code: def convert_imagegpt_checkpoint_to_pytorch(
      imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path
  ):
      # Construct configuration depending on size
      MODELS = {
          "small": (512, 8, 24),
          "medium": (1024, 8, 36),
          "large": (1536, 16, 48),
      }
      n_embd, n_head, n_layer = MODELS[
          model_size
      ]  # set model hyperparameters
      config = ImageGPTConfig(
          n_embd=n_embd, n_layer=n_layer, n_head=n_head
      )
      model = ImageGPTForCausalLM(config)
  
      # Load weights from numpy
      load_tf_weights_in_imagegpt(
          model, config, imagegpt_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 ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
  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_imagegpt_checkpoint_to_pytorch(imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path) without return types