class_docstr: Save the conversation:
  (user input, model output, rewards and learn_counter)
  during the RL training loop.
class_docstr_tok: ["Save","the","conversation",":","(","user","input",",","model","output",",","rewards","and","learn_counter",")","during","the","RL","training","loop","."]
docstr: Add a conversation to the log
  
  Args:
      user_input (str): User input / initial prompt
      model_output (str): Completion of the LLM model
      reward (float): Reward of the reward model assigned to the output
      learn_counter (int): Number of the learning iteration to
          distinguish the conversations that happens at different
          points of the training loopt
docstr_tok: ["Add","a","conversation","to","the","log","Args",":","user_input","(","str",")",":","User","input","\/","initial","prompt","model_output","(","str",")",":","Completion","of","the","LLM","model","reward","(","float",")",":","Reward","of","the","reward","model","assigned","to","the","output","learn_counter","(","int",")",":","Number","of","the","learning","iteration","to","distinguish","the","conversations","that","happens","at","different","points","of","the","training","loopt"]
code: def append(
      self,
      user_input: str,
      model_output: str,
      reward: float,
      learn_counter: int,
  ):
      """Add a conversation to the log
  
      Args:
          user_input (str): User input / initial prompt
          model_output (str): Completion of the LLM model
          reward (float): Reward of the reward model assigned to the output
          learn_counter (int): Number of the learning iteration to
              distinguish the conversations that happens at different
              points of the training loopt
      """
      self.conversation.append(
          {
              "user_input": user_input,
              "model_output": model_output,
              "reward": reward,
              "learn_counter": learn_counter,
          }
      )
  
file_path: nebuly/optimization/chatllama/chatllama/rlhf/utils.py
imports: import json
  import os
  from beartype import beartype
  from plotly import graph_objects
label_desc: Use image node_id 2 for calling the ConversationLog obj's underlying member method code with example usage: obj.append(user_input, model_output, reward, learn_counter) without return types