code: def convert_tf_checkpoint_to_pytorch(
      tf_checkpoint_path, albert_config_file, pytorch_dump_path
  ):
      # Initialise PyTorch model
      config = AlbertConfig.from_json_file(albert_config_file)
      print(f"Building PyTorch model from configuration: {config}")
      model = AlbertForPreTraining(config)
  
      # Load weights from tf checkpoint
      load_tf_weights_in_albert(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 ...utils import logging
  from .None import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
label_desc: Use image node_id 1 for calling a global function with example usage: convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path) without return types