class_docstr: Stacked model that generates first the knowledge, and then the dialogue response.
class_docstr_tok: ["Stacked","model","that","generates","first","the","knowledge",",","and","then","the","dialogue","response","."]
code: def _init_knowledge_model(self):
      # Initialize knowledge agent.
      if "knowledge_agent" in self.opts:
          self.knowledge_agent = create_agent(
              self.opts["knowledge_agent"], requireModelExists=True
          )
          print("Options for Knowledge Response Agent")
          self.knowledge_agent.opt.log()
      elif (
          self.opts["init"]["knowledge_response_model_path"] == "oracle"
      ):
          self.knowledge_agent = OracleKnowledgeAgent(self.opts["init"])
  
imports: from copy import deepcopy
  from typing import Optional, List
  from types import MethodType
  from collections import defaultdict
  from nltk.tokenize import sent_tokenize
  from nltk.corpus import stopwords
  import random
  import spacy
  import torch
  from parlai.core.agents import create_agent, create_agent_from_shared
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.message import Message
  from parlai.core.agents import Agent
  from parlai.core.metrics import F1Metric
  from parlai.agents.rag.args import setup_rag_args
  from parlai.agents.bart.bart import BartAgent
  from parlai.core.metrics import normalize_answer
  from parlai.tasks.wizard_of_wikipedia.agents import TOKEN_NOCHOSEN
  from parlai.tasks.wizard_of_wikipedia.agents import TOKEN_KNOWLEDGE, TOKEN_END_KNOWLEDGE
label_desc: Use image node_id 8 for calling the StackedKnowledgeDialogueAgent obj's underlying member method code with example usage: obj._init_knowledge_model() without return types