def METHOD_NAME(dataset, micro_batch_size=None):
    """Specifically one epoch to be used in an indexing job."""
    args = get_args()
    world_size = mpu.get_data_parallel_world_size()
    rank = mpu.get_data_parallel_rank()
    if micro_batch_size is None:
        micro_batch_size = args.micro_batch_size
    global_batch_size = micro_batch_size * world_size
    num_workers = args.num_workers
    sampler = torch.utils.data.SequentialSampler(dataset)
    # importantly, drop_last must be False to get all the data.
    assert False, 'DistributedBatchSampler deprecated, change the implementation'
    from megatron.data.samplers import DistributedBatchSampler
    batch_sampler = DistributedBatchSampler(sampler,
                                            batch_size=global_batch_size,
                                            drop_last=False,
                                            rank=rank,
                                            world_size=world_size)
    return torch.utils.data.DataLoader(dataset,
                                       batch_sampler=batch_sampler,
                                       num_workers=num_workers,
                                       pin_memory=True)