class_docstr: Interactive world for wizard of wikipedia.
  
  Used for models trained on the task `-t wizard_of_wikipedia`. Automatically
  retrieves knowledge from Wikipedia based on the conversation history using a TF-IDF
  retriever. Then uses a Transformer-based model to select a checked sentence from
  these retrieved passages.
class_docstr_tok: ["Interactive","world","for","wizard","of","wikipedia",".","Used","for","models","trained","on","the","task","`","-t","wizard_of_wikipedia","`",".","Automatically","retrieves","knowledge","from","Wikipedia","based","on","the","conversation","history","using","a","TF-IDF","retriever",".","Then","uses","a","Transformer-based","model","to","select","a","checked","sentence","from","these","retrieved","passages","."]
code: def _load_topics(self, opt):
      # Load possible chosen topics
      build(opt)
      topics_path = os.path.join(
          opt["datapath"], "wizard_of_wikipedia", "topic_splits.json"
      )
      # Get training set topics
      datatype = opt["datatype"].split(":")[0]
      self.topic_list = json.load(open(topics_path, "rb"))[datatype]
  
imports: from typing import Optional
  from parlai.core.opt import Opt
  from copy import deepcopy
  import json
  import random
  import os
  import string
  from parlai.core.params import ParlaiParser
  from parlai.core.agents import create_agent
  from parlai.core.message import Message
  from parlai.core.worlds import DialogPartnerWorld, validate
  from parlai.tasks.wizard_of_wikipedia.agents import TOKEN_KNOWLEDGE, TOKEN_END_KNOWLEDGE
  from parlai.tasks.self_chat.worlds import SelfChatWorld
  from parlai.utils.misc import warn_once
  from .build import build
  from projects.wizard_of_wikipedia.knowledge_retriever.knowledge_retriever import KnowledgeRetrieverAgent
label_desc: Use image node_id 4 for calling the InteractiveWorld obj's underlying member method code with example usage: obj._load_topics(opt) without return types