code: def _transform_predictor(self, predictor: Callable) -> Callable:
      # check if predictor returns predicted class or prediction probabilities for each class
      # if needed adjust predictor so it returns the predicted class
      x = np.zeros((1,) + self.image_shape, dtype=self.dtype)
      try:
          prediction = predictor(x)
      except Exception as e:
          msg = (
              f"Predictor failed to be called on {type(x)} of shape {x.shape} and dtype {x.dtype}. "
              f"Check that the parameter `image_shape` is correctly specified."
          )
          raise PredictorCallError(msg) from e
  
      if not isinstance(prediction, np.ndarray):
          msg = f"Excepted predictor return type to be {np.ndarray} but got {type(prediction)}."
          raise PredictorReturnTypeError(msg)
  
      if np.argmax(prediction.shape) == 0:
          return predictor
      else:
          transformer = ArgmaxTransformer(predictor)
          return transformer
  
imports: import copy
  import logging
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import numpy
  from skimage.segmentation import felzenszwalb, quickshift, slic
  from alibi.api.defaults import DEFAULT_DATA_ANCHOR_IMG, DEFAULT_META_ANCHOR
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.exceptions import PredictorCallError, PredictorReturnTypeError
  from alibi.utils.wrappers import ArgmaxTransformer
  from .anchor_base import AnchorBaseBeam
  from .anchor_explanation import AnchorExplanation
label_desc: Use image node_id 7 for calling the AnchorImage obj's underlying member method code with example usage: obj._transform_predictor(predictor) and returns: predictor, transformer