code: def _update_settings(self, X):
          
  
          self.logger.info("Loading default parameters for Sparse Logistic Regression")
          config = get_config()["learning"]["SparseLogisticRegression"]
  
          for key in config.keys():
              if key not in self.settings:
                  self.settings[key] = config[key]
  
          # Add one feature for padding vector (all 0s)
          self.settings["input_dim"] = X[1].shape[1] + 1
docs: Update the model argument.
  
          :param X: The input data of the model.
          :type X: list of (candidate, features) pair