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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def shap_values(self, X, ranked_outputs=None, output_rank_order='max'):
 """ 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". """

        return self.explainer.shap_values(X, ranked_outputs, output_rank_order)