code: def fit(self, X, y):
          
          if self.timeseries_container is None:
              raise RuntimeError("You have to provide a time series using the set_timeseries_container function before.")
  
          self.feature_extractor.set_timeseries_container(self.timeseries_container)
  
          if self.filter_only_tsfresh_features:
              # Do not merge the time series features to the old features
              X_tmp = pd.DataFrame(index=X.index)
          else:
              X_tmp = X
  
          X_augmented = self.feature_extractor.transform(X_tmp)
  
          self.col_to_max, self.col_to_min, self.col_to_median = get_range_values_per_column(X_augmented)
          X_augmented = impute_dataframe_range(X_augmented, col_to_max=self.col_to_max, col_to_median=self.col_to_median,
                                               col_to_min=self.col_to_min)
  
          self.feature_selector.fit(X_augmented, y)
  
          return self
docs: Use the given timeseries from :func:`~set_timeseries_container` and calculate features from it and add them
          to the data sample X (which can contain other manually-designed features).
  
          Then determine which of the features of X are relevant for the given target y.
          Store those relevant features internally to only extract them in the transform step.
  
          If filter_only_tsfresh_features is True, only reject newly, automatically added features. If it is False,
          also look at the features that are already present in the DataFrame.
  
          :param X: The data frame without the time series features. The index rows should be present in the timeseries
             and in the target vector.
          :type X: pandas.DataFrame or numpy.array
  
          :param y: The target vector to define, which features are relevant.
          :type y: pandas.Series or numpy.array
  
          :return: the fitted estimator with the information, which features are relevant.
          :rtype: RelevantFeatureAugmenter