class_docstr: The shard sync layer is a synchronization point between model shards.
  - In the forward pass, it drops parameters in the previous shard and
  loads parameters for the next shard.
  - In the backward pass, it does the reverse.
  It does not change or create any outputs at all, instead it just
  forwards the input as the output.
  NOTE: see https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function
code: def backward(ctx, *grad_outputs):  # type: ignore # pragma: no cover
      load_index = ctx.index
      drop_index = load_index + 1
      model_slices = ctx.model_slices
      model_instance = ctx.model_instance
  
      # TODO(anj-s): Are these redundant in the backward pass?
      if drop_index == len(model_slices):
          # Drop the last activation since it is still on the CPU
          # after the loss.backward() call.
          model_instance._activations[-1] = tuple(
              [a.cuda() for a in list(model_instance._activations[-1])]
          )
  
      if drop_index < len(model_slices):
          # Move shard from device to offload device.
          model_slices[drop_index].backward_drop()
          model_instance._activations[drop_index] = tuple(
              [
                  a.cpu()
                  for a in list(model_instance._activations[drop_index])
              ]
          )
  
      if load_index >= 0:
          # Load shard from offload device to device.
          model_slices[load_index].backward_load()
          model_instance._activations[load_index] = tuple(
              [
                  a.cuda()
                  for a in list(model_instance._activations[load_index])
              ]
          )
  
      # The returned variables need to mirror the forward inputs
      # TODO(anj-s): Why do we need to do this?
      if isinstance(grad_outputs, tuple):
          return grad_outputs[0], None, None, None
  
      return grad_outputs, None, None, None
  
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 ShardSyncLayer obj's underlying member method code with example usage: obj.backward(ctx) and returns: grad_outputs,  None,  None,  None, grad_outputs,  None,  None,  None