program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.22.1"}})]
{
    func main<ios18>(tensor<int32, [1, 128]> attention_mask, tensor<int32, [1, 128]> input_ids, tensor<int32, [1, 128]> token_type_ids) {
            tensor<int32, [3]> var_5 = const()[name = string("op_5"), val = tensor<int32, [3]>([1, 128, -1])];
            tensor<int32, [4]> var_7 = const()[name = string("op_7"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> var_8 = const()[name = string("op_8"), val = tensor<int32, [4]>([1, 128, 12, 32])];
            int32 var_19 = const()[name = string("op_19"), val = int32(-1)];
            int32 var_20 = const()[name = string("op_20"), val = int32(0)];
            int32 var_130_batch_dims_0 = const()[name = string("op_130_batch_dims_0"), val = int32(0)];
            bool var_130_validate_indices_0 = const()[name = string("op_130_validate_indices_0"), val = bool(false)];
            tensor<fp16, [30522, 384]> self_model_bert_embeddings_word_embeddings_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<uint8, [30522, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<uint8, [1, 1]>([[83]]), scale = tensor<fp16, [1, 1]>([[0x1.9p-8]]))[name = string("self_model_bert_embeddings_word_embeddings_weight_to_fp16_quantized")];
            string input_ids_to_int16_dtype_0 = const()[name = string("input_ids_to_int16_dtype_0"), val = string("int16")];
            string cast_38_dtype_0 = const()[name = string("cast_38_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int16, [1, 128]> input_ids_to_int16 = cast(dtype = input_ids_to_int16_dtype_0, x = input_ids)[name = string("cast_5")];
            tensor<int32, [1, 128]> cast_38 = cast(dtype = cast_38_dtype_0, x = input_ids_to_int16)[name = string("cast_4")];
            tensor<bool, [1, 128]> greater_equal_0 = greater_equal(x = cast_38, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(30522)];
            tensor<int32, [1, 128]> add_0 = add(x = cast_38, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 128]> select_0 = select(a = cast_38, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")];
            string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)];
            tensor<int16, [1, 128]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_3")];
            tensor<int32, [1, 128]> cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_2")];
            tensor<bool, [1, 128]> greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")];
            int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(30522)];
            tensor<int32, [1, 128]> add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")];
            tensor<int32, [1, 128]> select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")];
            int32 greater_equal_0_y_0_2 = const()[name = string("greater_equal_0_y_0_2"), val = int32(0)];
            tensor<bool, [1, 128]> greater_equal_0_2 = greater_equal(x = select_0_1, y = greater_equal_0_y_0_2)[name = string("greater_equal_0_2")];
            int32 slice_by_index_0_2 = const()[name = string("slice_by_index_0_2"), val = int32(30522)];
            tensor<int32, [1, 128]> add_0_2 = add(x = select_0_1, y = slice_by_index_0_2)[name = string("add_0_2")];
            tensor<int32, [1, 128]> select_0_2 = select(a = select_0_1, b = add_0_2, cond = greater_equal_0_2)[name = string("select_0_2")];
            int32 op_130_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_130_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)];
            tensor<fp16, [1, 128, 384]> op_130_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_130_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_130_batch_dims_0, indices = select_0_2, validate_indices = var_130_validate_indices_0, x = self_model_bert_embeddings_word_embeddings_weight_to_fp16_quantized)[name = string("op_130_cast_fp16_cast_uint16_cast_uint16")];
            tensor<fp16, [1, 128, 384]> var_18_to_fp16 = const()[name = string("op_18_to_fp16"), val = tensor<fp16, [1, 128, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11720576)))];
            tensor<fp16, [1, 128, 384]> var_131_cast_fp16 = add(x = op_130_cast_fp16_cast_uint16_cast_uint16, y = var_18_to_fp16)[name = string("op_131_cast_fp16")];
            int32 var_132_axis_0 = const()[name = string("op_132_axis_0"), val = int32(0)];
            int32 var_132_batch_dims_0 = const()[name = string("op_132_batch_dims_0"), val = int32(0)];
            bool var_132_validate_indices_0 = const()[name = string("op_132_validate_indices_0"), val = bool(false)];
            tensor<fp16, [2, 384]> self_model_bert_embeddings_token_type_embeddings_weight_to_fp16 = const()[name = string("self_model_bert_embeddings_token_type_embeddings_weight_to_fp16"), val = tensor<fp16, [2, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11818944)))];
            string token_type_ids_to_uint16_dtype_0 = const()[name = string("token_type_ids_to_uint16_dtype_0"), val = string("uint16")];
            tensor<uint16, [1, 128]> token_type_ids_to_uint16 = cast(dtype = token_type_ids_to_uint16_dtype_0, x = token_type_ids)[name = string("cast_1")];
            tensor<fp16, [1, 128, 384]> var_132_cast_fp16_cast_uint16 = gather(axis = var_132_axis_0, batch_dims = var_132_batch_dims_0, indices = token_type_ids_to_uint16, validate_indices = var_132_validate_indices_0, x = self_model_bert_embeddings_token_type_embeddings_weight_to_fp16)[name = string("op_132_cast_fp16_cast_uint16")];
            tensor<fp16, [1, 128, 384]> input_7_cast_fp16 = add(x = var_131_cast_fp16, y = var_132_cast_fp16_cast_uint16)[name = string("input_7_cast_fp16")];
            tensor<int32, [1]> input_9_axes_0 = const()[name = string("input_9_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_embeddings_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_embeddings_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11820544)))];
            tensor<fp16, [384]> self_model_bert_embeddings_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_embeddings_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11821376)))];
            fp16 var_15_to_fp16 = const()[name = string("op_15_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 128, 384]> input_9_cast_fp16 = layer_norm(axes = input_9_axes_0, beta = self_model_bert_embeddings_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_embeddings_LayerNorm_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")];
            tensor<int32, [1]> var_136_axes_0 = const()[name = string("op_136_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1, 1, 128]> var_136 = expand_dims(axes = var_136_axes_0, x = attention_mask)[name = string("op_136")];
            tensor<int32, [1]> var_137_axes_0 = const()[name = string("op_137_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 1, 1, 128]> var_137 = expand_dims(axes = var_137_axes_0, x = var_136)[name = string("op_137")];
            tensor<bool, [1, 1, 1, 128]> var_139 = not_equal(x = var_137, y = var_20)[name = string("op_139")];
            tensor<fp16, [1, 1, 1, 128]> var_13_after_broadcast_to_fp16 = const()[name = string("op_13_after_broadcast_to_fp16"), val = tensor<fp16, [1, 1, 1, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11822208)))];
            tensor<fp16, [1, 1, 1, 128]> var_12_after_broadcast_to_fp16 = const()[name = string("op_12_after_broadcast_to_fp16"), val = tensor<fp16, [1, 1, 1, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11822528)))];
            tensor<fp16, [1, 1, 1, 128]> additive_mask_cast_fp16 = select(a = var_13_after_broadcast_to_fp16, b = var_12_after_broadcast_to_fp16, cond = var_139)[name = string("additive_mask_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_0_attention_self_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11822848))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11970368))))[name = string("self_model_bert_encoder_layer_0_attention_self_query_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_attention_self_query_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_attention_self_query_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11971200)))];
            fp16 quantize_0_scale_0 = const()[name = string("quantize_0_scale_0"), val = fp16(0x1.b2cp-5)];
            uint8 quantize_0_zero_point_0 = const()[name = string("quantize_0_zero_point_0"), val = uint8(136)];
            string quantize_0_output_dtype_0 = const()[name = string("quantize_0_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_0 = quantize(input = input_9_cast_fp16, output_dtype = quantize_0_output_dtype_0, scale = quantize_0_scale_0, zero_point = quantize_0_zero_point_0)[name = string("quantize_0")];
            fp16 dequantize_0_scale_0 = const()[name = string("dequantize_0_scale_0"), val = fp16(0x1.b2cp-5)];
            uint8 dequantize_0_zero_point_0 = const()[name = string("dequantize_0_zero_point_0"), val = uint8(136)];
            tensor<fp16, [1, 128, 384]> dequantize_0 = dequantize(input = quantize_0, scale = dequantize_0_scale_0, zero_point = dequantize_0_zero_point_0)[name = string("dequantize_0")];
            tensor<fp16, [1, 128, 384]> linear_0_cast_fp16 = linear(bias = self_model_bert_encoder_layer_0_attention_self_query_bias_to_fp16, weight = self_model_bert_encoder_layer_0_attention_self_query_weight_to_fp16_quantized, x = dequantize_0)[name = string("linear_0_cast_fp16")];
            fp16 quantize_38_scale_0 = const()[name = string("quantize_38_scale_0"), val = fp16(0x1.0e8p-3)];
            uint8 quantize_38_zero_point_0 = const()[name = string("quantize_38_zero_point_0"), val = uint8(127)];
            string quantize_38_output_dtype_0 = const()[name = string("quantize_38_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_38 = quantize(input = linear_0_cast_fp16, output_dtype = quantize_38_output_dtype_0, scale = quantize_38_scale_0, zero_point = quantize_38_zero_point_0)[name = string("quantize_38")];
            fp16 dequantize_38_scale_0 = const()[name = string("dequantize_38_scale_0"), val = fp16(0x1.0e8p-3)];
            uint8 dequantize_38_zero_point_0 = const()[name = string("dequantize_38_zero_point_0"), val = uint8(127)];
            tensor<fp16, [1, 128, 384]> dequantize_38__post__dequant = dequantize(input = quantize_38, scale = dequantize_38_scale_0, zero_point = dequantize_38_zero_point_0)[name = string("dequantize_38")];
            tensor<fp16, [1, 128, 12, 32]> var_142_cast_fp16 = reshape(shape = var_8, x = dequantize_38__post__dequant)[name = string("op_142_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_0_attention_self_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11972032))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12119552))))[name = string("self_model_bert_encoder_layer_0_attention_self_key_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_attention_self_key_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_attention_self_key_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12120384)))];
            tensor<fp16, [1, 128, 384]> linear_1_cast_fp16 = linear(bias = self_model_bert_encoder_layer_0_attention_self_key_bias_to_fp16, weight = self_model_bert_encoder_layer_0_attention_self_key_weight_to_fp16_quantized, x = dequantize_0)[name = string("linear_1_cast_fp16")];
            fp16 quantize_39_scale_0 = const()[name = string("quantize_39_scale_0"), val = fp16(0x1.998p-4)];
            uint8 quantize_39_zero_point_0 = const()[name = string("quantize_39_zero_point_0"), val = uint8(130)];
            string quantize_39_output_dtype_0 = const()[name = string("quantize_39_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_39 = quantize(input = linear_1_cast_fp16, output_dtype = quantize_39_output_dtype_0, scale = quantize_39_scale_0, zero_point = quantize_39_zero_point_0)[name = string("quantize_39")];
            fp16 dequantize_39_scale_0 = const()[name = string("dequantize_39_scale_0"), val = fp16(0x1.998p-4)];
            uint8 dequantize_39_zero_point_0 = const()[name = string("dequantize_39_zero_point_0"), val = uint8(130)];
            tensor<fp16, [1, 128, 384]> dequantize_39__post__dequant = dequantize(input = quantize_39, scale = dequantize_39_scale_0, zero_point = dequantize_39_zero_point_0)[name = string("dequantize_39")];
            tensor<fp16, [1, 128, 12, 32]> var_145_cast_fp16 = reshape(shape = var_8, x = dequantize_39__post__dequant)[name = string("op_145_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_0_attention_self_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12121216))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12268736))))[name = string("self_model_bert_encoder_layer_0_attention_self_value_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_attention_self_value_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_attention_self_value_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12269568)))];
            tensor<fp16, [1, 128, 384]> linear_2_cast_fp16 = linear(bias = self_model_bert_encoder_layer_0_attention_self_value_bias_to_fp16, weight = self_model_bert_encoder_layer_0_attention_self_value_weight_to_fp16_quantized, x = dequantize_0)[name = string("linear_2_cast_fp16")];
            fp16 quantize_40_scale_0 = const()[name = string("quantize_40_scale_0"), val = fp16(0x1.924p-6)];
            uint8 quantize_40_zero_point_0 = const()[name = string("quantize_40_zero_point_0"), val = uint8(116)];
            string quantize_40_output_dtype_0 = const()[name = string("quantize_40_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_40 = quantize(input = linear_2_cast_fp16, output_dtype = quantize_40_output_dtype_0, scale = quantize_40_scale_0, zero_point = quantize_40_zero_point_0)[name = string("quantize_40")];
            fp16 dequantize_40_scale_0 = const()[name = string("dequantize_40_scale_0"), val = fp16(0x1.924p-6)];
            uint8 dequantize_40_zero_point_0 = const()[name = string("dequantize_40_zero_point_0"), val = uint8(116)];
            tensor<fp16, [1, 128, 384]> dequantize_40__post__dequant = dequantize(input = quantize_40, scale = dequantize_40_scale_0, zero_point = dequantize_40_zero_point_0)[name = string("dequantize_40")];
            tensor<fp16, [1, 128, 12, 32]> var_148_cast_fp16 = reshape(shape = var_8, x = dequantize_40__post__dequant)[name = string("op_148_cast_fp16")];
            bool var_151_transpose_x_0 = const()[name = string("op_151_transpose_x_0"), val = bool(false)];
            bool var_151_transpose_y_0 = const()[name = string("op_151_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_24_perm_0 = const()[name = string("transpose_24_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_25_perm_0 = const()[name = string("transpose_25_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 12, 32, 128]> transpose_25 = transpose(perm = transpose_25_perm_0, x = var_145_cast_fp16)[name = string("transpose_58")];
            tensor<fp16, [1, 12, 128, 32]> transpose_24 = transpose(perm = transpose_24_perm_0, x = var_142_cast_fp16)[name = string("transpose_59")];
            tensor<fp16, [1, 12, 128, 128]> var_151_cast_fp16 = matmul(transpose_x = var_151_transpose_x_0, transpose_y = var_151_transpose_y_0, x = transpose_24, y = transpose_25)[name = string("op_151_cast_fp16")];
            fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 12, 128, 128]> _inversed_scores_1_cast_fp16 = mul(x = var_151_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> var_154_cast_fp16 = add(x = _inversed_scores_1_cast_fp16, y = additive_mask_cast_fp16)[name = string("op_154_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> probabilities_1_cast_fp16 = softmax(axis = var_19, x = var_154_cast_fp16)[name = string("probabilities_1_cast_fp16")];
            bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)];
            bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 12, 128, 32]> values_1_cast_fp16 = transpose(perm = var_7, x = var_148_cast_fp16)[name = string("transpose_57")];
            tensor<fp16, [1, 12, 128, 32]> context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = probabilities_1_cast_fp16, y = values_1_cast_fp16)[name = string("context_1_cast_fp16")];
            tensor<fp16, [1, 128, 12, 32]> var_157_cast_fp16 = transpose(perm = var_7, x = context_1_cast_fp16)[name = string("transpose_56")];
            tensor<fp16, [1, 128, 384]> input_11_cast_fp16 = reshape(shape = var_5, x = var_157_cast_fp16)[name = string("input_11_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_0_attention_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12270400))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12417920))))[name = string("self_model_bert_encoder_layer_0_attention_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_attention_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_attention_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12418752)))];
            fp16 quantize_3_scale_0 = const()[name = string("quantize_3_scale_0"), val = fp16(0x1.0bp-6)];
            uint8 quantize_3_zero_point_0 = const()[name = string("quantize_3_zero_point_0"), val = uint8(174)];
            string quantize_3_output_dtype_0 = const()[name = string("quantize_3_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_3 = quantize(input = input_11_cast_fp16, output_dtype = quantize_3_output_dtype_0, scale = quantize_3_scale_0, zero_point = quantize_3_zero_point_0)[name = string("quantize_3")];
            fp16 dequantize_3_scale_0 = const()[name = string("dequantize_3_scale_0"), val = fp16(0x1.0bp-6)];
            uint8 dequantize_3_zero_point_0 = const()[name = string("dequantize_3_zero_point_0"), val = uint8(174)];
            tensor<fp16, [1, 128, 384]> dequantize_3 = dequantize(input = quantize_3, scale = dequantize_3_scale_0, zero_point = dequantize_3_zero_point_0)[name = string("dequantize_3")];
            tensor<fp16, [1, 128, 384]> linear_3_cast_fp16 = linear(bias = self_model_bert_encoder_layer_0_attention_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_0_attention_output_dense_weight_to_fp16_quantized, x = dequantize_3)[name = string("linear_3_cast_fp16")];
            fp16 quantize_41_scale_0 = const()[name = string("quantize_41_scale_0"), val = fp16(0x1.2e8p-6)];
            uint8 quantize_41_zero_point_0 = const()[name = string("quantize_41_zero_point_0"), val = uint8(35)];
            string quantize_41_output_dtype_0 = const()[name = string("quantize_41_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_41 = quantize(input = linear_3_cast_fp16, output_dtype = quantize_41_output_dtype_0, scale = quantize_41_scale_0, zero_point = quantize_41_zero_point_0)[name = string("quantize_41")];
            fp16 dequantize_41_scale_0 = const()[name = string("dequantize_41_scale_0"), val = fp16(0x1.2e8p-6)];
            uint8 dequantize_41_zero_point_0 = const()[name = string("dequantize_41_zero_point_0"), val = uint8(35)];
            tensor<fp16, [1, 128, 384]> dequantize_41__post__dequant = dequantize(input = quantize_41, scale = dequantize_41_scale_0, zero_point = dequantize_41_zero_point_0)[name = string("dequantize_41")];
            tensor<fp16, [1, 128, 384]> input_13_cast_fp16 = add(x = input_9_cast_fp16, y = dequantize_41__post__dequant)[name = string("input_13_cast_fp16")];
            tensor<int32, [1]> input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_attention_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_attention_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12419584)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_attention_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_attention_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12420416)))];
            tensor<fp16, [1, 128, 384]> input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = self_model_bert_encoder_layer_0_attention_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_0_attention_output_LayerNorm_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1536, 384]> self_model_bert_encoder_layer_0_intermediate_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1536, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12421248))), scale = tensor<fp16, [1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13011136))))[name = string("self_model_bert_encoder_layer_0_intermediate_dense_weight_to_fp16_quantized")];
            tensor<fp16, [1536]> self_model_bert_encoder_layer_0_intermediate_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13014272)))];
            fp16 quantize_4_scale_0 = const()[name = string("quantize_4_scale_0"), val = fp16(0x1.1f8p-3)];
            uint8 quantize_4_zero_point_0 = const()[name = string("quantize_4_zero_point_0"), val = uint8(53)];
            string quantize_4_output_dtype_0 = const()[name = string("quantize_4_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_4 = quantize(input = input_15_cast_fp16, output_dtype = quantize_4_output_dtype_0, scale = quantize_4_scale_0, zero_point = quantize_4_zero_point_0)[name = string("quantize_4")];
            fp16 dequantize_4_scale_0 = const()[name = string("dequantize_4_scale_0"), val = fp16(0x1.1f8p-3)];
            uint8 dequantize_4_zero_point_0 = const()[name = string("dequantize_4_zero_point_0"), val = uint8(53)];
            tensor<fp16, [1, 128, 384]> dequantize_4 = dequantize(input = quantize_4, scale = dequantize_4_scale_0, zero_point = dequantize_4_zero_point_0)[name = string("dequantize_4")];
            tensor<fp16, [1, 128, 1536]> linear_4_cast_fp16 = linear(bias = self_model_bert_encoder_layer_0_intermediate_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_0_intermediate_dense_weight_to_fp16_quantized, x = dequantize_4)[name = string("linear_4_cast_fp16")];
            fp16 quantize_42_scale_0 = const()[name = string("quantize_42_scale_0"), val = fp16(0x1.4c8p-3)];
            uint8 quantize_42_zero_point_0 = const()[name = string("quantize_42_zero_point_0"), val = uint8(105)];
            string quantize_42_output_dtype_0 = const()[name = string("quantize_42_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_42 = quantize(input = linear_4_cast_fp16, output_dtype = quantize_42_output_dtype_0, scale = quantize_42_scale_0, zero_point = quantize_42_zero_point_0)[name = string("quantize_42")];
            fp16 dequantize_42_scale_0 = const()[name = string("dequantize_42_scale_0"), val = fp16(0x1.4c8p-3)];
            uint8 dequantize_42_zero_point_0 = const()[name = string("dequantize_42_zero_point_0"), val = uint8(105)];
            tensor<fp16, [1, 128, 1536]> dequantize_42__post__dequant = dequantize(input = quantize_42, scale = dequantize_42_scale_0, zero_point = dequantize_42_zero_point_0)[name = string("dequantize_42")];
            string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 1536]> input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = dequantize_42__post__dequant)[name = string("input_17_cast_fp16")];
            tensor<fp16, [384, 1536]> self_model_bert_encoder_layer_0_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13017408))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13607296))))[name = string("self_model_bert_encoder_layer_0_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13608128)))];
            fp16 quantize_5_scale_0 = const()[name = string("quantize_5_scale_0"), val = fp16(0x1.898p-4)];
            uint8 quantize_5_zero_point_0 = const()[name = string("quantize_5_zero_point_0"), val = uint8(2)];
            string quantize_5_output_dtype_0 = const()[name = string("quantize_5_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_5 = quantize(input = input_17_cast_fp16, output_dtype = quantize_5_output_dtype_0, scale = quantize_5_scale_0, zero_point = quantize_5_zero_point_0)[name = string("quantize_5")];
            fp16 dequantize_5_scale_0 = const()[name = string("dequantize_5_scale_0"), val = fp16(0x1.898p-4)];
            uint8 dequantize_5_zero_point_0 = const()[name = string("dequantize_5_zero_point_0"), val = uint8(2)];
            tensor<fp16, [1, 128, 1536]> dequantize_5 = dequantize(input = quantize_5, scale = dequantize_5_scale_0, zero_point = dequantize_5_zero_point_0)[name = string("dequantize_5")];
            tensor<fp16, [1, 128, 384]> linear_5_cast_fp16 = linear(bias = self_model_bert_encoder_layer_0_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_0_output_dense_weight_to_fp16_quantized, x = dequantize_5)[name = string("linear_5_cast_fp16")];
            fp16 quantize_43_scale_0 = const()[name = string("quantize_43_scale_0"), val = fp16(0x1.89p-1)];
            uint8 quantize_43_zero_point_0 = const()[name = string("quantize_43_zero_point_0"), val = uint8(4)];
            string quantize_43_output_dtype_0 = const()[name = string("quantize_43_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_43 = quantize(input = linear_5_cast_fp16, output_dtype = quantize_43_output_dtype_0, scale = quantize_43_scale_0, zero_point = quantize_43_zero_point_0)[name = string("quantize_43")];
            fp16 dequantize_43_scale_0 = const()[name = string("dequantize_43_scale_0"), val = fp16(0x1.89p-1)];
            uint8 dequantize_43_zero_point_0 = const()[name = string("dequantize_43_zero_point_0"), val = uint8(4)];
            tensor<fp16, [1, 128, 384]> dequantize_43__post__dequant = dequantize(input = quantize_43, scale = dequantize_43_scale_0, zero_point = dequantize_43_zero_point_0)[name = string("dequantize_43")];
            tensor<fp16, [1, 128, 384]> input_19_cast_fp16 = add(x = input_15_cast_fp16, y = dequantize_43__post__dequant)[name = string("input_19_cast_fp16")];
            tensor<int32, [1]> input_21_axes_0 = const()[name = string("input_21_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13608960)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_0_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_0_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13609792)))];
            tensor<fp16, [1, 128, 384]> input_21_cast_fp16 = layer_norm(axes = input_21_axes_0, beta = self_model_bert_encoder_layer_0_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_0_output_LayerNorm_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_1_attention_self_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13610624))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13758144))))[name = string("self_model_bert_encoder_layer_1_attention_self_query_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_attention_self_query_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_attention_self_query_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13758976)))];
            fp16 quantize_6_scale_0 = const()[name = string("quantize_6_scale_0"), val = fp16(0x1.dccp-5)];
            uint8 quantize_6_zero_point_0 = const()[name = string("quantize_6_zero_point_0"), val = uint8(116)];
            string quantize_6_output_dtype_0 = const()[name = string("quantize_6_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_6 = quantize(input = input_21_cast_fp16, output_dtype = quantize_6_output_dtype_0, scale = quantize_6_scale_0, zero_point = quantize_6_zero_point_0)[name = string("quantize_6")];
            fp16 dequantize_6_scale_0 = const()[name = string("dequantize_6_scale_0"), val = fp16(0x1.dccp-5)];
            uint8 dequantize_6_zero_point_0 = const()[name = string("dequantize_6_zero_point_0"), val = uint8(116)];
            tensor<fp16, [1, 128, 384]> dequantize_6 = dequantize(input = quantize_6, scale = dequantize_6_scale_0, zero_point = dequantize_6_zero_point_0)[name = string("dequantize_6")];
            tensor<fp16, [1, 128, 384]> linear_6_cast_fp16 = linear(bias = self_model_bert_encoder_layer_1_attention_self_query_bias_to_fp16, weight = self_model_bert_encoder_layer_1_attention_self_query_weight_to_fp16_quantized, x = dequantize_6)[name = string("linear_6_cast_fp16")];
            fp16 quantize_44_scale_0 = const()[name = string("quantize_44_scale_0"), val = fp16(0x1.984p-5)];
            uint8 quantize_44_zero_point_0 = const()[name = string("quantize_44_zero_point_0"), val = uint8(125)];
            string quantize_44_output_dtype_0 = const()[name = string("quantize_44_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_44 = quantize(input = linear_6_cast_fp16, output_dtype = quantize_44_output_dtype_0, scale = quantize_44_scale_0, zero_point = quantize_44_zero_point_0)[name = string("quantize_44")];
            fp16 dequantize_44_scale_0 = const()[name = string("dequantize_44_scale_0"), val = fp16(0x1.984p-5)];
            uint8 dequantize_44_zero_point_0 = const()[name = string("dequantize_44_zero_point_0"), val = uint8(125)];
            tensor<fp16, [1, 128, 384]> dequantize_44__post__dequant = dequantize(input = quantize_44, scale = dequantize_44_scale_0, zero_point = dequantize_44_zero_point_0)[name = string("dequantize_44")];
            tensor<fp16, [1, 128, 12, 32]> var_168_cast_fp16 = reshape(shape = var_8, x = dequantize_44__post__dequant)[name = string("op_168_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_1_attention_self_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13759808))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13907328))))[name = string("self_model_bert_encoder_layer_1_attention_self_key_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_attention_self_key_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_attention_self_key_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13908160)))];
            tensor<fp16, [1, 128, 384]> linear_7_cast_fp16 = linear(bias = self_model_bert_encoder_layer_1_attention_self_key_bias_to_fp16, weight = self_model_bert_encoder_layer_1_attention_self_key_weight_to_fp16_quantized, x = dequantize_6)[name = string("linear_7_cast_fp16")];
            fp16 quantize_45_scale_0 = const()[name = string("quantize_45_scale_0"), val = fp16(0x1.92p-5)];
            uint8 quantize_45_zero_point_0 = const()[name = string("quantize_45_zero_point_0"), val = uint8(132)];
            string quantize_45_output_dtype_0 = const()[name = string("quantize_45_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_45 = quantize(input = linear_7_cast_fp16, output_dtype = quantize_45_output_dtype_0, scale = quantize_45_scale_0, zero_point = quantize_45_zero_point_0)[name = string("quantize_45")];
            fp16 dequantize_45_scale_0 = const()[name = string("dequantize_45_scale_0"), val = fp16(0x1.92p-5)];
            uint8 dequantize_45_zero_point_0 = const()[name = string("dequantize_45_zero_point_0"), val = uint8(132)];
            tensor<fp16, [1, 128, 384]> dequantize_45__post__dequant = dequantize(input = quantize_45, scale = dequantize_45_scale_0, zero_point = dequantize_45_zero_point_0)[name = string("dequantize_45")];
            tensor<fp16, [1, 128, 12, 32]> var_171_cast_fp16 = reshape(shape = var_8, x = dequantize_45__post__dequant)[name = string("op_171_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_1_attention_self_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13908992))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14056512))))[name = string("self_model_bert_encoder_layer_1_attention_self_value_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_attention_self_value_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_attention_self_value_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14057344)))];
