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_load(self, non_blocking: bool = True) -> None:
      with torch.cuda.stream(self._cpu_to_gpu_stream):
          # Restore all the parameter buffers
          self.model_shard.to(
              device=self.device, non_blocking=non_blocking
          )
  
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 6 for calling the ModelShard obj's underlying member method code with example usage: obj.forward_load(non_blocking) without return types