more args

          results.append(result)

        return np.concatenate(results, axis=0)

  def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=False):

  def evaluate(self,

               dataset,

               metrics,

               transformers=[],

               outputs=None,

               per_task_metrics=False):

    if not self.built:

      self.build()

    return self.evaluate_generator(

        self.default_generator(dataset, predict=True),

        metrics,

        labels=self.my_labels,

        transformers=transformers,

        outputs=outputs,

        weights=[self.my_task_weights],

        per_task_metrics=per_task_metrics)