code: def convert_tf_checkpoint_to_pytorch(
      tf_checkpoint_path,
      config_file,
      pytorch_dump_path,
      discriminator_or_generator,
  ):
      # Initialise PyTorch model
      config = ElectraConfig.from_json_file(config_file)
      print(f"Building PyTorch model from configuration: {config}")
  
      if discriminator_or_generator == "discriminator":
          model = ElectraForPreTraining(config)
      elif discriminator_or_generator == "generator":
          model = ElectraForMaskedLM(config)
      else:
          raise ValueError(
              "The discriminator_or_generator argument should be either 'discriminator' or 'generator'"
          )
  
      # Load weights from tf checkpoint
      load_tf_weights_in_electra(
          model,
          config,
          tf_checkpoint_path,
          discriminator_or_generator=discriminator_or_generator,
      )
  
      # 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 ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
  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, discriminator_or_generator) without return types