Conv2D computes wrongly in Windows OS
Hi **@Shuo-Sun20** ,
I tried to run your code on Colab using TF v2.16.1 and i am not facing any issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/b9af30cf64d9451c5100271495d67fca/64396_2-16-1.ipynb) here for reference.

Thank you! This issue only exists on Windows OS, so on Colab(linux OS) this issue will not show up. 
Please try it on Windows. @Shuo-Sun20 


1. 
Use following code, I got same result in both linux & windows:
```
from keras.layers import Conv2D
import numpy as np

x=np.random.rand(1,2,2,1)
l=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)
print(l(x).shape)
print(l.compute_output_shape(x.shape))

(1, 2, 2, 1)
(1, 0, 0, 1)
```

tensorflow                   2.16.1
keras                        3.1.1

2. The kernel is 3, but the input is <3. It's strange case.
Could you confirm if such input parameters are right? 1. You are right, kernel_size > input_size should be an invalid parameter combination, while now Conv2D can generate a reseult without warning. Maybe a checker should exist here?
2. The behavior of Conv2D is diffenrent on Linux and Windows (since I failed to reproduce it on colab), this inconsisdent may need deeper inspection. @Shuo-Sun20 
1. For the checker of abnormal input, could report it as a feature in another issue?
2. I run the case and got same result in both windows and linux.
You said they were different in your case.
Could you share the whole logs of them?
And share the result of 'pip list' in windows and linux.
 1. I'll report it in another issue.
2. I run the following code in both windows and linux:
```
from keras.layers import Conv2D
import numpy as np

x=np.random.rand(1,2,2,1)
l=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```

In windows the result is
```
2024-04-07 16:35:31.739978: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-07 16:35:32.406322: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-07 16:35:33.580266: 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.
(1, 2, 2, 1)
(1, 0, 0, 1)
```

The pip list in windows:
```
absl-py==2.1.0
astunparse==1.6.3
certifi==2024.2.2
charset-normalizer==3.3.2
flatbuffers==24.3.25
gast==0.5.4
google-pasta==0.2.0
grpcio==1.62.1
h5py==3.10.0
idna==3.6
importlib_metadata==7.1.0
keras==3.1.1
libclang==18.1.1
Markdown==3.6
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
ml-dtypes==0.3.2
namex==0.0.7
numpy==1.26.4
opt-einsum==3.3.0
optree==0.11.0
packaging==24.0
protobuf==4.25.3
Pygments==2.17.2
requests==2.31.0
rich==13.7.1
six==1.16.0
tensorboard==2.16.2
tensorboard-data-server==0.7.2
tensorflow==2.16.1
tensorflow-intel==2.16.1
tensorflow-io-gcs-filesystem==0.31.0
termcolor==2.4.0
typing_extensions==4.11.0
urllib3==2.2.1
Werkzeug==3.0.2
wrapt==1.16.0
zipp==3.18.1
```


while in this linux [colab](https://colab.research.google.com/drive/1OVe3gBmZGeog36-sDGMxT7j6D-Qs29Ap?usp=sharing), the result is:
```
(1, 0, 0, 1)
(1, 0, 0, 1)
```

I failed to install tensorflow-intel 2.16.1 in colab(linux), so I just installed tensorflow and keras using regular pip command. 
You can freely edit the code in the [colab](https://colab.research.google.com/drive/1OVe3gBmZGeog36-sDGMxT7j6D-Qs29Ap?usp=sharing) and install packages.

The pip list is a little different since colab pre installs many packages used in Deep Learning. The list is too long to show in this comment. You can find it yourself with the shared [link](https://colab.research.google.com/drive/1OVe3gBmZGeog36-sDGMxT7j6D-Qs29Ap?usp=sharing). @Shuo-Sun20 
In windows, my result is same as yours.
In linux, when enable oneDNN path in TF by TF_ENABLE_ONEDNN_OPTS=1, the result is same as windows.
If TF_ENABLE_ONEDNN_OPTS=0, the result is same as yours (different with windows).

I think it's the different between oneDNN code and Eigen code of TF.

But the following code can't work in colab env you provide.
I guess the TF in colab doesn't support oneDNN code path.
Maybe you could try in local/another linux.

```
import os
os.environ['TF_ENABLE_ONEDNN_OPTS']='1'

from keras.layers import Conv2D
import numpy as np

x=np.random.rand(1,2,2,1)
l=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```


### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

tf 2.16

### Custom code

Yes

### OS platform and distribution

Windows 10

### 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?

On Windows OS, Conv2D generates a wrong output in some cases, while performs correctly on some others.
This error does not occur on Linux OS, even with the same code.

An wrong execution example:
![图片](https://github.com/tensorflow/tensorflow/assets/97206823/51fab71a-9bc6-42a9-ab67-19a6c9ec50d0)

You can tell that the result `l(x)` has a wrong shape.

I notice that an exisiting issue #63860 points out the similar error in Conv3D. I guess Conv2D and Conv3D have similar problem since they have the same parent class BaseConv.

### Standalone code to reproduce the issue

```shell
#This test case works fine on linux OS, while goes wrongly on Windows.


from keras.layers import Conv2D
import numpy as np

x=np.random.rand(1,2,2,1)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```


### Relevant log output

```shell
TensorShape([1, 2, 2, 1])
(1, 0, 0, 1)
```