            tensor<fp16, [1, 128, 384]> linear_8_cast_fp16 = linear(bias = self_model_bert_encoder_layer_1_attention_self_value_bias_to_fp16, weight = self_model_bert_encoder_layer_1_attention_self_value_weight_to_fp16_quantized, x = dequantize_6)[name = string("linear_8_cast_fp16")];
            fp16 quantize_46_scale_0 = const()[name = string("quantize_46_scale_0"), val = fp16(0x1.81p-6)];
            uint8 quantize_46_zero_point_0 = const()[name = string("quantize_46_zero_point_0"), val = uint8(113)];
            string quantize_46_output_dtype_0 = const()[name = string("quantize_46_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_46 = quantize(input = linear_8_cast_fp16, output_dtype = quantize_46_output_dtype_0, scale = quantize_46_scale_0, zero_point = quantize_46_zero_point_0)[name = string("quantize_46")];
            fp16 dequantize_46_scale_0 = const()[name = string("dequantize_46_scale_0"), val = fp16(0x1.81p-6)];
            uint8 dequantize_46_zero_point_0 = const()[name = string("dequantize_46_zero_point_0"), val = uint8(113)];
            tensor<fp16, [1, 128, 384]> dequantize_46__post__dequant = dequantize(input = quantize_46, scale = dequantize_46_scale_0, zero_point = dequantize_46_zero_point_0)[name = string("dequantize_46")];
            tensor<fp16, [1, 128, 12, 32]> var_174_cast_fp16 = reshape(shape = var_8, x = dequantize_46__post__dequant)[name = string("op_174_cast_fp16")];
            bool var_177_transpose_x_0 = const()[name = string("op_177_transpose_x_0"), val = bool(false)];
            bool var_177_transpose_y_0 = const()[name = string("op_177_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_26_perm_0 = const()[name = string("transpose_26_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_27_perm_0 = const()[name = string("transpose_27_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 12, 32, 128]> transpose_27 = transpose(perm = transpose_27_perm_0, x = var_171_cast_fp16)[name = string("transpose_54")];
            tensor<fp16, [1, 12, 128, 32]> transpose_26 = transpose(perm = transpose_26_perm_0, x = var_168_cast_fp16)[name = string("transpose_55")];
            tensor<fp16, [1, 12, 128, 128]> var_177_cast_fp16 = matmul(transpose_x = var_177_transpose_x_0, transpose_y = var_177_transpose_y_0, x = transpose_26, y = transpose_27)[name = string("op_177_cast_fp16")];
            fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 12, 128, 128]> _inversed_scores_3_cast_fp16 = mul(x = var_177_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> var_180_cast_fp16 = add(x = _inversed_scores_3_cast_fp16, y = additive_mask_cast_fp16)[name = string("op_180_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> probabilities_3_cast_fp16 = softmax(axis = var_19, x = var_180_cast_fp16)[name = string("probabilities_3_cast_fp16")];
            bool context_3_transpose_x_0 = const()[name = string("context_3_transpose_x_0"), val = bool(false)];
            bool context_3_transpose_y_0 = const()[name = string("context_3_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 12, 128, 32]> values_3_cast_fp16 = transpose(perm = var_7, x = var_174_cast_fp16)[name = string("transpose_53")];
            tensor<fp16, [1, 12, 128, 32]> context_3_cast_fp16 = matmul(transpose_x = context_3_transpose_x_0, transpose_y = context_3_transpose_y_0, x = probabilities_3_cast_fp16, y = values_3_cast_fp16)[name = string("context_3_cast_fp16")];
            tensor<fp16, [1, 128, 12, 32]> var_183_cast_fp16 = transpose(perm = var_7, x = context_3_cast_fp16)[name = string("transpose_52")];
            tensor<fp16, [1, 128, 384]> input_23_cast_fp16 = reshape(shape = var_5, x = var_183_cast_fp16)[name = string("input_23_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_1_attention_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14058176))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14205696))))[name = string("self_model_bert_encoder_layer_1_attention_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_attention_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_attention_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14206528)))];
            fp16 quantize_9_scale_0 = const()[name = string("quantize_9_scale_0"), val = fp16(0x1.eap-7)];
            uint8 quantize_9_zero_point_0 = const()[name = string("quantize_9_zero_point_0"), val = uint8(127)];
            string quantize_9_output_dtype_0 = const()[name = string("quantize_9_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_9 = quantize(input = input_23_cast_fp16, output_dtype = quantize_9_output_dtype_0, scale = quantize_9_scale_0, zero_point = quantize_9_zero_point_0)[name = string("quantize_9")];
            fp16 dequantize_9_scale_0 = const()[name = string("dequantize_9_scale_0"), val = fp16(0x1.eap-7)];
            uint8 dequantize_9_zero_point_0 = const()[name = string("dequantize_9_zero_point_0"), val = uint8(127)];
            tensor<fp16, [1, 128, 384]> dequantize_9 = dequantize(input = quantize_9, scale = dequantize_9_scale_0, zero_point = dequantize_9_zero_point_0)[name = string("dequantize_9")];
            tensor<fp16, [1, 128, 384]> linear_9_cast_fp16 = linear(bias = self_model_bert_encoder_layer_1_attention_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_1_attention_output_dense_weight_to_fp16_quantized, x = dequantize_9)[name = string("linear_9_cast_fp16")];
            fp16 quantize_47_scale_0 = const()[name = string("quantize_47_scale_0"), val = fp16(0x1.5ccp-7)];
            uint8 quantize_47_zero_point_0 = const()[name = string("quantize_47_zero_point_0"), val = uint8(111)];
            string quantize_47_output_dtype_0 = const()[name = string("quantize_47_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_47 = quantize(input = linear_9_cast_fp16, output_dtype = quantize_47_output_dtype_0, scale = quantize_47_scale_0, zero_point = quantize_47_zero_point_0)[name = string("quantize_47")];
            fp16 dequantize_47_scale_0 = const()[name = string("dequantize_47_scale_0"), val = fp16(0x1.5ccp-7)];
            uint8 dequantize_47_zero_point_0 = const()[name = string("dequantize_47_zero_point_0"), val = uint8(111)];
            tensor<fp16, [1, 128, 384]> dequantize_47__post__dequant = dequantize(input = quantize_47, scale = dequantize_47_scale_0, zero_point = dequantize_47_zero_point_0)[name = string("dequantize_47")];
            tensor<fp16, [1, 128, 384]> input_25_cast_fp16 = add(x = input_21_cast_fp16, y = dequantize_47__post__dequant)[name = string("input_25_cast_fp16")];
            tensor<int32, [1]> input_27_axes_0 = const()[name = string("input_27_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_attention_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_attention_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14207360)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_attention_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_attention_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14208192)))];
            tensor<fp16, [1, 128, 384]> input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = self_model_bert_encoder_layer_1_attention_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_1_attention_output_LayerNorm_weight_to_fp16, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1536, 384]> self_model_bert_encoder_layer_1_intermediate_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1536, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14209024))), scale = tensor<fp16, [1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14798912))))[name = string("self_model_bert_encoder_layer_1_intermediate_dense_weight_to_fp16_quantized")];
            tensor<fp16, [1536]> self_model_bert_encoder_layer_1_intermediate_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14802048)))];
            fp16 quantize_10_scale_0 = const()[name = string("quantize_10_scale_0"), val = fp16(0x1.37cp-3)];
            uint8 quantize_10_zero_point_0 = const()[name = string("quantize_10_zero_point_0"), val = uint8(57)];
            string quantize_10_output_dtype_0 = const()[name = string("quantize_10_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_10 = quantize(input = input_27_cast_fp16, output_dtype = quantize_10_output_dtype_0, scale = quantize_10_scale_0, zero_point = quantize_10_zero_point_0)[name = string("quantize_10")];
            fp16 dequantize_10_scale_0 = const()[name = string("dequantize_10_scale_0"), val = fp16(0x1.37cp-3)];
            uint8 dequantize_10_zero_point_0 = const()[name = string("dequantize_10_zero_point_0"), val = uint8(57)];
            tensor<fp16, [1, 128, 384]> dequantize_10 = dequantize(input = quantize_10, scale = dequantize_10_scale_0, zero_point = dequantize_10_zero_point_0)[name = string("dequantize_10")];
            tensor<fp16, [1, 128, 1536]> linear_10_cast_fp16 = linear(bias = self_model_bert_encoder_layer_1_intermediate_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_1_intermediate_dense_weight_to_fp16_quantized, x = dequantize_10)[name = string("linear_10_cast_fp16")];
            fp16 quantize_48_scale_0 = const()[name = string("quantize_48_scale_0"), val = fp16(0x1.0e4p-3)];
            uint8 quantize_48_zero_point_0 = const()[name = string("quantize_48_zero_point_0"), val = uint8(100)];
            string quantize_48_output_dtype_0 = const()[name = string("quantize_48_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_48 = quantize(input = linear_10_cast_fp16, output_dtype = quantize_48_output_dtype_0, scale = quantize_48_scale_0, zero_point = quantize_48_zero_point_0)[name = string("quantize_48")];
            fp16 dequantize_48_scale_0 = const()[name = string("dequantize_48_scale_0"), val = fp16(0x1.0e4p-3)];
            uint8 dequantize_48_zero_point_0 = const()[name = string("dequantize_48_zero_point_0"), val = uint8(100)];
            tensor<fp16, [1, 128, 1536]> dequantize_48__post__dequant = dequantize(input = quantize_48, scale = dequantize_48_scale_0, zero_point = dequantize_48_zero_point_0)[name = string("dequantize_48")];
            string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 1536]> input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = dequantize_48__post__dequant)[name = string("input_29_cast_fp16")];
            tensor<fp16, [384, 1536]> self_model_bert_encoder_layer_1_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14805184))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15395072))))[name = string("self_model_bert_encoder_layer_1_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15395904)))];
            fp16 quantize_11_scale_0 = const()[name = string("quantize_11_scale_0"), val = fp16(0x1.4acp-4)];
            uint8 quantize_11_zero_point_0 = const()[name = string("quantize_11_zero_point_0"), val = uint8(2)];
            string quantize_11_output_dtype_0 = const()[name = string("quantize_11_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_11 = quantize(input = input_29_cast_fp16, output_dtype = quantize_11_output_dtype_0, scale = quantize_11_scale_0, zero_point = quantize_11_zero_point_0)[name = string("quantize_11")];
            fp16 dequantize_11_scale_0 = const()[name = string("dequantize_11_scale_0"), val = fp16(0x1.4acp-4)];
            uint8 dequantize_11_zero_point_0 = const()[name = string("dequantize_11_zero_point_0"), val = uint8(2)];
            tensor<fp16, [1, 128, 1536]> dequantize_11 = dequantize(input = quantize_11, scale = dequantize_11_scale_0, zero_point = dequantize_11_zero_point_0)[name = string("dequantize_11")];
            tensor<fp16, [1, 128, 384]> linear_11_cast_fp16 = linear(bias = self_model_bert_encoder_layer_1_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_1_output_dense_weight_to_fp16_quantized, x = dequantize_11)[name = string("linear_11_cast_fp16")];
            fp16 quantize_49_scale_0 = const()[name = string("quantize_49_scale_0"), val = fp16(0x1.8p-2)];
            uint8 quantize_49_zero_point_0 = const()[name = string("quantize_49_zero_point_0"), val = uint8(12)];
            string quantize_49_output_dtype_0 = const()[name = string("quantize_49_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_49 = quantize(input = linear_11_cast_fp16, output_dtype = quantize_49_output_dtype_0, scale = quantize_49_scale_0, zero_point = quantize_49_zero_point_0)[name = string("quantize_49")];
            fp16 dequantize_49_scale_0 = const()[name = string("dequantize_49_scale_0"), val = fp16(0x1.8p-2)];
            uint8 dequantize_49_zero_point_0 = const()[name = string("dequantize_49_zero_point_0"), val = uint8(12)];
            tensor<fp16, [1, 128, 384]> dequantize_49__post__dequant = dequantize(input = quantize_49, scale = dequantize_49_scale_0, zero_point = dequantize_49_zero_point_0)[name = string("dequantize_49")];
            tensor<fp16, [1, 128, 384]> input_31_cast_fp16 = add(x = input_27_cast_fp16, y = dequantize_49__post__dequant)[name = string("input_31_cast_fp16")];
            tensor<int32, [1]> input_33_axes_0 = const()[name = string("input_33_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15396736)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_1_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_1_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15397568)))];
            tensor<fp16, [1, 128, 384]> input_33_cast_fp16 = layer_norm(axes = input_33_axes_0, beta = self_model_bert_encoder_layer_1_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_1_output_LayerNorm_weight_to_fp16, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_2_attention_self_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15398400))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15545920))))[name = string("self_model_bert_encoder_layer_2_attention_self_query_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_attention_self_query_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_attention_self_query_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15546752)))];
            fp16 quantize_12_scale_0 = const()[name = string("quantize_12_scale_0"), val = fp16(0x1.e7p-5)];
            uint8 quantize_12_zero_point_0 = const()[name = string("quantize_12_zero_point_0"), val = uint8(121)];
            string quantize_12_output_dtype_0 = const()[name = string("quantize_12_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_12 = quantize(input = input_33_cast_fp16, output_dtype = quantize_12_output_dtype_0, scale = quantize_12_scale_0, zero_point = quantize_12_zero_point_0)[name = string("quantize_12")];
            fp16 dequantize_12_scale_0 = const()[name = string("dequantize_12_scale_0"), val = fp16(0x1.e7p-5)];
            uint8 dequantize_12_zero_point_0 = const()[name = string("dequantize_12_zero_point_0"), val = uint8(121)];
            tensor<fp16, [1, 128, 384]> dequantize_12 = dequantize(input = quantize_12, scale = dequantize_12_scale_0, zero_point = dequantize_12_zero_point_0)[name = string("dequantize_12")];
            tensor<fp16, [1, 128, 384]> linear_12_cast_fp16 = linear(bias = self_model_bert_encoder_layer_2_attention_self_query_bias_to_fp16, weight = self_model_bert_encoder_layer_2_attention_self_query_weight_to_fp16_quantized, x = dequantize_12)[name = string("linear_12_cast_fp16")];
            fp16 quantize_50_scale_0 = const()[name = string("quantize_50_scale_0"), val = fp16(0x1.49cp-5)];
            uint8 quantize_50_zero_point_0 = const()[name = string("quantize_50_zero_point_0"), val = uint8(124)];
            string quantize_50_output_dtype_0 = const()[name = string("quantize_50_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_50 = quantize(input = linear_12_cast_fp16, output_dtype = quantize_50_output_dtype_0, scale = quantize_50_scale_0, zero_point = quantize_50_zero_point_0)[name = string("quantize_50")];
