code: def transform(self, data):
          
  
          if not self._get("fitted"):
              raise RuntimeError("`transform` called before `fit` or `fit_transform`.")
  
          data = data.copy()
          output_column_prefix = self._get("output_column_prefix")
  
          if output_column_prefix is None:
              prefix = ""
          else:
              prefix = output_column_prefix + '.'
  
          transform_function = self._get("transform_function")
  
          feature_columns = self._get("features")
          feature_columns = _internal_utils.select_feature_subset(data, feature_columns)
  
          for f in feature_columns:
              data[prefix + f] = transform_function(data[f])
  
          return data
docs: Transforms the data.