code: def CrossBarrier(
      model,
      optimizer,
      named_parameters=None,
      compression=Compression.none,
      backward_passes_per_step=1,
      num_steps=10**6,
  ):
      """Wrap Torch optimizer using BytePS DistributedOptimizer and _CrossBarrier."""
      bps_opt = _bps_DistributedOptimizer(
          optimizer,
          named_parameters,
          compression,
          backward_passes_per_step,
      )
      return _CrossBarrier(model, bps_opt, num_steps)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from byteps.torch.compression import Compression
  from byteps.torch.ops import push_pull_async_inplace
  from byteps.torch.ops import poll, synchronize
  from byteps.torch.ops import init, shutdown
  from byteps.torch.ops import size, local_size, rank, local_rank
  import threading
  import logging
  import time
  import math
  import torch
  import byteps.torch
label_desc: Use image node_id 3 for calling a global function with example usage: CrossBarrier(model, optimizer, named_parameters, compression, backward_passes_per_step, num_steps) and returns: _CrossBarrier