            fp16 dequantize_50_scale_0 = const()[name = string("dequantize_50_scale_0"), val = fp16(0x1.49cp-5)];
            uint8 dequantize_50_zero_point_0 = const()[name = string("dequantize_50_zero_point_0"), val = uint8(124)];
            tensor<fp16, [1, 128, 384]> dequantize_50__post__dequant = dequantize(input = quantize_50, scale = dequantize_50_scale_0, zero_point = dequantize_50_zero_point_0)[name = string("dequantize_50")];
            tensor<fp16, [1, 128, 12, 32]> var_194_cast_fp16 = reshape(shape = var_8, x = dequantize_50__post__dequant)[name = string("op_194_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_2_attention_self_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15547584))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15695104))))[name = string("self_model_bert_encoder_layer_2_attention_self_key_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_attention_self_key_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_attention_self_key_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15695936)))];
            tensor<fp16, [1, 128, 384]> linear_13_cast_fp16 = linear(bias = self_model_bert_encoder_layer_2_attention_self_key_bias_to_fp16, weight = self_model_bert_encoder_layer_2_attention_self_key_weight_to_fp16_quantized, x = dequantize_12)[name = string("linear_13_cast_fp16")];
            fp16 quantize_51_scale_0 = const()[name = string("quantize_51_scale_0"), val = fp16(0x1.5p-5)];
            fp16 dequantize_51_scale_0 = const()[name = string("dequantize_51_scale_0"), val = fp16(0x1.5p-5)];
            string quantize_75_output_dtype_0 = const()[name = string("quantize_75_output_dtype_0"), val = string("int8")];
            tensor<int8, [1, 128, 384]> quantize_75 = quantize(input = linear_13_cast_fp16, output_dtype = quantize_75_output_dtype_0, scale = quantize_51_scale_0)[name = string("quantize_75")];
            tensor<fp16, [1, 128, 384]> dequantize_75 = dequantize(input = quantize_75, scale = dequantize_51_scale_0)[name = string("dequantize_75")];
            tensor<fp16, [1, 128, 12, 32]> var_197_cast_fp16 = reshape(shape = var_8, x = dequantize_75)[name = string("op_197_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_2_attention_self_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15696768))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15844288))))[name = string("self_model_bert_encoder_layer_2_attention_self_value_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_attention_self_value_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_attention_self_value_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15845120)))];
            tensor<fp16, [1, 128, 384]> linear_14_cast_fp16 = linear(bias = self_model_bert_encoder_layer_2_attention_self_value_bias_to_fp16, weight = self_model_bert_encoder_layer_2_attention_self_value_weight_to_fp16_quantized, x = dequantize_12)[name = string("linear_14_cast_fp16")];
            fp16 quantize_52_scale_0 = const()[name = string("quantize_52_scale_0"), val = fp16(0x1.a44p-6)];
            uint8 quantize_52_zero_point_0 = const()[name = string("quantize_52_zero_point_0"), val = uint8(120)];
            string quantize_52_output_dtype_0 = const()[name = string("quantize_52_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_52 = quantize(input = linear_14_cast_fp16, output_dtype = quantize_52_output_dtype_0, scale = quantize_52_scale_0, zero_point = quantize_52_zero_point_0)[name = string("quantize_52")];
            fp16 dequantize_52_scale_0 = const()[name = string("dequantize_52_scale_0"), val = fp16(0x1.a44p-6)];
            uint8 dequantize_52_zero_point_0 = const()[name = string("dequantize_52_zero_point_0"), val = uint8(120)];
            tensor<fp16, [1, 128, 384]> dequantize_52__post__dequant = dequantize(input = quantize_52, scale = dequantize_52_scale_0, zero_point = dequantize_52_zero_point_0)[name = string("dequantize_52")];
            tensor<fp16, [1, 128, 12, 32]> var_200_cast_fp16 = reshape(shape = var_8, x = dequantize_52__post__dequant)[name = string("op_200_cast_fp16")];
            bool var_203_transpose_x_0 = const()[name = string("op_203_transpose_x_0"), val = bool(false)];
            bool var_203_transpose_y_0 = const()[name = string("op_203_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_28_perm_0 = const()[name = string("transpose_28_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_29_perm_0 = const()[name = string("transpose_29_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 12, 32, 128]> transpose_29 = transpose(perm = transpose_29_perm_0, x = var_197_cast_fp16)[name = string("transpose_50")];
            tensor<fp16, [1, 12, 128, 32]> transpose_28 = transpose(perm = transpose_28_perm_0, x = var_194_cast_fp16)[name = string("transpose_51")];
            tensor<fp16, [1, 12, 128, 128]> var_203_cast_fp16 = matmul(transpose_x = var_203_transpose_x_0, transpose_y = var_203_transpose_y_0, x = transpose_28, y = transpose_29)[name = string("op_203_cast_fp16")];
            fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 12, 128, 128]> _inversed_scores_5_cast_fp16 = mul(x = var_203_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> var_206_cast_fp16 = add(x = _inversed_scores_5_cast_fp16, y = additive_mask_cast_fp16)[name = string("op_206_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> probabilities_5_cast_fp16 = softmax(axis = var_19, x = var_206_cast_fp16)[name = string("probabilities_5_cast_fp16")];
            bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)];
            bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 12, 128, 32]> values_5_cast_fp16 = transpose(perm = var_7, x = var_200_cast_fp16)[name = string("transpose_49")];
            tensor<fp16, [1, 12, 128, 32]> context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = probabilities_5_cast_fp16, y = values_5_cast_fp16)[name = string("context_5_cast_fp16")];
            tensor<fp16, [1, 128, 12, 32]> var_209_cast_fp16 = transpose(perm = var_7, x = context_5_cast_fp16)[name = string("transpose_48")];
            tensor<fp16, [1, 128, 384]> input_35_cast_fp16 = reshape(shape = var_5, x = var_209_cast_fp16)[name = string("input_35_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_2_attention_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15845952))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15993472))))[name = string("self_model_bert_encoder_layer_2_attention_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_attention_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_attention_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15994304)))];
            fp16 quantize_15_scale_0 = const()[name = string("quantize_15_scale_0"), val = fp16(0x1.a5p-7)];
            uint8 quantize_15_zero_point_0 = const()[name = string("quantize_15_zero_point_0"), val = uint8(123)];
            string quantize_15_output_dtype_0 = const()[name = string("quantize_15_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_15 = quantize(input = input_35_cast_fp16, output_dtype = quantize_15_output_dtype_0, scale = quantize_15_scale_0, zero_point = quantize_15_zero_point_0)[name = string("quantize_15")];
            fp16 dequantize_15_scale_0 = const()[name = string("dequantize_15_scale_0"), val = fp16(0x1.a5p-7)];
            uint8 dequantize_15_zero_point_0 = const()[name = string("dequantize_15_zero_point_0"), val = uint8(123)];
            tensor<fp16, [1, 128, 384]> dequantize_15 = dequantize(input = quantize_15, scale = dequantize_15_scale_0, zero_point = dequantize_15_zero_point_0)[name = string("dequantize_15")];
            tensor<fp16, [1, 128, 384]> linear_15_cast_fp16 = linear(bias = self_model_bert_encoder_layer_2_attention_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_2_attention_output_dense_weight_to_fp16_quantized, x = dequantize_15)[name = string("linear_15_cast_fp16")];
            fp16 quantize_53_scale_0 = const()[name = string("quantize_53_scale_0"), val = fp16(0x1.908p-7)];
            uint8 quantize_53_zero_point_0 = const()[name = string("quantize_53_zero_point_0"), val = uint8(143)];
            string quantize_53_output_dtype_0 = const()[name = string("quantize_53_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_53 = quantize(input = linear_15_cast_fp16, output_dtype = quantize_53_output_dtype_0, scale = quantize_53_scale_0, zero_point = quantize_53_zero_point_0)[name = string("quantize_53")];
            fp16 dequantize_53_scale_0 = const()[name = string("dequantize_53_scale_0"), val = fp16(0x1.908p-7)];
            uint8 dequantize_53_zero_point_0 = const()[name = string("dequantize_53_zero_point_0"), val = uint8(143)];
            tensor<fp16, [1, 128, 384]> dequantize_53__post__dequant = dequantize(input = quantize_53, scale = dequantize_53_scale_0, zero_point = dequantize_53_zero_point_0)[name = string("dequantize_53")];
            tensor<fp16, [1, 128, 384]> input_37_cast_fp16 = add(x = input_33_cast_fp16, y = dequantize_53__post__dequant)[name = string("input_37_cast_fp16")];
            tensor<int32, [1]> input_39_axes_0 = const()[name = string("input_39_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_attention_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_attention_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15995136)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_attention_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_attention_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15995968)))];
            tensor<fp16, [1, 128, 384]> input_39_cast_fp16 = layer_norm(axes = input_39_axes_0, beta = self_model_bert_encoder_layer_2_attention_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_2_attention_output_LayerNorm_weight_to_fp16, x = input_37_cast_fp16)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1536, 384]> self_model_bert_encoder_layer_2_intermediate_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1536, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15996800))), scale = tensor<fp16, [1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16586688))))[name = string("self_model_bert_encoder_layer_2_intermediate_dense_weight_to_fp16_quantized")];
            tensor<fp16, [1536]> self_model_bert_encoder_layer_2_intermediate_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16589824)))];
            fp16 quantize_16_scale_0 = const()[name = string("quantize_16_scale_0"), val = fp16(0x1.528p-3)];
            uint8 quantize_16_zero_point_0 = const()[name = string("quantize_16_zero_point_0"), val = uint8(88)];
            string quantize_16_output_dtype_0 = const()[name = string("quantize_16_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_16 = quantize(input = input_39_cast_fp16, output_dtype = quantize_16_output_dtype_0, scale = quantize_16_scale_0, zero_point = quantize_16_zero_point_0)[name = string("quantize_16")];
            fp16 dequantize_16_scale_0 = const()[name = string("dequantize_16_scale_0"), val = fp16(0x1.528p-3)];
            uint8 dequantize_16_zero_point_0 = const()[name = string("dequantize_16_zero_point_0"), val = uint8(88)];
            tensor<fp16, [1, 128, 384]> dequantize_16 = dequantize(input = quantize_16, scale = dequantize_16_scale_0, zero_point = dequantize_16_zero_point_0)[name = string("dequantize_16")];
            tensor<fp16, [1, 128, 1536]> linear_16_cast_fp16 = linear(bias = self_model_bert_encoder_layer_2_intermediate_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_2_intermediate_dense_weight_to_fp16_quantized, x = dequantize_16)[name = string("linear_16_cast_fp16")];
            fp16 quantize_54_scale_0 = const()[name = string("quantize_54_scale_0"), val = fp16(0x1.bbcp-4)];
            uint8 quantize_54_zero_point_0 = const()[name = string("quantize_54_zero_point_0"), val = uint8(94)];
            string quantize_54_output_dtype_0 = const()[name = string("quantize_54_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_54 = quantize(input = linear_16_cast_fp16, output_dtype = quantize_54_output_dtype_0, scale = quantize_54_scale_0, zero_point = quantize_54_zero_point_0)[name = string("quantize_54")];
            fp16 dequantize_54_scale_0 = const()[name = string("dequantize_54_scale_0"), val = fp16(0x1.bbcp-4)];
            uint8 dequantize_54_zero_point_0 = const()[name = string("dequantize_54_zero_point_0"), val = uint8(94)];
            tensor<fp16, [1, 128, 1536]> dequantize_54__post__dequant = dequantize(input = quantize_54, scale = dequantize_54_scale_0, zero_point = dequantize_54_zero_point_0)[name = string("dequantize_54")];
            string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 1536]> input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = dequantize_54__post__dequant)[name = string("input_41_cast_fp16")];
            tensor<fp16, [384, 1536]> self_model_bert_encoder_layer_2_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16592960))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17182848))))[name = string("self_model_bert_encoder_layer_2_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17183680)))];
            fp16 quantize_17_scale_0 = const()[name = string("quantize_17_scale_0"), val = fp16(0x1.1b8p-4)];
            uint8 quantize_17_zero_point_0 = const()[name = string("quantize_17_zero_point_0"), val = uint8(2)];
            string quantize_17_output_dtype_0 = const()[name = string("quantize_17_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_17 = quantize(input = input_41_cast_fp16, output_dtype = quantize_17_output_dtype_0, scale = quantize_17_scale_0, zero_point = quantize_17_zero_point_0)[name = string("quantize_17")];
            fp16 dequantize_17_scale_0 = const()[name = string("dequantize_17_scale_0"), val = fp16(0x1.1b8p-4)];
            uint8 dequantize_17_zero_point_0 = const()[name = string("dequantize_17_zero_point_0"), val = uint8(2)];
            tensor<fp16, [1, 128, 1536]> dequantize_17 = dequantize(input = quantize_17, scale = dequantize_17_scale_0, zero_point = dequantize_17_zero_point_0)[name = string("dequantize_17")];
            tensor<fp16, [1, 128, 384]> linear_17_cast_fp16 = linear(bias = self_model_bert_encoder_layer_2_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_2_output_dense_weight_to_fp16_quantized, x = dequantize_17)[name = string("linear_17_cast_fp16")];
            fp16 quantize_55_scale_0 = const()[name = string("quantize_55_scale_0"), val = fp16(0x1.4e4p-1)];
            uint8 quantize_55_zero_point_0 = const()[name = string("quantize_55_zero_point_0"), val = uint8(5)];
            string quantize_55_output_dtype_0 = const()[name = string("quantize_55_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_55 = quantize(input = linear_17_cast_fp16, output_dtype = quantize_55_output_dtype_0, scale = quantize_55_scale_0, zero_point = quantize_55_zero_point_0)[name = string("quantize_55")];
            fp16 dequantize_55_scale_0 = const()[name = string("dequantize_55_scale_0"), val = fp16(0x1.4e4p-1)];
            uint8 dequantize_55_zero_point_0 = const()[name = string("dequantize_55_zero_point_0"), val = uint8(5)];
            tensor<fp16, [1, 128, 384]> dequantize_55__post__dequant = dequantize(input = quantize_55, scale = dequantize_55_scale_0, zero_point = dequantize_55_zero_point_0)[name = string("dequantize_55")];
            tensor<fp16, [1, 128, 384]> input_43_cast_fp16 = add(x = input_39_cast_fp16, y = dequantize_55__post__dequant)[name = string("input_43_cast_fp16")];
            tensor<int32, [1]> input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17184512)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_2_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_2_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17185344)))];
