code: def get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None, pbar:Optional[PBar]=None,
                    ordered:bool=False) -> List[Tensor]:
          "Return predictions and targets on the valid, train, or test set, depending on `ds_type`."
          self.model.reset()
          if ordered: np.random.seed(42)
          preds = super().get_preds(ds_type=ds_type, with_loss=with_loss, n_batch=n_batch, pbar=pbar)
          if ordered and hasattr(self.dl(ds_type), 'sampler'):
              np.random.seed(42)
              sampler = [i for i in self.dl(ds_type).sampler]
              reverse_sampler = np.argsort(sampler)
              preds = [p[reverse_sampler] for p in preds] 
          return(predsf get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None, pbar:Optional[PBar]=None,
                    ordered:bool=False) -> List[Tensor]:
          "Return predictions and targets on the valid, train, or test set, depending on `ds_type`."
          self.model.reset()
          if ordered: np.random.seed(42)
          preds = super().get_preds(ds_type=ds_type, with_loss=with_loss, n_batch=n_batch, pbar=pbar)
          if ordered and hasattr(self.dl(ds_type), 'sampler'):
              np.random.seed(42)
              sampler = [i for i in self.dl(ds_type).sampler]
              reverse_sampler = np.argsort(sampler)
              preds = [p[reverse_sampler] for p in preds] 
          return(preds)
docs: Return predictions and targets on the valid, train, or test set, depending on `ds_type`.