin_0 = const()[name = tensor<string, []>("op_2900_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_2900_end_0 = const()[name = tensor<string, []>("op_2900_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_2900_end_mask_0 = const()[name = tensor<string, []>("op_2900_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_2900_cast_fp16 = slice_by_index(begin = var_2900_begin_0, end = var_2900_end_0, end_mask = var_2900_end_mask_0, x = x_341_cast_fp16)[name = tensor<string, []>("op_2900_cast_fp16")];
            tensor<int32, [4]> var_2901 = const()[name = tensor<string, []>("op_2901"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_2901, x = var_2900_cast_fp16)[name = tensor<string, []>("matrix_bd_61_cast_fp16")];
            tensor<bool, []> matrix_ac_31_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_31_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_31_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_126_perm_0 = const()[name = tensor<string, []>("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_127_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_205")];
            tensor<fp16, [1, 8, 188, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_2884_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_31_cast_fp16")];
            tensor<int32, [4]> matrix_bd_63_begin_0 = const()[name = tensor<string, []>("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_63_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2910_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = tensor<string, []>("op_2910_cast_fp16")];
            tensor<fp16, []> _inversed_scores_61_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_61_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_2910_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_61_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_63_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_2916_cast_fp16 = softmax(axis = var_30, x = scores_63_cast_fp16)[name = tensor<string, []>("op_2916_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_813_cast_fp16 = select(a = var_11_to_fp16, b = var_2916_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_813_cast_fp16")];
            tensor<bool, []> x_343_transpose_x_0 = const()[name = tensor<string, []>("x_343_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_343_transpose_y_0 = const()[name = tensor<string, []>("x_343_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_31_cast_fp16)[name = tensor<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_813_cast_fp16, y = value_33_cast_fp16)[name = tensor<string, []>("x_343_cast_fp16")];
            tensor<int32, [4]> var_2920_perm_0 = const()[name = tensor<string, []>("op_2920_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2921 = const()[name = tensor<string, []>("op_2921"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_2920_cast_fp16 = transpose(perm = var_2920_perm_0, x = x_343_cast_fp16)[name = tensor<string, []>("transpose_203")];
            tensor<fp16, [1, 188, 1024]> input_815_cast_fp16 = reshape(shape = var_2921, x = var_2920_cast_fp16)[name = tensor<string, []>("input_815_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_15_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383899008))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384947648))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384949760)))];
            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = module_layers_15_self_attn_linear_out_bias_to_fp16, weight = module_layers_15_self_attn_linear_out_weight_to_fp16_quantized, x = input_815_cast_fp16)[name = tensor<string, []>("linear_142_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_819_cast_fp16 = add(x = input_811_cast_fp16, y = linear_142_cast_fp16)[name = tensor<string, []>("input_819_cast_fp16")];
            tensor<int32, [1]> x_347_axes_0 = const()[name = tensor<string, []>("x_347_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_15_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384951872)))];
            tensor<fp16, [1024]> module_layers_15_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384953984)))];
            tensor<fp16, [1, 188, 1024]> x_347_cast_fp16 = layer_norm(axes = x_347_axes_0, beta = module_layers_15_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_conv_weight_to_fp16, x = input_819_cast_fp16)[name = tensor<string, []>("x_347_cast_fp16")];
            tensor<int32, [3]> input_821_perm_0 = const()[name = tensor<string, []>("input_821_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_823_pad_type_0 = const()[name = tensor<string, []>("input_823_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_823_strides_0 = const()[name = tensor<string, []>("input_823_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_823_pad_0 = const()[name = tensor<string, []>("input_823_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_823_dilations_0 = const()[name = tensor<string, []>("input_823_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_823_groups_0 = const()[name = tensor<string, []>("input_823_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(384956096))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387053312))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387057472)))];
            tensor<fp16, [1, 1024, 188]> input_821_cast_fp16 = transpose(perm = input_821_perm_0, x = x_347_cast_fp16)[name = tensor<string, []>("transpose_202")];
            tensor<fp16, [1, 2048, 188]> input_823_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv1_bias_to_fp16, dilations = input_823_dilations_0, groups = input_823_groups_0, pad = input_823_pad_0, pad_type = input_823_pad_type_0, strides = input_823_strides_0, weight = module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_821_cast_fp16)[name = tensor<string, []>("input_823_cast_fp16")];
            tensor<int32, []> x_349_split_num_splits_0 = const()[name = tensor<string, []>("x_349_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_349_split_axis_0 = const()[name = tensor<string, []>("x_349_split_axis_0"), val = tensor<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_823_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_349_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_825_cast_fp16 = select(a = var_11_to_fp16, b = x_349_cast_fp16, cond = var_335)[name = tensor<string, []>("input_825_cast_fp16")];
            tensor<int32, [6]> input_827_pad_0 = const()[name = tensor<string, []>("input_827_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_827_mode_0 = const()[name = tensor<string, []>("input_827_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_167_to_fp16 = const()[name = tensor<string, []>("const_167_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_827_cast_fp16 = pad(constant_val = const_167_to_fp16, mode = input_827_mode_0, pad = input_827_pad_0, x = input_825_cast_fp16)[name = tensor<string, []>("input_827_cast_fp16")];
            tensor<string, []> input_829_pad_type_0 = const()[name = tensor<string, []>("input_829_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_829_groups_0 = const()[name = tensor<string, []>("input_829_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_829_strides_0 = const()[name = tensor<string, []>("input_829_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_829_pad_0 = const()[name = tensor<string, []>("input_829_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_829_dilations_0 = const()[name = tensor<string, []>("input_829_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_278_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_278_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387061632))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387070912))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_279_to_fp16 = const()[name = tensor<string, []>("const_279_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387073024)))];
            tensor<fp16, [1, 1024, 188]> input_831_cast_fp16 = conv(bias = const_279_to_fp16, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_278_to_fp16_quantized, x = input_827_cast_fp16)[name = tensor<string, []>("input_831_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_833_cast_fp16 = silu(x = input_831_cast_fp16)[name = tensor<string, []>("input_833_cast_fp16")];
            tensor<string, []> x_351_pad_type_0 = const()[name = tensor<string, []>("x_351_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_351_strides_0 = const()[name = tensor<string, []>("x_351_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_351_pad_0 = const()[name = tensor<string, []>("x_351_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_351_dilations_0 = const()[name = tensor<string, []>("x_351_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_351_groups_0 = const()[name = tensor<string, []>("x_351_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387075136))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388123776))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388125888)))];
            tensor<fp16, [1, 1024, 188]> x_351_cast_fp16 = conv(bias = module_layers_15_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_833_cast_fp16)[name = tensor<string, []>("x_351_cast_fp16")];
            tensor<int32, [3]> input_835_perm_0 = const()[name = tensor<string, []>("input_835_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_835_cast_fp16 = transpose(perm = input_835_perm_0, x = x_351_cast_fp16)[name = tensor<string, []>("transpose_201")];
            tensor<fp16, [1, 188, 1024]> input_837_cast_fp16 = add(x = input_819_cast_fp16, y = input_835_cast_fp16)[name = tensor<string, []>("input_837_cast_fp16")];
            tensor<int32, [1]> input_839_axes_0 = const()[name = tensor<string, []>("input_839_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388128000)))];
            tensor<fp16, [1024]> module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388130112)))];
            tensor<fp16, [1, 188, 1024]> input_839_cast_fp16 = layer_norm(axes = input_839_axes_0, beta = module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_837_cast_fp16)[name = tensor<string, []>("input_839_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388132224))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(392326592))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(392334848)))];
            tensor<fp16, [1, 188, 4096]> linear_143_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized, x = input_839_cast_fp16)[name = tensor<string, []>("linear_143_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_843_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor<string, []>("input_843_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(392343104))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396537472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396539584)))];
            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized, x = input_843_cast_fp16)[name = tensor<string, []>("linear_144_cast_fp16")];
            tensor<fp16, []> var_2987_to_fp16 = const()[name = tensor<string, []>("op_2987_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_2988_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_2987_to_fp16)[name = tensor<string, []>("op_2988_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_849_cast_fp16 = add(x = input_837_cast_fp16, y = var_2988_cast_fp16)[name = tensor<string, []>("input_849_cast_fp16")];
            tensor<int32, [1]> input_851_axes_0 = const()[name = tensor<string, []>("input_851_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_15_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396541696)))];
            tensor<fp16, [1024]> module_layers_15_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396543808)))];
            tensor<fp16, [1, 188, 1024]> input_851_cast_fp16 = layer_norm(axes = input_851_axes_0, beta = module_layers_15_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_15_norm_out_weight_to_fp16, x = input_849_cast_fp16)[name = tensor<string, []>("input_851_cast_fp16")];
            tensor<int32, [1]> input_853_axes_0 = const()[name = tensor<string, []>("input_853_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396545920)))];
            tensor<fp16, [1024]> module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396548032)))];
            tensor<fp16, [1, 188, 1024]> input_853_cast_fp16 = layer_norm(axes = input_853_axes_0, beta = module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_851_cast_fp16)[name = tensor<string, []>("input_853_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396550144))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(400744512))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(400752768)))];
            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized, x = input_853_cast_fp16)[name = tensor<string, []>("linear_145_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_857_cast_fp16 = silu(x = linear_145_cast_fp16)[name = tensor<string, []>("input_857_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(400761024))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404955392))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404957504)))];
            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized, x = input_857_cast_fp16)[name = tensor<string, []>("linear_146_cast_fp16")];
            tensor<fp16, []> var_3018_to_fp16 = const()[name = tensor<string, []>("op_3018_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3019_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3018_to_fp16)[name = tensor<string, []>("op_3019_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_863_cast_fp16 = add(x = input_851_cast_fp16, y = var_3019_cast_fp16)[name = tensor<string, []>("input_863_cast_fp16")];
            tensor<int32, [1]> query_33_axes_0 = const()[name = tensor<string, []>("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_16_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404959616)))];
            tensor<fp16, [1024]> module_layers_16_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404961728)))];
            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_self_att_weight_to_fp16, x = input_863_cast_fp16)[name = tensor<string, []>("query_33_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(404963840))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406012480))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406014592)))];
            tensor<fp16, [1, 188, 1024]> linear_147_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_q_bias_to_fp16, weight = module_layers_16_self_attn_linear_q_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = tensor<string, []>("linear_147_cast_fp16")];
            tensor<int32, [4]> var_3036 = const()[name = tensor<string, []>("op_3036"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3036, x = linear_147_cast_fp16)[name = tensor<string, []>("q_97_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406016704))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407065344))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407067456)))];
            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_k_bias_to_fp16, weight = module_layers_16_self_attn_linear_k_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = tensor<string, []>("linear_148_cast_fp16")];
            tensor<int32, [4]> var_3041 = const()[name = tensor<string, []>("op_3041"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3041, x = linear_148_cast_fp16)[name = tensor<string, []>("k_65_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(407069568))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408118208))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408120320)))];
            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_v_bias_to_fp16, weight = module_layers_16_self_attn_linear_v_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = tensor<string, []>("linear_149_cast_fp16")];
            tensor<int32, [4]> var_3046 = const()[name = tensor<string, []>("op_3046"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3046, x = linear_149_cast_fp16)[name = tensor<string, []>("v_33_cast_fp16")];
            tensor<int32, [4]> value_35_perm_0 = const()[name = tensor<string, []>("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_16_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408122432))), scale = tensor<fp16, [8]>([0x1.044p-7, 0x1.1fp-7, 0x1.95p-9, 0x1.8cp-8, 0x1.2c8p-7, 0x1.054p-7, 0x1.4e4p-8, 0x1.224p-7]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3058_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3058_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_16_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408123520))), scale = tensor<fp16, [8]>([0x1.7fcp-9, 0x1.15cp-7, 0x1.5e4p-9, 0x1.61cp-8, 0x1.d4p-9, 0x1.164p-8, 0x1.c54p-9, 0x1.76cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3060_cast_fp16 = add(x = q_97_cast_fp16, y = module_layers_16_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3060_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_359_transpose_x_0 = const()[name = tensor<string, []>("x_359_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_359_transpose_y_0 = const()[name = tensor<string, []>("x_359_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3062_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3062_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408124608))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408508672))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3060_cast_fp16)[name = tensor<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_3062_to_fp16_quantized)[name = tensor<string, []>("x_359_cast_fp16")];
            tensor<int32, [8]> x_361_pad_0 = const()[name = tensor<string, []>("x_361_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_361_mode_0 = const()[name = tensor<string, []>("x_361_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_174_to_fp16 = const()[name = tensor<string, []>("const_174_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_361_cast_fp16 = pad(constant_val = const_174_to_fp16, mode = x_361_mode_0, pad = x_361_pad_0, x = x_359_cast_fp16)[name = tensor<string, []>("x_361_cast_fp16")];
            tensor<int32, [4]> var_3070 = const()[name = tensor<string, []>("op_3070"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_363_cast_fp16 = reshape(shape = var_3070, x = x_361_cast_fp16)[name = tensor<string, []>("x_363_cast_fp16")];
            tensor<int32, [4]> var_3074_begin_0 = const()[name = tensor<string, []>("op_3074_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3074_end_0 = const()[name = tensor<string, []>("op_3074_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3074_end_mask_0 = const()[name = tensor<string, []>("op_3074_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3074_cast_fp16 = slice_by_index(begin = var_3074_begin_0, end = var_3074_end_0, end_mask = var_3074_end_mask_0, x = x_363_cast_fp16)[name = tensor<string, []>("op_3074_cast_fp16")];
            tensor<int32, [4]> var_3075 = const()[name = tensor<string, []>("op_3075"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3075, x = var_3074_cast_fp16)[name = tensor<string, []>("matrix_bd_65_cast_fp16")];
            tensor<bool, []> matrix_ac_33_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_33_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_33_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_33_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_128_perm_0 = const()[name = tensor<string, []>("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_129_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_198")];
            tensor<fp16, [1, 8, 188, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3058_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_33_cast_fp16")];
            tensor<int32, [4]> matrix_bd_67_begin_0 = const()[name = tensor<string, []>("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_67_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3084_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = tensor<string, []>("op_3084_cast_fp16")];
            tensor<fp16, []> _inversed_scores_65_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_65_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3084_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_65_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_67_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3090_cast_fp16 = softmax(axis = var_30, x = scores_67_cast_fp16)[name = tensor<string, []>("op_3090_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_865_cast_fp16 = select(a = var_11_to_fp16, b = var_3090_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_865_cast_fp16")];
            tensor<bool, []> x_365_transpose_x_0 = const()[name = tensor<string, []>("x_365_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_365_transpose_y_0 = const()[name = tensor<string, []>("x_365_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_33_cast_fp16)[name = tensor<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_865_cast_fp16, y = value_35_cast_fp16)[name = tensor<string, []>("x_365_cast_fp16")];
