Change activation modules in C++ from using Tensor& to Tensor (#28501)

Summary:
Sequential does not like modules added to it to take Tensor&
(const Tensor& and Tensor are both OK).
Functional and others use Tensor when they want to potentially
change things in-place.
This changes ReLU and friends to also do that.

Unfortunately, this seems to be BC breaking on the ABI level.
On the other hand, use of the module ReLU seems rare enough outside
Sequential (in particular in C++ models, the standard seems to be
to use torch::relu instead).

is the BC breaking OK here? (yf225 or anyone else)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28501

Differential Revision: D18089978

Pulled By: yf225

fbshipit-source-id: ac9aba6dc2081117dece57cd8a15bafe14ec8f51

test/cpp/api/sequential.cpp

@@ -13,6 +13,10 @@ using namespace torch::test;
 
 struct SequentialTest : torch::test::SeedingFixture {};
 
+TEST_F(SequentialTest, CanContainThings) {
+  Sequential sequential(Linear(3, 4), ReLU(), BatchNorm(3));
+}
+
 TEST_F(SequentialTest, ConstructsFromSharedPointer) {
   struct M : torch::nn::Module {
     explicit M(int value_) : value(value_) {}