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drop_last=True just removes the last batch if the number of its example < batch size, so at most you might lose 9 examples (for a batch of 10) at a given iteration, but then in the next you will see these examples since we train with a shuffle, i don't think it'll have any significant impact; tho it shouldn't be used for validation
in dataloader
drop_last=True
?Otherwise,
dataloader = iter(zip(cycle(self.supervised_loader), self.unsupervised_loader))
might get different size
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