class_docstr: Implementation of the permutation feature importance for tabular datasets. The method measure the importance
  of a feature as the relative increase/decrease in the loss/score function when the feature values are permuted.
  Supports black-box models.
  
  For details of the method see the papers:
  
   - https://link.springer.com/article/10.1023/A:1010933404324
  
   - https://arxiv.org/abs/1801.01489
docstr: Resets the predictor function.
  
  Parameters
  ----------
  predictor
      New predictor function.
docstr_tok: ["Resets","the","predictor","function",".","Parameters","--","--","--","--","--","predictor","New","predictor","function","."]
code: def reset_predictor(self, predictor: Callable) -> None:
      """
      Resets the predictor function.
  
      Parameters
      ----------
      predictor
          New predictor function.
      """
      self.predictor = predictor
  
imports: import copy
  import inspect
  import logging
  import math
  import numbers
  import sys
  from collections import defaultdict
  from copy import deepcopy
  from enum import Enum
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union, no_type_check
  import matplotlib.pyplot
  import numpy
  import sklearn.metrics
  from tqdm import tqdm
  from alibi.api.defaults import DEFAULT_DATA_PERMUTATION_IMPORTANCE, DEFAULT_META_PERMUTATION_IMPORTANCE
  from alibi.api.interfaces import Explainer, Explanation
label_desc: Use image node_id 12 for calling the PermutationImportance obj's underlying member method code with example usage: obj.reset_predictor(predictor) without return types