            tensor<int32, [4]> var_3094_perm_0 = const()[name = tensor<string, []>("op_3094_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3095 = const()[name = tensor<string, []>("op_3095"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3094_cast_fp16 = transpose(perm = var_3094_perm_0, x = x_365_cast_fp16)[name = tensor<string, []>("transpose_196")];
            tensor<fp16, [1, 188, 1024]> input_867_cast_fp16 = reshape(shape = var_3095, x = var_3094_cast_fp16)[name = tensor<string, []>("input_867_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_16_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(408509504))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409558144))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409560256)))];
            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = module_layers_16_self_attn_linear_out_bias_to_fp16, weight = module_layers_16_self_attn_linear_out_weight_to_fp16_quantized, x = input_867_cast_fp16)[name = tensor<string, []>("linear_151_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_871_cast_fp16 = add(x = input_863_cast_fp16, y = linear_151_cast_fp16)[name = tensor<string, []>("input_871_cast_fp16")];
            tensor<int32, [1]> x_369_axes_0 = const()[name = tensor<string, []>("x_369_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_16_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409562368)))];
            tensor<fp16, [1024]> module_layers_16_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409564480)))];
            tensor<fp16, [1, 188, 1024]> x_369_cast_fp16 = layer_norm(axes = x_369_axes_0, beta = module_layers_16_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_conv_weight_to_fp16, x = input_871_cast_fp16)[name = tensor<string, []>("x_369_cast_fp16")];
            tensor<int32, [3]> input_873_perm_0 = const()[name = tensor<string, []>("input_873_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_875_pad_type_0 = const()[name = tensor<string, []>("input_875_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_875_strides_0 = const()[name = tensor<string, []>("input_875_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_875_pad_0 = const()[name = tensor<string, []>("input_875_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_875_dilations_0 = const()[name = tensor<string, []>("input_875_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_875_groups_0 = const()[name = tensor<string, []>("input_875_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409566592))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411663808))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411667968)))];
            tensor<fp16, [1, 1024, 188]> input_873_cast_fp16 = transpose(perm = input_873_perm_0, x = x_369_cast_fp16)[name = tensor<string, []>("transpose_195")];
            tensor<fp16, [1, 2048, 188]> input_875_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv1_bias_to_fp16, dilations = input_875_dilations_0, groups = input_875_groups_0, pad = input_875_pad_0, pad_type = input_875_pad_type_0, strides = input_875_strides_0, weight = module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_873_cast_fp16)[name = tensor<string, []>("input_875_cast_fp16")];
            tensor<int32, []> x_371_split_num_splits_0 = const()[name = tensor<string, []>("x_371_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_371_split_axis_0 = const()[name = tensor<string, []>("x_371_split_axis_0"), val = tensor<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_875_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_371_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_877_cast_fp16 = select(a = var_11_to_fp16, b = x_371_cast_fp16, cond = var_335)[name = tensor<string, []>("input_877_cast_fp16")];
            tensor<int32, [6]> input_879_pad_0 = const()[name = tensor<string, []>("input_879_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_879_mode_0 = const()[name = tensor<string, []>("input_879_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_177_to_fp16 = const()[name = tensor<string, []>("const_177_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_879_cast_fp16 = pad(constant_val = const_177_to_fp16, mode = input_879_mode_0, pad = input_879_pad_0, x = input_877_cast_fp16)[name = tensor<string, []>("input_879_cast_fp16")];
            tensor<string, []> input_881_pad_type_0 = const()[name = tensor<string, []>("input_881_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_881_groups_0 = const()[name = tensor<string, []>("input_881_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_881_strides_0 = const()[name = tensor<string, []>("input_881_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_881_pad_0 = const()[name = tensor<string, []>("input_881_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_881_dilations_0 = const()[name = tensor<string, []>("input_881_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_280_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_280_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411672128))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411681408))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_281_to_fp16 = const()[name = tensor<string, []>("const_281_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411683520)))];
            tensor<fp16, [1, 1024, 188]> input_883_cast_fp16 = conv(bias = const_281_to_fp16, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = const_280_to_fp16_quantized, x = input_879_cast_fp16)[name = tensor<string, []>("input_883_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_885_cast_fp16 = silu(x = input_883_cast_fp16)[name = tensor<string, []>("input_885_cast_fp16")];
            tensor<string, []> x_373_pad_type_0 = const()[name = tensor<string, []>("x_373_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_373_strides_0 = const()[name = tensor<string, []>("x_373_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_373_pad_0 = const()[name = tensor<string, []>("x_373_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_373_dilations_0 = const()[name = tensor<string, []>("x_373_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_373_groups_0 = const()[name = tensor<string, []>("x_373_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411685632))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412734272))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412736384)))];
            tensor<fp16, [1, 1024, 188]> x_373_cast_fp16 = conv(bias = module_layers_16_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_885_cast_fp16)[name = tensor<string, []>("x_373_cast_fp16")];
            tensor<int32, [3]> input_887_perm_0 = const()[name = tensor<string, []>("input_887_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_887_cast_fp16 = transpose(perm = input_887_perm_0, x = x_373_cast_fp16)[name = tensor<string, []>("transpose_194")];
            tensor<fp16, [1, 188, 1024]> input_889_cast_fp16 = add(x = input_871_cast_fp16, y = input_887_cast_fp16)[name = tensor<string, []>("input_889_cast_fp16")];
            tensor<int32, [1]> input_891_axes_0 = const()[name = tensor<string, []>("input_891_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412738496)))];
            tensor<fp16, [1024]> module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412740608)))];
            tensor<fp16, [1, 188, 1024]> input_891_cast_fp16 = layer_norm(axes = input_891_axes_0, beta = module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_889_cast_fp16)[name = tensor<string, []>("input_891_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412742720))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416937088))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416945344)))];
            tensor<fp16, [1, 188, 4096]> linear_152_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized, x = input_891_cast_fp16)[name = tensor<string, []>("linear_152_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_895_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor<string, []>("input_895_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416953600))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421147968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421150080)))];
            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized, x = input_895_cast_fp16)[name = tensor<string, []>("linear_153_cast_fp16")];
            tensor<fp16, []> var_3161_to_fp16 = const()[name = tensor<string, []>("op_3161_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3162_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3161_to_fp16)[name = tensor<string, []>("op_3162_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_901_cast_fp16 = add(x = input_889_cast_fp16, y = var_3162_cast_fp16)[name = tensor<string, []>("input_901_cast_fp16")];
            tensor<int32, [1]> input_903_axes_0 = const()[name = tensor<string, []>("input_903_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_16_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421152192)))];
            tensor<fp16, [1024]> module_layers_16_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421154304)))];
            tensor<fp16, [1, 188, 1024]> input_903_cast_fp16 = layer_norm(axes = input_903_axes_0, beta = module_layers_16_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_16_norm_out_weight_to_fp16, x = input_901_cast_fp16)[name = tensor<string, []>("input_903_cast_fp16")];
            tensor<int32, [1]> input_905_axes_0 = const()[name = tensor<string, []>("input_905_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421156416)))];
            tensor<fp16, [1024]> module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421158528)))];
            tensor<fp16, [1, 188, 1024]> input_905_cast_fp16 = layer_norm(axes = input_905_axes_0, beta = module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_903_cast_fp16)[name = tensor<string, []>("input_905_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(421160640))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425355008))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425363264)))];
            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = module_layers_17_feed_forward1_linear1_bias_to_fp16, weight = module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized, x = input_905_cast_fp16)[name = tensor<string, []>("linear_154_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_909_cast_fp16 = silu(x = linear_154_cast_fp16)[name = tensor<string, []>("input_909_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(425371520))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429565888))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429568000)))];
            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = module_layers_17_feed_forward1_linear2_bias_to_fp16, weight = module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized, x = input_909_cast_fp16)[name = tensor<string, []>("linear_155_cast_fp16")];
            tensor<fp16, []> var_3192_to_fp16 = const()[name = tensor<string, []>("op_3192_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3193_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3192_to_fp16)[name = tensor<string, []>("op_3193_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_915_cast_fp16 = add(x = input_903_cast_fp16, y = var_3193_cast_fp16)[name = tensor<string, []>("input_915_cast_fp16")];
            tensor<int32, [1]> query_35_axes_0 = const()[name = tensor<string, []>("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_17_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429570112)))];
            tensor<fp16, [1024]> module_layers_17_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429572224)))];
            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_self_att_weight_to_fp16, x = input_915_cast_fp16)[name = tensor<string, []>("query_35_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429574336))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430622976))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430625088)))];
            tensor<fp16, [1, 188, 1024]> linear_156_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_q_bias_to_fp16, weight = module_layers_17_self_attn_linear_q_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = tensor<string, []>("linear_156_cast_fp16")];
            tensor<int32, [4]> var_3210 = const()[name = tensor<string, []>("op_3210"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3210, x = linear_156_cast_fp16)[name = tensor<string, []>("q_103_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430627200))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431675840))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431677952)))];
            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_k_bias_to_fp16, weight = module_layers_17_self_attn_linear_k_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = tensor<string, []>("linear_157_cast_fp16")];
            tensor<int32, [4]> var_3215 = const()[name = tensor<string, []>("op_3215"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3215, x = linear_157_cast_fp16)[name = tensor<string, []>("k_69_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431680064))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432728704))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432730816)))];
            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_v_bias_to_fp16, weight = module_layers_17_self_attn_linear_v_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = tensor<string, []>("linear_158_cast_fp16")];
            tensor<int32, [4]> var_3220 = const()[name = tensor<string, []>("op_3220"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3220, x = linear_158_cast_fp16)[name = tensor<string, []>("v_35_cast_fp16")];
            tensor<int32, [4]> value_37_perm_0 = const()[name = tensor<string, []>("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_17_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432732928))), scale = tensor<fp16, [8]>([0x1.cf4p-8, 0x1.1c8p-8, 0x1.f08p-9, 0x1.438p-8, 0x1.ee8p-9, 0x1.298p-7, 0x1.468p-8, 0x1.2p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3232_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3232_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_17_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432734016))), scale = tensor<fp16, [8]>([0x1.5b4p-8, 0x1.474p-8, 0x1.364p-9, 0x1.a14p-8, 0x1.408p-8, 0x1.6f4p-8, 0x1.168p-9, 0x1.f6p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3234_cast_fp16 = add(x = q_103_cast_fp16, y = module_layers_17_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3234_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_381_transpose_x_0 = const()[name = tensor<string, []>("x_381_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_381_transpose_y_0 = const()[name = tensor<string, []>("x_381_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3236_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3236_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432735104))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433119168))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3234_cast_fp16)[name = tensor<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_3236_to_fp16_quantized)[name = tensor<string, []>("x_381_cast_fp16")];
            tensor<int32, [8]> x_383_pad_0 = const()[name = tensor<string, []>("x_383_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_383_mode_0 = const()[name = tensor<string, []>("x_383_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_184_to_fp16 = const()[name = tensor<string, []>("const_184_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_383_cast_fp16 = pad(constant_val = const_184_to_fp16, mode = x_383_mode_0, pad = x_383_pad_0, x = x_381_cast_fp16)[name = tensor<string, []>("x_383_cast_fp16")];
            tensor<int32, [4]> var_3244 = const()[name = tensor<string, []>("op_3244"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_385_cast_fp16 = reshape(shape = var_3244, x = x_383_cast_fp16)[name = tensor<string, []>("x_385_cast_fp16")];
            tensor<int32, [4]> var_3248_begin_0 = const()[name = tensor<string, []>("op_3248_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3248_end_0 = const()[name = tensor<string, []>("op_3248_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3248_end_mask_0 = const()[name = tensor<string, []>("op_3248_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3248_cast_fp16 = slice_by_index(begin = var_3248_begin_0, end = var_3248_end_0, end_mask = var_3248_end_mask_0, x = x_385_cast_fp16)[name = tensor<string, []>("op_3248_cast_fp16")];
            tensor<int32, [4]> var_3249 = const()[name = tensor<string, []>("op_3249"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3249, x = var_3248_cast_fp16)[name = tensor<string, []>("matrix_bd_69_cast_fp16")];
            tensor<bool, []> matrix_ac_35_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_35_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_35_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_35_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_130_perm_0 = const()[name = tensor<string, []>("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_131_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_191")];
            tensor<fp16, [1, 8, 188, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3232_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_35_cast_fp16")];
            tensor<int32, [4]> matrix_bd_71_begin_0 = const()[name = tensor<string, []>("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_71_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3258_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = tensor<string, []>("op_3258_cast_fp16")];
            tensor<fp16, []> _inversed_scores_69_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_69_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3258_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_69_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_71_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3264_cast_fp16 = softmax(axis = var_30, x = scores_71_cast_fp16)[name = tensor<string, []>("op_3264_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_917_cast_fp16 = select(a = var_11_to_fp16, b = var_3264_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_917_cast_fp16")];
            tensor<bool, []> x_387_transpose_x_0 = const()[name = tensor<string, []>("x_387_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_387_transpose_y_0 = const()[name = tensor<string, []>("x_387_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_35_cast_fp16)[name = tensor<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_917_cast_fp16, y = value_37_cast_fp16)[name = tensor<string, []>("x_387_cast_fp16")];
            tensor<int32, [4]> var_3268_perm_0 = const()[name = tensor<string, []>("op_3268_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3269 = const()[name = tensor<string, []>("op_3269"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3268_cast_fp16 = transpose(perm = var_3268_perm_0, x = x_387_cast_fp16)[name = tensor<string, []>("transpose_189")];
            tensor<fp16, [1, 188, 1024]> input_919_cast_fp16 = reshape(shape = var_3269, x = var_3268_cast_fp16)[name = tensor<string, []>("input_919_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_17_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433120000))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434168640))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434170752)))];
            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = module_layers_17_self_attn_linear_out_bias_to_fp16, weight = module_layers_17_self_attn_linear_out_weight_to_fp16_quantized, x = input_919_cast_fp16)[name = tensor<string, []>("linear_160_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_923_cast_fp16 = add(x = input_915_cast_fp16, y = linear_160_cast_fp16)[name = tensor<string, []>("input_923_cast_fp16")];
            tensor<int32, [1]> x_391_axes_0 = const()[name = tensor<string, []>("x_391_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_17_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434172864)))];
            tensor<fp16, [1024]> module_layers_17_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434174976)))];
            tensor<fp16, [1, 188, 1024]> x_391_cast_fp16 = layer_norm(axes = x_391_axes_0, beta = module_layers_17_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_conv_weight_to_fp16, x = input_923_cast_fp16)[name = tensor<string, []>("x_391_cast_fp16")];
