[Typing] 修复一些类型标注问题 (PaddlePaddle#66677)

---------

Co-authored-by: SigureMo <sigure.qaq@gmail.com>

python/paddle/incubate/autograd/functional.py

@@ -295,7 +295,9 @@ def __init__(
         else:
             self._jacobian = _JacobianBatchFirst(func, xs)
 
-    def __getitem__(self, indexes: int | slice) -> Tensor:
+    def __getitem__(
+        self, indexes: int | slice | tuple[int | slice, ...]
+    ) -> Tensor:
         return self._jacobian[indexes]
 
     @property

python/paddle/io/dataloader/dataset.py

@@ -100,6 +100,11 @@ def __len__(self) -> int:
             "{}".format('__len__', self.__class__.__name__)
         )
 
+    if TYPE_CHECKING:
+        # A virtual method for type checking only
+        def __iter__(self) -> Iterator[_T]:
+            ...
+
 
 class IterableDataset(Dataset[_T]):
     """
@@ -482,19 +487,32 @@ class Subset(Dataset[_T]):
         .. code-block:: python
 
             >>> import paddle
-            >>> from paddle.io import Subset
 
-            >>> # example 1:
-            >>> a = paddle.io.Subset(dataset=range(1, 4), indices=[0, 2])
+            >>> class RangeDataset(paddle.io.Dataset):  # type: ignore[type-arg]
+            ...     def __init__(self, start, stop):
+            ...         self.start = start
+            ...         self.stop = stop
+            ...
+            ...     def __getitem__(self, index):
+            ...         return index + self.start
+            ...
+            ...     def __len__(self):
+            ...         return self.stop - self.start
+
+            >>> # Example 1:
+            >>> a = paddle.io.Subset(dataset=RangeDataset(1, 4), indices=[0, 2])
             >>> print(list(a))
             [1, 3]
 
-            >>> # example 2:
-            >>> b = paddle.io.Subset(dataset=range(1, 4), indices=[1, 1])
+            >>> # Example 2:
+            >>> b = paddle.io.Subset(dataset=RangeDataset(1, 4), indices=[1, 1])
             >>> print(list(b))
             [2, 2]
     """
 
+    dataset: Dataset[_T]
+    indices: Sequence[int]
+
     def __init__(self, dataset: Dataset[_T], indices: Sequence[int]) -> None:
         self.dataset = dataset
         self.indices = indices

python/paddle/io/dataloader/worker.py

@@ -17,6 +17,7 @@
 import queue
 import sys
 import traceback
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 
@@ -31,6 +32,9 @@
 from .fetcher import _IterableDatasetFetcher, _MapDatasetFetcher
 from .flat import _flatten_batch
 
+if TYPE_CHECKING:
+    from paddle.io import Dataset
+
 
 class _IterableDatasetStopIteration:
     def __init__(self, worker_id):
@@ -77,7 +81,7 @@ def is_alive(self):
 _worker_info = None
 
 
-def get_worker_info() -> WorkerInfo | None:
+def get_worker_info() -> WorkerInfo:
     """
     Get DataLoader worker process information function, this function is
     used to split data copy in worker process for IterableDataset
@@ -105,7 +109,7 @@ def get_worker_info() -> WorkerInfo | None:
             >>> import numpy as np
             >>> from paddle.io import IterableDataset, DataLoader, get_worker_info
 
-            >>> class SplitedIterableDataset(IterableDataset):
+            >>> class SplitedIterableDataset(IterableDataset): # type: ignore[type-arg]
             ...     def __init__(self, start, end):
             ...         self.start = start
             ...         self.end = end
@@ -118,8 +122,8 @@ def get_worker_info() -> WorkerInfo | None:
             ...         else:
             ...             per_worker = int(
             ...                 math.ceil((self.end - self.start) / float(
-            ...                     worker_info.num_workers)))  # type: ignore[attr-defined]
-            ...             worker_id = worker_info.id  # type: ignore[attr-defined]
+            ...                     worker_info.num_workers)))
+            ...             worker_id = worker_info.id
             ...             iter_start = self.start + worker_id * per_worker
             ...             iter_end = min(iter_start + per_worker, self.end)
             ...
@@ -157,6 +161,11 @@ def get_worker_info() -> WorkerInfo | None:
 
 
 class WorkerInfo:
+    num_workers: int
+    id: int
+    dataset: Dataset[Any]
+    seed: int
+
     __initialized = False
 
     def __init__(self, **kwargs):
@@ -390,9 +399,11 @@ def numpy2lodtensor(arr):
                         return lodtensor
 
                     tensor_list = [
-                        numpy2lodtensor(b)
-                        if isinstance(b, np.ndarray)
-                        else b.get_tensor()
+                        (
+                            numpy2lodtensor(b)
+                            if isinstance(b, np.ndarray)
+                            else b.get_tensor()
+                        )
                         for b in batch
                     ]
                     out_queue.put((idx, tensor_list, structure))