[2/n] Support module.to("cuda:0") in FakeTensorMode on cuda-less machine (#163433)

Summary:
To support exporting a cuda model on a CPU-only machine under fake tensor mode.
User commonly need to move sample inputs to the cuda device with .to("cuda:0") or .to("cuda") call.
This diff supports this.

I expect the following pattern to work

```
with FakeTensorMode(allow_non_fake_inputs=True):
    cuda_module = module.to("cuda:0")
    cuda_sample_inputs = tuple([x.to("cuda:0") for x in sample_inputs])

    with torch.no_grad():
        ep = torch.export.export(cuda_module, cuda_sample_inputs)

```

Before
Moving module.to("cuda:0") under fake tensor mode would have parameter on `meta` device.

After
parameters would be on "cuda:0" .

Test Plan: buck2 run  fbcode//caffe2/test:fake_tensor -- --r test_move_module

Reviewed By: mikaylagawarecki

Differential Revision: D80102876

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163433
Approved by: https://github.com/albanD

torch/nn/modules/module.py

@@ -929,8 +929,12 @@ class Module:
             for module in self.children():
                 module._apply(fn)
 
+        from torch._subclasses.fake_tensor import FakeTensor
+
         def compute_should_use_set_data(tensor, tensor_applied) -> bool:
-            if torch._has_compatible_shallow_copy_type(tensor, tensor_applied):
+            if torch._has_compatible_shallow_copy_type(
+                tensor, tensor_applied
+            ) and not isinstance(tensor_applied, FakeTensor):
                 # If the new tensor has compatible tensor type as the existing tensor,
                 # the current behavior is to change the tensor in-place using `.data =`,
                 # and the future behavior is to overwrite the existing tensor. However,
@@ -957,8 +961,6 @@ class Module:
                 param_applied = fn(param)
             p_should_use_set_data = compute_should_use_set_data(param, param_applied)
 
-            from torch._subclasses.fake_tensor import FakeTensor
-
             # subclasses may have multiple child tensors so we need to use swap_tensors
             p_should_use_swap_tensors = (
                 should_use_swap_tensors