Fix wrong type stubs

stubs/torch/__init__.pyi

@@ -373,10 +373,14 @@ class Tensor:
     def is_coalesced(self) -> bool: ...
     def is_complex(self) -> bool: ...
     def is_contiguous(self) -> bool: ...
-    def is_cuda(self) -> bool: ...
+#MODIFIED BY TORCHGPIPE
+    is_cuda: bool
+#END
     def is_distributed(self) -> bool: ...
     def is_floating_point(self) -> bool: ...
-    def is_leaf(self) -> bool: ...
+#MODIFIED BY TORCHGPIPE
+    is_leaf: bool
+#END
     def is_nonzero(self) -> bool: ...
     def is_same_size(self, other: Tensor) -> bool: ...
     def is_set_to(self, tensor: Tensor) -> bool: ...

stubs/torch/nn/__init__.pyi

@@ -1,5 +1,5 @@
 #MODIFIED BY TORCHGPIPE
-from typing import Any, Callable, Iterable, Iterator, TypeVar
+from typing import Any, Callable, Iterable, Iterator, Tuple, TypeVar
 
 from torch import Tensor, device
 
@@ -33,6 +33,9 @@ class Module:
     def register_forward_pre_hook(self, hook: __Hook1) -> __RemovableHandle: ...
     def register_forward_hook(self, hook: __Hook2) -> __RemovableHandle: ...
 
+    def named_children(self) -> Iterable[Tuple[str, Module]]: ...
+    def add_module(self, name: str, module: Module) -> None: ...
+
 
 class Sequential(Module):
     def __init__(self, *args: Module) -> None: ...

stubs/torch/nn/modules/batchnorm.pyi

@@ -12,8 +12,11 @@ class _BatchNorm(Module):
     affine: bool
     track_running_stats: bool
 
+    weight: Tensor
+    bias: Tensor
     running_mean: Tensor
     running_var: Tensor
+    num_batches_tracked: Tensor
 
     # for DeferredBatchNorm
     sum: Tensor