Conv3D Operation Error on Windows platform
Hi @AGFACBNNR ,

Is this issue is specific for Windows OS? I have tested on colab(Linux) with TF package and tensorflow-intel package which are working fine as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/613a61b4dee3431d20b3ca9046560fb6/63860.ipynb).

 Yes, I can only reproduce it on Windows OS.
I also found that this issue cannot be reproduced on colab, which confused me for a whole day. Therefore, I tested it on another Winodws OS machine and a Linux OS machine and checked the difference in package lists. Finally  I guess the reason is hidden in tensorflow-intel>=2.15.0, which can not be installed on colab. 

Sorry for missing the version info.
### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

tf-2.15.0, tf-2.16.0, tf-2.16.1

### Custom code

Yes

### OS platform and distribution

Windows 10

### Mobile device

_No response_

### Python version

3.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I found an output shape inconsistent in Conv3D layer:
```
from tensorflow.keras.layers import Conv3D
import numpy as np

x=np.random.rand(3,5,5,5,4)
l=Conv3D(5,[2,2,3],[1,1,1],'valid','channels_first',[2,2,2],1)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```
The output is:
```
(3, 5, 5, 5, 4)
(3, 5, 3, 3, 0)
```

However, I failed to reproduce it on google colab. After checking the package and re-running it on a Linux machine and another Windows machine, I found this bug may come from the package 'tensorflow-intel', which exists only on Windows platform.

Could you please check it?

### Standalone code to reproduce the issue

```shell
from tensorflow.keras.layers import Conv3D
import numpy as np

x=np.random.rand(3,5,5,5,4)
l=Conv3D(5,[2,2,3],[1,1,1],'valid','channels_first',[2,2,2],1)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```
```


### Relevant log output

```shell
(3, 5, 5, 5, 4)
(3, 5, 3, 3, 0)
```
