I do not understand how that would have the same effect. It would if all of the batches have the same batch_size (normal case)
But when every batch can have a different length, you would be using the same learning rate for a loss averaged over 1 sample than one averaged over a 1000 samples. But we want a larger learning rate in the second case, don’t we?