class_docstr: Grouped Conditional Expectation plots are generated for a given instance and set of features.
  They show how the model prediction is affected when a pair of features of a given instance are
  perturbed. The perturbed features can be either a provided subset of the input covariates, or
  the top K features based on the importance rank obtained by a global explainer (e.g., SHAP).
  If the user provides a single feature then the algorithm produces a standard ICE plot where the
  selected feature varies according to a linespace grid. If the user chooses more than one feature,
  then the explainer produces 3D ICE plots. Here, two features vary simultaneously according to a
  meshgrid, the output of the model is stored for each pair of values.
code: def _batch_predict(self, x_perturbations):
      f_predict_samples = None
  
      try:
          f_predict_samples = self.model(x_perturbations)
      except Exception as ex:
          warnings.warn(
              "Batch scoring failed with error: {}. Scoring sequentially...".format(
                  ex
              )
          )
          f_predict_samples = [
              self.model(x_perturbations[i])
              for i in range(x_perturbations.shape[0])
          ]
          f_predict_samples = np.array(f_predict_samples)
  
      if f_predict_samples is None:
          raise Exception("Model prediction could not be computed.")
      return f_predict_samples
  
file_path: AIX360/aix360/algorithms/gce/gce.py
imports: import warnings
  from typing import List, Union, Callable
  import numpy
  import pandas
  import shap
  from aix360.algorithms.lbbe import LocalBBExplainer
label_desc: Use image node_id 7 for calling the GroupedCEExplainer obj's underlying member method code with example usage: obj._batch_predict(x_perturbations) and returns: f_predict_samples