class_docstr: This container identifies which methods need to be overridden in addition to
  the base container to enable use in the RLHF pipeline. These methods are not
  necessary for inference alone.
  
  NOTE: If you are using this feature with a container that
  also inherits from `MetaTensorContainer`, ensure that `MetaTensorContainer`
  is inherited before `HybridEngineContainer` in the class definition.
class_docstr_tok: ["This","container","identifies","which","methods","need","to","be","overridden","in","addition","to","the","base","container","to","enable","use","in","the","RLHF","pipeline",".","These","methods","are","not","necessary","for","inference","alone",".","NOTE",":","If","you","are","using","this","feature","with","a","container","that","also","inherits","from","`","MetaTensorContainer","`",",","ensure","that","`","MetaTensorContainer","`","is","inherited","before","`","HybridEngineContainer","`","in","the","class","definition","."]
docstr: Unfuse the LoRA parameters for the training mode.
docstr_tok: ["Unfuse","the","LoRA","parameters","for","the","training","mode","."]
code: def unfuse_lora(self):
      """Unfuse the LoRA parameters for the training mode."""
      for maybe_lora_param, param in self.get_lora_matched_pair():
          if len(maybe_lora_param) == 3:
              (
                  lora_right_weight,
                  lora_left_weight,
                  lora_scaling,
              ) = maybe_lora_param
              param.data -= lora_scaling * torch.matmul(
                  lora_left_weight.t(), lora_right_weight.t()
              )
  
imports: from abc import ABC, abstractmethod
  from typing import List, Tuple
  import torch
label_desc: Use image node_id 7 for calling the HybridEngineContainer obj's underlying member method code with example usage: obj.unfuse_lora() without return types