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 backward_load(
      self, non_blocking: bool = True
  ) -> None:  # pragma: no cover
      with torch.cuda.stream(self._cpu_to_gpu_stream):
          self.model_shard.to(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 7 for calling the ModelShard obj's underlying member method code with example usage: obj.backward_load(non_blocking) without return types