data_utils/functions.py [331:355]:
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    else:
        train_dataset = []

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        # per GPU for DistributedDataParallel
        batch_size = int(args.batch_size / args.world_size)
        print(f"batch size per GPU is {batch_size}")
    else:
        train_sampler = None
        batch_size = args.batch_size

    if return_train:
        train_loader = torch.utils.data.DataLoader(
            train_dataset,
            batch_size=batch_size,
            shuffle=(train_sampler is None),
            num_workers=args.workers,
            sampler=train_sampler,
            pin_memory=True,
        )
    else:
        train_loader = None
    print("Train loader initiated")
    val_loader = torch.utils.data.DataLoader(
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data_utils/functions_bis.py [105:129]:
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    else:
        train_dataset = []

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        # per GPU for DistributedDataParallel
        batch_size = int(args.batch_size / args.world_size)
        print(f"batch size per GPU is {batch_size}")
    else:
        train_sampler = None
        batch_size = args.batch_size

    if return_train:
        train_loader = torch.utils.data.DataLoader(
            train_dataset,
            batch_size=batch_size,
            shuffle=(train_sampler is None),
            num_workers=args.workers,
            sampler=train_sampler,
            pin_memory=True,
        )
    else:
        train_loader = None
    print("Train loader initiated")
    val_loader = torch.utils.data.DataLoader(
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