code: def shap_values(self, X, ranked_outputs=None, output_rank_order='max'):
          
          return self.explainer.shap_values(X, ranked_outputs, output_rank_order)
docs: Return approximate SHAP values for the model applied to the data given by X.
  
          Parameters
          ----------
          X : list,
              if framework == 'tensorflow': numpy.array, or pandas.DataFrame
              if framework == 'pytorch': torch.tensor
              A tensor (or list of tensors) of samples (where X.shape[0] == # samples) on which to
              explain the model's output.
  
          ranked_outputs : None or int
              If ranked_outputs is None then we explain all the outputs in a multi-output model. If
              ranked_outputs is a positive integer then we only explain that many of the top model
              outputs (where "top" is determined by output_rank_order). Note that this causes a pair
              of values to be returned (shap_values, indexes), where shap_values is a list of numpy
              arrays for each of the output ranks, and indexes is a matrix that indicates for each sample
              which output indexes were choses as "top".
  
          output_rank_order : "max", "min", or "max_abs"
              How to order the model outputs when using ranked_outputs, either by maximum, minimum, or
              maximum absolute value.
  
          Returns
          -------
          For a models with a single output this returns a tensor of SHAP values with the same shape
          as X. For a model with multiple outputs this returns a list of SHAP value tensors, each of
          which are the same shape as X. If ranked_outputs is None then this list of tensors matches
          the number of model outputs. If ranked_outputs is a positive integer a pair is returned
          (shap_values, indexes), where shap_values is a list of tensors with a length of
          ranked_outputs, and indexes is a matrix that indicates for each sample which output indexes
          were chosen as "top".