class_docstr: ## Sequential module for `Zero3Layer` layers
class_docstr_tok: ["#","#","Sequential","module","for","`","Zero3Layer","`","layers"]
code: def get_trainable_chunk(self):
      # Return the list of trainable chunks from each layer
      return sum(
          [m.get_trainable_chunk() for m in self.module_list], []
      )
  
imports: import functools
  from typing import List, Optional, Tuple
  import torch
  import torch.distributed
  from torch import nn
label_desc: Use image node_id 2 for calling the Zero3Sequential obj's underlying member method code with example usage: obj.get_trainable_chunk() and returns: sum