    def __init__(self, name2loader):
        """Iterates over multiple dataloaders, it ensures all processes
        work on data from the same dataloader. This loader will end when
        the shorter dataloader raises StopIteration exception.

        loaders: Dict, {name: dataloader}
        """
        self.name2loader = name2loader
        self.name2iter = {name: iter(l) for name, l in name2loader.items()}
        name2index = {name: idx for idx, (name, l) in enumerate(name2loader.items())}
        index2name = {v: k for k, v in name2index.items()}

        iter_order = []
        for n, l in name2loader.items():
            iter_order.extend([name2index[n]]*len(l))

        random.shuffle(iter_order)
        iter_order = torch.Tensor(iter_order).to(torch.device("cuda")).to(torch.uint8)

        # sync
        if is_dist_avail_and_initialized():
            # make sure all processes have the same order so that
            # each step they will have data from the same loader
            dist.broadcast(iter_order, src=0)
        self.iter_order = [index2name[int(e.item())] for e in iter_order.cpu()]

        logger.info(str(self))
Iterates over multiple dataloaders, it ensures all processes
work on data from the same dataloader. This loader will end when
the shorter dataloader raises StopIteration exception.

loaders: Dict, {name: dataloader}
videomamba/video_mm/dataset/dataloader.py