            tensor<int32, [3]> input_925_perm_0 = const()[name = tensor<string, []>("input_925_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_927_pad_type_0 = const()[name = tensor<string, []>("input_927_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_927_strides_0 = const()[name = tensor<string, []>("input_927_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_927_pad_0 = const()[name = tensor<string, []>("input_927_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_927_dilations_0 = const()[name = tensor<string, []>("input_927_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_927_groups_0 = const()[name = tensor<string, []>("input_927_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(434177088))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436274304))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_17_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436278464)))];
            tensor<fp16, [1, 1024, 188]> input_925_cast_fp16 = transpose(perm = input_925_perm_0, x = x_391_cast_fp16)[name = tensor<string, []>("transpose_188")];
            tensor<fp16, [1, 2048, 188]> input_927_cast_fp16 = conv(bias = module_layers_17_conv_pointwise_conv1_bias_to_fp16, dilations = input_927_dilations_0, groups = input_927_groups_0, pad = input_927_pad_0, pad_type = input_927_pad_type_0, strides = input_927_strides_0, weight = module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_925_cast_fp16)[name = tensor<string, []>("input_927_cast_fp16")];
            tensor<int32, []> x_393_split_num_splits_0 = const()[name = tensor<string, []>("x_393_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_393_split_axis_0 = const()[name = tensor<string, []>("x_393_split_axis_0"), val = tensor<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_927_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_393_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_929_cast_fp16 = select(a = var_11_to_fp16, b = x_393_cast_fp16, cond = var_335)[name = tensor<string, []>("input_929_cast_fp16")];
            tensor<int32, [6]> input_931_pad_0 = const()[name = tensor<string, []>("input_931_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_931_mode_0 = const()[name = tensor<string, []>("input_931_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_187_to_fp16 = const()[name = tensor<string, []>("const_187_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_931_cast_fp16 = pad(constant_val = const_187_to_fp16, mode = input_931_mode_0, pad = input_931_pad_0, x = input_929_cast_fp16)[name = tensor<string, []>("input_931_cast_fp16")];
            tensor<string, []> input_933_pad_type_0 = const()[name = tensor<string, []>("input_933_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_933_groups_0 = const()[name = tensor<string, []>("input_933_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_933_strides_0 = const()[name = tensor<string, []>("input_933_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_933_pad_0 = const()[name = tensor<string, []>("input_933_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_933_dilations_0 = const()[name = tensor<string, []>("input_933_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_282_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_282_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436282624))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436291904))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_283_to_fp16 = const()[name = tensor<string, []>("const_283_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436294016)))];
            tensor<fp16, [1, 1024, 188]> input_935_cast_fp16 = conv(bias = const_283_to_fp16, dilations = input_933_dilations_0, groups = input_933_groups_0, pad = input_933_pad_0, pad_type = input_933_pad_type_0, strides = input_933_strides_0, weight = const_282_to_fp16_quantized, x = input_931_cast_fp16)[name = tensor<string, []>("input_935_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_937_cast_fp16 = silu(x = input_935_cast_fp16)[name = tensor<string, []>("input_937_cast_fp16")];
            tensor<string, []> x_395_pad_type_0 = const()[name = tensor<string, []>("x_395_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_395_strides_0 = const()[name = tensor<string, []>("x_395_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_395_pad_0 = const()[name = tensor<string, []>("x_395_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_395_dilations_0 = const()[name = tensor<string, []>("x_395_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_395_groups_0 = const()[name = tensor<string, []>("x_395_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(436296128))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437344768))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_17_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437346880)))];
            tensor<fp16, [1, 1024, 188]> x_395_cast_fp16 = conv(bias = module_layers_17_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_937_cast_fp16)[name = tensor<string, []>("x_395_cast_fp16")];
            tensor<int32, [3]> input_939_perm_0 = const()[name = tensor<string, []>("input_939_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_939_cast_fp16 = transpose(perm = input_939_perm_0, x = x_395_cast_fp16)[name = tensor<string, []>("transpose_187")];
            tensor<fp16, [1, 188, 1024]> input_941_cast_fp16 = add(x = input_923_cast_fp16, y = input_939_cast_fp16)[name = tensor<string, []>("input_941_cast_fp16")];
            tensor<int32, [1]> input_943_axes_0 = const()[name = tensor<string, []>("input_943_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437348992)))];
            tensor<fp16, [1024]> module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437351104)))];
            tensor<fp16, [1, 188, 1024]> input_943_cast_fp16 = layer_norm(axes = input_943_axes_0, beta = module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_941_cast_fp16)[name = tensor<string, []>("input_943_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(437353216))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441547584))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441555840)))];
            tensor<fp16, [1, 188, 4096]> linear_161_cast_fp16 = linear(bias = module_layers_17_feed_forward2_linear1_bias_to_fp16, weight = module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized, x = input_943_cast_fp16)[name = tensor<string, []>("linear_161_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_947_cast_fp16 = silu(x = linear_161_cast_fp16)[name = tensor<string, []>("input_947_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441564096))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445758464))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445760576)))];
            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = module_layers_17_feed_forward2_linear2_bias_to_fp16, weight = module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized, x = input_947_cast_fp16)[name = tensor<string, []>("linear_162_cast_fp16")];
            tensor<fp16, []> var_3335_to_fp16 = const()[name = tensor<string, []>("op_3335_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3336_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3335_to_fp16)[name = tensor<string, []>("op_3336_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_953_cast_fp16 = add(x = input_941_cast_fp16, y = var_3336_cast_fp16)[name = tensor<string, []>("input_953_cast_fp16")];
            tensor<int32, [1]> input_955_axes_0 = const()[name = tensor<string, []>("input_955_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_17_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445762688)))];
            tensor<fp16, [1024]> module_layers_17_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445764800)))];
            tensor<fp16, [1, 188, 1024]> input_955_cast_fp16 = layer_norm(axes = input_955_axes_0, beta = module_layers_17_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_17_norm_out_weight_to_fp16, x = input_953_cast_fp16)[name = tensor<string, []>("input_955_cast_fp16")];
            tensor<int32, [1]> input_957_axes_0 = const()[name = tensor<string, []>("input_957_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445766912)))];
            tensor<fp16, [1024]> module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445769024)))];
            tensor<fp16, [1, 188, 1024]> input_957_cast_fp16 = layer_norm(axes = input_957_axes_0, beta = module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_955_cast_fp16)[name = tensor<string, []>("input_957_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445771136))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449965504))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449973760)))];
            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = module_layers_18_feed_forward1_linear1_bias_to_fp16, weight = module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized, x = input_957_cast_fp16)[name = tensor<string, []>("linear_163_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_961_cast_fp16 = silu(x = linear_163_cast_fp16)[name = tensor<string, []>("input_961_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449982016))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454176384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454178496)))];
            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = module_layers_18_feed_forward1_linear2_bias_to_fp16, weight = module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized, x = input_961_cast_fp16)[name = tensor<string, []>("linear_164_cast_fp16")];
            tensor<fp16, []> var_3366_to_fp16 = const()[name = tensor<string, []>("op_3366_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3367_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3366_to_fp16)[name = tensor<string, []>("op_3367_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_967_cast_fp16 = add(x = input_955_cast_fp16, y = var_3367_cast_fp16)[name = tensor<string, []>("input_967_cast_fp16")];
            tensor<int32, [1]> query_37_axes_0 = const()[name = tensor<string, []>("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_18_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454180608)))];
            tensor<fp16, [1024]> module_layers_18_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454182720)))];
            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_self_att_weight_to_fp16, x = input_967_cast_fp16)[name = tensor<string, []>("query_37_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454184832))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455233472))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455235584)))];
            tensor<fp16, [1, 188, 1024]> linear_165_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_q_bias_to_fp16, weight = module_layers_18_self_attn_linear_q_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = tensor<string, []>("linear_165_cast_fp16")];
            tensor<int32, [4]> var_3384 = const()[name = tensor<string, []>("op_3384"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3384, x = linear_165_cast_fp16)[name = tensor<string, []>("q_109_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455237696))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456286336))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456288448)))];
            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_k_bias_to_fp16, weight = module_layers_18_self_attn_linear_k_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = tensor<string, []>("linear_166_cast_fp16")];
            tensor<int32, [4]> var_3389 = const()[name = tensor<string, []>("op_3389"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3389, x = linear_166_cast_fp16)[name = tensor<string, []>("k_73_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(456290560))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457339200))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457341312)))];
            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_v_bias_to_fp16, weight = module_layers_18_self_attn_linear_v_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = tensor<string, []>("linear_167_cast_fp16")];
            tensor<int32, [4]> var_3394 = const()[name = tensor<string, []>("op_3394"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3394, x = linear_167_cast_fp16)[name = tensor<string, []>("v_37_cast_fp16")];
            tensor<int32, [4]> value_39_perm_0 = const()[name = tensor<string, []>("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_18_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457343424))), scale = tensor<fp16, [8]>([0x1.26p-8, 0x1.ddcp-9, 0x1.be8p-9, 0x1.6a8p-8, 0x1.a5cp-9, 0x1.c84p-9, 0x1.014p-8, 0x1.2b4p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3406_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3406_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_18_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457344512))), scale = tensor<fp16, [8]>([0x1.50cp-9, 0x1.83cp-9, 0x1.544p-9, 0x1.9d4p-8, 0x1p-7, 0x1.f44p-8, 0x1.ca8p-8, 0x1.9fp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3408_cast_fp16 = add(x = q_109_cast_fp16, y = module_layers_18_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3408_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_403_transpose_x_0 = const()[name = tensor<string, []>("x_403_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_403_transpose_y_0 = const()[name = tensor<string, []>("x_403_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3410_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3410_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457345600))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457729664))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3408_cast_fp16)[name = tensor<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_3410_to_fp16_quantized)[name = tensor<string, []>("x_403_cast_fp16")];
            tensor<int32, [8]> x_405_pad_0 = const()[name = tensor<string, []>("x_405_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_405_mode_0 = const()[name = tensor<string, []>("x_405_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_194_to_fp16 = const()[name = tensor<string, []>("const_194_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_405_cast_fp16 = pad(constant_val = const_194_to_fp16, mode = x_405_mode_0, pad = x_405_pad_0, x = x_403_cast_fp16)[name = tensor<string, []>("x_405_cast_fp16")];
            tensor<int32, [4]> var_3418 = const()[name = tensor<string, []>("op_3418"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_407_cast_fp16 = reshape(shape = var_3418, x = x_405_cast_fp16)[name = tensor<string, []>("x_407_cast_fp16")];
            tensor<int32, [4]> var_3422_begin_0 = const()[name = tensor<string, []>("op_3422_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3422_end_0 = const()[name = tensor<string, []>("op_3422_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3422_end_mask_0 = const()[name = tensor<string, []>("op_3422_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3422_cast_fp16 = slice_by_index(begin = var_3422_begin_0, end = var_3422_end_0, end_mask = var_3422_end_mask_0, x = x_407_cast_fp16)[name = tensor<string, []>("op_3422_cast_fp16")];
            tensor<int32, [4]> var_3423 = const()[name = tensor<string, []>("op_3423"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3423, x = var_3422_cast_fp16)[name = tensor<string, []>("matrix_bd_73_cast_fp16")];
            tensor<bool, []> matrix_ac_37_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_37_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_132_perm_0 = const()[name = tensor<string, []>("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_133_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_184")];
            tensor<fp16, [1, 8, 188, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3406_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_37_cast_fp16")];
            tensor<int32, [4]> matrix_bd_75_begin_0 = const()[name = tensor<string, []>("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_75_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3432_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = tensor<string, []>("op_3432_cast_fp16")];
            tensor<fp16, []> _inversed_scores_73_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_73_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3432_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_73_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_75_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3438_cast_fp16 = softmax(axis = var_30, x = scores_75_cast_fp16)[name = tensor<string, []>("op_3438_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_969_cast_fp16 = select(a = var_11_to_fp16, b = var_3438_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_969_cast_fp16")];
            tensor<bool, []> x_409_transpose_x_0 = const()[name = tensor<string, []>("x_409_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_409_transpose_y_0 = const()[name = tensor<string, []>("x_409_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_37_cast_fp16)[name = tensor<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_969_cast_fp16, y = value_39_cast_fp16)[name = tensor<string, []>("x_409_cast_fp16")];
            tensor<int32, [4]> var_3442_perm_0 = const()[name = tensor<string, []>("op_3442_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3443 = const()[name = tensor<string, []>("op_3443"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3442_cast_fp16 = transpose(perm = var_3442_perm_0, x = x_409_cast_fp16)[name = tensor<string, []>("transpose_182")];
            tensor<fp16, [1, 188, 1024]> input_971_cast_fp16 = reshape(shape = var_3443, x = var_3442_cast_fp16)[name = tensor<string, []>("input_971_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457730496))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458779136))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458781248)))];
            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = module_layers_18_self_attn_linear_out_bias_to_fp16, weight = module_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_971_cast_fp16)[name = tensor<string, []>("linear_169_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_975_cast_fp16 = add(x = input_967_cast_fp16, y = linear_169_cast_fp16)[name = tensor<string, []>("input_975_cast_fp16")];
            tensor<int32, [1]> x_413_axes_0 = const()[name = tensor<string, []>("x_413_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_18_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458783360)))];
            tensor<fp16, [1024]> module_layers_18_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458785472)))];
            tensor<fp16, [1, 188, 1024]> x_413_cast_fp16 = layer_norm(axes = x_413_axes_0, beta = module_layers_18_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_conv_weight_to_fp16, x = input_975_cast_fp16)[name = tensor<string, []>("x_413_cast_fp16")];
            tensor<int32, [3]> input_977_perm_0 = const()[name = tensor<string, []>("input_977_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_979_pad_type_0 = const()[name = tensor<string, []>("input_979_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_979_strides_0 = const()[name = tensor<string, []>("input_979_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_979_pad_0 = const()[name = tensor<string, []>("input_979_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_979_dilations_0 = const()[name = tensor<string, []>("input_979_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_979_groups_0 = const()[name = tensor<string, []>("input_979_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458787584))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460884800))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_18_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460888960)))];
            tensor<fp16, [1, 1024, 188]> input_977_cast_fp16 = transpose(perm = input_977_perm_0, x = x_413_cast_fp16)[name = tensor<string, []>("transpose_181")];
            tensor<fp16, [1, 2048, 188]> input_979_cast_fp16 = conv(bias = module_layers_18_conv_pointwise_conv1_bias_to_fp16, dilations = input_979_dilations_0, groups = input_979_groups_0, pad = input_979_pad_0, pad_type = input_979_pad_type_0, strides = input_979_strides_0, weight = module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_977_cast_fp16)[name = tensor<string, []>("input_979_cast_fp16")];
            tensor<int32, []> x_415_split_num_splits_0 = const()[name = tensor<string, []>("x_415_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_415_split_axis_0 = const()[name = tensor<string, []>("x_415_split_axis_0"), val = tensor<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_979_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_415_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_981_cast_fp16 = select(a = var_11_to_fp16, b = x_415_cast_fp16, cond = var_335)[name = tensor<string, []>("input_981_cast_fp16")];
            tensor<int32, [6]> input_983_pad_0 = const()[name = tensor<string, []>("input_983_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_983_mode_0 = const()[name = tensor<string, []>("input_983_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_197_to_fp16 = const()[name = tensor<string, []>("const_197_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_983_cast_fp16 = pad(constant_val = const_197_to_fp16, mode = input_983_mode_0, pad = input_983_pad_0, x = input_981_cast_fp16)[name = tensor<string, []>("input_983_cast_fp16")];
