code: def one_batch(self, ds_type:DatasetType=DatasetType.Train, detach:bool=True, denorm:bool=True, cpu:bool=True)->Collection[Tensor]:
          "Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`."
          dl = self.dl(ds_type)
          w = self.num_workers
          self.num_workers = 0
          try:     x,y = next(iter(dl))
          finally: self.num_workers = w
          if detach: x,y = to_detach(x,cpu=cpu),to_detach(y,cpu=cpu)
          norm = getattr(self,'norm',False)
          if denorm and norm:
              x = self.denorm(x)
              if norm.keywords.get('do_y',False): y = self.denorm(y, do_x=True)
          return x,f one_batch(self, ds_type:DatasetType=DatasetType.Train, detach:bool=True, denorm:bool=True, cpu:bool=True)->Collection[Tensor]:
          "Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`."
          dl = self.dl(ds_type)
          w = self.num_workers
          self.num_workers = 0
          try:     x,y = next(iter(dl))
          finally: self.num_workers = w
          if detach: x,y = to_detach(x,cpu=cpu),to_detach(y,cpu=cpu)
          norm = getattr(self,'norm',False)
          if denorm and norm:
              x = self.denorm(x)
              if norm.keywords.get('do_y',False): y = self.denorm(y, do_x=True)
          return x,y
docs: Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`.