docstr: Parameters
  ----------
  only_different_prediction
      If ``True``, will only return examples where the result makes a different prediction than the
      original model.
  only_same_prediction
      If ``True``, will only return examples where the result makes the same prediction than the
      original model.
  partial_index
      Get the examples from the partial result until a certain index.
  
  Returns
  -------
  Examples covered by result.
code: def examples(
      self,
      only_different_prediction: bool = False,
      only_same_prediction: bool = False,
      partial_index: Optional[int] = None,
  ) -> Union[list, np.ndarray]:
      """
      Parameters
      ----------
      only_different_prediction
          If ``True``, will only return examples where the result makes a different prediction than the
          original model.
      only_same_prediction
          If ``True``, will only return examples where the result makes the same prediction than the
          original model.
      partial_index
          Get the examples from the partial result until a certain index.
  
      Returns
      -------
      Examples covered by result.
      """
      if only_different_prediction and only_same_prediction:
          print(
              "Error: you cannot have only_different_prediction and only_same_prediction at the same time"
          )
          return []
      key = "covered"
      if only_different_prediction:
          key = "covered_false"
      if only_same_prediction:
          key = "covered_true"
      size = len(self.exp_map["examples"])
      idx = partial_index if partial_index is not None else size - 1
      if idx < 0 or idx > size:
          return []
      return self.exp_map["examples"][idx][key]
  
file_path: alibi/alibi/explainers/anchors/anchor_explanation.py
imports: from typing import Optional, Union
  import numpy
label_desc: Use image node_id 6 for calling the AnchorExplanation obj's underlying member method code with example usage: obj.examples(only_different_prediction, only_same_prediction, partial_index) and returns: self, list, list