code: def _canonize_content_input(self, dataset, single_style):
          
          unpack = lambda x: x
          if isinstance(dataset, _tc.SArray):
              dataset = _tc.SFrame({self.content_feature: dataset})
              if single_style:
                  unpack = lambda sf: sf['stylized_' + self.content_feature]
          elif isinstance(dataset, _tc.Image):
              dataset = _tc.SFrame({self.content_feature: [dataset]})
              if single_style:
                  unpack = lambda sf: sf['stylized_' + self.content_feature][0]
          return dataset, unpack
docs: Takes input and returns tuple of the input in canonical form (SFrame)
          along with an unpack callback function that can be applied to
          prediction results to "undo" the canonization.