code: def cross_entropy_error(self, input_data, targets, average=True,
                              cache=None, prediction=False,
                              sum_errors=True):
          
  
          loss = []
          if cache is None:
              cache = self.n_tasks * [None]
  
          for targets_task, cache_task, task in \
              izip(targets, cache, self.tasks):
              loss.append(task.cross_entropy_error(
                  input_data, targets_task, average=average,
                  cache=cache_task,
                  prediction=prediction))
  
          if sum_errors:
              return sum(loss)
          else:
              return loss
docs: Computes the cross-entropy error for all tasks.