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: Return a list of all parameters that would have LoRA for the module.
docstr_tok: ["Return","a","list","of","all","parameters","that","would","have","LoRA","for","the","module","."]
code: def get_lora_params(self):
      """
      Return a list of all parameters that would have LoRA for the module.
      """
      if not hasattr(self, "lora_params"):
          self.set_lora_params()
      return self.lora_params
  
imports: from abc import ABC, abstractmethod
  from typing import List, Tuple
  import torch
label_desc: Use image node_id 16 for calling the HybridEngineContainer obj's underlying member method code with example usage: obj.get_lora_params() and returns: self