            tensor<string, []> input_985_pad_type_0 = const()[name = tensor<string, []>("input_985_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_985_groups_0 = const()[name = tensor<string, []>("input_985_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_985_strides_0 = const()[name = tensor<string, []>("input_985_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_985_pad_0 = const()[name = tensor<string, []>("input_985_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_985_dilations_0 = const()[name = tensor<string, []>("input_985_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_284_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_284_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460893120))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460902400))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_285_to_fp16 = const()[name = tensor<string, []>("const_285_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460904512)))];
            tensor<fp16, [1, 1024, 188]> input_987_cast_fp16 = conv(bias = const_285_to_fp16, dilations = input_985_dilations_0, groups = input_985_groups_0, pad = input_985_pad_0, pad_type = input_985_pad_type_0, strides = input_985_strides_0, weight = const_284_to_fp16_quantized, x = input_983_cast_fp16)[name = tensor<string, []>("input_987_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_989_cast_fp16 = silu(x = input_987_cast_fp16)[name = tensor<string, []>("input_989_cast_fp16")];
            tensor<string, []> x_417_pad_type_0 = const()[name = tensor<string, []>("x_417_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_417_strides_0 = const()[name = tensor<string, []>("x_417_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_417_pad_0 = const()[name = tensor<string, []>("x_417_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_417_dilations_0 = const()[name = tensor<string, []>("x_417_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_417_groups_0 = const()[name = tensor<string, []>("x_417_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(460906624))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461955264))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_18_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461957376)))];
            tensor<fp16, [1, 1024, 188]> x_417_cast_fp16 = conv(bias = module_layers_18_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_989_cast_fp16)[name = tensor<string, []>("x_417_cast_fp16")];
            tensor<int32, [3]> input_991_perm_0 = const()[name = tensor<string, []>("input_991_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_991_cast_fp16 = transpose(perm = input_991_perm_0, x = x_417_cast_fp16)[name = tensor<string, []>("transpose_180")];
            tensor<fp16, [1, 188, 1024]> input_993_cast_fp16 = add(x = input_975_cast_fp16, y = input_991_cast_fp16)[name = tensor<string, []>("input_993_cast_fp16")];
            tensor<int32, [1]> input_995_axes_0 = const()[name = tensor<string, []>("input_995_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461959488)))];
            tensor<fp16, [1024]> module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461961600)))];
            tensor<fp16, [1, 188, 1024]> input_995_cast_fp16 = layer_norm(axes = input_995_axes_0, beta = module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_993_cast_fp16)[name = tensor<string, []>("input_995_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461963712))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466158080))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466166336)))];
            tensor<fp16, [1, 188, 4096]> linear_170_cast_fp16 = linear(bias = module_layers_18_feed_forward2_linear1_bias_to_fp16, weight = module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_995_cast_fp16)[name = tensor<string, []>("linear_170_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_999_cast_fp16 = silu(x = linear_170_cast_fp16)[name = tensor<string, []>("input_999_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(466174592))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470368960))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470371072)))];
            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = module_layers_18_feed_forward2_linear2_bias_to_fp16, weight = module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_999_cast_fp16)[name = tensor<string, []>("linear_171_cast_fp16")];
            tensor<fp16, []> var_3509_to_fp16 = const()[name = tensor<string, []>("op_3509_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3510_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3509_to_fp16)[name = tensor<string, []>("op_3510_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1005_cast_fp16 = add(x = input_993_cast_fp16, y = var_3510_cast_fp16)[name = tensor<string, []>("input_1005_cast_fp16")];
            tensor<int32, [1]> input_1007_axes_0 = const()[name = tensor<string, []>("input_1007_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_18_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470373184)))];
            tensor<fp16, [1024]> module_layers_18_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470375296)))];
            tensor<fp16, [1, 188, 1024]> input_1007_cast_fp16 = layer_norm(axes = input_1007_axes_0, beta = module_layers_18_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_18_norm_out_weight_to_fp16, x = input_1005_cast_fp16)[name = tensor<string, []>("input_1007_cast_fp16")];
            tensor<int32, [1]> input_1009_axes_0 = const()[name = tensor<string, []>("input_1009_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470377408)))];
            tensor<fp16, [1024]> module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470379520)))];
            tensor<fp16, [1, 188, 1024]> input_1009_cast_fp16 = layer_norm(axes = input_1009_axes_0, beta = module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1007_cast_fp16)[name = tensor<string, []>("input_1009_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470381632))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474576000))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474584256)))];
            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = module_layers_19_feed_forward1_linear1_bias_to_fp16, weight = module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1009_cast_fp16)[name = tensor<string, []>("linear_172_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1013_cast_fp16 = silu(x = linear_172_cast_fp16)[name = tensor<string, []>("input_1013_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474592512))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478786880))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478788992)))];
            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = module_layers_19_feed_forward1_linear2_bias_to_fp16, weight = module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1013_cast_fp16)[name = tensor<string, []>("linear_173_cast_fp16")];
            tensor<fp16, []> var_3540_to_fp16 = const()[name = tensor<string, []>("op_3540_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3541_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3540_to_fp16)[name = tensor<string, []>("op_3541_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1019_cast_fp16 = add(x = input_1007_cast_fp16, y = var_3541_cast_fp16)[name = tensor<string, []>("input_1019_cast_fp16")];
            tensor<int32, [1]> query_39_axes_0 = const()[name = tensor<string, []>("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_19_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478791104)))];
            tensor<fp16, [1024]> module_layers_19_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478793216)))];
            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_self_att_weight_to_fp16, x = input_1019_cast_fp16)[name = tensor<string, []>("query_39_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478795328))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479843968))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479846080)))];
            tensor<fp16, [1, 188, 1024]> linear_174_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_q_bias_to_fp16, weight = module_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = tensor<string, []>("linear_174_cast_fp16")];
            tensor<int32, [4]> var_3558 = const()[name = tensor<string, []>("op_3558"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3558, x = linear_174_cast_fp16)[name = tensor<string, []>("q_115_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479848192))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480896832))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480898944)))];
            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_k_bias_to_fp16, weight = module_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = tensor<string, []>("linear_175_cast_fp16")];
            tensor<int32, [4]> var_3563 = const()[name = tensor<string, []>("op_3563"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3563, x = linear_175_cast_fp16)[name = tensor<string, []>("k_77_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(480901056))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481949696))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481951808)))];
            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_v_bias_to_fp16, weight = module_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = tensor<string, []>("linear_176_cast_fp16")];
            tensor<int32, [4]> var_3568 = const()[name = tensor<string, []>("op_3568"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3568, x = linear_176_cast_fp16)[name = tensor<string, []>("v_39_cast_fp16")];
            tensor<int32, [4]> value_41_perm_0 = const()[name = tensor<string, []>("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_19_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481953920))), scale = tensor<fp16, [8]>([0x1.af8p-9, 0x1.42p-8, 0x1.c94p-9, 0x1.ba8p-9, 0x1.f88p-9, 0x1.11cp-8, 0x1.ccp-8, 0x1.acp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3580_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3580_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_19_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481955008))), scale = tensor<fp16, [8]>([0x1.dd8p-8, 0x1.66p-8, 0x1.34p-8, 0x1.95cp-8, 0x1.1b8p-8, 0x1.464p-8, 0x1.ba8p-9, 0x1.82p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3582_cast_fp16 = add(x = q_115_cast_fp16, y = module_layers_19_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3582_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_425_transpose_x_0 = const()[name = tensor<string, []>("x_425_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_425_transpose_y_0 = const()[name = tensor<string, []>("x_425_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3584_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3584_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(481956096))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482340160))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3582_cast_fp16)[name = tensor<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_3584_to_fp16_quantized)[name = tensor<string, []>("x_425_cast_fp16")];
            tensor<int32, [8]> x_427_pad_0 = const()[name = tensor<string, []>("x_427_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_427_mode_0 = const()[name = tensor<string, []>("x_427_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_204_to_fp16 = const()[name = tensor<string, []>("const_204_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_427_cast_fp16 = pad(constant_val = const_204_to_fp16, mode = x_427_mode_0, pad = x_427_pad_0, x = x_425_cast_fp16)[name = tensor<string, []>("x_427_cast_fp16")];
            tensor<int32, [4]> var_3592 = const()[name = tensor<string, []>("op_3592"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_429_cast_fp16 = reshape(shape = var_3592, x = x_427_cast_fp16)[name = tensor<string, []>("x_429_cast_fp16")];
            tensor<int32, [4]> var_3596_begin_0 = const()[name = tensor<string, []>("op_3596_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3596_end_0 = const()[name = tensor<string, []>("op_3596_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3596_end_mask_0 = const()[name = tensor<string, []>("op_3596_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3596_cast_fp16 = slice_by_index(begin = var_3596_begin_0, end = var_3596_end_0, end_mask = var_3596_end_mask_0, x = x_429_cast_fp16)[name = tensor<string, []>("op_3596_cast_fp16")];
            tensor<int32, [4]> var_3597 = const()[name = tensor<string, []>("op_3597"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3597, x = var_3596_cast_fp16)[name = tensor<string, []>("matrix_bd_77_cast_fp16")];
            tensor<bool, []> matrix_ac_39_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_39_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_39_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_39_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_134_perm_0 = const()[name = tensor<string, []>("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_135_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_177")];
            tensor<fp16, [1, 8, 188, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3580_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_39_cast_fp16")];
            tensor<int32, [4]> matrix_bd_79_begin_0 = const()[name = tensor<string, []>("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_79_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3606_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = tensor<string, []>("op_3606_cast_fp16")];
            tensor<fp16, []> _inversed_scores_77_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_77_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3606_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_77_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_79_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3612_cast_fp16 = softmax(axis = var_30, x = scores_79_cast_fp16)[name = tensor<string, []>("op_3612_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1021_cast_fp16 = select(a = var_11_to_fp16, b = var_3612_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1021_cast_fp16")];
            tensor<bool, []> x_431_transpose_x_0 = const()[name = tensor<string, []>("x_431_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_431_transpose_y_0 = const()[name = tensor<string, []>("x_431_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_39_cast_fp16)[name = tensor<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_1021_cast_fp16, y = value_41_cast_fp16)[name = tensor<string, []>("x_431_cast_fp16")];
            tensor<int32, [4]> var_3616_perm_0 = const()[name = tensor<string, []>("op_3616_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3617 = const()[name = tensor<string, []>("op_3617"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3616_cast_fp16 = transpose(perm = var_3616_perm_0, x = x_431_cast_fp16)[name = tensor<string, []>("transpose_175")];
            tensor<fp16, [1, 188, 1024]> input_1023_cast_fp16 = reshape(shape = var_3617, x = var_3616_cast_fp16)[name = tensor<string, []>("input_1023_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(482340992))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483389632))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483391744)))];
            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = module_layers_19_self_attn_linear_out_bias_to_fp16, weight = module_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1023_cast_fp16)[name = tensor<string, []>("linear_178_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1027_cast_fp16 = add(x = input_1019_cast_fp16, y = linear_178_cast_fp16)[name = tensor<string, []>("input_1027_cast_fp16")];
            tensor<int32, [1]> x_435_axes_0 = const()[name = tensor<string, []>("x_435_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_19_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483393856)))];
            tensor<fp16, [1024]> module_layers_19_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483395968)))];
            tensor<fp16, [1, 188, 1024]> x_435_cast_fp16 = layer_norm(axes = x_435_axes_0, beta = module_layers_19_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_conv_weight_to_fp16, x = input_1027_cast_fp16)[name = tensor<string, []>("x_435_cast_fp16")];
            tensor<int32, [3]> input_1029_perm_0 = const()[name = tensor<string, []>("input_1029_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1031_pad_type_0 = const()[name = tensor<string, []>("input_1031_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1031_strides_0 = const()[name = tensor<string, []>("input_1031_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1031_pad_0 = const()[name = tensor<string, []>("input_1031_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1031_dilations_0 = const()[name = tensor<string, []>("input_1031_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1031_groups_0 = const()[name = tensor<string, []>("input_1031_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(483398080))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(485495296))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_19_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(485499456)))];
            tensor<fp16, [1, 1024, 188]> input_1029_cast_fp16 = transpose(perm = input_1029_perm_0, x = x_435_cast_fp16)[name = tensor<string, []>("transpose_174")];
            tensor<fp16, [1, 2048, 188]> input_1031_cast_fp16 = conv(bias = module_layers_19_conv_pointwise_conv1_bias_to_fp16, dilations = input_1031_dilations_0, groups = input_1031_groups_0, pad = input_1031_pad_0, pad_type = input_1031_pad_type_0, strides = input_1031_strides_0, weight = module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1029_cast_fp16)[name = tensor<string, []>("input_1031_cast_fp16")];
            tensor<int32, []> x_437_split_num_splits_0 = const()[name = tensor<string, []>("x_437_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_437_split_axis_0 = const()[name = tensor<string, []>("x_437_split_axis_0"), val = tensor<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_1031_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_437_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1033_cast_fp16 = select(a = var_11_to_fp16, b = x_437_cast_fp16, cond = var_335)[name = tensor<string, []>("input_1033_cast_fp16")];
            tensor<int32, [6]> input_1035_pad_0 = const()[name = tensor<string, []>("input_1035_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1035_mode_0 = const()[name = tensor<string, []>("input_1035_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_207_to_fp16 = const()[name = tensor<string, []>("const_207_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1035_cast_fp16 = pad(constant_val = const_207_to_fp16, mode = input_1035_mode_0, pad = input_1035_pad_0, x = input_1033_cast_fp16)[name = tensor<string, []>("input_1035_cast_fp16")];
            tensor<string, []> input_1037_pad_type_0 = const()[name = tensor<string, []>("input_1037_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1037_groups_0 = const()[name = tensor<string, []>("input_1037_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1037_strides_0 = const()[name = tensor<string, []>("input_1037_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1037_pad_0 = const()[name = tensor<string, []>("input_1037_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1037_dilations_0 = const()[name = tensor<string, []>("input_1037_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_286_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_286_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(485503616))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(485512896))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_287_to_fp16 = const()[name = tensor<string, []>("const_287_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(485515008)))];
            tensor<fp16, [1, 1024, 188]> input_1039_cast_fp16 = conv(bias = const_287_to_fp16, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = const_286_to_fp16_quantized, x = input_1035_cast_fp16)[name = tensor<string, []>("input_1039_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1041_cast_fp16 = silu(x = input_1039_cast_fp16)[name = tensor<string, []>("input_1041_cast_fp16")];
            tensor<string, []> x_439_pad_type_0 = const()[name = tensor<string, []>("x_439_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_439_strides_0 = const()[name = tensor<string, []>("x_439_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_439_pad_0 = const()[name = tensor<string, []>("x_439_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_439_dilations_0 = const()[name = tensor<string, []>("x_439_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_439_groups_0 = const()[name = tensor<string, []>("x_439_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(485517120))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486565760))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_19_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486567872)))];
