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 forward(self, *inputs):  # type: ignore
      return (
          self.model_shard(*inputs)
          if isinstance(inputs, tuple)
          else self.model_shard(inputs)
      )
  
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 2 for calling the ModelShard obj's underlying member method code with example usage: obj.forward() and returns: unknown