use ESRGAN super resolution on iOS device by TensorFlowLiteSwift, the output is not identical as the output in python env.
the tflite model is convert from https://tfhub.dev/captain-pool/esrgan-tf2/1 @OwenHongJinmu 
Could you please elaborate more and provide detailed steps to replicate the issue reported here ?

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. Hi @OwenHongJinmu 

The TFLite model converted from the TF model might loose some accuracy because of quantization.

You can keep the float model by turning off the optimization by `converter.optimizations =[]` while converting the model to TFLite which can preserve better accuracy.

Thanks.

 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. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. 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/60308">Yes</a>
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I use the sample image for ESRGAN lr-1 
![lr1](https://user-images.githubusercontent.com/7868069/231623423-1431788d-dc0c-451e-9e52-d811f410276c.jpg)
and inference by TensorFlowLiteSwift, compared with python env in Colab , the input pixel data array was identical, but the output pixel data was different totally。partial output data：
iOS:    [[[ 59. 78. 70.] [ 77. 137. 42.] [ 76. 136. 79.]  ...
Colab:[[[ 59. 79. 71.] [ 72. 103.  72.] [ 86. 124.  65.] ...