            tensor<fp16, [1, 1024, 188]> x_439_cast_fp16 = conv(bias = module_layers_19_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1041_cast_fp16)[name = tensor<string, []>("x_439_cast_fp16")];
            tensor<int32, [3]> input_1043_perm_0 = const()[name = tensor<string, []>("input_1043_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1043_cast_fp16 = transpose(perm = input_1043_perm_0, x = x_439_cast_fp16)[name = tensor<string, []>("transpose_173")];
            tensor<fp16, [1, 188, 1024]> input_1045_cast_fp16 = add(x = input_1027_cast_fp16, y = input_1043_cast_fp16)[name = tensor<string, []>("input_1045_cast_fp16")];
            tensor<int32, [1]> input_1047_axes_0 = const()[name = tensor<string, []>("input_1047_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486569984)))];
            tensor<fp16, [1024]> module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486572096)))];
            tensor<fp16, [1, 188, 1024]> input_1047_cast_fp16 = layer_norm(axes = input_1047_axes_0, beta = module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1045_cast_fp16)[name = tensor<string, []>("input_1047_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486574208))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490768576))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490776832)))];
            tensor<fp16, [1, 188, 4096]> linear_179_cast_fp16 = linear(bias = module_layers_19_feed_forward2_linear1_bias_to_fp16, weight = module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1047_cast_fp16)[name = tensor<string, []>("linear_179_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1051_cast_fp16 = silu(x = linear_179_cast_fp16)[name = tensor<string, []>("input_1051_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(490785088))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494979456))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494981568)))];
            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = module_layers_19_feed_forward2_linear2_bias_to_fp16, weight = module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1051_cast_fp16)[name = tensor<string, []>("linear_180_cast_fp16")];
            tensor<fp16, []> var_3683_to_fp16 = const()[name = tensor<string, []>("op_3683_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3684_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3683_to_fp16)[name = tensor<string, []>("op_3684_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1057_cast_fp16 = add(x = input_1045_cast_fp16, y = var_3684_cast_fp16)[name = tensor<string, []>("input_1057_cast_fp16")];
            tensor<int32, [1]> input_1059_axes_0 = const()[name = tensor<string, []>("input_1059_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_19_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494983680)))];
            tensor<fp16, [1024]> module_layers_19_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494985792)))];
            tensor<fp16, [1, 188, 1024]> input_1059_cast_fp16 = layer_norm(axes = input_1059_axes_0, beta = module_layers_19_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_19_norm_out_weight_to_fp16, x = input_1057_cast_fp16)[name = tensor<string, []>("input_1059_cast_fp16")];
            tensor<int32, [1]> input_1061_axes_0 = const()[name = tensor<string, []>("input_1061_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494987904)))];
            tensor<fp16, [1024]> module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494990016)))];
            tensor<fp16, [1, 188, 1024]> input_1061_cast_fp16 = layer_norm(axes = input_1061_axes_0, beta = module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1059_cast_fp16)[name = tensor<string, []>("input_1061_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(494992128))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499186496))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499194752)))];
            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = module_layers_20_feed_forward1_linear1_bias_to_fp16, weight = module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1061_cast_fp16)[name = tensor<string, []>("linear_181_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1065_cast_fp16 = silu(x = linear_181_cast_fp16)[name = tensor<string, []>("input_1065_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499203008))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503397376))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503399488)))];
            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = module_layers_20_feed_forward1_linear2_bias_to_fp16, weight = module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1065_cast_fp16)[name = tensor<string, []>("linear_182_cast_fp16")];
            tensor<fp16, []> var_3714_to_fp16 = const()[name = tensor<string, []>("op_3714_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3715_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3714_to_fp16)[name = tensor<string, []>("op_3715_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1071_cast_fp16 = add(x = input_1059_cast_fp16, y = var_3715_cast_fp16)[name = tensor<string, []>("input_1071_cast_fp16")];
            tensor<int32, [1]> query_41_axes_0 = const()[name = tensor<string, []>("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_20_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503401600)))];
            tensor<fp16, [1024]> module_layers_20_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503403712)))];
            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_self_att_weight_to_fp16, x = input_1071_cast_fp16)[name = tensor<string, []>("query_41_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503405824))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504454464))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504456576)))];
            tensor<fp16, [1, 188, 1024]> linear_183_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_q_bias_to_fp16, weight = module_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = tensor<string, []>("linear_183_cast_fp16")];
            tensor<int32, [4]> var_3732 = const()[name = tensor<string, []>("op_3732"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_3732, x = linear_183_cast_fp16)[name = tensor<string, []>("q_121_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504458688))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505507328))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505509440)))];
            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_k_bias_to_fp16, weight = module_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = tensor<string, []>("linear_184_cast_fp16")];
            tensor<int32, [4]> var_3737 = const()[name = tensor<string, []>("op_3737"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_3737, x = linear_184_cast_fp16)[name = tensor<string, []>("k_81_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505511552))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506560192))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506562304)))];
            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_v_bias_to_fp16, weight = module_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = tensor<string, []>("linear_185_cast_fp16")];
            tensor<int32, [4]> var_3742 = const()[name = tensor<string, []>("op_3742"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_3742, x = linear_185_cast_fp16)[name = tensor<string, []>("v_41_cast_fp16")];
            tensor<int32, [4]> value_43_perm_0 = const()[name = tensor<string, []>("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_20_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506564416))), scale = tensor<fp16, [8]>([0x1.07cp-8, 0x1.08cp-7, 0x1.a2p-9, 0x1.7e8p-8, 0x1.75p-8, 0x1.b1p-8, 0x1.74cp-8, 0x1.56p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3754_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3754_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_20_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506565504))), scale = tensor<fp16, [8]>([0x1.144p-8, 0x1.068p-9, 0x1.298p-8, 0x1.178p-8, 0x1.a6p-8, 0x1.2cp-8, 0x1.3b8p-8, 0x1.0ecp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3756_cast_fp16 = add(x = q_121_cast_fp16, y = module_layers_20_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3756_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_447_transpose_x_0 = const()[name = tensor<string, []>("x_447_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_447_transpose_y_0 = const()[name = tensor<string, []>("x_447_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3758_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3758_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506566592))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506950656))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_3756_cast_fp16)[name = tensor<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_3758_to_fp16_quantized)[name = tensor<string, []>("x_447_cast_fp16")];
            tensor<int32, [8]> x_449_pad_0 = const()[name = tensor<string, []>("x_449_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_449_mode_0 = const()[name = tensor<string, []>("x_449_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_214_to_fp16 = const()[name = tensor<string, []>("const_214_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_449_cast_fp16 = pad(constant_val = const_214_to_fp16, mode = x_449_mode_0, pad = x_449_pad_0, x = x_447_cast_fp16)[name = tensor<string, []>("x_449_cast_fp16")];
            tensor<int32, [4]> var_3766 = const()[name = tensor<string, []>("op_3766"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_451_cast_fp16 = reshape(shape = var_3766, x = x_449_cast_fp16)[name = tensor<string, []>("x_451_cast_fp16")];
            tensor<int32, [4]> var_3770_begin_0 = const()[name = tensor<string, []>("op_3770_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3770_end_0 = const()[name = tensor<string, []>("op_3770_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3770_end_mask_0 = const()[name = tensor<string, []>("op_3770_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3770_cast_fp16 = slice_by_index(begin = var_3770_begin_0, end = var_3770_end_0, end_mask = var_3770_end_mask_0, x = x_451_cast_fp16)[name = tensor<string, []>("op_3770_cast_fp16")];
            tensor<int32, [4]> var_3771 = const()[name = tensor<string, []>("op_3771"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_3771, x = var_3770_cast_fp16)[name = tensor<string, []>("matrix_bd_81_cast_fp16")];
            tensor<bool, []> matrix_ac_41_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_41_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_41_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_41_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_136_perm_0 = const()[name = tensor<string, []>("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_137_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_170")];
            tensor<fp16, [1, 8, 188, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_3754_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_41_cast_fp16")];
            tensor<int32, [4]> matrix_bd_83_begin_0 = const()[name = tensor<string, []>("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_83_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3780_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = tensor<string, []>("op_3780_cast_fp16")];
            tensor<fp16, []> _inversed_scores_81_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_81_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_3780_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_81_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_83_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3786_cast_fp16 = softmax(axis = var_30, x = scores_83_cast_fp16)[name = tensor<string, []>("op_3786_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1073_cast_fp16 = select(a = var_11_to_fp16, b = var_3786_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1073_cast_fp16")];
            tensor<bool, []> x_453_transpose_x_0 = const()[name = tensor<string, []>("x_453_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_453_transpose_y_0 = const()[name = tensor<string, []>("x_453_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_41_cast_fp16)[name = tensor<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_1073_cast_fp16, y = value_43_cast_fp16)[name = tensor<string, []>("x_453_cast_fp16")];
            tensor<int32, [4]> var_3790_perm_0 = const()[name = tensor<string, []>("op_3790_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3791 = const()[name = tensor<string, []>("op_3791"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3790_cast_fp16 = transpose(perm = var_3790_perm_0, x = x_453_cast_fp16)[name = tensor<string, []>("transpose_168")];
            tensor<fp16, [1, 188, 1024]> input_1075_cast_fp16 = reshape(shape = var_3791, x = var_3790_cast_fp16)[name = tensor<string, []>("input_1075_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506951488))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508000128))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508002240)))];
            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = module_layers_20_self_attn_linear_out_bias_to_fp16, weight = module_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1075_cast_fp16)[name = tensor<string, []>("linear_187_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1079_cast_fp16 = add(x = input_1071_cast_fp16, y = linear_187_cast_fp16)[name = tensor<string, []>("input_1079_cast_fp16")];
            tensor<int32, [1]> x_457_axes_0 = const()[name = tensor<string, []>("x_457_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_20_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508004352)))];
            tensor<fp16, [1024]> module_layers_20_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508006464)))];
            tensor<fp16, [1, 188, 1024]> x_457_cast_fp16 = layer_norm(axes = x_457_axes_0, beta = module_layers_20_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_conv_weight_to_fp16, x = input_1079_cast_fp16)[name = tensor<string, []>("x_457_cast_fp16")];
            tensor<int32, [3]> input_1081_perm_0 = const()[name = tensor<string, []>("input_1081_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1083_pad_type_0 = const()[name = tensor<string, []>("input_1083_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1083_strides_0 = const()[name = tensor<string, []>("input_1083_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1083_pad_0 = const()[name = tensor<string, []>("input_1083_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1083_dilations_0 = const()[name = tensor<string, []>("input_1083_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1083_groups_0 = const()[name = tensor<string, []>("input_1083_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508008576))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510105792))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_20_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510109952)))];
            tensor<fp16, [1, 1024, 188]> input_1081_cast_fp16 = transpose(perm = input_1081_perm_0, x = x_457_cast_fp16)[name = tensor<string, []>("transpose_167")];
            tensor<fp16, [1, 2048, 188]> input_1083_cast_fp16 = conv(bias = module_layers_20_conv_pointwise_conv1_bias_to_fp16, dilations = input_1083_dilations_0, groups = input_1083_groups_0, pad = input_1083_pad_0, pad_type = input_1083_pad_type_0, strides = input_1083_strides_0, weight = module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1081_cast_fp16)[name = tensor<string, []>("input_1083_cast_fp16")];
            tensor<int32, []> x_459_split_num_splits_0 = const()[name = tensor<string, []>("x_459_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_459_split_axis_0 = const()[name = tensor<string, []>("x_459_split_axis_0"), val = tensor<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_1083_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_459_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1085_cast_fp16 = select(a = var_11_to_fp16, b = x_459_cast_fp16, cond = var_335)[name = tensor<string, []>("input_1085_cast_fp16")];
            tensor<int32, [6]> input_1087_pad_0 = const()[name = tensor<string, []>("input_1087_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1087_mode_0 = const()[name = tensor<string, []>("input_1087_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_217_to_fp16 = const()[name = tensor<string, []>("const_217_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1087_cast_fp16 = pad(constant_val = const_217_to_fp16, mode = input_1087_mode_0, pad = input_1087_pad_0, x = input_1085_cast_fp16)[name = tensor<string, []>("input_1087_cast_fp16")];
            tensor<string, []> input_1089_pad_type_0 = const()[name = tensor<string, []>("input_1089_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1089_groups_0 = const()[name = tensor<string, []>("input_1089_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1089_strides_0 = const()[name = tensor<string, []>("input_1089_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1089_pad_0 = const()[name = tensor<string, []>("input_1089_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1089_dilations_0 = const()[name = tensor<string, []>("input_1089_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_288_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_288_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510114112))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510123392))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_289_to_fp16 = const()[name = tensor<string, []>("const_289_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510125504)))];
            tensor<fp16, [1, 1024, 188]> input_1091_cast_fp16 = conv(bias = const_289_to_fp16, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_288_to_fp16_quantized, x = input_1087_cast_fp16)[name = tensor<string, []>("input_1091_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1093_cast_fp16 = silu(x = input_1091_cast_fp16)[name = tensor<string, []>("input_1093_cast_fp16")];
            tensor<string, []> x_461_pad_type_0 = const()[name = tensor<string, []>("x_461_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_461_strides_0 = const()[name = tensor<string, []>("x_461_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_461_pad_0 = const()[name = tensor<string, []>("x_461_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_461_dilations_0 = const()[name = tensor<string, []>("x_461_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_461_groups_0 = const()[name = tensor<string, []>("x_461_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(510127616))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511176256))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_20_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511178368)))];
            tensor<fp16, [1, 1024, 188]> x_461_cast_fp16 = conv(bias = module_layers_20_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1093_cast_fp16)[name = tensor<string, []>("x_461_cast_fp16")];
            tensor<int32, [3]> input_1095_perm_0 = const()[name = tensor<string, []>("input_1095_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1095_cast_fp16 = transpose(perm = input_1095_perm_0, x = x_461_cast_fp16)[name = tensor<string, []>("transpose_166")];
            tensor<fp16, [1, 188, 1024]> input_1097_cast_fp16 = add(x = input_1079_cast_fp16, y = input_1095_cast_fp16)[name = tensor<string, []>("input_1097_cast_fp16")];
            tensor<int32, [1]> input_1099_axes_0 = const()[name = tensor<string, []>("input_1099_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511180480)))];
            tensor<fp16, [1024]> module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511182592)))];
            tensor<fp16, [1, 188, 1024]> input_1099_cast_fp16 = layer_norm(axes = input_1099_axes_0, beta = module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1097_cast_fp16)[name = tensor<string, []>("input_1099_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511184704))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(515379072))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(515387328)))];
            tensor<fp16, [1, 188, 4096]> linear_188_cast_fp16 = linear(bias = module_layers_20_feed_forward2_linear1_bias_to_fp16, weight = module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1099_cast_fp16)[name = tensor<string, []>("linear_188_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1103_cast_fp16 = silu(x = linear_188_cast_fp16)[name = tensor<string, []>("input_1103_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(515395584))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519589952))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519592064)))];
