def __init__(self, repeat_factors, *, shuffle=True, seed=None):
        """
        Args:
            repeat_factors (Tensor): a float vector, the repeat factor for each indice. When it's
                full of ones, it is equivalent to ``TrainingSampler(len(repeat_factors), ...)``.
            shuffle (bool): whether to shuffle the indices or not
            seed (int): the initial seed of the shuffle. Must be the same
                across all workers. If None, will use a random seed shared
                among workers (require synchronization among all workers).
        """
        self._shuffle = shuffle
        if seed is None:
            seed = comm.shared_random_seed()
        self._seed = int(seed)

        self._rank = comm.get_rank()
        self._world_size = comm.get_world_size()

        # Split into whole number (_int_part) and fractional (_frac_part) parts.
        self._int_part = torch.trunc(repeat_factors)
        self._frac_part = repeat_factors - self._int_part

        Args:
            repeat_factors (Tensor): a float vector, the repeat factor for each indice. When it's
                full of ones, it is equivalent to ``TrainingSampler(len(repeat_factors), ...)``.
            shuffle (bool): whether to shuffle the indices or not
            seed (int): the initial seed of the shuffle. Must be the same
                across all workers. If None, will use a random seed shared
                among workers (require synchronization among all workers).
        
ControlNet-v1-1-nightly/annotator/oneformer/detectron2/data/samplers/distributed_sampler.py