Use is_available instead of device_count to check for CUDA availability (#97043)
There are some tests that incorrectly uses the number of GPU devices `torch.cuda.device_count() > 0` to check for CUDA availability instead of the default `torch.cuda.is_available()` call. This makes these tests more brittle when encountering infra flakiness on G5 runner using A10G, for example [test_pytorch_np](https://hud.pytorch.org/failure/FAILED%20test_tensorboard.py%3A%3ATestTensorBoardPyTorchNumpy%3A%3Atest_pytorch_np%20-%20RuntimeError%3A%20No%20CUDA%20GPUs%20are%20available).
The underlying problem is that GPU devices could crash on these runner. While the root cause for that is unclear and we will try to upgrade to a new NVIDIA driver https://github.com/pytorch/pytorch/pull/96904 to see if it helps, we can also make these tests more resilient by using the correct check to skip tests correctly when GPU crashes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97043
Approved by: https://github.com/clee2000
