code: def explain_instance(self, data_row, classifier_fn, labels=(1,),
                           top_labels=None, num_features=10, num_samples=5000,
                           distance_metric='euclidean', model_regressor=None):
          
  
          # Flatten input so that the normal explainer can handle it
          data_row = data_row.T.reshape(self.n_timesteps * self.n_features)
  
          # Wrap the classifier to reshape input
          classifier_fn = self._make_predict_proba(classifier_fn)
          return super(RecurrentTabularExplainer, self).explain_instance(
              data_row, classifier_fn,
              labels=labels,
              top_labels=top_labels,
              num_features=num_features,
              num_samples=num_samples,
              distance_metric=distance_metric,
              model_regressor=model_regressor)
docs: Generates explanations for a prediction.
  
          First, we generate neighborhood data by randomly perturbing features
          from the instance (see __data_inverse). We then learn locally weighted
          linear models on this neighborhood data to explain each of the classes
          in an interpretable way (see lime_base.py).
  
          Args:
              data_row: 2d numpy array, corresponding to a row
              classifier_fn: classifier prediction probability function, which
                  takes a numpy array and outputs prediction probabilities. For
                  ScikitClassifiers , this is classifier.predict_proba.
              labels: iterable with labels to be explained.
              top_labels: if not None, ignore labels and produce explanations for
                  the K labels with highest prediction probabilities, where K is
                  this parameter.
              num_features: maximum number of features present in explanation
              num_samples: size of the neighborhood to learn the linear model
              distance_metric: the distance metric to use for weights.
              model_regressor: sklearn regressor to use in explanation. Defaults
                  to Ridge regression in LimeBase. Must have
                  model_regressor.coef_ and 'sample_weight' as a parameter
                  to model_regressor.fit()
  
          Returns:
              An Explanation object (see explanation.py) with the corresponding
              explanations.