Image augmentation is a common technique used when training computer vision models in order to generate artificial training data by transforming in your actual training data, for example, random rotations and shifts.
However, bugs are tricky because no errors will be raised. Instead the result would be that the model will not perform as well on the un-augmented test dataset as it could have.
You can use this tool to develop and sanity check your transforms on actual images before using them in a training script.