[functorch] fix batching rule for dropout (#92975)
Fixes https://github.com/pytorch/pytorch/issues/92283
The repro now works:
```python
import torch
import torch.func
import torch.nn as nn
x = torch.randn(3, device='cuda')
y = torch.randn(1, 3, device='cuda')
def fn(x, y):
# previously output of dropout used to be incorrect [B, 3] (B=1) and thus `mean(1)` used to fail
# post the fix output of dropout is [B, 1, 3] and `mean(1)` works.
return x + nn.functional.dropout(y, 0.3).mean(1)
o = torch.func.vmap(fn, in_dims=(0, None), randomness='different')(x, y)
```
**NOTE**:
`native_dropout_batching_rule(const Tensor& tensor, double p, c10::optional<bool> train)` was called only for CUDA tensor. Hence this issue only affected CUDA tensors and not CPU tensors
Ref:
https://github.com/pytorch/pytorch/blob/a6ac922eabee8fce7a48dedac81e82ac8cfe9a45/aten/src/ATen/functorch/PyTorchOperatorHacks.cpp#L251-L258
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92975
Approved by: https://github.com/Chillee, https://github.com/Skylion007
