docstr: Explain the predictor's predictions for a given input.
  
  Computes the similarity score between the inputs and the training set. Returns an explainer object
  containing the scores, the indices of the training set instances sorted by descending similarity and the
  most similar and least similar instances of the data set for the input. Note that the input may be a single
  instance or a batch of instances.
  
  Parameters
  ----------
  X
      `X` can be a `numpy` array, `tensorflow` tensor, `pytorch` tensor of the same shape as the training data
      or a list of objects, with or without a leading batch dimension. If the batch dimension is missing it's
      added.
  Y
      `Y` can be a `numpy` array, `tensorflow` tensor or a `pytorch` tensor. In the case of a regression task, the
      `Y` argument must be present. If the task is classification then `Y` defaults to the model prediction.
  
  Returns
  -------
  `Explanation` object containing the ordered similarity scores for the test instance(s) with additional         metadata as attributes. Contains the following data-related attributes
      - `scores`: ``np.ndarray`` - similarity scores for each pair of instances in the training and test set             sorted in descending order.
      - `ordered_indices`: ``np.ndarray`` - indices of the paired training and test set instances sorted by the             similarity score in descending order.
      - `most_similar`: ``np.ndarray`` - 5 most similar instances in the training set for each test instance             The first element is the most similar instance.
      -  `least_similar`: ``np.ndarray`` - 5 least similar instances in the training set for each test instance.             The first element is the least similar instance.
  
  Raises
  ------
  ValueError
      If `Y` is ``None`` and the `task` is ``'regression'``.
  ValueError
      If the shape of `X` or `Y` does not match the shape of the training or target data.
  ValueError
      If the fit method has not been called prior to calling this method.
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code: def explain(
      self,
      X: "Union[np.ndarray, tensorflow.Tensor, torch.Tensor, Any, List[Any]]",
      Y: "Optional[Union[np.ndarray, tensorflow.Tensor, torch.Tensor]]" = None,
  ) -> "Explanation":
      """Explain the predictor's predictions for a given input.
  
          Computes the similarity score between the inputs and the training set. Returns an explainer object
          containing the scores, the indices of the training set instances sorted by descending similarity and the
          most similar and least similar instances of the data set for the input. Note that the input may be a single
          instance or a batch of instances.
  
          Parameters
          ----------
          X
              `X` can be a `numpy` array, `tensorflow` tensor, `pytorch` tensor of the same shape as the training data
              or a list of objects, with or without a leading batch dimension. If the batch dimension is missing it's
              added.
          Y
              `Y` can be a `numpy` array, `tensorflow` tensor or a `pytorch` tensor. In the case of a regression task, the
              `Y` argument must be present. If the task is classification then `Y` defaults to the model prediction.
  
          Returns
          -------
          `Explanation` object containing the ordered similarity scores for the test instance(s) with additional \
          metadata as attributes. Contains the following data-related attributes
              - `scores`: ``np.ndarray`` - similarity scores for each pair of instances in the training and test set \
              sorted in descending order.
              - `ordered_indices`: ``np.ndarray`` - indices of the paired training and test set instances sorted by the \
              similarity score in descending order.
              - `most_similar`: ``np.ndarray`` - 5 most similar instances in the training set for each test instance \
              The first element is the most similar instance.
              -  `least_similar`: ``np.ndarray`` - 5 least similar instances in the training set for each test instance. \
              The first element is the least similar instance.
  
          Raises
          ------
          ValueError
              If `Y` is ``None`` and the `task` is ``'regression'``.
          ValueError
              If the shape of `X` or `Y` does not match the shape of the training or target data.
          ValueError
              If the fit method has not been called prior to calling this method.
          """
      self._verify_fit()
      X, Y = self._preprocess_args(X, Y)
      test_grads = []
      for x, y in zip(X, Y):
          test_grads.append(
              self._compute_grad(self._format(x), y[None])[None]
          )
      grads_X_test = np.concatenate(np.array(test_grads), axis=0)
      if not self.precompute_grads:
          scores = self._compute_adhoc_similarity(grads_X_test)
      else:
          scores = self.sim_fn(grads_X_test, self.grad_X_train)
      return self._build_explanation(scores)
  
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 4 for calling the GradientSimilarity obj's underlying member method code with example usage: obj.explain(X, Y) and returns: self