            tensor<fp16, [1, 128, 384]> input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = self_model_bert_encoder_layer_2_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_2_output_LayerNorm_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_3_attention_self_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17186176))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17333696))))[name = string("self_model_bert_encoder_layer_3_attention_self_query_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_attention_self_query_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_attention_self_query_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17334528)))];
            fp16 quantize_18_scale_0 = const()[name = string("quantize_18_scale_0"), val = fp16(0x1.06cp-4)];
            uint8 quantize_18_zero_point_0 = const()[name = string("quantize_18_zero_point_0"), val = uint8(105)];
            string quantize_18_output_dtype_0 = const()[name = string("quantize_18_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_18 = quantize(input = input_45_cast_fp16, output_dtype = quantize_18_output_dtype_0, scale = quantize_18_scale_0, zero_point = quantize_18_zero_point_0)[name = string("quantize_18")];
            fp16 dequantize_18_scale_0 = const()[name = string("dequantize_18_scale_0"), val = fp16(0x1.06cp-4)];
            uint8 dequantize_18_zero_point_0 = const()[name = string("dequantize_18_zero_point_0"), val = uint8(105)];
            tensor<fp16, [1, 128, 384]> dequantize_18 = dequantize(input = quantize_18, scale = dequantize_18_scale_0, zero_point = dequantize_18_zero_point_0)[name = string("dequantize_18")];
            tensor<fp16, [1, 128, 384]> linear_18_cast_fp16 = linear(bias = self_model_bert_encoder_layer_3_attention_self_query_bias_to_fp16, weight = self_model_bert_encoder_layer_3_attention_self_query_weight_to_fp16_quantized, x = dequantize_18)[name = string("linear_18_cast_fp16")];
            fp16 quantize_56_scale_0 = const()[name = string("quantize_56_scale_0"), val = fp16(0x1.0a8p-5)];
            uint8 quantize_56_zero_point_0 = const()[name = string("quantize_56_zero_point_0"), val = uint8(134)];
            string quantize_56_output_dtype_0 = const()[name = string("quantize_56_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_56 = quantize(input = linear_18_cast_fp16, output_dtype = quantize_56_output_dtype_0, scale = quantize_56_scale_0, zero_point = quantize_56_zero_point_0)[name = string("quantize_56")];
            fp16 dequantize_56_scale_0 = const()[name = string("dequantize_56_scale_0"), val = fp16(0x1.0a8p-5)];
            uint8 dequantize_56_zero_point_0 = const()[name = string("dequantize_56_zero_point_0"), val = uint8(134)];
            tensor<fp16, [1, 128, 384]> dequantize_56__post__dequant = dequantize(input = quantize_56, scale = dequantize_56_scale_0, zero_point = dequantize_56_zero_point_0)[name = string("dequantize_56")];
            tensor<fp16, [1, 128, 12, 32]> var_220_cast_fp16 = reshape(shape = var_8, x = dequantize_56__post__dequant)[name = string("op_220_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_3_attention_self_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17335360))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17482880))))[name = string("self_model_bert_encoder_layer_3_attention_self_key_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_attention_self_key_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_attention_self_key_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17483712)))];
            tensor<fp16, [1, 128, 384]> linear_19_cast_fp16 = linear(bias = self_model_bert_encoder_layer_3_attention_self_key_bias_to_fp16, weight = self_model_bert_encoder_layer_3_attention_self_key_weight_to_fp16_quantized, x = dequantize_18)[name = string("linear_19_cast_fp16")];
            fp16 quantize_57_scale_0 = const()[name = string("quantize_57_scale_0"), val = fp16(0x1.13p-5)];
            uint8 quantize_57_zero_point_0 = const()[name = string("quantize_57_zero_point_0"), val = uint8(134)];
            string quantize_57_output_dtype_0 = const()[name = string("quantize_57_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_57 = quantize(input = linear_19_cast_fp16, output_dtype = quantize_57_output_dtype_0, scale = quantize_57_scale_0, zero_point = quantize_57_zero_point_0)[name = string("quantize_57")];
            fp16 dequantize_57_scale_0 = const()[name = string("dequantize_57_scale_0"), val = fp16(0x1.13p-5)];
            uint8 dequantize_57_zero_point_0 = const()[name = string("dequantize_57_zero_point_0"), val = uint8(134)];
            tensor<fp16, [1, 128, 384]> dequantize_57__post__dequant = dequantize(input = quantize_57, scale = dequantize_57_scale_0, zero_point = dequantize_57_zero_point_0)[name = string("dequantize_57")];
            tensor<fp16, [1, 128, 12, 32]> var_223_cast_fp16 = reshape(shape = var_8, x = dequantize_57__post__dequant)[name = string("op_223_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_3_attention_self_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17484544))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17632064))))[name = string("self_model_bert_encoder_layer_3_attention_self_value_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_attention_self_value_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_attention_self_value_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17632896)))];
            tensor<fp16, [1, 128, 384]> linear_20_cast_fp16 = linear(bias = self_model_bert_encoder_layer_3_attention_self_value_bias_to_fp16, weight = self_model_bert_encoder_layer_3_attention_self_value_weight_to_fp16_quantized, x = dequantize_18)[name = string("linear_20_cast_fp16")];
            fp16 quantize_58_scale_0 = const()[name = string("quantize_58_scale_0"), val = fp16(0x1.a84p-6)];
            uint8 quantize_58_zero_point_0 = const()[name = string("quantize_58_zero_point_0"), val = uint8(138)];
            string quantize_58_output_dtype_0 = const()[name = string("quantize_58_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_58 = quantize(input = linear_20_cast_fp16, output_dtype = quantize_58_output_dtype_0, scale = quantize_58_scale_0, zero_point = quantize_58_zero_point_0)[name = string("quantize_58")];
            fp16 dequantize_58_scale_0 = const()[name = string("dequantize_58_scale_0"), val = fp16(0x1.a84p-6)];
            uint8 dequantize_58_zero_point_0 = const()[name = string("dequantize_58_zero_point_0"), val = uint8(138)];
            tensor<fp16, [1, 128, 384]> dequantize_58__post__dequant = dequantize(input = quantize_58, scale = dequantize_58_scale_0, zero_point = dequantize_58_zero_point_0)[name = string("dequantize_58")];
            tensor<fp16, [1, 128, 12, 32]> var_226_cast_fp16 = reshape(shape = var_8, x = dequantize_58__post__dequant)[name = string("op_226_cast_fp16")];
            bool var_229_transpose_x_0 = const()[name = string("op_229_transpose_x_0"), val = bool(false)];
            bool var_229_transpose_y_0 = const()[name = string("op_229_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_30_perm_0 = const()[name = string("transpose_30_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_31_perm_0 = const()[name = string("transpose_31_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 12, 32, 128]> transpose_31 = transpose(perm = transpose_31_perm_0, x = var_223_cast_fp16)[name = string("transpose_46")];
            tensor<fp16, [1, 12, 128, 32]> transpose_30 = transpose(perm = transpose_30_perm_0, x = var_220_cast_fp16)[name = string("transpose_47")];
            tensor<fp16, [1, 12, 128, 128]> var_229_cast_fp16 = matmul(transpose_x = var_229_transpose_x_0, transpose_y = var_229_transpose_y_0, x = transpose_30, y = transpose_31)[name = string("op_229_cast_fp16")];
            fp16 _inversed_scores_7_y_0_to_fp16 = const()[name = string("_inversed_scores_7_y_0_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 12, 128, 128]> _inversed_scores_7_cast_fp16 = mul(x = var_229_cast_fp16, y = _inversed_scores_7_y_0_to_fp16)[name = string("_inversed_scores_7_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> var_232_cast_fp16 = add(x = _inversed_scores_7_cast_fp16, y = additive_mask_cast_fp16)[name = string("op_232_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> probabilities_7_cast_fp16 = softmax(axis = var_19, x = var_232_cast_fp16)[name = string("probabilities_7_cast_fp16")];
            bool context_7_transpose_x_0 = const()[name = string("context_7_transpose_x_0"), val = bool(false)];
            bool context_7_transpose_y_0 = const()[name = string("context_7_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 12, 128, 32]> values_7_cast_fp16 = transpose(perm = var_7, x = var_226_cast_fp16)[name = string("transpose_45")];
            tensor<fp16, [1, 12, 128, 32]> context_7_cast_fp16 = matmul(transpose_x = context_7_transpose_x_0, transpose_y = context_7_transpose_y_0, x = probabilities_7_cast_fp16, y = values_7_cast_fp16)[name = string("context_7_cast_fp16")];
            tensor<fp16, [1, 128, 12, 32]> var_235_cast_fp16 = transpose(perm = var_7, x = context_7_cast_fp16)[name = string("transpose_44")];
            tensor<fp16, [1, 128, 384]> input_47_cast_fp16 = reshape(shape = var_5, x = var_235_cast_fp16)[name = string("input_47_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_3_attention_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17633728))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17781248))))[name = string("self_model_bert_encoder_layer_3_attention_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_attention_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_attention_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17782080)))];
            fp16 quantize_21_scale_0 = const()[name = string("quantize_21_scale_0"), val = fp16(0x1.4ecp-6)];
            uint8 quantize_21_zero_point_0 = const()[name = string("quantize_21_zero_point_0"), val = uint8(161)];
            string quantize_21_output_dtype_0 = const()[name = string("quantize_21_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_21 = quantize(input = input_47_cast_fp16, output_dtype = quantize_21_output_dtype_0, scale = quantize_21_scale_0, zero_point = quantize_21_zero_point_0)[name = string("quantize_21")];
            fp16 dequantize_21_scale_0 = const()[name = string("dequantize_21_scale_0"), val = fp16(0x1.4ecp-6)];
            uint8 dequantize_21_zero_point_0 = const()[name = string("dequantize_21_zero_point_0"), val = uint8(161)];
            tensor<fp16, [1, 128, 384]> dequantize_21 = dequantize(input = quantize_21, scale = dequantize_21_scale_0, zero_point = dequantize_21_zero_point_0)[name = string("dequantize_21")];
            tensor<fp16, [1, 128, 384]> linear_21_cast_fp16 = linear(bias = self_model_bert_encoder_layer_3_attention_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_3_attention_output_dense_weight_to_fp16_quantized, x = dequantize_21)[name = string("linear_21_cast_fp16")];
            fp16 quantize_59_scale_0 = const()[name = string("quantize_59_scale_0"), val = fp16(0x1.27p-6)];
            uint8 quantize_59_zero_point_0 = const()[name = string("quantize_59_zero_point_0"), val = uint8(118)];
            string quantize_59_output_dtype_0 = const()[name = string("quantize_59_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_59 = quantize(input = linear_21_cast_fp16, output_dtype = quantize_59_output_dtype_0, scale = quantize_59_scale_0, zero_point = quantize_59_zero_point_0)[name = string("quantize_59")];
            fp16 dequantize_59_scale_0 = const()[name = string("dequantize_59_scale_0"), val = fp16(0x1.27p-6)];
            uint8 dequantize_59_zero_point_0 = const()[name = string("dequantize_59_zero_point_0"), val = uint8(118)];
            tensor<fp16, [1, 128, 384]> dequantize_59__post__dequant = dequantize(input = quantize_59, scale = dequantize_59_scale_0, zero_point = dequantize_59_zero_point_0)[name = string("dequantize_59")];
            tensor<fp16, [1, 128, 384]> input_49_cast_fp16 = add(x = input_45_cast_fp16, y = dequantize_59__post__dequant)[name = string("input_49_cast_fp16")];
            tensor<int32, [1]> input_51_axes_0 = const()[name = string("input_51_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_attention_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_attention_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17782912)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_attention_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_attention_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17783744)))];
            tensor<fp16, [1, 128, 384]> input_51_cast_fp16 = layer_norm(axes = input_51_axes_0, beta = self_model_bert_encoder_layer_3_attention_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_3_attention_output_LayerNorm_weight_to_fp16, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1536, 384]> self_model_bert_encoder_layer_3_intermediate_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1536, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17784576))), scale = tensor<fp16, [1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18374464))))[name = string("self_model_bert_encoder_layer_3_intermediate_dense_weight_to_fp16_quantized")];
            tensor<fp16, [1536]> self_model_bert_encoder_layer_3_intermediate_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18377600)))];
            fp16 quantize_22_scale_0 = const()[name = string("quantize_22_scale_0"), val = fp16(0x1.30cp-3)];
            uint8 quantize_22_zero_point_0 = const()[name = string("quantize_22_zero_point_0"), val = uint8(64)];
            string quantize_22_output_dtype_0 = const()[name = string("quantize_22_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_22 = quantize(input = input_51_cast_fp16, output_dtype = quantize_22_output_dtype_0, scale = quantize_22_scale_0, zero_point = quantize_22_zero_point_0)[name = string("quantize_22")];
            fp16 dequantize_22_scale_0 = const()[name = string("dequantize_22_scale_0"), val = fp16(0x1.30cp-3)];
            uint8 dequantize_22_zero_point_0 = const()[name = string("dequantize_22_zero_point_0"), val = uint8(64)];
            tensor<fp16, [1, 128, 384]> dequantize_22 = dequantize(input = quantize_22, scale = dequantize_22_scale_0, zero_point = dequantize_22_zero_point_0)[name = string("dequantize_22")];
            tensor<fp16, [1, 128, 1536]> linear_22_cast_fp16 = linear(bias = self_model_bert_encoder_layer_3_intermediate_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_3_intermediate_dense_weight_to_fp16_quantized, x = dequantize_22)[name = string("linear_22_cast_fp16")];
            fp16 quantize_60_scale_0 = const()[name = string("quantize_60_scale_0"), val = fp16(0x1.7f8p-4)];
            uint8 quantize_60_zero_point_0 = const()[name = string("quantize_60_zero_point_0"), val = uint8(112)];
            string quantize_60_output_dtype_0 = const()[name = string("quantize_60_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_60 = quantize(input = linear_22_cast_fp16, output_dtype = quantize_60_output_dtype_0, scale = quantize_60_scale_0, zero_point = quantize_60_zero_point_0)[name = string("quantize_60")];
            fp16 dequantize_60_scale_0 = const()[name = string("dequantize_60_scale_0"), val = fp16(0x1.7f8p-4)];
            uint8 dequantize_60_zero_point_0 = const()[name = string("dequantize_60_zero_point_0"), val = uint8(112)];
            tensor<fp16, [1, 128, 1536]> dequantize_60__post__dequant = dequantize(input = quantize_60, scale = dequantize_60_scale_0, zero_point = dequantize_60_zero_point_0)[name = string("dequantize_60")];
