docstr: Explain an instance and return the counterfactual with metadata.
  
  Parameters
  ----------
  X
      Instance to be explained.
  
  Returns
  -------
  explanation
      `Explanation` object containing the counterfactual with additional metadata as attributes.
      See usage at `Counterfactual examples`_ for details.
  
      .. _Counterfactual examples:
          https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html
docstr_tok: ["Explain","an","instance","and","return","the","counterfactual","with","metadata",".","Parameters","--","--","--","--","--","X","Instance","to","be","explained",".","Returns","--","--","--","-","explanation","`","Explanation","`","object","containing","the","counterfactual","with","additional","metadata","as","attributes",".","See","usage","at","`","Counterfactual","examples","`","_","for","details",".","..","_Counterfactual","examples",":","https",":","\/\/docs.seldon.io\/projects\/alibi\/en\/stable\/methods\/CF.html"]
code: def explain(self, X: np.ndarray) -> Explanation:
      """
      Explain an instance and return the counterfactual with metadata.
  
      Parameters
      ----------
      X
          Instance to be explained.
  
      Returns
      -------
      explanation
          `Explanation` object containing the counterfactual with additional metadata as attributes.
          See usage at `Counterfactual examples`_ for details.
  
          .. _Counterfactual examples:
              https://docs.seldon.io/projects/alibi/en/stable/methods/CF.html
      """
      # TODO change init parameters on the fly
  
      if X.shape[0] != 1:
          logger.warning(
              "Currently only single instance explanations supported (first dim = 1), "
              "but first dim = %s",
              X.shape[0],
          )
  
      # make a prediction
      Y = self.predict_fn(X)
  
      pred_class = Y.argmax(axis=1).item()
      pred_prob = Y.max(axis=1).item()
      self.return_dict["orig_class"] = pred_class
      self.return_dict["orig_proba"] = pred_prob
  
      logger.debug(
          "Initial prediction: %s with p=%s", pred_class, pred_prob
      )
  
      # define the class-specific prediction function
      self.predict_class_fn, t_class = _define_func(
          self.predict_fn, pred_class, self.target_class
      )
  
      # initialize with an instance
      X_init = self._initialize(X)
  
      # minimize loss iteratively
      self._minimize_loss(X, X_init, Y)
  
      return_dict = self.return_dict.copy()
      self.instance_dict = dict.fromkeys(
          ["X", "distance", "lambda", "index", "class", "proba", "loss"]
      )
      self.return_dict = {
          "cf": None,
          "all": {i: [] for i in range(self.max_lam_steps)},
          "orig_class": None,
          "orig_proba": None,
      }
  
      # create explanation object
      explanation = Explanation(
          meta=copy.deepcopy(self.meta), data=return_dict
      )
  
      return explanation
  
imports: import copy
  import logging
  from typing import Callable, Optional, Tuple, Union
  import numpy
  import tensorflow.compat.v1
  from alibi.api.defaults import DEFAULT_DATA_CF, DEFAULT_META_CF
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.utils.gradients import num_grad_batch
label_desc: Use image node_id 4 for calling the Counterfactual obj's underlying member method code with example usage: obj.explain(X) and returns: explanation