code: def convert_tf_checkpoint_to_pytorch(
      tf_checkpoint_path, config_file, pytorch_dump_path
  ):
      # Initialise PyTorch model
      config_json = json.load(open(config_file, "r"))
      config = GPTNeoConfig(
          hidden_size=config_json["n_embd"],
          num_layers=config_json["n_layer"],
          num_heads=config_json["n_head"],
          attention_types=config_json["attention_types"],
          max_position_embeddings=config_json["n_positions"],
          resid_dropout=config_json["res_dropout"],
          embed_dropout=config_json["embed_dropout"],
          attention_dropout=config_json["attn_dropout"],
      )
      print(f"Building PyTorch model from configuration: {config}")
      model = GPTNeoForCausalLM(config)
  
      # Load weights from tf checkpoint
      load_tf_weights_in_gpt_neo(model, config, tf_checkpoint_path)
  
      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}")
      model.save_pretrained(pytorch_dump_path)
  
imports: import argparse
  import json
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  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, config_file, pytorch_dump_path) without return types