[Typing][B-40] Add type annotations for `python/paddle/autograd/backw…

…ard_mode.py` (PaddlePaddle#66277)

python/paddle/autograd/backward_mode.py

@@ -12,15 +12,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Sequence
+
 import paddle
 from paddle.base import core, framework
 from paddle.base.backward import gradients_with_optimizer  # noqa: F401
 
+if TYPE_CHECKING:
+    from paddle import Tensor
+
+
 __all__ = []
 
 
 @framework.dygraph_only
-def backward(tensors, grad_tensors=None, retain_graph=False):
+def backward(
+    tensors: list[Tensor],
+    grad_tensors: list[Tensor | None] | None = None,
+    retain_graph: bool = False,
+) -> None:
     """
     Compute the backward gradients of given tensors.
 
@@ -80,19 +92,21 @@ def backward(tensors, grad_tensors=None, retain_graph=False):
 
     """
 
-    def check_tensors(in_out_list, name):
+    def check_tensors(
+        in_out_list: Sequence[Tensor] | Tensor, name: str
+    ) -> Sequence[Tensor]:
         assert in_out_list is not None, f"{name} should not be None"
 
         if isinstance(in_out_list, (list, tuple)):
             assert len(in_out_list) > 0, f"{name} cannot be empty"
             for each_var in in_out_list:
                 assert isinstance(
-                    each_var, (paddle.Tensor, core.eager.Tensor)
+                    each_var, paddle.Tensor
                 ), f"Elements of {name} must be paddle.Tensor"
             return in_out_list
         else:
             assert isinstance(
-                in_out_list, (paddle.Tensor, core.eager.Tensor)
+                in_out_list, paddle.Tensor
             ), f"{name} must be Tensor or list of Tensor"
             return [in_out_list]
 
@@ -109,7 +123,7 @@ def check_tensors(in_out_list, name):
         for each_tensor in grad_tensors:
             if each_tensor is not None:
                 assert isinstance(
-                    each_tensor, (paddle.Tensor, core.eager.Tensor)
+                    each_tensor, paddle.Tensor
                 ), "The argument 'grad_tensors' of paddle.autograd.backward is invalid, it can be 'None', 'paddle.Tensor' or 'list[None/paddle.Tensor]'."
     else:
         grad_tensors = []