human
Complete the below
def data_parallelism(all_workers=False):

    ''''Over which devices do we split each training batch.
    In old-fashioned async mode, we split the batch over all GPUs on the
    current worker.
    In sync mode, we split the batch over all the parameter server GPUs.
    This function returns an expert_utils.Parallelism object, which can be used
    to build the model.  It is configured in a way that any variables created
    by `tf.get_variable` will be assigned to the parameter servers and shared
    between datashards.
    Args:
    all_workers: whether the devices are all async workers or just this one.
    Returns:
    a expert_utils.Parallelism.'
    '''
 ... Can you provide a the Python program you requested?
assistant
#Here is the Python program you requested.
    def _replica_device_setter(worker_device):
        if (FLAGS.ps_replicas == 0):
            return worker_device
        return tf.train.replica_device_setter(worker_device=worker_device, ps_tasks=FLAGS.ps_replicas, ps_device=((FLAGS.ps_job + '/GPU:0') if (FLAGS.ps_gpu > 0) else FLAGS.ps_job))
    if (FLAGS.schedule == 'local_run'):
        assert (not FLAGS.sync)
        datashard_devices = [('gpu:%d' % d) for d in _gpu_order(FLAGS.worker_gpu)]
        if (FLAGS.locally_shard_to_cpu or (FLAGS.worker_gpu < 1)):
            datashard_devices += ['cpu:0']
        caching_devices = None
    elif FLAGS.sync:
        assert (FLAGS.ps_replicas > 0)
        datashard_devices = [_replica_device_setter(d) for d in ps_devices(all_workers=all_workers)]
        if ((FLAGS.ps_gpu > 0) and (FLAGS.ps_replicas > 1)):
            caching_devices = [(FLAGS.ps_job + ('/task:%d/cpu:0' % d)) for (d, _) in _ps_gpus(all_workers=all_workers)]
        else:
            caching_devices = None
    elif (FLAGS.worker_gpu > 1):
        datashard_devices = [_replica_device_setter((FLAGS.worker_job + ('/GPU:%d' % d))) for d in _gpu_order(FLAGS.worker_gpu)]
        caching_devices = ([(FLAGS.worker_job + '/GPU:0')] * FLAGS.worker_gpu)
    else:
        datashard_devices = [_replica_device_setter(FLAGS.worker_job)]
        caching_devices = None
    tf.logging.info('datashard_devices:    %s', datashard_devices)
    tf.logging.info('caching_devices:    %s', caching_devices)
    return eu.Parallelism(datashard_devices, reuse=True, caching_devices=caching_devices, daisy_chain_variables=FLAGS.daisy_chain_variables)