class_docstr: ## Sequential module for `Zero3Layer` layers
class_docstr_tok: ["#","#","Sequential","module","for","`","Zero3Layer","`","layers"]
code: def forward(self, x: torch.Tensor):
      # Make sure gradient back up is complete
      torch.cuda.current_stream().wait_stream(self.backup_stream)
  
      # Forward pass
      for m in self.module_list:
          x = m(x)
  
      #
      return x
  
imports: import functools
  from typing import List, Optional, Tuple
  import torch
  import torch.distributed
  from torch import nn
label_desc: Use image node_id 3 for calling the Zero3Sequential obj's underlying member method code with example usage: obj.forward(x) and returns: x