src/transformers/models/canine/convert_canine_original_tf_checkpoint_to_pytorch.py/convert_tf_checkpoint_to_pytorch
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)