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 _is_numeric_feature(
      self,
      feature_name,
  ) -> bool:
      if not self._is_fitted:
          raise RuntimeError(
              f"Error: can not verify feature [{feature_name}] dtype without fit operation."
          )
      return (feature_name in self.feature_range) and (
          "max" in self.feature_range[feature_name]
      )
  
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 3 for calling the GroupedCEExplainer obj's underlying member method code with example usage: obj._is_numeric_feature(feature_name) and returns: unknown