v2.15.0 docker image print error messages like "Unable to register cuDNN/cuFFT... factory "
Hi @fancyerii ,

Thanks for reporting.Its known issue.It seems this might be due to duplicate registry AFAIK. Though its logging an error it seems the GPU works fine.In your logs also the GPUs also detected and it will not affecting the execution. Could you confirm whether there is a problem with execution of your code?

Same issue was discussed in #62002, #62075.May please refer for more responses there. Thanks! CC: @learning-to-play @fancyerii , Just to verify please cross check whether cudnn_version: 8 & cuda_version 12.2 are there or not using the code below.

```
from tensorflow.python.platform import build_info

print("cudnn_version",build_info.build_info['cudnn_version'])
print("cuda_version",build_info.build_info['cuda_version'])
``` @SuryanarayanaY , I'm having the same problem as the OP on latest-gpu, just run your recommended cross check and am getting:

```
>>> print("cudnn_version",build_info.build_info['cudnn_version'])
cudnn_version 8
>>> print("cuda_version",build_info.build_info['cuda_version'])
cuda_version 12.2
```

So you're saying I just should ignore the "unable to register..." then? They are the only reason I at all went through the trouble of attempting to use Docker images... I'll revert back to my native install then. Thanks.

Note: lines starting with "E" tend to be interpreted as "errors", I guess, i.e. possibly blocking problems. I guess this makes this issue quite a serious one. In fact, there is one more difference for me between native and Docker. With native, I am also getting:

```
W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
```

And that seems to be fixed by neither a regular nor a local `pip install` of `tensorrt` and friends. So I guess the Docker image still has an added value for me. Unless there is an obvious fix for the local TensorRT problem (I am installing TensorRT according to NVIDIA's instructions as far as I know)? same problem here :(
### Issue type

Bug

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

No

### Source

binary

### TensorFlow version

2.15.0

### Custom code

No

### OS platform and distribution

ubuntu 18.04

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

```
$ docker pull tensorflow/tensorflow:2.15.0-gpu
$ docker run -it --gpus all tensorflow/tensorflow:2.15.0-gpu
$import tensorflow as tf
2024-02-18 02:43:26.283147: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-02-18 02:43:26.283204: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-02-18 02:43:26.283926: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-02-18 02:43:26.289011: I tensorflow/core/platform/cpu_feature_guard.cc:182] 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.
>>> print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
Num GPUs Available:  8
```

I tried some examples and it worked. But why this error messages show?

### Standalone code to reproduce the issue

```shell
Ubuntu 18.04 nvidia-driver 535.104.12
```


### Relevant log output

_No response_