class_docstr: Base Megatron module inhertied by all Models.
  
  Megatron specific extensions of torch Module with support
  for pipelining
  
  Args:
      config (TransformerConfig): Transformer config
docstr: Override state dict for saving checkpoints Use this function to override the
  state dict for saving checkpoints.
  
  Args:
      prefix (str, optional): _description_. Defaults to ''.
      keep_vars (bool, optional): _description_. Defaults to False.
  
  Returns:
      _type_: _description_
docstr_tok: ["Override","state","dict","for","saving","checkpoints","Use","this","function","to","override","the","state","dict","for","saving","checkpoints",".","Args",":","prefix","(","str",",","optional",")",":","_description_",".","Defaults","to","``",".","keep_vars","(","bool",",","optional",")",":","_description_",".","Defaults","to","False",".","Returns",":","_type_",":","_description_"]
code: def state_dict_for_save_checkpoint(
      self, prefix: str = "", keep_vars: bool = False
  ):
      """Override state dict for saving checkpoints Use this function to override the
      state dict for saving checkpoints.
  
      Args:
          prefix (str, optional): _description_. Defaults to ''.
          keep_vars (bool, optional): _description_. Defaults to False.
  
      Returns:
          _type_: _description_
      """
  
      return self.state_dict(prefix=prefix, keep_vars=keep_vars)
  
imports: import torch
  from torch.autograd import Variable
  from torch.nn.parameter import Parameter
  from megatron.core import parallel_state
  from megatron.core.transformer.transformer_config import TransformerConfig
label_desc: Use image node_id 2 for calling the MegatronModule obj's underlying member method code with example usage: obj.state_dict_for_save_checkpoint(prefix, keep_vars) and returns: self