code: def predictor(
      self, predictor: Optional[Callable]
  ) -> None:  # Optional here because in saving.py we set it to None
      # deal with the case from saving.py first
      # TODO: how do we prevent users from passing predictor=None? Probably beartype.
      if predictor is None:
          self._predictor = None
          if self.ohe:
              self._ohe_predictor = None
      else:
          # if input is one-hot encoded
          if self.ohe:
              # this predictor expects ordinal/labels encoded categorical variables
              ord_predictor = lambda x: predictor(
                  ord_to_ohe(x, self.cat_vars_ord)[0]
              )  # noqa: E731
              self._predictor = self._transform_predictor(ord_predictor)
  
              # this predictor expects one-hot encoded categorical variable
              self._ohe_predictor = self._transform_ohe_predictor(
                  predictor
              )
  
          else:
              # set the predictor
              self._predictor = self._transform_predictor(predictor)
  
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 8 for calling the AnchorTabular obj's underlying member method code with example usage: obj.predictor(predictor) without return types