more documentation for batch sampler

    (sequential, random, WeightedRandomSampler, etc.) with this batch

    sampler.

    The `interleave` argument specifies how to distribute a batch.

    The `interleave` argument specifies how to distribute a batch. A value

    of True combined with the above random sampler is equivalent to pytorch's

    torch.utils.data.distributed.DistributedSampler.

    For the following batch [0,1,2,3,4,5,6,7] and data parallelism of 2 

    specifying True will result in the following samples for each gpu:

        GPU0: [0,2,4,6] GPU1: [1,3,5,7]