            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = module_layers_20_feed_forward2_linear2_bias_to_fp16, weight = module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1103_cast_fp16)[name = tensor<string, []>("linear_189_cast_fp16")];
            tensor<fp16, []> var_3857_to_fp16 = const()[name = tensor<string, []>("op_3857_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3858_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_3857_to_fp16)[name = tensor<string, []>("op_3858_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1109_cast_fp16 = add(x = input_1097_cast_fp16, y = var_3858_cast_fp16)[name = tensor<string, []>("input_1109_cast_fp16")];
            tensor<int32, [1]> input_1111_axes_0 = const()[name = tensor<string, []>("input_1111_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_20_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519594176)))];
            tensor<fp16, [1024]> module_layers_20_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519596288)))];
            tensor<fp16, [1, 188, 1024]> input_1111_cast_fp16 = layer_norm(axes = input_1111_axes_0, beta = module_layers_20_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_20_norm_out_weight_to_fp16, x = input_1109_cast_fp16)[name = tensor<string, []>("input_1111_cast_fp16")];
            tensor<int32, [1]> input_1113_axes_0 = const()[name = tensor<string, []>("input_1113_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519598400)))];
            tensor<fp16, [1024]> module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519600512)))];
            tensor<fp16, [1, 188, 1024]> input_1113_cast_fp16 = layer_norm(axes = input_1113_axes_0, beta = module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1111_cast_fp16)[name = tensor<string, []>("input_1113_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(519602624))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523796992))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523805248)))];
            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = module_layers_21_feed_forward1_linear1_bias_to_fp16, weight = module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1113_cast_fp16)[name = tensor<string, []>("linear_190_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1117_cast_fp16 = silu(x = linear_190_cast_fp16)[name = tensor<string, []>("input_1117_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523813504))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528007872))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528009984)))];
            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = module_layers_21_feed_forward1_linear2_bias_to_fp16, weight = module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1117_cast_fp16)[name = tensor<string, []>("linear_191_cast_fp16")];
            tensor<fp16, []> var_3888_to_fp16 = const()[name = tensor<string, []>("op_3888_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_3889_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_3888_to_fp16)[name = tensor<string, []>("op_3889_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1123_cast_fp16 = add(x = input_1111_cast_fp16, y = var_3889_cast_fp16)[name = tensor<string, []>("input_1123_cast_fp16")];
            tensor<int32, [1]> query_43_axes_0 = const()[name = tensor<string, []>("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_21_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528012096)))];
            tensor<fp16, [1024]> module_layers_21_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528014208)))];
            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_self_att_weight_to_fp16, x = input_1123_cast_fp16)[name = tensor<string, []>("query_43_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(528016320))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529064960))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529067072)))];
            tensor<fp16, [1, 188, 1024]> linear_192_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_q_bias_to_fp16, weight = module_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = tensor<string, []>("linear_192_cast_fp16")];
            tensor<int32, [4]> var_3906 = const()[name = tensor<string, []>("op_3906"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_3906, x = linear_192_cast_fp16)[name = tensor<string, []>("q_127_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(529069184))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530117824))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530119936)))];
            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_k_bias_to_fp16, weight = module_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = tensor<string, []>("linear_193_cast_fp16")];
            tensor<int32, [4]> var_3911 = const()[name = tensor<string, []>("op_3911"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_3911, x = linear_193_cast_fp16)[name = tensor<string, []>("k_85_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530122048))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(531170688))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(531172800)))];
            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_v_bias_to_fp16, weight = module_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = tensor<string, []>("linear_194_cast_fp16")];
            tensor<int32, [4]> var_3916 = const()[name = tensor<string, []>("op_3916"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_3916, x = linear_194_cast_fp16)[name = tensor<string, []>("v_43_cast_fp16")];
            tensor<int32, [4]> value_45_perm_0 = const()[name = tensor<string, []>("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_21_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(531174912))), scale = tensor<fp16, [8]>([0x1.0b4p-8, 0x1.ebcp-9, 0x1.904p-9, 0x1.9c4p-9, 0x1.6p-9, 0x1.794p-8, 0x1.098p-8, 0x1.704p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3928_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_3928_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_21_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(531176000))), scale = tensor<fp16, [8]>([0x1.1b4p-8, 0x1.b3p-9, 0x1.32cp-8, 0x1.404p-8, 0x1.cf8p-9, 0x1.3cp-9, 0x1.a2cp-8, 0x1.548p-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_3930_cast_fp16 = add(x = q_127_cast_fp16, y = module_layers_21_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_3930_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_469_transpose_x_0 = const()[name = tensor<string, []>("x_469_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_469_transpose_y_0 = const()[name = tensor<string, []>("x_469_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_3932_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_3932_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(531177088))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(531561152))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_3930_cast_fp16)[name = tensor<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_3932_to_fp16_quantized)[name = tensor<string, []>("x_469_cast_fp16")];
            tensor<int32, [8]> x_471_pad_0 = const()[name = tensor<string, []>("x_471_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_471_mode_0 = const()[name = tensor<string, []>("x_471_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_224_to_fp16 = const()[name = tensor<string, []>("const_224_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_471_cast_fp16 = pad(constant_val = const_224_to_fp16, mode = x_471_mode_0, pad = x_471_pad_0, x = x_469_cast_fp16)[name = tensor<string, []>("x_471_cast_fp16")];
            tensor<int32, [4]> var_3940 = const()[name = tensor<string, []>("op_3940"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_473_cast_fp16 = reshape(shape = var_3940, x = x_471_cast_fp16)[name = tensor<string, []>("x_473_cast_fp16")];
            tensor<int32, [4]> var_3944_begin_0 = const()[name = tensor<string, []>("op_3944_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_3944_end_0 = const()[name = tensor<string, []>("op_3944_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_3944_end_mask_0 = const()[name = tensor<string, []>("op_3944_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_3944_cast_fp16 = slice_by_index(begin = var_3944_begin_0, end = var_3944_end_0, end_mask = var_3944_end_mask_0, x = x_473_cast_fp16)[name = tensor<string, []>("op_3944_cast_fp16")];
            tensor<int32, [4]> var_3945 = const()[name = tensor<string, []>("op_3945"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_3945, x = var_3944_cast_fp16)[name = tensor<string, []>("matrix_bd_85_cast_fp16")];
            tensor<bool, []> matrix_ac_43_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_43_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_43_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_43_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_138_perm_0 = const()[name = tensor<string, []>("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_139_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_163")];
            tensor<fp16, [1, 8, 188, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_3928_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_43_cast_fp16")];
            tensor<int32, [4]> matrix_bd_87_begin_0 = const()[name = tensor<string, []>("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_87_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3954_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = tensor<string, []>("op_3954_cast_fp16")];
            tensor<fp16, []> _inversed_scores_85_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_85_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_3954_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_85_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_87_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_3960_cast_fp16 = softmax(axis = var_30, x = scores_87_cast_fp16)[name = tensor<string, []>("op_3960_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1125_cast_fp16 = select(a = var_11_to_fp16, b = var_3960_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1125_cast_fp16")];
            tensor<bool, []> x_475_transpose_x_0 = const()[name = tensor<string, []>("x_475_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_475_transpose_y_0 = const()[name = tensor<string, []>("x_475_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_43_cast_fp16)[name = tensor<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_1125_cast_fp16, y = value_45_cast_fp16)[name = tensor<string, []>("x_475_cast_fp16")];
            tensor<int32, [4]> var_3964_perm_0 = const()[name = tensor<string, []>("op_3964_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_3965 = const()[name = tensor<string, []>("op_3965"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_3964_cast_fp16 = transpose(perm = var_3964_perm_0, x = x_475_cast_fp16)[name = tensor<string, []>("transpose_161")];
            tensor<fp16, [1, 188, 1024]> input_1127_cast_fp16 = reshape(shape = var_3965, x = var_3964_cast_fp16)[name = tensor<string, []>("input_1127_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_21_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(531561984))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532610624))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532612736)))];
            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = module_layers_21_self_attn_linear_out_bias_to_fp16, weight = module_layers_21_self_attn_linear_out_weight_to_fp16_quantized, x = input_1127_cast_fp16)[name = tensor<string, []>("linear_196_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1131_cast_fp16 = add(x = input_1123_cast_fp16, y = linear_196_cast_fp16)[name = tensor<string, []>("input_1131_cast_fp16")];
            tensor<int32, [1]> x_479_axes_0 = const()[name = tensor<string, []>("x_479_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_21_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532614848)))];
            tensor<fp16, [1024]> module_layers_21_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532616960)))];
            tensor<fp16, [1, 188, 1024]> x_479_cast_fp16 = layer_norm(axes = x_479_axes_0, beta = module_layers_21_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_conv_weight_to_fp16, x = input_1131_cast_fp16)[name = tensor<string, []>("x_479_cast_fp16")];
            tensor<int32, [3]> input_1133_perm_0 = const()[name = tensor<string, []>("input_1133_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1135_pad_type_0 = const()[name = tensor<string, []>("input_1135_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1135_strides_0 = const()[name = tensor<string, []>("input_1135_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1135_pad_0 = const()[name = tensor<string, []>("input_1135_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1135_dilations_0 = const()[name = tensor<string, []>("input_1135_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1135_groups_0 = const()[name = tensor<string, []>("input_1135_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(532619072))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534716288))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_21_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534720448)))];
            tensor<fp16, [1, 1024, 188]> input_1133_cast_fp16 = transpose(perm = input_1133_perm_0, x = x_479_cast_fp16)[name = tensor<string, []>("transpose_160")];
            tensor<fp16, [1, 2048, 188]> input_1135_cast_fp16 = conv(bias = module_layers_21_conv_pointwise_conv1_bias_to_fp16, dilations = input_1135_dilations_0, groups = input_1135_groups_0, pad = input_1135_pad_0, pad_type = input_1135_pad_type_0, strides = input_1135_strides_0, weight = module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1133_cast_fp16)[name = tensor<string, []>("input_1135_cast_fp16")];
            tensor<int32, []> x_481_split_num_splits_0 = const()[name = tensor<string, []>("x_481_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_481_split_axis_0 = const()[name = tensor<string, []>("x_481_split_axis_0"), val = tensor<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_1135_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_481_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1137_cast_fp16 = select(a = var_11_to_fp16, b = x_481_cast_fp16, cond = var_335)[name = tensor<string, []>("input_1137_cast_fp16")];
            tensor<int32, [6]> input_1139_pad_0 = const()[name = tensor<string, []>("input_1139_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1139_mode_0 = const()[name = tensor<string, []>("input_1139_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_227_to_fp16 = const()[name = tensor<string, []>("const_227_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1139_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = input_1139_mode_0, pad = input_1139_pad_0, x = input_1137_cast_fp16)[name = tensor<string, []>("input_1139_cast_fp16")];
            tensor<string, []> input_1141_pad_type_0 = const()[name = tensor<string, []>("input_1141_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1141_groups_0 = const()[name = tensor<string, []>("input_1141_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1141_strides_0 = const()[name = tensor<string, []>("input_1141_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1141_pad_0 = const()[name = tensor<string, []>("input_1141_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1141_dilations_0 = const()[name = tensor<string, []>("input_1141_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_290_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_290_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534724608))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534733888))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_291_to_fp16 = const()[name = tensor<string, []>("const_291_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534736000)))];
            tensor<fp16, [1, 1024, 188]> input_1143_cast_fp16 = conv(bias = const_291_to_fp16, dilations = input_1141_dilations_0, groups = input_1141_groups_0, pad = input_1141_pad_0, pad_type = input_1141_pad_type_0, strides = input_1141_strides_0, weight = const_290_to_fp16_quantized, x = input_1139_cast_fp16)[name = tensor<string, []>("input_1143_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1145_cast_fp16 = silu(x = input_1143_cast_fp16)[name = tensor<string, []>("input_1145_cast_fp16")];
            tensor<string, []> x_483_pad_type_0 = const()[name = tensor<string, []>("x_483_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_483_strides_0 = const()[name = tensor<string, []>("x_483_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_483_pad_0 = const()[name = tensor<string, []>("x_483_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_483_dilations_0 = const()[name = tensor<string, []>("x_483_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_483_groups_0 = const()[name = tensor<string, []>("x_483_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534738112))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(535786752))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_21_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(535788864)))];
            tensor<fp16, [1, 1024, 188]> x_483_cast_fp16 = conv(bias = module_layers_21_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1145_cast_fp16)[name = tensor<string, []>("x_483_cast_fp16")];
            tensor<int32, [3]> input_1147_perm_0 = const()[name = tensor<string, []>("input_1147_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1147_cast_fp16 = transpose(perm = input_1147_perm_0, x = x_483_cast_fp16)[name = tensor<string, []>("transpose_159")];
            tensor<fp16, [1, 188, 1024]> input_1149_cast_fp16 = add(x = input_1131_cast_fp16, y = input_1147_cast_fp16)[name = tensor<string, []>("input_1149_cast_fp16")];
            tensor<int32, [1]> input_1151_axes_0 = const()[name = tensor<string, []>("input_1151_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(535790976)))];
            tensor<fp16, [1024]> module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(535793088)))];
            tensor<fp16, [1, 188, 1024]> input_1151_cast_fp16 = layer_norm(axes = input_1151_axes_0, beta = module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1149_cast_fp16)[name = tensor<string, []>("input_1151_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(535795200))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539989568))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(539997824)))];
            tensor<fp16, [1, 188, 4096]> linear_197_cast_fp16 = linear(bias = module_layers_21_feed_forward2_linear1_bias_to_fp16, weight = module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1151_cast_fp16)[name = tensor<string, []>("linear_197_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1155_cast_fp16 = silu(x = linear_197_cast_fp16)[name = tensor<string, []>("input_1155_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540006080))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544200448))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544202560)))];
            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = module_layers_21_feed_forward2_linear2_bias_to_fp16, weight = module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1155_cast_fp16)[name = tensor<string, []>("linear_198_cast_fp16")];
            tensor<fp16, []> var_4031_to_fp16 = const()[name = tensor<string, []>("op_4031_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4032_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_4031_to_fp16)[name = tensor<string, []>("op_4032_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1161_cast_fp16 = add(x = input_1149_cast_fp16, y = var_4032_cast_fp16)[name = tensor<string, []>("input_1161_cast_fp16")];
            tensor<int32, [1]> input_1163_axes_0 = const()[name = tensor<string, []>("input_1163_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_21_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544204672)))];
            tensor<fp16, [1024]> module_layers_21_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544206784)))];
            tensor<fp16, [1, 188, 1024]> input_1163_cast_fp16 = layer_norm(axes = input_1163_axes_0, beta = module_layers_21_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_21_norm_out_weight_to_fp16, x = input_1161_cast_fp16)[name = tensor<string, []>("input_1163_cast_fp16")];
            tensor<int32, [1]> input_1165_axes_0 = const()[name = tensor<string, []>("input_1165_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544208896)))];
            tensor<fp16, [1024]> module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544211008)))];
