code: def shap_values(self, X, nsamples=200, ranked_outputs=None, output_rank_order="max", rseed=None):
          
          return self.explainer.shap_values(X, nsamples, ranked_outputs, output_rank_order, rseed)
docs: Return the values for the model applied to 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 phi is a list of numpy arrays for each of
              the output ranks, and indexes is a matrix that tells for each sample which output indexes
              were choses as "top".
  
          output_rank_order : "max", "min", "max_abs", or "custom"
              How to order the model outputs when using ranked_outputs, either by maximum, minimum, or
              maximum absolute value. If "custom" Then "ranked_outputs" contains a list of output nodes.
  
          rseed : None or int
              Seeding the randomness in shap value computation  (background example choice, 
              interpolation between current and background example, smoothing).
  
          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 tells for each sample which output indexes
          were chosen as "top".