code: def parallel_ctx(self: Learner, device_ids=None):
      "A context manager to adapt a learner to train in data parallel mode."
      try:
          self.to_parallel(device_ids)
          yield self
      finally:
          self.detach_parallel()
  
imports: from __future__ import annotations
  from .basics import *
  from .callback.progress import ProgressCallback
  from torch.nn.parallel import DistributedDataParallel, DataParallel
  from .data.load import _FakeLoader, _loaders
  from .optimizer import OptimWrapper
label_desc: Use image node_id 4 for calling a global function with example usage: parallel_ctx(device_ids) without return types