            tensor<fp16, [1, 188, 1024]> input_1165_cast_fp16 = layer_norm(axes = input_1165_axes_0, beta = module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1163_cast_fp16)[name = tensor<string, []>("input_1165_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544213120))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548407488))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548415744)))];
            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = module_layers_22_feed_forward1_linear1_bias_to_fp16, weight = module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1165_cast_fp16)[name = tensor<string, []>("linear_199_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1169_cast_fp16 = silu(x = linear_199_cast_fp16)[name = tensor<string, []>("input_1169_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(548424000))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552618368))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552620480)))];
            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = module_layers_22_feed_forward1_linear2_bias_to_fp16, weight = module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1169_cast_fp16)[name = tensor<string, []>("linear_200_cast_fp16")];
            tensor<fp16, []> var_4062_to_fp16 = const()[name = tensor<string, []>("op_4062_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4063_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_4062_to_fp16)[name = tensor<string, []>("op_4063_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1175_cast_fp16 = add(x = input_1163_cast_fp16, y = var_4063_cast_fp16)[name = tensor<string, []>("input_1175_cast_fp16")];
            tensor<int32, [1]> query_45_axes_0 = const()[name = tensor<string, []>("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_22_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552622592)))];
            tensor<fp16, [1024]> module_layers_22_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552624704)))];
            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_self_att_weight_to_fp16, x = input_1175_cast_fp16)[name = tensor<string, []>("query_45_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(552626816))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553675456))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553677568)))];
            tensor<fp16, [1, 188, 1024]> linear_201_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_q_bias_to_fp16, weight = module_layers_22_self_attn_linear_q_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = tensor<string, []>("linear_201_cast_fp16")];
            tensor<int32, [4]> var_4080 = const()[name = tensor<string, []>("op_4080"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_4080, x = linear_201_cast_fp16)[name = tensor<string, []>("q_133_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553679680))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554728320))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554730432)))];
            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_k_bias_to_fp16, weight = module_layers_22_self_attn_linear_k_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = tensor<string, []>("linear_202_cast_fp16")];
            tensor<int32, [4]> var_4085 = const()[name = tensor<string, []>("op_4085"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_4085, x = linear_202_cast_fp16)[name = tensor<string, []>("k_89_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(554732544))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555781184))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555783296)))];
            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_v_bias_to_fp16, weight = module_layers_22_self_attn_linear_v_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = tensor<string, []>("linear_203_cast_fp16")];
            tensor<int32, [4]> var_4090 = const()[name = tensor<string, []>("op_4090"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4090, x = linear_203_cast_fp16)[name = tensor<string, []>("v_45_cast_fp16")];
            tensor<int32, [4]> value_47_perm_0 = const()[name = tensor<string, []>("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_22_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555785408))), scale = tensor<fp16, [8]>([0x1.c34p-9, 0x1.30cp-9, 0x1.668p-9, 0x1.694p-9, 0x1.cbp-8, 0x1.204p-8, 0x1.0a8p-8, 0x1.f6cp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_4102_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_4102_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_22_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555786496))), scale = tensor<fp16, [8]>([0x1.1ccp-8, 0x1.cdcp-9, 0x1.2c8p-8, 0x1.aa4p-9, 0x1.3ecp-8, 0x1.bacp-8, 0x1.6e4p-8, 0x1.acp-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_4104_cast_fp16 = add(x = q_133_cast_fp16, y = module_layers_22_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_4104_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_491_transpose_x_0 = const()[name = tensor<string, []>("x_491_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_491_transpose_y_0 = const()[name = tensor<string, []>("x_491_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_4106_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_4106_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(555787584))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(556171648))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4104_cast_fp16)[name = tensor<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_4106_to_fp16_quantized)[name = tensor<string, []>("x_491_cast_fp16")];
            tensor<int32, [8]> x_493_pad_0 = const()[name = tensor<string, []>("x_493_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_493_mode_0 = const()[name = tensor<string, []>("x_493_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_234_to_fp16 = const()[name = tensor<string, []>("const_234_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_493_cast_fp16 = pad(constant_val = const_234_to_fp16, mode = x_493_mode_0, pad = x_493_pad_0, x = x_491_cast_fp16)[name = tensor<string, []>("x_493_cast_fp16")];
            tensor<int32, [4]> var_4114 = const()[name = tensor<string, []>("op_4114"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_495_cast_fp16 = reshape(shape = var_4114, x = x_493_cast_fp16)[name = tensor<string, []>("x_495_cast_fp16")];
            tensor<int32, [4]> var_4118_begin_0 = const()[name = tensor<string, []>("op_4118_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_4118_end_0 = const()[name = tensor<string, []>("op_4118_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_4118_end_mask_0 = const()[name = tensor<string, []>("op_4118_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_4118_cast_fp16 = slice_by_index(begin = var_4118_begin_0, end = var_4118_end_0, end_mask = var_4118_end_mask_0, x = x_495_cast_fp16)[name = tensor<string, []>("op_4118_cast_fp16")];
            tensor<int32, [4]> var_4119 = const()[name = tensor<string, []>("op_4119"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4119, x = var_4118_cast_fp16)[name = tensor<string, []>("matrix_bd_89_cast_fp16")];
            tensor<bool, []> matrix_ac_45_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_45_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_45_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_45_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_140_perm_0 = const()[name = tensor<string, []>("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_141_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_156")];
            tensor<fp16, [1, 8, 188, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_4102_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_45_cast_fp16")];
            tensor<int32, [4]> matrix_bd_91_begin_0 = const()[name = tensor<string, []>("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = tensor<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 = tensor<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 = tensor<string, []>("matrix_bd_91_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4128_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = tensor<string, []>("op_4128_cast_fp16")];
            tensor<fp16, []> _inversed_scores_89_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_89_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4128_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_89_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_91_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4134_cast_fp16 = softmax(axis = var_30, x = scores_91_cast_fp16)[name = tensor<string, []>("op_4134_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1177_cast_fp16 = select(a = var_11_to_fp16, b = var_4134_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1177_cast_fp16")];
            tensor<bool, []> x_497_transpose_x_0 = const()[name = tensor<string, []>("x_497_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_497_transpose_y_0 = const()[name = tensor<string, []>("x_497_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_45_cast_fp16)[name = tensor<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_1177_cast_fp16, y = value_47_cast_fp16)[name = tensor<string, []>("x_497_cast_fp16")];
            tensor<int32, [4]> var_4138_perm_0 = const()[name = tensor<string, []>("op_4138_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_4139 = const()[name = tensor<string, []>("op_4139"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_4138_cast_fp16 = transpose(perm = var_4138_perm_0, x = x_497_cast_fp16)[name = tensor<string, []>("transpose_154")];
            tensor<fp16, [1, 188, 1024]> input_1179_cast_fp16 = reshape(shape = var_4139, x = var_4138_cast_fp16)[name = tensor<string, []>("input_1179_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_22_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(556172480))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557221120))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557223232)))];
            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = module_layers_22_self_attn_linear_out_bias_to_fp16, weight = module_layers_22_self_attn_linear_out_weight_to_fp16_quantized, x = input_1179_cast_fp16)[name = tensor<string, []>("linear_205_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1183_cast_fp16 = add(x = input_1175_cast_fp16, y = linear_205_cast_fp16)[name = tensor<string, []>("input_1183_cast_fp16")];
            tensor<int32, [1]> x_501_axes_0 = const()[name = tensor<string, []>("x_501_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_22_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557225344)))];
            tensor<fp16, [1024]> module_layers_22_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557227456)))];
            tensor<fp16, [1, 188, 1024]> x_501_cast_fp16 = layer_norm(axes = x_501_axes_0, beta = module_layers_22_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_conv_weight_to_fp16, x = input_1183_cast_fp16)[name = tensor<string, []>("x_501_cast_fp16")];
            tensor<int32, [3]> input_1185_perm_0 = const()[name = tensor<string, []>("input_1185_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1187_pad_type_0 = const()[name = tensor<string, []>("input_1187_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1187_strides_0 = const()[name = tensor<string, []>("input_1187_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1187_pad_0 = const()[name = tensor<string, []>("input_1187_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1187_dilations_0 = const()[name = tensor<string, []>("input_1187_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1187_groups_0 = const()[name = tensor<string, []>("input_1187_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557229568))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559326784))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_22_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559330944)))];
            tensor<fp16, [1, 1024, 188]> input_1185_cast_fp16 = transpose(perm = input_1185_perm_0, x = x_501_cast_fp16)[name = tensor<string, []>("transpose_153")];
            tensor<fp16, [1, 2048, 188]> input_1187_cast_fp16 = conv(bias = module_layers_22_conv_pointwise_conv1_bias_to_fp16, dilations = input_1187_dilations_0, groups = input_1187_groups_0, pad = input_1187_pad_0, pad_type = input_1187_pad_type_0, strides = input_1187_strides_0, weight = module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1185_cast_fp16)[name = tensor<string, []>("input_1187_cast_fp16")];
            tensor<int32, []> x_503_split_num_splits_0 = const()[name = tensor<string, []>("x_503_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_503_split_axis_0 = const()[name = tensor<string, []>("x_503_split_axis_0"), val = tensor<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_1187_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_503_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1189_cast_fp16 = select(a = var_11_to_fp16, b = x_503_cast_fp16, cond = var_335)[name = tensor<string, []>("input_1189_cast_fp16")];
            tensor<int32, [6]> input_1191_pad_0 = const()[name = tensor<string, []>("input_1191_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1191_mode_0 = const()[name = tensor<string, []>("input_1191_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_237_to_fp16 = const()[name = tensor<string, []>("const_237_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1191_cast_fp16 = pad(constant_val = const_237_to_fp16, mode = input_1191_mode_0, pad = input_1191_pad_0, x = input_1189_cast_fp16)[name = tensor<string, []>("input_1191_cast_fp16")];
            tensor<string, []> input_1193_pad_type_0 = const()[name = tensor<string, []>("input_1193_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1193_groups_0 = const()[name = tensor<string, []>("input_1193_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1193_strides_0 = const()[name = tensor<string, []>("input_1193_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1193_pad_0 = const()[name = tensor<string, []>("input_1193_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1193_dilations_0 = const()[name = tensor<string, []>("input_1193_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_292_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_292_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559335104))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559344384))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_293_to_fp16 = const()[name = tensor<string, []>("const_293_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559346496)))];
            tensor<fp16, [1, 1024, 188]> input_1195_cast_fp16 = conv(bias = const_293_to_fp16, dilations = input_1193_dilations_0, groups = input_1193_groups_0, pad = input_1193_pad_0, pad_type = input_1193_pad_type_0, strides = input_1193_strides_0, weight = const_292_to_fp16_quantized, x = input_1191_cast_fp16)[name = tensor<string, []>("input_1195_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1197_cast_fp16 = silu(x = input_1195_cast_fp16)[name = tensor<string, []>("input_1197_cast_fp16")];
            tensor<string, []> x_505_pad_type_0 = const()[name = tensor<string, []>("x_505_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_505_strides_0 = const()[name = tensor<string, []>("x_505_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_505_pad_0 = const()[name = tensor<string, []>("x_505_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_505_dilations_0 = const()[name = tensor<string, []>("x_505_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_505_groups_0 = const()[name = tensor<string, []>("x_505_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559348608))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(560397248))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_22_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(560399360)))];
            tensor<fp16, [1, 1024, 188]> x_505_cast_fp16 = conv(bias = module_layers_22_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1197_cast_fp16)[name = tensor<string, []>("x_505_cast_fp16")];
            tensor<int32, [3]> input_1199_perm_0 = const()[name = tensor<string, []>("input_1199_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1199_cast_fp16 = transpose(perm = input_1199_perm_0, x = x_505_cast_fp16)[name = tensor<string, []>("transpose_152")];
            tensor<fp16, [1, 188, 1024]> input_1201_cast_fp16 = add(x = input_1183_cast_fp16, y = input_1199_cast_fp16)[name = tensor<string, []>("input_1201_cast_fp16")];
            tensor<int32, [1]> input_1203_axes_0 = const()[name = tensor<string, []>("input_1203_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(560401472)))];
            tensor<fp16, [1024]> module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(560403584)))];
            tensor<fp16, [1, 188, 1024]> input_1203_cast_fp16 = layer_norm(axes = input_1203_axes_0, beta = module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1201_cast_fp16)[name = tensor<string, []>("input_1203_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(560405696))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564600064))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564608320)))];
            tensor<fp16, [1, 188, 4096]> linear_206_cast_fp16 = linear(bias = module_layers_22_feed_forward2_linear1_bias_to_fp16, weight = module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1203_cast_fp16)[name = tensor<string, []>("linear_206_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1207_cast_fp16 = silu(x = linear_206_cast_fp16)[name = tensor<string, []>("input_1207_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564616576))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568810944))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568813056)))];
            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = module_layers_22_feed_forward2_linear2_bias_to_fp16, weight = module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1207_cast_fp16)[name = tensor<string, []>("linear_207_cast_fp16")];
            tensor<fp16, []> var_4205_to_fp16 = const()[name = tensor<string, []>("op_4205_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4206_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_4205_to_fp16)[name = tensor<string, []>("op_4206_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1213_cast_fp16 = add(x = input_1201_cast_fp16, y = var_4206_cast_fp16)[name = tensor<string, []>("input_1213_cast_fp16")];
            tensor<int32, [1]> input_1215_axes_0 = const()[name = tensor<string, []>("input_1215_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_22_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568815168)))];
            tensor<fp16, [1024]> module_layers_22_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568817280)))];
            tensor<fp16, [1, 188, 1024]> input_1215_cast_fp16 = layer_norm(axes = input_1215_axes_0, beta = module_layers_22_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_22_norm_out_weight_to_fp16, x = input_1213_cast_fp16)[name = tensor<string, []>("input_1215_cast_fp16")];
            tensor<int32, [1]> input_1217_axes_0 = const()[name = tensor<string, []>("input_1217_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568819392)))];
            tensor<fp16, [1024]> module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568821504)))];
            tensor<fp16, [1, 188, 1024]> input_1217_cast_fp16 = layer_norm(axes = input_1217_axes_0, beta = module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1215_cast_fp16)[name = tensor<string, []>("input_1217_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(568823616))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573017984))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573026240)))];
            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = module_layers_23_feed_forward1_linear1_bias_to_fp16, weight = module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1217_cast_fp16)[name = tensor<string, []>("linear_208_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1221_cast_fp16 = silu(x = linear_208_cast_fp16)[name = tensor<string, []>("input_1221_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573034496))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577228864))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577230976)))];
            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = module_layers_23_feed_forward1_linear2_bias_to_fp16, weight = module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1221_cast_fp16)[name = tensor<string, []>("linear_209_cast_fp16")];
            tensor<fp16, []> var_4236_to_fp16 = const()[name = tensor<string, []>("op_4236_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4237_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4236_to_fp16)[name = tensor<string, []>("op_4237_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1227_cast_fp16 = add(x = input_1215_cast_fp16, y = var_4237_cast_fp16)[name = tensor<string, []>("input_1227_cast_fp16")];
