misusing percision value in test_cuda function in torch/testing/_internal/common_nn.py.
Labels: triaged, module: testing

### 🐛 Describe the bug

I found tf32_precision of NewModuleTest class. is not using  tf32 test. Like Conv2d_groups testcase.
In test_nn.py file, using add_test to collect testcases and enable tf32 testcases, which is using with tf32_on(self, test.tf32_precision) to set tf32 config and also modify self.precision value. But self.precision is not using in test_cuda.
we can find self.precision in test_cuda function is not tf32_percision in add_test function. From add_test to test_cuda, self variable is to testcase, and test in add_test is self in test_cuda.
def add_test(test, decorator=None) path:  https://github.com/pytorch/pytorch/blob/main/test/test_nn.py#L7484
def tf32_on(self, tf32_precision=1e-5): https://github.com/pytorch/pytorch/blob/main/torch/testing/_internal/common_cuda.py#L94
def test_cuda(self, test_case):  https://github.com/pytorch/pytorch/blob/main/torch/testing/_internal/common_nn.py#L4450

```
# before enter to test_cuda function.
-> test.test_cuda(self, **kwargs)
(Pdb) p self.percision
(Pdb) p test.__dict__["precision"]
0.0002
(Pdb) p self.__dict__["_precision"]
0.005
(Pdb) p test.tf32_precision
0.005
# enter test_cuda function
(Pdb) n
> /projs/platform/shangang/anaconda3/envs/shangang_conda_torch19/lib/python3.7/site-packages/torch/testing/_internal/common_nn.py(6029)test_cuda()
-> if not TEST_CUDA or not self.should_test_cuda:
(Pdb) p test_case.precision
0.005
(Pdb) p self.precision
0.0002 // still using 0.0002 to compare cuda tf32 result to cpu result, without using tf32_percision.
````

Is this a bug of testcases?

### Versions

Collecting environment information...
PyTorch version: 1.13.0a0+gitd922c29
Is debug build: True
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.27.4
Libc version: glibc-2.17

Python version: 3.7.16 (default, Jan 17 2023, 22:20:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.15.0-161-generic-x86_64-with-debian-buster-sid
Is CUDA available: True
CUDA runtime version: 11.6.55
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla V100-PCIE-32GB
GPU 1: Tesla V100-PCIE-32GB
GPU 2: Tesla V100-PCIE-32GB
GPU 3: Tesla V100-PCIE-32GB
GPU 4: Tesla V100-PCIE-32GB
GPU 5: Tesla V100-PCIE-32GB
GPU 6: Tesla V100-PCIE-32GB
GPU 7: Tesla V100-PCIE-32GB

Nvidia driver version: 510.85.02
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.6
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.20.3
[pip3] pytorch-lightning==1.6.5
[pip3] torch==1.13.0a0+gitd922c29
[pip3] torchaudio==0.9.0
[pip3] torchmetrics==0.11.0
[pip3] torchvision==0.10.0+cu111
[conda] cudatoolkit               11.3.1               h2bc3f7f_2    defaults
[conda] cudatoolkit-dev           11.6.0               h72bdee0_5    conda-forge
[conda] numpy                     1.20.3                   pypi_0    pypi
[conda] pytorch-lightning         1.6.5                    pypi_0    pypi
[conda] torch                     1.13.0a0+gitd922c29          pypi_0    pypi
[conda] torchaudio                0.9.0                    pypi_0    pypi
[conda] torchmetrics              0.11.0                   pypi_0    pypi
[conda] torchvision               0.10.0+cu111             pypi_0    pypi