code: def __init__(self, loss, efficient=False):
      super().__init__()
      if not isinstance(
          loss, (BaseMetricLossFunction, CrossBatchMemory)
      ):
          raise TypeError(
              "The input loss must extend BaseMetricLossFunction or CrossBatchMemory"
          )
      if isinstance(loss, CrossBatchMemory) and efficient:
          raise ValueError(
              "CrossBatchMemory with efficient=True is not currently supported"
          )
      self.loss = loss
      self.efficient = efficient
  
imports: import torch
  from ..losses import BaseMetricLossFunction, CrossBatchMemory
  from ..miners import BaseMiner
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
label_desc: Use image node_id 1 to create a new DistributedLossWrapper object from inherited base classes: torch.nn with example: obj = DistributedLossWrapper(loss, efficient)