Utilize GPU for tf 2.15
@FutureGoose Please ensure you have a compatible Nvidia GPU and drivers installed. Also kindly verify that the paths to CUDA, cuDNN, and TensorFlow DLLs are added to your system's environment variable PATH as well.
Thank you!
### Issue type

Build/Install

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

2.15

### Custom code

No

### OS platform and distribution

WSL Ubuntu 22.04.3 (Windows 10)

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

11.8/8.6

### GPU model and memory

4095MB NVIDIA GeForce RTX 2070 SUPER

### Current behavior?

TensorFlow is not finding the GPU.

### Standalone code to reproduce the issue

```shell
`python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"`

I have a lot of training data, so I'm trying to get TensorFlow to utilize the computer's GPU. I have been unsuccessful so far. Any help would be greatly appreciated.

The project I'm working on requires tf 2.15.

My understanding is that these are the options for tf to make it run on the GPU:
1. Python 3.9 with tf 2.10
2. Python 3.10 with the [DirectML plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-) (tf 2.10)
3. Linux (WSL in this case)

Since the project requirement is tf 2.15, I'm left with only option 3.

I've been following the installation instructions here:
[](https://www.tensorflow.org/install/pip#windows-wsl2)
and here
[](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-wsl-2)

I.e. Cuda 11.8 and cuDNN 8.6.
```


### Relevant log output

```shell
2024-05-30 21:40:47.425057: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

2024-05-30 21:40:48.004912: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

2024-05-30 21:40:48.594078: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.

2024-05-30 21:40:48.613551: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
```

Some other, perhaps, relevant information:

```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
```

```
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 520.61.05    Driver Version: 555.85       CUDA Version: 12.5     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  On   | 00000000:01:00.0  On |                  N/A |
|  0%   55C    P0    42W / 215W |    725MiB /  8192MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
```
```
