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 = ["Hello world"]
      try:
          prediction = predictor(x)
      except Exception as e:
          msg = (
              f"Predictor failed to be called on x={x}. "
              f"Check that `predictor` works with inputs of type List[str]."
          )
          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
  import string
  from copy import deepcopy
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import spacy
  from alibi.utils.missing_optional_dependency import import_optional
  from alibi.api.defaults import DEFAULT_DATA_ANCHOR, 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
  from .text_samplers import UnknownSampler, SimilaritySampler, load_spacy_lexeme_prob
label_desc: Use image node_id 7 for calling the AnchorText obj's underlying member method code with example usage: obj._transform_predictor(predictor) and returns: predictor, transformer