def __iter__(self):
        """Return an iterator over the shards."""
        if isinstance(self.epoch, SharedEpoch):
            epoch = self.epoch.get_value()
        else:
            # NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)
            # situation as different workers may wrap at different times (or not at all).
            self.epoch += 1
            epoch = self.epoch
        if self.deterministic:
            # reset seed w/ epoch if deterministic
            if self.worker_seed is None:
                # pytorch worker seed should be deterministic due to being init by arg.seed + rank + worker id
                seed = pytorch_worker_seed(epoch)
            else:
                seed = self.worker_seed() + epoch
            self.rng.seed(seed)
        for _ in range(self.nshards):
            yield dict(url=self.rng.choice(self.urls))
Return an iterator over the shards.
EVA-CLIP/rei/training/data.py