code: def export_saved_model_to_torchscript(
      saved_model_path: str, path: str, export_config: ExportConfig
  ) -> None:
      cuda.CUDA_ENABLED = False
      precision.FP16_ENABLED = False
      task, train_config, _training_state = load(saved_model_path)
      jit_model = task.torchscript_export(
          task.model, path, export_config=export_config
      )
      if export_config.export_lite_path is not None:
          task.lite_export(
              task.model, jit_model, export_config.export_lite_path
          )
  
imports: import gzip
  import json
  import os
  import sys
  import traceback
  from typing import Any, Dict, get_type_hints, IO, Iterator, List, Optional, Tuple, Union
  import torch
  from pytext.common.constants import Stage
  from pytext.config import ExportConfig, PyTextConfig, TestConfig
  from pytext.config.component import ComponentType, create_component, create_exporter
  from pytext.data.data import Batcher
  from pytext.data.data_handler import CommonMetadata
  from pytext.metric_reporters.channel import Channel
  from pytext.task import create_task, get_latest_checkpoint_path, load, NewTask, save, Task_Deprecated
  from pytext.task.disjoint_multitask import NewDisjointMultitask
  from pytext.trainers import TrainingState
  from pytext.utils import cuda, distributed, precision, round_seq, set_random_seeds, timing
  from pytext.utils.file_io import PathManager
label_desc: Use image node_id 10 for calling a global function with example usage: export_saved_model_to_torchscript(saved_model_path, path, export_config) without return types