code: def _transform_predictor(self, predictor: Callable) -> Callable:
      # define data instance full of zeros
      x = np.zeros([1, len(self.feature_names)], dtype=self.dtype)
  
      try:
          # check if predictor returns predicted class or prediction probabilities for each class
          # if needed adjust predictor so it returns the predicted class
          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 `feature_names` 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
  from collections import OrderedDict, defaultdict
  from itertools import accumulate
  from typing import Any, Callable, DefaultDict, Dict, List, Optional, Set, Tuple, Type, Union
  import numpy
  from alibi.api.defaults import DEFAULT_DATA_ANCHOR, DEFAULT_META_ANCHOR
  from alibi.api.interfaces import Explainer, Explanation, FitMixin
  from alibi.exceptions import NotFittedError, PredictorCallError, PredictorReturnTypeError
  from alibi.utils.discretizer import Discretizer
  from alibi.utils.mapping import ohe_to_ord, ord_to_ohe
  from alibi.utils.wrappers import ArgmaxTransformer
  from .anchor_base import AnchorBaseBeam
  from .anchor_explanation import AnchorExplanation
label_desc: Use image node_id 9 for calling the AnchorTabular obj's underlying member method code with example usage: obj._transform_predictor(predictor) and returns: predictor, transformer