code: def predict_batch_transformer(
      x: Union[list, np.ndarray],
      model: tf.keras.Model,
      tokenizer: Callable,
      max_len: int,
      batch_size: int = int(1e10),
      dtype: Union[Type[np.generic], tf.DType] = np.float32,
  ) -> Union[np.ndarray, tf.Tensor]:
      """
      Make batch predictions using a transformers tokenizer and model.
  
      Parameters
      ----------
      x
          Batch of instances.
      model
          Transformer model.
      tokenizer
          Tokenizer for model.
      max_len
          Max token length.
      batch_size
          Batch size.
      dtype
          Model output type, e.g. np.float32 or tf.float32.
  
      Returns
      -------
      Numpy array or tensorflow tensor with model outputs.
      """
      preprocess_fn = partial(
          tokenize_transformer,
          tokenizer=tokenizer,
          max_len=max_len,
          backend="tf",
      )
      return predict_batch(
          x,
          model,
          preprocess_fn=preprocess_fn,
          batch_size=batch_size,
          dtype=dtype,
      )
  
imports: from functools import partial
  from typing import Callable, Type, Union
  import numpy
  import tensorflow
  from alibi_detect.utils.prediction import tokenize_transformer
label_desc: Use image node_id 2 for calling a global function with example usage: predict_batch_transformer(x, model, tokenizer, max_len, batch_size, dtype) and returns: predict_batch