code: def convert_gpt2_checkpoint_to_pytorch(
      gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path
  ):
      # Construct model
      if gpt2_config_file == "":
          config = GPT2Config()
      else:
          config = GPT2Config.from_json_file(gpt2_config_file)
      model = GPT2Model(config)
  
      # Load weights from numpy
      load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
  
      # Save pytorch-model
      pytorch_weights_dump_path = (
          pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      )
      pytorch_config_dump_path = (
          pytorch_dump_folder_path + "/" + CONFIG_NAME
      )
      print(f"Save PyTorch model to {pytorch_weights_dump_path}")
      torch.save(model.state_dict(), pytorch_weights_dump_path)
      print(f"Save configuration file to {pytorch_config_dump_path}")
      with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
          f.write(config.to_json_string())
  
imports: import argparse
  import torch
  from transformers import GPT2Config, GPT2Model, load_tf_weights_in_gpt2
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
label_desc: Use image node_id 1 for calling a global function with example usage: convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path) without return types