code: def load_model_tokenizer(model_name, device="cpu"):
      huggingface_model = (
          model_dict[model_name].from_pretrained(model_name).to(device)
      )
      tokenizer = tokenizer_dict[model_name].from_pretrained(model_name)
  
      if model_name in ["facebook/bart-base"]:
          huggingface_model.config.no_repeat_ngram_size = 0
          huggingface_model.config.forced_bos_token_id = None
          huggingface_model.config.min_length = 0
  
      return huggingface_model, tokenizer
  
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 2 for calling a global function with example usage: load_model_tokenizer(model_name, device) and returns: huggingface_model,  tokenizer