code: def detach_distributed(self: Learner):
      "Remove `DistributedTrainer` from a learner"
      if num_distrib() <= 1:
          return self
      self.remove_cb(DistributedTrainer)
      if rank_distrib() and not hasattr(self, "progress"):
          self.add_cb(ProgressCallback())
      return self
  
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 10 for calling a global function with example usage: detach_distributed() and returns: self, self