def all_gather_list(data, group=None, max_size=16384):
    """Gathers arbitrary data from all nodes into a list.

    Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python
    data. Note that *data* must be picklable and any CUDA tensors will be moved
    to CPU and returned on CPU as well.

    Args:
        data (Any): data from the local worker to be gathered on other workers
        group: group of the collective
        max_size (int, optional): maximum size of the data to be gathered
            across workers
    """
    from fairseq import utils

    if group is None:
        group = get_global_group()
    rank = get_rank(group=group)
    world_size = get_world_size(group=group)

    buffer_size = max_size * world_size
    if (
        not hasattr(all_gather_list, "_buffer")
        or all_gather_list._buffer.numel() < buffer_size
    ):
        all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size)
        all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory()
    buffer = all_gather_list._buffer
    buffer.zero_()
    cpu_buffer = all_gather_list._cpu_buffer

    data = utils.move_to_cpu(data)
    enc = pickle.dumps(data)
    enc_size = len(enc)
    header_size = 4  # size of header that contains the length of the encoded data
    size = header_size + enc_size
    if size > max_size:
        raise ValueError(
            "encoded data size ({}) exceeds max_size ({})".format(size, max_size)
        )

    header = struct.pack(">I", enc_size)
    cpu_buffer[:size] = torch.ByteTensor(list(header + enc))
    start = rank * max_size
    buffer[start : start + size].copy_(cpu_buffer[:size])

    all_reduce(buffer, group=group)

    buffer = buffer.cpu()
    try:
        result = []
        for i in range(world_size):
            out_buffer = buffer[i * max_size : (i + 1) * max_size]
            (enc_size,) = struct.unpack(">I", bytes(out_buffer[:header_size].tolist()))
            if enc_size > 0:
                result.append(
                    pickle.loads(
                        bytes(out_buffer[header_size : header_size + enc_size].tolist())
                    )
                )
        return result
    except pickle.UnpicklingError:
        raise Exception(
            "Unable to unpickle data from other workers. all_gather_list requires all "
            "workers to enter the function together, so this error usually indicates "
            "that the workers have fallen out of sync somehow. Workers can fall out of "
            "sync if one of them runs out of memory, or if there are other conditions "
            "in your training script that can cause one worker to finish an epoch "
            "while other workers are still iterating over their portions of the data. "
            "Try rerunning with --ddp-backend=legacy_ddp and see if that helps."
        )
Gathers arbitrary data from all nodes into a list.

    Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python
    data. Note that *data* must be picklable and any CUDA tensors will be moved
    to CPU and returned on CPU as well.

    Args:
        data (Any): data from the local worker to be gathered on other workers
        group: group of the collective
        max_size (int, optional): maximum size of the data to be gathered
            across workers
    
understand_icl/fairseq/fairseq/distributed/utils.py