class_docstr: Float 16 Module.
  
  Attributes:
      config (TransformerConfig): Transformer config
      fp16 (bool) : Specifies if the model runs in fp16 mode
      bf16 (bool) : Specifies if the model runs in bf16 mode
  
  Args:
      config (TransformerConfig): The transformer config used to initalize the model
docstr: Retrieve state_dict from the module being wrapped.
  
  When using distributed checkpointing, keep_vars must always be set to True.
docstr_tok: ["Retrieve","state_dict","from","the","module","being","wrapped",".","When","using","distributed","checkpointing",",","keep_vars","must","always","be","set","to","True","."]
code: def sharded_state_dict(self, prefix=""):
      """Retrieve state_dict from the module being wrapped.
  
      When using distributed checkpointing, keep_vars must always be set to True.
      """
      return self.module.sharded_state_dict(prefix=prefix)
  
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 6 for calling the Float16Module obj's underlying member method code with example usage: obj.sharded_state_dict(prefix) and returns: self