          standard = [t.numpy() for t in self._model.model([feed_a, feed_b])]
          swapped = [t.numpy() for t in self._model.model([feed_b, feed_a])]
  
          if self._model.config["learn_mask"]:  # Add mask to 4th channel of final output
              standard = [np.concatenate(side[-2:], axis=-1) for side in standard]
              swapped = [np.concatenate(side[-2:], axis=-1) for side in swapped]
          else:  # Retrieve final output
              standard = [side[-1] if isinstance(side, list) else side for side in standard]
              swapped = [side[-1] if isinstance(side, list) else side for side in swapped]
  
          preds["a_a"] = standard[0]
 
</s> remove         standard = [t.numpy() for t in self._model.model([feed_a, feed_b])]
        swapped = [t.numpy() for t in self._model.model([feed_b, feed_a])]
 </s> add         standard = self._model.model([feed_a, feed_b])
        swapped = self._model.model([feed_b, feed_a]) </s> remove             standard = [side[-1] if isinstance(side, list) else side for side in standard]
            swapped = [side[-1] if isinstance(side, list) else side for side in swapped]
 </s> add             standard = [side[-1] if isinstance(side, list) else side
                        for side in [t.numpy() for t in standard]]
            swapped = [side[-1] if isinstance(side, list) else side
                       for side in [t.numpy() for t in swapped]]
            standard = [np.concatenate(side[-2:], axis=-1)
                        for side in [[s.numpy() for s in t] for t in standard]]
            swapped = [np.concatenate(side[-2:], axis=-1)
                       for side in [[s.numpy() for s in t] for t in swapped]]
          standard = [t.numpy() for t in self._model.model([feed_a, feed_b])]
          swapped = [t.numpy() for t in self._model.model([feed_b, feed_a])]
  
          if self._model.config["learn_mask"]:  # Add mask to 4th channel of final output
            standard = [np.concatenate(side[-2:], axis=-1)
                        for side in [[s.numpy() for s in t] for t in standard]]
            swapped = [np.concatenate(side[-2:], axis=-1)
                       for side in [[s.numpy() for s in t] for t in swapped]]
            standard = [np.concatenate(side[-2:], axis=-1)
                        for side in [[s.numpy() for s in t] for t in standard]]
            swapped = [np.concatenate(side[-2:], axis=-1)
                       for side in [[s.numpy() for s in t] for t in swapped]]
          else:  # Retrieve final output
              standard = [side[-1] if isinstance(side, list) else side for side in standard]
              swapped = [side[-1] if isinstance(side, list) else side for side in swapped]
  
          preds["a_a"] = standard[0]
keep keep keep keep replace replace keep keep keep keep keep
 bugfix: train - fix learn_mask training 
plugins/train/trainer/_base.py