            string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 1536]> input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = dequantize_60__post__dequant)[name = string("input_53_cast_fp16")];
            tensor<fp16, [384, 1536]> self_model_bert_encoder_layer_3_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18380736))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18970624))))[name = string("self_model_bert_encoder_layer_3_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18971456)))];
            fp16 quantize_23_scale_0 = const()[name = string("quantize_23_scale_0"), val = fp16(0x1.b34p-5)];
            uint8 quantize_23_zero_point_0 = const()[name = string("quantize_23_zero_point_0"), val = uint8(3)];
            string quantize_23_output_dtype_0 = const()[name = string("quantize_23_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_23 = quantize(input = input_53_cast_fp16, output_dtype = quantize_23_output_dtype_0, scale = quantize_23_scale_0, zero_point = quantize_23_zero_point_0)[name = string("quantize_23")];
            fp16 dequantize_23_scale_0 = const()[name = string("dequantize_23_scale_0"), val = fp16(0x1.b34p-5)];
            uint8 dequantize_23_zero_point_0 = const()[name = string("dequantize_23_zero_point_0"), val = uint8(3)];
            tensor<fp16, [1, 128, 1536]> dequantize_23 = dequantize(input = quantize_23, scale = dequantize_23_scale_0, zero_point = dequantize_23_zero_point_0)[name = string("dequantize_23")];
            tensor<fp16, [1, 128, 384]> linear_23_cast_fp16 = linear(bias = self_model_bert_encoder_layer_3_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_3_output_dense_weight_to_fp16_quantized, x = dequantize_23)[name = string("linear_23_cast_fp16")];
            fp16 quantize_61_scale_0 = const()[name = string("quantize_61_scale_0"), val = fp16(0x1.c64p-3)];
            uint8 quantize_61_zero_point_0 = const()[name = string("quantize_61_zero_point_0"), val = uint8(45)];
            string quantize_61_output_dtype_0 = const()[name = string("quantize_61_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_61 = quantize(input = linear_23_cast_fp16, output_dtype = quantize_61_output_dtype_0, scale = quantize_61_scale_0, zero_point = quantize_61_zero_point_0)[name = string("quantize_61")];
            fp16 dequantize_61_scale_0 = const()[name = string("dequantize_61_scale_0"), val = fp16(0x1.c64p-3)];
            uint8 dequantize_61_zero_point_0 = const()[name = string("dequantize_61_zero_point_0"), val = uint8(45)];
            tensor<fp16, [1, 128, 384]> dequantize_61__post__dequant = dequantize(input = quantize_61, scale = dequantize_61_scale_0, zero_point = dequantize_61_zero_point_0)[name = string("dequantize_61")];
            tensor<fp16, [1, 128, 384]> input_55_cast_fp16 = add(x = input_51_cast_fp16, y = dequantize_61__post__dequant)[name = string("input_55_cast_fp16")];
            tensor<int32, [1]> input_57_axes_0 = const()[name = string("input_57_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18972288)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_3_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_3_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18973120)))];
            tensor<fp16, [1, 128, 384]> input_57_cast_fp16 = layer_norm(axes = input_57_axes_0, beta = self_model_bert_encoder_layer_3_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_3_output_LayerNorm_weight_to_fp16, x = input_55_cast_fp16)[name = string("input_57_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_4_attention_self_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18973952))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19121472))))[name = string("self_model_bert_encoder_layer_4_attention_self_query_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_attention_self_query_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_attention_self_query_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19122304)))];
            fp16 quantize_24_scale_0 = const()[name = string("quantize_24_scale_0"), val = fp16(0x1.e3p-5)];
            uint8 quantize_24_zero_point_0 = const()[name = string("quantize_24_zero_point_0"), val = uint8(79)];
            string quantize_24_output_dtype_0 = const()[name = string("quantize_24_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_24 = quantize(input = input_57_cast_fp16, output_dtype = quantize_24_output_dtype_0, scale = quantize_24_scale_0, zero_point = quantize_24_zero_point_0)[name = string("quantize_24")];
            fp16 dequantize_24_scale_0 = const()[name = string("dequantize_24_scale_0"), val = fp16(0x1.e3p-5)];
            uint8 dequantize_24_zero_point_0 = const()[name = string("dequantize_24_zero_point_0"), val = uint8(79)];
            tensor<fp16, [1, 128, 384]> dequantize_24 = dequantize(input = quantize_24, scale = dequantize_24_scale_0, zero_point = dequantize_24_zero_point_0)[name = string("dequantize_24")];
            tensor<fp16, [1, 128, 384]> linear_24_cast_fp16 = linear(bias = self_model_bert_encoder_layer_4_attention_self_query_bias_to_fp16, weight = self_model_bert_encoder_layer_4_attention_self_query_weight_to_fp16_quantized, x = dequantize_24)[name = string("linear_24_cast_fp16")];
            fp16 quantize_62_scale_0 = const()[name = string("quantize_62_scale_0"), val = fp16(0x1.238p-5)];
            uint8 quantize_62_zero_point_0 = const()[name = string("quantize_62_zero_point_0"), val = uint8(118)];
            string quantize_62_output_dtype_0 = const()[name = string("quantize_62_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_62 = quantize(input = linear_24_cast_fp16, output_dtype = quantize_62_output_dtype_0, scale = quantize_62_scale_0, zero_point = quantize_62_zero_point_0)[name = string("quantize_62")];
            fp16 dequantize_62_scale_0 = const()[name = string("dequantize_62_scale_0"), val = fp16(0x1.238p-5)];
            uint8 dequantize_62_zero_point_0 = const()[name = string("dequantize_62_zero_point_0"), val = uint8(118)];
            tensor<fp16, [1, 128, 384]> dequantize_62__post__dequant = dequantize(input = quantize_62, scale = dequantize_62_scale_0, zero_point = dequantize_62_zero_point_0)[name = string("dequantize_62")];
            tensor<fp16, [1, 128, 12, 32]> var_246_cast_fp16 = reshape(shape = var_8, x = dequantize_62__post__dequant)[name = string("op_246_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_4_attention_self_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19123136))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19270656))))[name = string("self_model_bert_encoder_layer_4_attention_self_key_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_attention_self_key_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_attention_self_key_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19271488)))];
            tensor<fp16, [1, 128, 384]> linear_25_cast_fp16 = linear(bias = self_model_bert_encoder_layer_4_attention_self_key_bias_to_fp16, weight = self_model_bert_encoder_layer_4_attention_self_key_weight_to_fp16_quantized, x = dequantize_24)[name = string("linear_25_cast_fp16")];
            fp16 quantize_63_scale_0 = const()[name = string("quantize_63_scale_0"), val = fp16(0x1.0e8p-5)];
            uint8 quantize_63_zero_point_0 = const()[name = string("quantize_63_zero_point_0"), val = uint8(117)];
            string quantize_63_output_dtype_0 = const()[name = string("quantize_63_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_63 = quantize(input = linear_25_cast_fp16, output_dtype = quantize_63_output_dtype_0, scale = quantize_63_scale_0, zero_point = quantize_63_zero_point_0)[name = string("quantize_63")];
            fp16 dequantize_63_scale_0 = const()[name = string("dequantize_63_scale_0"), val = fp16(0x1.0e8p-5)];
            uint8 dequantize_63_zero_point_0 = const()[name = string("dequantize_63_zero_point_0"), val = uint8(117)];
            tensor<fp16, [1, 128, 384]> dequantize_63__post__dequant = dequantize(input = quantize_63, scale = dequantize_63_scale_0, zero_point = dequantize_63_zero_point_0)[name = string("dequantize_63")];
            tensor<fp16, [1, 128, 12, 32]> var_249_cast_fp16 = reshape(shape = var_8, x = dequantize_63__post__dequant)[name = string("op_249_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_4_attention_self_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19272320))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19419840))))[name = string("self_model_bert_encoder_layer_4_attention_self_value_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_attention_self_value_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_attention_self_value_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19420672)))];
            tensor<fp16, [1, 128, 384]> linear_26_cast_fp16 = linear(bias = self_model_bert_encoder_layer_4_attention_self_value_bias_to_fp16, weight = self_model_bert_encoder_layer_4_attention_self_value_weight_to_fp16_quantized, x = dequantize_24)[name = string("linear_26_cast_fp16")];
            fp16 quantize_64_scale_0 = const()[name = string("quantize_64_scale_0"), val = fp16(0x1.d38p-6)];
            uint8 quantize_64_zero_point_0 = const()[name = string("quantize_64_zero_point_0"), val = uint8(136)];
            string quantize_64_output_dtype_0 = const()[name = string("quantize_64_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_64 = quantize(input = linear_26_cast_fp16, output_dtype = quantize_64_output_dtype_0, scale = quantize_64_scale_0, zero_point = quantize_64_zero_point_0)[name = string("quantize_64")];
            fp16 dequantize_64_scale_0 = const()[name = string("dequantize_64_scale_0"), val = fp16(0x1.d38p-6)];
            uint8 dequantize_64_zero_point_0 = const()[name = string("dequantize_64_zero_point_0"), val = uint8(136)];
            tensor<fp16, [1, 128, 384]> dequantize_64__post__dequant = dequantize(input = quantize_64, scale = dequantize_64_scale_0, zero_point = dequantize_64_zero_point_0)[name = string("dequantize_64")];
            tensor<fp16, [1, 128, 12, 32]> var_252_cast_fp16 = reshape(shape = var_8, x = dequantize_64__post__dequant)[name = string("op_252_cast_fp16")];
            bool var_255_transpose_x_0 = const()[name = string("op_255_transpose_x_0"), val = bool(false)];
            bool var_255_transpose_y_0 = const()[name = string("op_255_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 12, 32, 128]> transpose_33 = transpose(perm = transpose_33_perm_0, x = var_249_cast_fp16)[name = string("transpose_42")];
            tensor<fp16, [1, 12, 128, 32]> transpose_32 = transpose(perm = transpose_32_perm_0, x = var_246_cast_fp16)[name = string("transpose_43")];
            tensor<fp16, [1, 12, 128, 128]> var_255_cast_fp16 = matmul(transpose_x = var_255_transpose_x_0, transpose_y = var_255_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_255_cast_fp16")];
            fp16 _inversed_scores_9_y_0_to_fp16 = const()[name = string("_inversed_scores_9_y_0_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 12, 128, 128]> _inversed_scores_9_cast_fp16 = mul(x = var_255_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> var_258_cast_fp16 = add(x = _inversed_scores_9_cast_fp16, y = additive_mask_cast_fp16)[name = string("op_258_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> probabilities_9_cast_fp16 = softmax(axis = var_19, x = var_258_cast_fp16)[name = string("probabilities_9_cast_fp16")];
            bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)];
            bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 12, 128, 32]> values_9_cast_fp16 = transpose(perm = var_7, x = var_252_cast_fp16)[name = string("transpose_41")];
            tensor<fp16, [1, 12, 128, 32]> context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = probabilities_9_cast_fp16, y = values_9_cast_fp16)[name = string("context_9_cast_fp16")];
            tensor<fp16, [1, 128, 12, 32]> var_261_cast_fp16 = transpose(perm = var_7, x = context_9_cast_fp16)[name = string("transpose_40")];
            tensor<fp16, [1, 128, 384]> input_59_cast_fp16 = reshape(shape = var_5, x = var_261_cast_fp16)[name = string("input_59_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_4_attention_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19421504))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19569024))))[name = string("self_model_bert_encoder_layer_4_attention_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_attention_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_attention_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19569856)))];
            fp16 quantize_27_scale_0 = const()[name = string("quantize_27_scale_0"), val = fp16(0x1.984p-6)];
            uint8 quantize_27_zero_point_0 = const()[name = string("quantize_27_zero_point_0"), val = uint8(135)];
            string quantize_27_output_dtype_0 = const()[name = string("quantize_27_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_27 = quantize(input = input_59_cast_fp16, output_dtype = quantize_27_output_dtype_0, scale = quantize_27_scale_0, zero_point = quantize_27_zero_point_0)[name = string("quantize_27")];
            fp16 dequantize_27_scale_0 = const()[name = string("dequantize_27_scale_0"), val = fp16(0x1.984p-6)];
            uint8 dequantize_27_zero_point_0 = const()[name = string("dequantize_27_zero_point_0"), val = uint8(135)];
            tensor<fp16, [1, 128, 384]> dequantize_27 = dequantize(input = quantize_27, scale = dequantize_27_scale_0, zero_point = dequantize_27_zero_point_0)[name = string("dequantize_27")];
            tensor<fp16, [1, 128, 384]> linear_27_cast_fp16 = linear(bias = self_model_bert_encoder_layer_4_attention_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_4_attention_output_dense_weight_to_fp16_quantized, x = dequantize_27)[name = string("linear_27_cast_fp16")];
            fp16 quantize_65_scale_0 = const()[name = string("quantize_65_scale_0"), val = fp16(0x1.838p-6)];
            uint8 quantize_65_zero_point_0 = const()[name = string("quantize_65_zero_point_0"), val = uint8(140)];
            string quantize_65_output_dtype_0 = const()[name = string("quantize_65_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_65 = quantize(input = linear_27_cast_fp16, output_dtype = quantize_65_output_dtype_0, scale = quantize_65_scale_0, zero_point = quantize_65_zero_point_0)[name = string("quantize_65")];
            fp16 dequantize_65_scale_0 = const()[name = string("dequantize_65_scale_0"), val = fp16(0x1.838p-6)];
            uint8 dequantize_65_zero_point_0 = const()[name = string("dequantize_65_zero_point_0"), val = uint8(140)];
            tensor<fp16, [1, 128, 384]> dequantize_65__post__dequant = dequantize(input = quantize_65, scale = dequantize_65_scale_0, zero_point = dequantize_65_zero_point_0)[name = string("dequantize_65")];
            tensor<fp16, [1, 128, 384]> input_61_cast_fp16 = add(x = input_57_cast_fp16, y = dequantize_65__post__dequant)[name = string("input_61_cast_fp16")];
            tensor<int32, [1]> input_63_axes_0 = const()[name = string("input_63_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_attention_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_attention_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19570688)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_attention_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_attention_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19571520)))];
            tensor<fp16, [1, 128, 384]> input_63_cast_fp16 = layer_norm(axes = input_63_axes_0, beta = self_model_bert_encoder_layer_4_attention_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_4_attention_output_LayerNorm_weight_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")];
