The Depthwise Convolution operation result is incorrect from interpreter
Have you considered a Re install i or re-writing the
interpreter to make sure that it matched what is required.
Sincerely, Hamish Leahy @Bajirak Could you please try to reinstall the interpreter and check the compatibility. Please try using the command Python: Clear cache. Also do mention the expected output for this operation. Thank you! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. Sorry for being late. @Hamish-Leahy , @sushreebarsa 

I created a new virtual environment and installed the nightly version of tensorflow.

`python3.8 -m venv issue_tracking`
`source issue_tracking/bin/activate`
`pip install --upgrade pip`
`pip install tf-nightly-cpu`
`pip freeze | grep tf-nightly`

It says
`tf-nightly-cpu==2.14.0.dev20230706`

After installation, I tried the tests described above again, but the output is still incorrect. 
(*The weight data of DwConv is randomly generated. Therefore, the same result as the issue cannot be derived.)

0) input[0, 61:64, 65:68, 24]
[[0.978893   0.03620292 0.9301994 ]
 [0.34403667 0.4492776  0.8247143 ]
 [0.8075199  0.77361435 0.1642472 ]]

1) weight[..., :24]
[[[ 0.04345933 -0.04809386  0.07144216]
  [-0.08781118  0.01062268 -0.05774678]
  [-0.03802773  0.07661463  0.0889118 ]]]

2) bias[24]
-0.029987096786499023

3) Deptwise Convoultion output
tflite output[0, 31, 33, 24] : 0.01883140206336975
test output[0, 31, 33, 24]: 0.04737246036529541 Hi @Bajirak 

I have tested both the keras and TFLite model and they produce same output given the same input. Please find this [gist](https://colab.research.google.com/gist/pjpratik/ece6465f351b841bc40e8810a0732989/61934.ipynb).

As their result goes I don't think there is an issue. Can you elaborate more about why the partial data is being tested? 

Thanks.

 Hi @pjpratik 

You are right. This isn't an issue. I thought the interpreter's operation result was invalid.

So, I tried to prove that the interpreter's results were wrong by using partial input and partial weight.

However, I was considering the 'SAME' Pad value incorrectly. After considering this, I checked my results and the interpreter's results.

Thank you for checking so carefully. Sorry for taking up your time. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61934">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61934">No</a>

### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 18.04

### Mobile device

_No response_

### Python version

3.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I am testing network inference using Interpreter for the '.tflite'  that supports dynamic input shape.

However, the operational results of Depthwise Convolution are different from what was expected.

I simply reproduced the issue.

### Standalone code to reproduce the issue

```shell
https://colab.research.google.com/drive/1WCOZBuWTYU7kn4EcjHHrG_fZm7mZcojN?usp=drive_link
```


### Relevant log output

```shell
# input : Inputs corresponding to output [0, 31, 33, 24] 
0) input[0, 61:64, 65:68, 24] 
[[0.978893   0.03620292 0.9301994 ]
 [0.34403667 0.4492776  0.8247143 ]
 [0.8075199  0.77361435 0.1642472 ]]

# weight: corresponding to output [0, 31, 33, 24]
1) weight[..., :24]
[[[ 0.09494528 -0.0925059   0.07752301]
  [-0.07614094  0.06238136 -0.03187032]
  [ 0.06566823  0.09477414 -0.05954333]]]

# bias: corresponding to output [0, 31, 33, 24]
2) bias[24]
0.024211471900343895

# tflite output: Interpreter result
# test output: Formula result of Depthwise Convolution
3) Deptwise Convoultion output
tflite output[0, 31, 33, 24] : 0.0
test output[0, 31, 33, 24]: 0.2780301570892334
```
