code: def convert_tf_checkpoint_to_pytorch(
      tf_checkpoint_path, bert_config_file, pytorch_dump_path
  ):
      # Initialise PyTorch model
      config = BertConfig.from_json_file(bert_config_file)
      print(f"Building PyTorch model from configuration: {config}")
      model = BertForPreTraining(config)
  
      # Load weights from tf checkpoint
      load_tf_weights_in_bert(model, config, tf_checkpoint_path)
  
      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}")
      torch.save(model.state_dict(), pytorch_dump_path)
  
imports: import argparse
  import torch
  from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
  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, bert_config_file, pytorch_dump_path) without return types