src/data_manager.py [164:191]:
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    if not stratify:
        dist_sampler = torch.utils.data.distributed.DistributedSampler(
            dataset=dataset,
            num_replicas=world_size,
            rank=rank)
        data_loader = torch.utils.data.DataLoader(
            dataset,
            sampler=dist_sampler,
            batch_size=batch_size,
            drop_last=drop_last,
            pin_memory=True,
            num_workers=8)
    else:
        dist_sampler = ClassStratifiedSampler(
            data_source=dataset,
            world_size=world_size,
            rank=rank,
            batch_size=batch_size,
            classes_per_batch=classes_per_batch,
            seed=_GLOBAL_SEED,
            unique_classes=unique_classes)
        data_loader = torch.utils.data.DataLoader(
            dataset,
            batch_sampler=dist_sampler,
            pin_memory=True,
            num_workers=8)

    return (data_loader, dist_sampler)
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src/data_manager.py [294:321]:
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    if not stratify:
        dist_sampler = torch.utils.data.distributed.DistributedSampler(
            dataset=dataset,
            num_replicas=world_size,
            rank=rank)
        data_loader = torch.utils.data.DataLoader(
            dataset,
            sampler=dist_sampler,
            batch_size=batch_size,
            drop_last=drop_last,
            pin_memory=True,
            num_workers=8)
    else:
        dist_sampler = ClassStratifiedSampler(
            data_source=dataset,
            world_size=world_size,
            rank=rank,
            batch_size=batch_size,
            classes_per_batch=classes_per_batch,
            seed=_GLOBAL_SEED,
            unique_classes=unique_classes)
        data_loader = torch.utils.data.DataLoader(
            dataset,
            batch_sampler=dist_sampler,
            pin_memory=True,
            num_workers=8)

    return (data_loader, dist_sampler)
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