code: def convert_tf_checkpoint_to_pytorch(
      tf_checkpoint_path, pytorch_dump_path
  ):
      # Initialize PyTorch model
      config = CanineConfig()
      model = CanineModel(config)
      model.eval()
  
      print(f"Building PyTorch model from configuration: {config}")
  
      # Load weights from tf checkpoint
      load_tf_weights_in_canine(model, config, tf_checkpoint_path)
  
      # Save pytorch-model (weights and configuration)
      print(f"Save PyTorch model to {pytorch_dump_path}")
      model.save_pretrained(pytorch_dump_path)
  
      # Save tokenizer files
      tokenizer = CanineTokenizer()
      print(f"Save tokenizer files to {pytorch_dump_path}")
      tokenizer.save_pretrained(pytorch_dump_path)
  
imports: import argparse
  from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
  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, pytorch_dump_path) without return types