            tensor<fp16, [1536, 384]> self_model_bert_encoder_layer_4_intermediate_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1536, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19572352))), scale = tensor<fp16, [1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20162240))))[name = string("self_model_bert_encoder_layer_4_intermediate_dense_weight_to_fp16_quantized")];
            tensor<fp16, [1536]> self_model_bert_encoder_layer_4_intermediate_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20165376)))];
            fp16 quantize_28_scale_0 = const()[name = string("quantize_28_scale_0"), val = fp16(0x1.23p-4)];
            uint8 quantize_28_zero_point_0 = const()[name = string("quantize_28_zero_point_0"), val = uint8(56)];
            string quantize_28_output_dtype_0 = const()[name = string("quantize_28_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_28 = quantize(input = input_63_cast_fp16, output_dtype = quantize_28_output_dtype_0, scale = quantize_28_scale_0, zero_point = quantize_28_zero_point_0)[name = string("quantize_28")];
            fp16 dequantize_28_scale_0 = const()[name = string("dequantize_28_scale_0"), val = fp16(0x1.23p-4)];
            uint8 dequantize_28_zero_point_0 = const()[name = string("dequantize_28_zero_point_0"), val = uint8(56)];
            tensor<fp16, [1, 128, 384]> dequantize_28 = dequantize(input = quantize_28, scale = dequantize_28_scale_0, zero_point = dequantize_28_zero_point_0)[name = string("dequantize_28")];
            tensor<fp16, [1, 128, 1536]> linear_28_cast_fp16 = linear(bias = self_model_bert_encoder_layer_4_intermediate_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_4_intermediate_dense_weight_to_fp16_quantized, x = dequantize_28)[name = string("linear_28_cast_fp16")];
            fp16 quantize_66_scale_0 = const()[name = string("quantize_66_scale_0"), val = fp16(0x1.3f8p-5)];
            uint8 quantize_66_zero_point_0 = const()[name = string("quantize_66_zero_point_0"), val = uint8(159)];
            string quantize_66_output_dtype_0 = const()[name = string("quantize_66_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_66 = quantize(input = linear_28_cast_fp16, output_dtype = quantize_66_output_dtype_0, scale = quantize_66_scale_0, zero_point = quantize_66_zero_point_0)[name = string("quantize_66")];
            fp16 dequantize_66_scale_0 = const()[name = string("dequantize_66_scale_0"), val = fp16(0x1.3f8p-5)];
            uint8 dequantize_66_zero_point_0 = const()[name = string("dequantize_66_zero_point_0"), val = uint8(159)];
            tensor<fp16, [1, 128, 1536]> dequantize_66__post__dequant = dequantize(input = quantize_66, scale = dequantize_66_scale_0, zero_point = dequantize_66_zero_point_0)[name = string("dequantize_66")];
            string input_65_mode_0 = const()[name = string("input_65_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 1536]> input_65_cast_fp16 = gelu(mode = input_65_mode_0, x = dequantize_66__post__dequant)[name = string("input_65_cast_fp16")];
            tensor<fp16, [384, 1536]> self_model_bert_encoder_layer_4_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20168512))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20758400))))[name = string("self_model_bert_encoder_layer_4_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20759232)))];
            fp16 quantize_29_scale_0 = const()[name = string("quantize_29_scale_0"), val = fp16(0x1.f6cp-7)];
            uint8 quantize_29_zero_point_0 = const()[name = string("quantize_29_zero_point_0"), val = uint8(11)];
            string quantize_29_output_dtype_0 = const()[name = string("quantize_29_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_29 = quantize(input = input_65_cast_fp16, output_dtype = quantize_29_output_dtype_0, scale = quantize_29_scale_0, zero_point = quantize_29_zero_point_0)[name = string("quantize_29")];
            fp16 dequantize_29_scale_0 = const()[name = string("dequantize_29_scale_0"), val = fp16(0x1.f6cp-7)];
            uint8 dequantize_29_zero_point_0 = const()[name = string("dequantize_29_zero_point_0"), val = uint8(11)];
            tensor<fp16, [1, 128, 1536]> dequantize_29 = dequantize(input = quantize_29, scale = dequantize_29_scale_0, zero_point = dequantize_29_zero_point_0)[name = string("dequantize_29")];
            tensor<fp16, [1, 128, 384]> linear_29_cast_fp16 = linear(bias = self_model_bert_encoder_layer_4_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_4_output_dense_weight_to_fp16_quantized, x = dequantize_29)[name = string("linear_29_cast_fp16")];
            fp16 quantize_67_scale_0 = const()[name = string("quantize_67_scale_0"), val = fp16(0x1.c4cp-5)];
            uint8 quantize_67_zero_point_0 = const()[name = string("quantize_67_zero_point_0"), val = uint8(181)];
            string quantize_67_output_dtype_0 = const()[name = string("quantize_67_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_67 = quantize(input = linear_29_cast_fp16, output_dtype = quantize_67_output_dtype_0, scale = quantize_67_scale_0, zero_point = quantize_67_zero_point_0)[name = string("quantize_67")];
            fp16 dequantize_67_scale_0 = const()[name = string("dequantize_67_scale_0"), val = fp16(0x1.c4cp-5)];
            uint8 dequantize_67_zero_point_0 = const()[name = string("dequantize_67_zero_point_0"), val = uint8(181)];
            tensor<fp16, [1, 128, 384]> dequantize_67__post__dequant = dequantize(input = quantize_67, scale = dequantize_67_scale_0, zero_point = dequantize_67_zero_point_0)[name = string("dequantize_67")];
            tensor<fp16, [1, 128, 384]> input_67_cast_fp16 = add(x = input_63_cast_fp16, y = dequantize_67__post__dequant)[name = string("input_67_cast_fp16")];
            tensor<int32, [1]> input_69_axes_0 = const()[name = string("input_69_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20760064)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_4_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_4_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20760896)))];
            tensor<fp16, [1, 128, 384]> input_69_cast_fp16 = layer_norm(axes = input_69_axes_0, beta = self_model_bert_encoder_layer_4_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_4_output_LayerNorm_weight_to_fp16, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_5_attention_self_query_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20761728))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20909248))))[name = string("self_model_bert_encoder_layer_5_attention_self_query_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_attention_self_query_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_attention_self_query_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20910080)))];
            fp16 quantize_30_scale_0 = const()[name = string("quantize_30_scale_0"), val = fp16(0x1.338p-5)];
            uint8 quantize_30_zero_point_0 = const()[name = string("quantize_30_zero_point_0"), val = uint8(91)];
            string quantize_30_output_dtype_0 = const()[name = string("quantize_30_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_30 = quantize(input = input_69_cast_fp16, output_dtype = quantize_30_output_dtype_0, scale = quantize_30_scale_0, zero_point = quantize_30_zero_point_0)[name = string("quantize_30")];
            fp16 dequantize_30_scale_0 = const()[name = string("dequantize_30_scale_0"), val = fp16(0x1.338p-5)];
            uint8 dequantize_30_zero_point_0 = const()[name = string("dequantize_30_zero_point_0"), val = uint8(91)];
            tensor<fp16, [1, 128, 384]> dequantize_30 = dequantize(input = quantize_30, scale = dequantize_30_scale_0, zero_point = dequantize_30_zero_point_0)[name = string("dequantize_30")];
            tensor<fp16, [1, 128, 384]> linear_30_cast_fp16 = linear(bias = self_model_bert_encoder_layer_5_attention_self_query_bias_to_fp16, weight = self_model_bert_encoder_layer_5_attention_self_query_weight_to_fp16_quantized, x = dequantize_30)[name = string("linear_30_cast_fp16")];
            fp16 quantize_68_scale_0 = const()[name = string("quantize_68_scale_0"), val = fp16(0x1.9fp-6)];
            uint8 quantize_68_zero_point_0 = const()[name = string("quantize_68_zero_point_0"), val = uint8(129)];
            string quantize_68_output_dtype_0 = const()[name = string("quantize_68_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_68 = quantize(input = linear_30_cast_fp16, output_dtype = quantize_68_output_dtype_0, scale = quantize_68_scale_0, zero_point = quantize_68_zero_point_0)[name = string("quantize_68")];
            fp16 dequantize_68_scale_0 = const()[name = string("dequantize_68_scale_0"), val = fp16(0x1.9fp-6)];
            uint8 dequantize_68_zero_point_0 = const()[name = string("dequantize_68_zero_point_0"), val = uint8(129)];
            tensor<fp16, [1, 128, 384]> dequantize_68__post__dequant = dequantize(input = quantize_68, scale = dequantize_68_scale_0, zero_point = dequantize_68_zero_point_0)[name = string("dequantize_68")];
            tensor<fp16, [1, 128, 12, 32]> var_272_cast_fp16 = reshape(shape = var_8, x = dequantize_68__post__dequant)[name = string("op_272_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_5_attention_self_key_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20910912))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21058432))))[name = string("self_model_bert_encoder_layer_5_attention_self_key_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_attention_self_key_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_attention_self_key_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21059264)))];
            tensor<fp16, [1, 128, 384]> linear_31_cast_fp16 = linear(bias = self_model_bert_encoder_layer_5_attention_self_key_bias_to_fp16, weight = self_model_bert_encoder_layer_5_attention_self_key_weight_to_fp16_quantized, x = dequantize_30)[name = string("linear_31_cast_fp16")];
            fp16 quantize_69_scale_0 = const()[name = string("quantize_69_scale_0"), val = fp16(0x1.bccp-6)];
            uint8 quantize_69_zero_point_0 = const()[name = string("quantize_69_zero_point_0"), val = uint8(121)];
            string quantize_69_output_dtype_0 = const()[name = string("quantize_69_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_69 = quantize(input = linear_31_cast_fp16, output_dtype = quantize_69_output_dtype_0, scale = quantize_69_scale_0, zero_point = quantize_69_zero_point_0)[name = string("quantize_69")];
            fp16 dequantize_69_scale_0 = const()[name = string("dequantize_69_scale_0"), val = fp16(0x1.bccp-6)];
            uint8 dequantize_69_zero_point_0 = const()[name = string("dequantize_69_zero_point_0"), val = uint8(121)];
            tensor<fp16, [1, 128, 384]> dequantize_69__post__dequant = dequantize(input = quantize_69, scale = dequantize_69_scale_0, zero_point = dequantize_69_zero_point_0)[name = string("dequantize_69")];
            tensor<fp16, [1, 128, 12, 32]> var_275_cast_fp16 = reshape(shape = var_8, x = dequantize_69__post__dequant)[name = string("op_275_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_5_attention_self_value_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21060096))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21207616))))[name = string("self_model_bert_encoder_layer_5_attention_self_value_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_attention_self_value_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_attention_self_value_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21208448)))];
            tensor<fp16, [1, 128, 384]> linear_32_cast_fp16 = linear(bias = self_model_bert_encoder_layer_5_attention_self_value_bias_to_fp16, weight = self_model_bert_encoder_layer_5_attention_self_value_weight_to_fp16_quantized, x = dequantize_30)[name = string("linear_32_cast_fp16")];
            fp16 quantize_70_scale_0 = const()[name = string("quantize_70_scale_0"), val = fp16(0x1.aap-6)];
            uint8 quantize_70_zero_point_0 = const()[name = string("quantize_70_zero_point_0"), val = uint8(124)];
            string quantize_70_output_dtype_0 = const()[name = string("quantize_70_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_70 = quantize(input = linear_32_cast_fp16, output_dtype = quantize_70_output_dtype_0, scale = quantize_70_scale_0, zero_point = quantize_70_zero_point_0)[name = string("quantize_70")];
            fp16 dequantize_70_scale_0 = const()[name = string("dequantize_70_scale_0"), val = fp16(0x1.aap-6)];
            uint8 dequantize_70_zero_point_0 = const()[name = string("dequantize_70_zero_point_0"), val = uint8(124)];
            tensor<fp16, [1, 128, 384]> dequantize_70__post__dequant = dequantize(input = quantize_70, scale = dequantize_70_scale_0, zero_point = dequantize_70_zero_point_0)[name = string("dequantize_70")];
            tensor<fp16, [1, 128, 12, 32]> var_278_cast_fp16 = reshape(shape = var_8, x = dequantize_70__post__dequant)[name = string("op_278_cast_fp16")];
            bool var_281_transpose_x_0 = const()[name = string("op_281_transpose_x_0"), val = bool(false)];
            bool var_281_transpose_y_0 = const()[name = string("op_281_transpose_y_0"), val = bool(false)];
            tensor<int32, [4]> transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp16, [1, 12, 32, 128]> transpose_35 = transpose(perm = transpose_35_perm_0, x = var_275_cast_fp16)[name = string("transpose_38")];
            tensor<fp16, [1, 12, 128, 32]> transpose_34 = transpose(perm = transpose_34_perm_0, x = var_272_cast_fp16)[name = string("transpose_39")];
            tensor<fp16, [1, 12, 128, 128]> var_281_cast_fp16 = matmul(transpose_x = var_281_transpose_x_0, transpose_y = var_281_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_281_cast_fp16")];
            fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 12, 128, 128]> _inversed_scores_cast_fp16 = mul(x = var_281_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> var_284_cast_fp16 = add(x = _inversed_scores_cast_fp16, y = additive_mask_cast_fp16)[name = string("op_284_cast_fp16")];
            tensor<fp16, [1, 12, 128, 128]> probabilities_cast_fp16 = softmax(axis = var_19, x = var_284_cast_fp16)[name = string("probabilities_cast_fp16")];
            bool context_transpose_x_0 = const()[name = string("context_transpose_x_0"), val = bool(false)];
            bool context_transpose_y_0 = const()[name = string("context_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 12, 128, 32]> values_cast_fp16 = transpose(perm = var_7, x = var_278_cast_fp16)[name = string("transpose_37")];
            tensor<fp16, [1, 12, 128, 32]> context_cast_fp16 = matmul(transpose_x = context_transpose_x_0, transpose_y = context_transpose_y_0, x = probabilities_cast_fp16, y = values_cast_fp16)[name = string("context_cast_fp16")];
            tensor<fp16, [1, 128, 12, 32]> var_287_cast_fp16 = transpose(perm = var_7, x = context_cast_fp16)[name = string("transpose_36")];
            tensor<fp16, [1, 128, 384]> input_71_cast_fp16 = reshape(shape = var_5, x = var_287_cast_fp16)[name = string("input_71_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_encoder_layer_5_attention_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21209280))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21356800))))[name = string("self_model_bert_encoder_layer_5_attention_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_attention_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_attention_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21357632)))];
            fp16 quantize_33_scale_0 = const()[name = string("quantize_33_scale_0"), val = fp16(0x1.2bcp-6)];
