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: Show the conversation log
  
  Args:
      current_iteration (int): Current iteration of the training loop,
          if not None, print only the conversations that happened at
          <current_iteration>
docstr_tok: ["Show","the","conversation","log","Args",":","current_iteration","(","int",")",":","Current","iteration","of","the","training","loop",",","if","not","None",",","print","only","the","conversations","that","happened","at","<","current_iteration",">"]
code: def show(self, current_iteration: int = None):
      """Show the conversation log
  
      Args:
          current_iteration (int): Current iteration of the training loop,
              if not None, print only the conversations that happened at
              <current_iteration>
      """
      for i, c in enumerate(self.conversation):
          if current_iteration is None:
              print(
                  f"##########################################\n"
                  f"Conversation {i} at learn_counter "
                  f"{c['learn_counter']}\n"
                  f"##########################################\n"
                  f"## User Input:\n\n{c['user_input']}\n\n"
                  f"## Model Output:\n\n{c['model_output']}\n\n"
                  f"## Reward: {c['reward']}\n\n"
              )
          else:
              if current_iteration == c["learn_counter"]:
                  print(
                      f"##########################################\n"
                      f"Conversation {i} at learn_counter "
                      f"{c['learn_counter']}\n"
                      f"##########################################\n"
                      f"## User Input:\n\n{c['user_input']}\n\n"
                      f"## Model Output:\n\n{c['model_output']}\n\n"
                      f"## Reward: {c['reward']}\n\n"
                  )
  
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 6 for calling the ConversationLog obj's underlying member method code with example usage: obj.show(current_iteration) without return types