code: def shard_(optimizer, group):
      if not _has_fairscale:
          raise ImportError(
              "\n\nPlease install the fairscale package:"
              "\n\n  pip install fairscale"
          )
  
      class FairseqOSS(OSS):
          @property
          def disable_mem_eff_fp16_loading_hack(self):
              return True
  
          def __getattr__(self, name):
              if name.startswith("supports") and hasattr(
                  self.optim, name
              ):
                  return getattr(self.optim, name)
              raise AttributeError(
                  "'FairseqOSS' object has no attribute {0!r}".format(
                      name
                  )
              )
  
          def broadcast_global_state_dict(
              self, state_dict: Dict[str, Any]
          ) -> Dict[str, Any]:
              """
              Broadcasts the entire state_dict to all other ranks
              each rank is responsible to load their own partition of data
              """
              return utils.broadcast_object(
                  state_dict,
                  src_rank=0,
                  group=self.group,
              )
  
      torch_optimizer = optimizer.optimizer
      optim_cls = type(torch_optimizer)
  
      optimizer.optimizer = FairseqOSS(
          torch_optimizer.param_groups,
          optim_cls,
          group=group,
          **optimizer.optimizer_config,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: shard_(optimizer, group) and returns: True, utils, getattr