Not initialized delegate kernel after tflite conversion
Hi @Tachimura 

The DepthConv2D layer in TF has known limitation of specifying any stride value != 1 is incompatible with specifying any dilation_rate value !=1.  

As given in [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv2D?hl=en&version=nightly#args)

>An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Current implementation only supports equal length strides in row and column dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value !=1.

Thanks.


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### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Windows 11
- TensorFlow installation (pip package or built from source):
pip package
- TensorFlow library (version, if pip package or github SHA, if built from source):
TensorFlow 2.10.0

### 2. Code

Code to reproduce my issue is attached to this issue.
[tf_issue.zip](https://github.com/tensorflow/tensorflow/files/11871912/tf_issue.zip)

### 3. Failure after conversion

```
File "tf_issue\test_ocr.py", line 62, in __call__
    self._interpreter.invoke()
File "...\venv\lib\site-packages\tensorflow\lite\python\interpreter.py", line 917, in invoke
    self._interpreter.Invoke()
RuntimeError: Current implementation only supports equal length strides in the row and column dimensions.Delegate kernel was not initializedNode number 510 (TfLiteFlexDelegate) failed to prepare.
```


### 5. (optional) Any other info / logs
Hello, 
I downloaded the OCR model called **en_PP-OCRv3_rec_infer** from the [Paddle repository](https://github.com/PaddlePaddle/PaddleOCR). To prepare it for my purposes, I converted it into the ONNX format and optimized it, following the guidelines provided [here](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/README_en.md#command-line-conversion). To ensure compatibility, I defined a static input/output size for the model.

Subsequently, I proceeded to convert the ONNX format to TFLite using this [repository](https://github.com/sithu31296/PyTorch-ONNX-TFLite/tree/master#onnx-to-tf). Once the conversion was complete, I loaded the resulting .tflite model into [Neutron](https://netron.app/) without any issues, as it successfully read and visualized the model.

However, the problem arises when I attempt to test this model using Python (3.10). The attached zip file contains the code, the model itself, and a sample testing image (It also contain a requirements file with all the packages of my environment).

In my case, I utilized an input_shape_dict of "{'x': [1, 3, 48, 320]}" and exported the model in both fp16 and fp32 formats. I also experimented with opset_versions 10 and 16. However, despite these attempts, I encountered the reported failure repeatedly.
