code: def convert_tf_checkpoint_to_pytorch(
      tf_checkpoint_path,
      big_bird_config_file,
      pytorch_dump_path,
      is_trivia_qa,
  ):
      # Initialise PyTorch model
      config = BigBirdConfig.from_json_file(big_bird_config_file)
      print(f"Building PyTorch model from configuration: {config}")
  
      if is_trivia_qa:
          model = BigBirdForQuestionAnswering(config)
      else:
          model = BigBirdForPreTraining(config)
  
      # Load weights from tf checkpoint
      load_tf_weights_in_big_bird(
          model, tf_checkpoint_path, is_trivia_qa=is_trivia_qa
      )
  
      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}")
      model.save_pretrained(pytorch_dump_path)
  
imports: import argparse
  from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, big_bird_config_file, pytorch_dump_path, is_trivia_qa) without return types