typing hell

pytorch_lightning/lite/lite.py

@@ -16,7 +16,7 @@
 from contextlib import contextmanager
 from functools import partial
 from pathlib import Path
-from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Sequence, Tuple, Union, cast
 
 import torch
 import torch.nn as nn
@@ -188,7 +188,7 @@ def setup(
 
     def setup_dataloaders(
         self, *dataloaders: DataLoader, replace_sampler: bool = True, move_to_device: bool = True
-    ) -> Union[_LiteDataLoader, List[_LiteDataLoader]]:
+    ) -> Union[DataLoader, List[DataLoader]]:
         """Setup one or multiple dataloaders for accelerated training. If you need different settings for each
         dataloader, call this method individually for each one.
 
@@ -213,7 +213,7 @@ def setup_dataloaders(
 
     def _setup_dataloader(
         self, dataloader: DataLoader, replace_sampler: bool = True, move_to_device: bool = True
-    ) -> _LiteDataLoader:
+    ) -> DataLoader:
         """Setup a single dataloader for accelerated training.
 
         Args:
@@ -246,7 +246,9 @@ def _setup_dataloader(
 
         dataloader = self._strategy.process_dataloader(dataloader)
         device = self.device if move_to_device and not isinstance(self._strategy, TPUSpawnPlugin) else None
-        return _LiteDataLoader(dataloader=dataloader, device=device)
+        lite_dataloader = _LiteDataLoader(dataloader=dataloader, device=device)
+        lite_dataloader = cast(DataLoader, lite_dataloader)
+        return lite_dataloader
 
     def backward(self, tensor: Tensor, *args: Any, model: Optional[_LiteModule] = None, **kwargs: Any) -> None:
         """Replaces ``loss.backward()`` in your training loop. Handles precision and automatically for you.