            tensor<int32, [1]> query_axes_0 = const()[name = tensor<string, []>("query_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_23_norm_self_att_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577233088)))];
            tensor<fp16, [1024]> module_layers_23_norm_self_att_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577235200)))];
            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_self_att_weight_to_fp16, x = input_1227_cast_fp16)[name = tensor<string, []>("query_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_q_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_self_attn_linear_q_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(577237312))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578285952))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578288064)))];
            tensor<fp16, [1, 188, 1024]> linear_210_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_q_bias_to_fp16, weight = module_layers_23_self_attn_linear_q_weight_to_fp16_quantized, x = query_cast_fp16)[name = tensor<string, []>("linear_210_cast_fp16")];
            tensor<int32, [4]> var_4254 = const()[name = tensor<string, []>("op_4254"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4254, x = linear_210_cast_fp16)[name = tensor<string, []>("q_139_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_k_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_self_attn_linear_k_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578290176))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579338816))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579340928)))];
            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_k_bias_to_fp16, weight = module_layers_23_self_attn_linear_k_weight_to_fp16_quantized, x = query_cast_fp16)[name = tensor<string, []>("linear_211_cast_fp16")];
            tensor<int32, [4]> var_4259 = const()[name = tensor<string, []>("op_4259"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4259, x = linear_211_cast_fp16)[name = tensor<string, []>("k_93_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_v_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_self_attn_linear_v_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579343040))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580391680))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580393792)))];
            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_v_bias_to_fp16, weight = module_layers_23_self_attn_linear_v_weight_to_fp16_quantized, x = query_cast_fp16)[name = tensor<string, []>("linear_212_cast_fp16")];
            tensor<int32, [4]> var_4264 = const()[name = tensor<string, []>("op_4264"), val = tensor<int32, [4]>([1, -1, 8, 128])];
            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4264, x = linear_212_cast_fp16)[name = tensor<string, []>("v_cast_fp16")];
            tensor<int32, [4]> value_perm_0 = const()[name = tensor<string, []>("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<fp16, [8, 128]> module_layers_23_self_attn_pos_bias_u_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_self_attn_pos_bias_u_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580395904))), scale = tensor<fp16, [8]>([0x1.4e4p-8, 0x1.798p-9, 0x1.3acp-8, 0x1.a64p-9, 0x1.8a4p-9, 0x1.5dp-8, 0x1.b4p-9, 0x1.73cp-8]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_4276_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_u_to_fp16_quantized)[name = tensor<string, []>("op_4276_cast_fp16")];
            tensor<fp16, [8, 128]> module_layers_23_self_attn_pos_bias_v_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_self_attn_pos_bias_v_to_fp16_quantized"), quantized_data = tensor<int8, [8, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580396992))), scale = tensor<fp16, [8]>([0x1.21p-8, 0x1.e7p-9, 0x1.bbp-8, 0x1.488p-8, 0x1.288p-8, 0x1.b5cp-9, 0x1.77cp-8, 0x1.a98p-9]), zero_point = tensor<int8, [8]>([0, 0, 0, 0, 0, 0, 0, 0])];
            tensor<fp16, [1, 188, 8, 128]> var_4278_cast_fp16 = add(x = q_139_cast_fp16, y = module_layers_23_self_attn_pos_bias_v_to_fp16_quantized)[name = tensor<string, []>("op_4278_cast_fp16")];
            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = tensor<string, []>("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<bool, []> x_513_transpose_x_0 = const()[name = tensor<string, []>("x_513_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_513_transpose_y_0 = const()[name = tensor<string, []>("x_513_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 128, 375]> op_4280_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(3), name = tensor<string, []>("op_4280_to_fp16_quantized"), quantized_data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580398080))), scale = tensor<fp16, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580782144))), zero_point = tensor<int8, [375]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14738176)))];
            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4278_cast_fp16)[name = tensor<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_4280_to_fp16_quantized)[name = tensor<string, []>("x_513_cast_fp16")];
            tensor<int32, [8]> x_515_pad_0 = const()[name = tensor<string, []>("x_515_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_515_mode_0 = const()[name = tensor<string, []>("x_515_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_244_to_fp16 = const()[name = tensor<string, []>("const_244_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 8, 188, 376]> x_515_cast_fp16 = pad(constant_val = const_244_to_fp16, mode = x_515_mode_0, pad = x_515_pad_0, x = x_513_cast_fp16)[name = tensor<string, []>("x_515_cast_fp16")];
            tensor<int32, [4]> var_4288 = const()[name = tensor<string, []>("op_4288"), val = tensor<int32, [4]>([1, 8, -1, 188])];
            tensor<fp16, [1, 8, 376, 188]> x_517_cast_fp16 = reshape(shape = var_4288, x = x_515_cast_fp16)[name = tensor<string, []>("x_517_cast_fp16")];
            tensor<int32, [4]> var_4292_begin_0 = const()[name = tensor<string, []>("op_4292_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
            tensor<int32, [4]> var_4292_end_0 = const()[name = tensor<string, []>("op_4292_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
            tensor<bool, [4]> var_4292_end_mask_0 = const()[name = tensor<string, []>("op_4292_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 8, 375, 188]> var_4292_cast_fp16 = slice_by_index(begin = var_4292_begin_0, end = var_4292_end_0, end_mask = var_4292_end_mask_0, x = x_517_cast_fp16)[name = tensor<string, []>("op_4292_cast_fp16")];
            tensor<int32, [4]> var_4293 = const()[name = tensor<string, []>("op_4293"), val = tensor<int32, [4]>([1, 8, 188, 375])];
            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4293, x = var_4292_cast_fp16)[name = tensor<string, []>("matrix_bd_93_cast_fp16")];
            tensor<bool, []> matrix_ac_transpose_x_0 = const()[name = tensor<string, []>("matrix_ac_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matrix_ac_transpose_y_0 = const()[name = tensor<string, []>("matrix_ac_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_142_perm_0 = const()[name = tensor<string, []>("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_143_perm_0 = const()[name = tensor<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 = tensor<string, []>("transpose_149")];
            tensor<fp16, [1, 8, 188, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4276_cast_fp16)[name = tensor<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 = tensor<string, []>("matrix_ac_cast_fp16")];
            tensor<int32, [4]> matrix_bd_begin_0 = const()[name = tensor<string, []>("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> matrix_bd_end_0 = const()[name = tensor<string, []>("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = tensor<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 = tensor<string, []>("matrix_bd_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4302_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = tensor<string, []>("op_4302_cast_fp16")];
            tensor<fp16, []> _inversed_scores_93_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_scores_93_y_0_to_fp16"), val = tensor<fp16, []>(0x1.6ap-4)];
            tensor<fp16, [1, 8, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4302_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = tensor<string, []>("_inversed_scores_93_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_12_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_3)[name = tensor<string, []>("scores_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> var_4308_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = tensor<string, []>("op_4308_cast_fp16")];
            tensor<fp16, [1, 8, 188, 188]> input_1229_cast_fp16 = select(a = var_11_to_fp16, b = var_4308_cast_fp16, cond = mask_3)[name = tensor<string, []>("input_1229_cast_fp16")];
            tensor<bool, []> x_519_transpose_x_0 = const()[name = tensor<string, []>("x_519_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> x_519_transpose_y_0 = const()[name = tensor<string, []>("x_519_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 8, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = tensor<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_1229_cast_fp16, y = value_cast_fp16)[name = tensor<string, []>("x_519_cast_fp16")];
            tensor<int32, [4]> var_4312_perm_0 = const()[name = tensor<string, []>("op_4312_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_4313 = const()[name = tensor<string, []>("op_4313"), val = tensor<int32, [3]>([1, -1, 1024])];
            tensor<fp16, [1, 188, 8, 128]> var_4312_cast_fp16 = transpose(perm = var_4312_perm_0, x = x_519_cast_fp16)[name = tensor<string, []>("transpose_147")];
            tensor<fp16, [1, 188, 1024]> input_1231_cast_fp16 = reshape(shape = var_4313, x = var_4312_cast_fp16)[name = tensor<string, []>("input_1231_cast_fp16")];
            tensor<fp16, [1024, 1024]> module_layers_23_self_attn_linear_out_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_self_attn_linear_out_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580782976))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581831616))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581833728)))];
            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = module_layers_23_self_attn_linear_out_bias_to_fp16, weight = module_layers_23_self_attn_linear_out_weight_to_fp16_quantized, x = input_1231_cast_fp16)[name = tensor<string, []>("linear_214_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_1235_cast_fp16 = add(x = input_1227_cast_fp16, y = linear_214_cast_fp16)[name = tensor<string, []>("input_1235_cast_fp16")];
            tensor<int32, [1]> x_523_axes_0 = const()[name = tensor<string, []>("x_523_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_23_norm_conv_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581835840)))];
            tensor<fp16, [1024]> module_layers_23_norm_conv_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581837952)))];
            tensor<fp16, [1, 188, 1024]> x_523_cast_fp16 = layer_norm(axes = x_523_axes_0, beta = module_layers_23_norm_conv_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_conv_weight_to_fp16, x = input_1235_cast_fp16)[name = tensor<string, []>("x_523_cast_fp16")];
            tensor<int32, [3]> input_1237_perm_0 = const()[name = tensor<string, []>("input_1237_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> input_1239_pad_type_0 = const()[name = tensor<string, []>("input_1239_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_1239_strides_0 = const()[name = tensor<string, []>("input_1239_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1239_pad_0 = const()[name = tensor<string, []>("input_1239_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1239_dilations_0 = const()[name = tensor<string, []>("input_1239_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_1239_groups_0 = const()[name = tensor<string, []>("input_1239_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2048, 1024, 1]> module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581840064))), scale = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583937280))), zero_point = tensor<int8, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17893760)))];
            tensor<fp16, [2048]> module_layers_23_conv_pointwise_conv1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583941440)))];
            tensor<fp16, [1, 1024, 188]> input_1237_cast_fp16 = transpose(perm = input_1237_perm_0, x = x_523_cast_fp16)[name = tensor<string, []>("transpose_146")];
            tensor<fp16, [1, 2048, 188]> input_1239_cast_fp16 = conv(bias = module_layers_23_conv_pointwise_conv1_bias_to_fp16, dilations = input_1239_dilations_0, groups = input_1239_groups_0, pad = input_1239_pad_0, pad_type = input_1239_pad_type_0, strides = input_1239_strides_0, weight = module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1237_cast_fp16)[name = tensor<string, []>("input_1239_cast_fp16")];
            tensor<int32, []> x_525_split_num_splits_0 = const()[name = tensor<string, []>("x_525_split_num_splits_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> x_525_split_axis_0 = const()[name = tensor<string, []>("x_525_split_axis_0"), val = tensor<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_1239_cast_fp16)[name = tensor<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 = tensor<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 = tensor<string, []>("x_525_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1241_cast_fp16 = select(a = var_11_to_fp16, b = x_525_cast_fp16, cond = var_335)[name = tensor<string, []>("input_1241_cast_fp16")];
            tensor<int32, [6]> input_1243_pad_0 = const()[name = tensor<string, []>("input_1243_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> input_1243_mode_0 = const()[name = tensor<string, []>("input_1243_mode_0"), val = tensor<string, []>("constant")];
            tensor<fp16, []> const_247_to_fp16 = const()[name = tensor<string, []>("const_247_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1024, 196]> input_1243_cast_fp16 = pad(constant_val = const_247_to_fp16, mode = input_1243_mode_0, pad = input_1243_pad_0, x = input_1241_cast_fp16)[name = tensor<string, []>("input_1243_cast_fp16")];
            tensor<string, []> input_1245_pad_type_0 = const()[name = tensor<string, []>("input_1245_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, []> input_1245_groups_0 = const()[name = tensor<string, []>("input_1245_groups_0"), val = tensor<int32, []>(1024)];
            tensor<int32, [1]> input_1245_strides_0 = const()[name = tensor<string, []>("input_1245_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_1245_pad_0 = const()[name = tensor<string, []>("input_1245_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_1245_dilations_0 = const()[name = tensor<string, []>("input_1245_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 9]> const_294_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("const_294_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583945600))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583954880))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> const_295_to_fp16 = const()[name = tensor<string, []>("const_295_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583956992)))];
            tensor<fp16, [1, 1024, 188]> input_1247_cast_fp16 = conv(bias = const_295_to_fp16, dilations = input_1245_dilations_0, groups = input_1245_groups_0, pad = input_1245_pad_0, pad_type = input_1245_pad_type_0, strides = input_1245_strides_0, weight = const_294_to_fp16_quantized, x = input_1243_cast_fp16)[name = tensor<string, []>("input_1247_cast_fp16")];
            tensor<fp16, [1, 1024, 188]> input_1249_cast_fp16 = silu(x = input_1247_cast_fp16)[name = tensor<string, []>("input_1249_cast_fp16")];
            tensor<string, []> x_527_pad_type_0 = const()[name = tensor<string, []>("x_527_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> x_527_strides_0 = const()[name = tensor<string, []>("x_527_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_527_pad_0 = const()[name = tensor<string, []>("x_527_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_527_dilations_0 = const()[name = tensor<string, []>("x_527_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_527_groups_0 = const()[name = tensor<string, []>("x_527_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583959104))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(585007744))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_23_conv_pointwise_conv2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(585009856)))];
            tensor<fp16, [1, 1024, 188]> x_527_cast_fp16 = conv(bias = module_layers_23_conv_pointwise_conv2_bias_to_fp16, 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 = module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1249_cast_fp16)[name = tensor<string, []>("x_527_cast_fp16")];
            tensor<int32, [3]> input_1251_perm_0 = const()[name = tensor<string, []>("input_1251_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 188, 1024]> input_1251_cast_fp16 = transpose(perm = input_1251_perm_0, x = x_527_cast_fp16)[name = tensor<string, []>("transpose_145")];
            tensor<fp16, [1, 188, 1024]> input_1253_cast_fp16 = add(x = input_1235_cast_fp16, y = input_1251_cast_fp16)[name = tensor<string, []>("input_1253_cast_fp16")];
            tensor<int32, [1]> input_1255_axes_0 = const()[name = tensor<string, []>("input_1255_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(585011968)))];
            tensor<fp16, [1024]> module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(585014080)))];
            tensor<fp16, [1, 188, 1024]> input_1255_cast_fp16 = layer_norm(axes = input_1255_axes_0, beta = module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1253_cast_fp16)[name = tensor<string, []>("input_1255_cast_fp16")];
            tensor<fp16, [4096, 1024]> module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [4096, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(585016192))), scale = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589210560))), zero_point = tensor<int8, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6969856)))];
            tensor<fp16, [4096]> module_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589218816)))];
            tensor<fp16, [1, 188, 4096]> linear_215_cast_fp16 = linear(bias = module_layers_23_feed_forward2_linear1_bias_to_fp16, weight = module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1255_cast_fp16)[name = tensor<string, []>("linear_215_cast_fp16")];
            tensor<fp16, [1, 188, 4096]> input_1259_cast_fp16 = silu(x = linear_215_cast_fp16)[name = tensor<string, []>("input_1259_cast_fp16")];
            tensor<fp16, [1024, 4096]> module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589227072))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593421440))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765952)))];
            tensor<fp16, [1024]> module_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593423552)))];
            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = module_layers_23_feed_forward2_linear2_bias_to_fp16, weight = module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1259_cast_fp16)[name = tensor<string, []>("linear_216_cast_fp16")];
            tensor<fp16, []> var_4379_to_fp16 = const()[name = tensor<string, []>("op_4379_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
            tensor<fp16, [1, 188, 1024]> var_4380_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4379_to_fp16)[name = tensor<string, []>("op_4380_cast_fp16")];
            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1253_cast_fp16, y = var_4380_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
            tensor<int32, [1]> audio_signal_axes_0 = const()[name = tensor<string, []>("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> module_layers_23_norm_out_weight_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593425664)))];
            tensor<fp16, [1024]> module_layers_23_norm_out_bias_to_fp16 = const()[name = tensor<string, []>("module_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593427776)))];
            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = module_layers_23_norm_out_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("audio_signal_cast_fp16")];
            tensor<int32, [3]> obj_1_perm_0 = const()[name = tensor<string, []>("obj_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> obj_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("obj_1_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp16, [1, 1024, 188]> obj_1_cast_fp16 = transpose(perm = obj_1_perm_0, x = audio_signal_cast_fp16)[name = tensor<string, []>("transpose_144")];
            tensor<fp32, [1, 1024, 188]> encoder_output = cast(dtype = obj_1_cast_fp16_to_fp32_dtype_0, x = obj_1_cast_fp16)[name = tensor<string, []>("cast_0")];
        } -> (encoder_output, encoded_length);
}