            uint8 quantize_33_zero_point_0 = const()[name = string("quantize_33_zero_point_0"), val = uint8(120)];
            string quantize_33_output_dtype_0 = const()[name = string("quantize_33_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_33 = quantize(input = input_71_cast_fp16, output_dtype = quantize_33_output_dtype_0, scale = quantize_33_scale_0, zero_point = quantize_33_zero_point_0)[name = string("quantize_33")];
            fp16 dequantize_33_scale_0 = const()[name = string("dequantize_33_scale_0"), val = fp16(0x1.2bcp-6)];
            uint8 dequantize_33_zero_point_0 = const()[name = string("dequantize_33_zero_point_0"), val = uint8(120)];
            tensor<fp16, [1, 128, 384]> dequantize_33 = dequantize(input = quantize_33, scale = dequantize_33_scale_0, zero_point = dequantize_33_zero_point_0)[name = string("dequantize_33")];
            tensor<fp16, [1, 128, 384]> linear_33_cast_fp16 = linear(bias = self_model_bert_encoder_layer_5_attention_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_5_attention_output_dense_weight_to_fp16_quantized, x = dequantize_33)[name = string("linear_33_cast_fp16")];
            fp16 quantize_71_scale_0 = const()[name = string("quantize_71_scale_0"), val = fp16(0x1.47cp-6)];
            uint8 quantize_71_zero_point_0 = const()[name = string("quantize_71_zero_point_0"), val = uint8(120)];
            string quantize_71_output_dtype_0 = const()[name = string("quantize_71_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_71 = quantize(input = linear_33_cast_fp16, output_dtype = quantize_71_output_dtype_0, scale = quantize_71_scale_0, zero_point = quantize_71_zero_point_0)[name = string("quantize_71")];
            fp16 dequantize_71_scale_0 = const()[name = string("dequantize_71_scale_0"), val = fp16(0x1.47cp-6)];
            uint8 dequantize_71_zero_point_0 = const()[name = string("dequantize_71_zero_point_0"), val = uint8(120)];
            tensor<fp16, [1, 128, 384]> dequantize_71__post__dequant = dequantize(input = quantize_71, scale = dequantize_71_scale_0, zero_point = dequantize_71_zero_point_0)[name = string("dequantize_71")];
            tensor<fp16, [1, 128, 384]> input_73_cast_fp16 = add(x = input_69_cast_fp16, y = dequantize_71__post__dequant)[name = string("input_73_cast_fp16")];
            tensor<int32, [1]> input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_attention_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_attention_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21358464)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_attention_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_attention_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21359296)))];
            tensor<fp16, [1, 128, 384]> input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = self_model_bert_encoder_layer_5_attention_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_5_attention_output_LayerNorm_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")];
            tensor<fp16, [1536, 384]> self_model_bert_encoder_layer_5_intermediate_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1536, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21360128))), scale = tensor<fp16, [1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21950016))))[name = string("self_model_bert_encoder_layer_5_intermediate_dense_weight_to_fp16_quantized")];
            tensor<fp16, [1536]> self_model_bert_encoder_layer_5_intermediate_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_intermediate_dense_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21953152)))];
            fp16 quantize_34_scale_0 = const()[name = string("quantize_34_scale_0"), val = fp16(0x1.524p-6)];
            uint8 quantize_34_zero_point_0 = const()[name = string("quantize_34_zero_point_0"), val = uint8(118)];
            string quantize_34_output_dtype_0 = const()[name = string("quantize_34_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_34 = quantize(input = input_75_cast_fp16, output_dtype = quantize_34_output_dtype_0, scale = quantize_34_scale_0, zero_point = quantize_34_zero_point_0)[name = string("quantize_34")];
            fp16 dequantize_34_scale_0 = const()[name = string("dequantize_34_scale_0"), val = fp16(0x1.524p-6)];
            uint8 dequantize_34_zero_point_0 = const()[name = string("dequantize_34_zero_point_0"), val = uint8(118)];
            tensor<fp16, [1, 128, 384]> dequantize_34 = dequantize(input = quantize_34, scale = dequantize_34_scale_0, zero_point = dequantize_34_zero_point_0)[name = string("dequantize_34")];
            tensor<fp16, [1, 128, 1536]> linear_34_cast_fp16 = linear(bias = self_model_bert_encoder_layer_5_intermediate_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_5_intermediate_dense_weight_to_fp16_quantized, x = dequantize_34)[name = string("linear_34_cast_fp16")];
            fp16 quantize_72_scale_0 = const()[name = string("quantize_72_scale_0"), val = fp16(0x1.7dcp-6)];
            uint8 quantize_72_zero_point_0 = const()[name = string("quantize_72_zero_point_0"), val = uint8(121)];
            string quantize_72_output_dtype_0 = const()[name = string("quantize_72_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_72 = quantize(input = linear_34_cast_fp16, output_dtype = quantize_72_output_dtype_0, scale = quantize_72_scale_0, zero_point = quantize_72_zero_point_0)[name = string("quantize_72")];
            fp16 dequantize_72_scale_0 = const()[name = string("dequantize_72_scale_0"), val = fp16(0x1.7dcp-6)];
            uint8 dequantize_72_zero_point_0 = const()[name = string("dequantize_72_zero_point_0"), val = uint8(121)];
            tensor<fp16, [1, 128, 1536]> dequantize_72__post__dequant = dequantize(input = quantize_72, scale = dequantize_72_scale_0, zero_point = dequantize_72_zero_point_0)[name = string("dequantize_72")];
            string input_77_mode_0 = const()[name = string("input_77_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 1536]> input_77_cast_fp16 = gelu(mode = input_77_mode_0, x = dequantize_72__post__dequant)[name = string("input_77_cast_fp16")];
            tensor<fp16, [384, 1536]> self_model_bert_encoder_layer_5_output_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21956288))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22546176))))[name = string("self_model_bert_encoder_layer_5_output_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_output_dense_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_output_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22547008)))];
            fp16 quantize_35_scale_0 = const()[name = string("quantize_35_scale_0"), val = fp16(0x1.a5cp-7)];
            uint8 quantize_35_zero_point_0 = const()[name = string("quantize_35_zero_point_0"), val = uint8(13)];
            string quantize_35_output_dtype_0 = const()[name = string("quantize_35_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 1536]> quantize_35 = quantize(input = input_77_cast_fp16, output_dtype = quantize_35_output_dtype_0, scale = quantize_35_scale_0, zero_point = quantize_35_zero_point_0)[name = string("quantize_35")];
            fp16 dequantize_35_scale_0 = const()[name = string("dequantize_35_scale_0"), val = fp16(0x1.a5cp-7)];
            uint8 dequantize_35_zero_point_0 = const()[name = string("dequantize_35_zero_point_0"), val = uint8(13)];
            tensor<fp16, [1, 128, 1536]> dequantize_35 = dequantize(input = quantize_35, scale = dequantize_35_scale_0, zero_point = dequantize_35_zero_point_0)[name = string("dequantize_35")];
            tensor<fp16, [1, 128, 384]> linear_35_cast_fp16 = linear(bias = self_model_bert_encoder_layer_5_output_dense_bias_to_fp16, weight = self_model_bert_encoder_layer_5_output_dense_weight_to_fp16_quantized, x = dequantize_35)[name = string("linear_35_cast_fp16")];
            fp16 quantize_73_scale_0 = const()[name = string("quantize_73_scale_0"), val = fp16(0x1.334p-4)];
            uint8 quantize_73_zero_point_0 = const()[name = string("quantize_73_zero_point_0"), val = uint8(95)];
            string quantize_73_output_dtype_0 = const()[name = string("quantize_73_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 128, 384]> quantize_73 = quantize(input = linear_35_cast_fp16, output_dtype = quantize_73_output_dtype_0, scale = quantize_73_scale_0, zero_point = quantize_73_zero_point_0)[name = string("quantize_73")];
            fp16 dequantize_73_scale_0 = const()[name = string("dequantize_73_scale_0"), val = fp16(0x1.334p-4)];
            uint8 dequantize_73_zero_point_0 = const()[name = string("dequantize_73_zero_point_0"), val = uint8(95)];
            tensor<fp16, [1, 128, 384]> dequantize_73__post__dequant = dequantize(input = quantize_73, scale = dequantize_73_scale_0, zero_point = dequantize_73_zero_point_0)[name = string("dequantize_73")];
            tensor<fp16, [1, 128, 384]> input_79_cast_fp16 = add(x = input_75_cast_fp16, y = dequantize_73__post__dequant)[name = string("input_79_cast_fp16")];
            tensor<int32, [1]> value_1_axes_0 = const()[name = string("value_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_output_LayerNorm_weight_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_output_LayerNorm_weight_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22547840)))];
            tensor<fp16, [384]> self_model_bert_encoder_layer_5_output_LayerNorm_bias_to_fp16 = const()[name = string("self_model_bert_encoder_layer_5_output_LayerNorm_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22548672)))];
            tensor<fp16, [1, 128, 384]> value_1_cast_fp16 = layer_norm(axes = value_1_axes_0, beta = self_model_bert_encoder_layer_5_output_LayerNorm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = self_model_bert_encoder_layer_5_output_LayerNorm_weight_to_fp16, x = input_79_cast_fp16)[name = string("value_1_cast_fp16")];
            tensor<int32, [3]> input_81_begin_0 = const()[name = string("input_81_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> input_81_end_0 = const()[name = string("input_81_end_0"), val = tensor<int32, [3]>([1, 1, 384])];
            tensor<bool, [3]> input_81_end_mask_0 = const()[name = string("input_81_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<bool, [3]> input_81_squeeze_mask_0 = const()[name = string("input_81_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])];
            tensor<fp16, [1, 384]> input_81_cast_fp16 = slice_by_index(begin = input_81_begin_0, end = input_81_end_0, end_mask = input_81_end_mask_0, squeeze_mask = input_81_squeeze_mask_0, x = value_1_cast_fp16)[name = string("input_81_cast_fp16")];
            tensor<fp16, [384, 384]> self_model_bert_pooler_dense_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [384, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22549504))), scale = tensor<fp16, [384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22697024))))[name = string("self_model_bert_pooler_dense_weight_to_fp16_quantized")];
            tensor<fp16, [384]> self_model_bert_pooler_dense_bias_to_fp16 = const()[name = string("self_model_bert_pooler_dense_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22697856)))];
            fp16 quantize_36_scale_0 = const()[name = string("quantize_36_scale_0"), val = fp16(0x1.fdp-7)];
            uint8 quantize_36_zero_point_0 = const()[name = string("quantize_36_zero_point_0"), val = uint8(130)];
            string quantize_36_output_dtype_0 = const()[name = string("quantize_36_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 384]> quantize_36 = quantize(input = input_81_cast_fp16, output_dtype = quantize_36_output_dtype_0, scale = quantize_36_scale_0, zero_point = quantize_36_zero_point_0)[name = string("quantize_36")];
            fp16 dequantize_36_scale_0 = const()[name = string("dequantize_36_scale_0"), val = fp16(0x1.fdp-7)];
            uint8 dequantize_36_zero_point_0 = const()[name = string("dequantize_36_zero_point_0"), val = uint8(130)];
            tensor<fp16, [1, 384]> dequantize_36 = dequantize(input = quantize_36, scale = dequantize_36_scale_0, zero_point = dequantize_36_zero_point_0)[name = string("dequantize_36")];
            tensor<fp16, [1, 384]> linear_36_cast_fp16 = linear(bias = self_model_bert_pooler_dense_bias_to_fp16, weight = self_model_bert_pooler_dense_weight_to_fp16_quantized, x = dequantize_36)[name = string("linear_36_cast_fp16")];
            tensor<fp16, [1, 384]> input_cast_fp16 = tanh(x = linear_36_cast_fp16)[name = string("input_cast_fp16")];
            tensor<fp16, [8, 384]> self_model_classifier_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [8, 384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22698688))), scale = tensor<fp16, [8, 1]>([[0x1.2bp-11], [0x1.1e8p-11], [0x1.214p-11], [0x1.17p-11], [0x1.568p-11], [0x1.4fp-11], [0x1.178p-11], [0x1.69p-11]]))[name = string("self_model_classifier_weight_to_fp16_quantized")];
            tensor<fp16, [8]> self_model_classifier_bias_to_fp16 = const()[name = string("self_model_classifier_bias_to_fp16"), val = tensor<fp16, [8]>([-0x1.e7cp-14, -0x1.88p-19, -0x1.eap-12, 0x1.1ep-13, -0x1.b18p-15, 0x1.9ccp-14, -0x1.2cp-18, 0x1.f54p-13])];
            fp16 quantize_37_scale_0 = const()[name = string("quantize_37_scale_0"), val = fp16(0x1.89p-8)];
            uint8 quantize_37_zero_point_0 = const()[name = string("quantize_37_zero_point_0"), val = uint8(129)];
            string quantize_37_output_dtype_0 = const()[name = string("quantize_37_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 384]> quantize_37 = quantize(input = input_cast_fp16, output_dtype = quantize_37_output_dtype_0, scale = quantize_37_scale_0, zero_point = quantize_37_zero_point_0)[name = string("quantize_37")];
            fp16 dequantize_37_scale_0 = const()[name = string("dequantize_37_scale_0"), val = fp16(0x1.89p-8)];
            uint8 dequantize_37_zero_point_0 = const()[name = string("dequantize_37_zero_point_0"), val = uint8(129)];
            tensor<fp16, [1, 384]> dequantize_37 = dequantize(input = quantize_37, scale = dequantize_37_scale_0, zero_point = dequantize_37_zero_point_0)[name = string("dequantize_37")];
            tensor<fp16, [1, 8]> linear_37_cast_fp16 = linear(bias = self_model_classifier_bias_to_fp16, weight = self_model_classifier_weight_to_fp16_quantized, x = dequantize_37)[name = string("linear_37_cast_fp16")];
            fp16 quantize_74_scale_0 = const()[name = string("quantize_74_scale_0"), val = fp16(0x1.5bcp-6)];
            uint8 quantize_74_zero_point_0 = const()[name = string("quantize_74_zero_point_0"), val = uint8(70)];
            string quantize_74_output_dtype_0 = const()[name = string("quantize_74_output_dtype_0"), val = string("uint8")];
            tensor<uint8, [1, 8]> quantize_74 = quantize(input = linear_37_cast_fp16, output_dtype = quantize_74_output_dtype_0, scale = quantize_74_scale_0, zero_point = quantize_74_zero_point_0)[name = string("quantize_74")];
            fp16 dequantize_74_scale_0 = const()[name = string("dequantize_74_scale_0"), val = fp16(0x1.5bcp-6)];
            uint8 dequantize_74_zero_point_0 = const()[name = string("dequantize_74_zero_point_0"), val = uint8(70)];
            tensor<fp16, [1, 8]> dequantize_74__post__dequant = dequantize(input = quantize_74, scale = dequantize_74_scale_0, zero_point = dequantize_74_zero_point_0)[name = string("dequantize_74")];
            string linear_37_cast_fp16_to_fp32_dtype_0 = const()[name = string("linear_37_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 8]> logits = cast(dtype = linear_37_cast_fp16_to_fp32_dtype_0, x = dequantize_74__post__dequant)[name = string("cast_0")];
        } -> (logits);
}