class_docstr: Wrap one shard of the model, make it possible to load parameters on the
  fly for the FW and BW pass on the given device.
code: def train(self, mode: bool = True) -> "ModelShard":
      # Make sure that the lookahead and lookback shards are not captured by this call
      self.model_shard.train(mode)
      return self
  
imports: from builtins import isinstance
  import functools
  import logging
  from typing import Any, List, Tuple
  import torch
  from torch import nn
label_desc: Use image node_id 4 for calling the ModelShard obj's underlying member method code with example usage: obj.train(mode) and returns: self