Returning None in validation_end method raises error about lightning HOT 3 CLOSED

    def validation_epoch_end(self, validation_outputs):
        return {}

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-27-f49f97955aea> in <module>
      4     check_val_every_n_epoch=3,
      5 )
----> 6 trainer.fit(model)

~/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/states.py in wrapped_fn(self, *args, **kwargs)
     46             if entering is not None:
     47                 self.state = entering
---> 48             result = fn(self, *args, **kwargs)
     49 
     50             # The INTERRUPTED state can be set inside the run function. To indicate that run was interrupted

~/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
   1082             self.accelerator_backend = CPUBackend(self)
   1083             self.accelerator_backend.setup(model)
-> 1084             results = self.accelerator_backend.train(model)
   1085 
   1086         # on fit end callback

~/anaconda3/lib/python3.8/site-packages/pytorch_lightning/accelerators/cpu_backend.py in train(self, model)
     37 
     38     def train(self, model):
---> 39         results = self.trainer.run_pretrain_routine(model)
     40         return results

~/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in run_pretrain_routine(self, model)
   1222 
   1223         # run a few val batches before training starts
-> 1224         self._run_sanity_check(ref_model, model)
   1225 
   1226         # clear cache before training

~/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in _run_sanity_check(self, ref_model, model)
   1255             num_loaders = len(self.val_dataloaders)
   1256             max_batches = [self.num_sanity_val_steps] * num_loaders
-> 1257             eval_results = self._evaluate(model, self.val_dataloaders, max_batches, False)
   1258 
   1259             # allow no returns from eval

~/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/evaluation_loop.py in _evaluate(self, model, dataloaders, max_batches, test_mode)
    397 
    398         # log callback metrics
--> 399         self.__update_callback_metrics(eval_results, using_eval_result)
    400 
    401         # Write predictions to disk if they're available.

~/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/evaluation_loop.py in __update_callback_metrics(self, eval_results, using_eval_result)
    419             if isinstance(eval_results, list):
    420                 for eval_result in eval_results:
--> 421                     self.callback_metrics = eval_result.callback_metrics
    422             else:
    423                 self.callback_metrics = eval_results.callback_metrics

AttributeError: 'dict' object has no attribute 'callback_metrics'