docstr: Builds an explanation object.
  
  Parameters
  ----------
  scores
      The scores for each of the instances in the data set computed by the similarity method.
docstr_tok: ["Builds","an","explanation","object",".","Parameters","--","--","--","--","--","scores","The","scores","for","each","of","the","instances","in","the","data","set","computed","by","the","similarity","method","."]
code: def _build_explanation(self, scores: np.ndarray) -> "Explanation":
      """Builds an explanation object.
  
      Parameters
      ----------
      scores
          The scores for each of the instances in the data set computed by the similarity method.
      """
      data = copy.deepcopy(DEFAULT_DATA_SIM)
      sorted_score_indices = np.argsort(scores)[:, ::-1]
      most_similar: Union[np.ndarray, List[Any]]
      least_similar: Union[np.ndarray, List[Any]]
  
      if isinstance(self.X_train, np.ndarray):
          broadcast_indices = np.expand_dims(
              sorted_score_indices,
              axis=tuple(range(2, len(self.X_train[None].shape))),
          )
          most_similar = np.take_along_axis(
              self.X_train[None], broadcast_indices[:, :5], axis=1
          )
          least_similar = np.take_along_axis(
              self.X_train[None], broadcast_indices[:, -1:-6:-1], axis=1
          )
      else:
          most_similar = [
              [self.X_train[i] for i in ssi[:5]]
              for ssi in sorted_score_indices
          ]
          least_similar = [
              [self.X_train[i] for i in ssi[-1:-6:-1]]
              for ssi in sorted_score_indices
          ]
  
      data.update(
          scores=np.take_along_axis(
              scores, sorted_score_indices, axis=1
          ),
          ordered_indices=sorted_score_indices,
          most_similar=most_similar,
          least_similar=least_similar,
      )
      return Explanation(meta=self.meta, data=data)
  
imports: import copy
  import warnings
  from enum import Enum
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  from alibi.api.defaults import DEFAULT_DATA_SIM, DEFAULT_META_SIM
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.explainers.similarity.base import BaseSimilarityExplainer
  from alibi.explainers.similarity.metrics import asym_dot, cos, dot
  from alibi.utils import _get_options_string
  from alibi.utils.frameworks import Framework
  from typing_extensions import Literal
label_desc: Use image node_id 5 for calling the GradientSimilarity obj's underlying member method code with example usage: obj._build_explanation(scores) and returns: Explanation