code: def main():
      args = parse_args()
      max_length = 5
      num_beams = 4
  
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
  
      logger.setLevel(logging.INFO)
      transformers.utils.logging.set_verbosity_error()
  
      device = torch.device(args.device)
  
      model, tokenizer = load_model_tokenizer(
          args.model_name_or_path, device
      )
  
      if model.config.decoder_start_token_id is None:
          raise ValueError(
              "Make sure that `config.decoder_start_token_id` is correctly defined"
          )
  
      model.to(device)
  
      if args.max_length:
          max_length = args.max_length
  
      if args.num_beams:
          num_beams = args.num_beams
  
      if args.output_file_path:
          output_name = args.output_file_path
      else:
          output_name = "BART.onnx"
  
      logger.info("Exporting model to ONNX")
      export_and_validate_model(
          model, tokenizer, output_name, num_beams, max_length
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/onnx/summarization/run_onnx_exporter.py
imports: import argparse
  import logging
  import os
  import sys
  import numpy
  import torch
  import onnxruntime
  import transformers
  from bart_onnx.generation_onnx import BARTBeamSearchGenerator
  from bart_onnx.reduce_onnx_size import remove_dup_initializers
  from transformers import BartForConditionalGeneration, BartTokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types