code: def predict_batch(
      x: Union[list, np.ndarray, tf.Tensor],
      model: Union[Callable, tf.keras.Model],
      batch_size: int = int(1e10),
      preprocess_fn: Callable = None,
      dtype: Union[Type[np.generic], tf.DType] = np.float32,
  ) -> Union[np.ndarray, tf.Tensor, tuple]:
      """
      Make batch predictions on a model.
  
      Parameters
      ----------
      x
          Batch of instances.
      model
          tf.keras model or one of the other permitted types defined in Data.
      batch_size
          Batch size used during prediction.
      preprocess_fn
          Optional preprocessing function for each batch.
      dtype
          Model output type, e.g. np.float32 or tf.float32.
  
      Returns
      -------
      Numpy array, tensorflow tensor or tuples of those with model outputs.
      """
      n = len(x)
      n_minibatch = int(np.ceil(n / batch_size))
      return_np = not isinstance(dtype, tf.DType)
      return_list = False
      preds: Union[list, tuple] = []
      for i in range(n_minibatch):
          istart, istop = i * batch_size, min((i + 1) * batch_size, n)
          x_batch = x[istart:istop]
          if isinstance(preprocess_fn, Callable):  # type: ignore
              x_batch = preprocess_fn(x_batch)
          preds_tmp = model(x_batch)
          if isinstance(preds_tmp, (list, tuple)):
              if (
                  len(preds) == 0
              ):  # init tuple with lists to store predictions
                  preds = tuple([] for _ in range(len(preds_tmp)))
                  return_list = isinstance(preds_tmp, list)
              for j, p in enumerate(preds_tmp):
                  preds[j].append(
                      p
                      if not return_np or isinstance(p, np.ndarray)
                      else p.numpy()
                  )
          elif isinstance(preds_tmp, (np.ndarray, tf.Tensor)):
              preds.append(
                  preds_tmp
                  if not return_np or isinstance(preds_tmp, np.ndarray)  # type: ignore
                  else preds_tmp.numpy()
              )
          else:
              raise TypeError(
                  f"Model output type {type(preds_tmp)} not supported. The model output "
                  f"type needs to be one of list, tuple, np.ndarray or tf.Tensor."
              )
      concat = np.concatenate if return_np else tf.concat
      out = (
          tuple(concat(p, axis=0) for p in preds)
          if isinstance(preds, tuple)
          else concat(preds, axis=0)
      )
      if return_list:
          out = list(out)
      return out
  
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 1 for calling a global function with example usage: predict_batch(x, model, batch_size, preprocess_fn, dtype) and returns: out