class_docstr: LM Chunk Teacher, base.
  
  Provides functionality for subclassed teachers for extracting data from world log
  files, generated via `generate_lm_data.py`
class_docstr_tok: ["LM","Chunk","Teacher",",","base",".","Provides","functionality","for","subclassed","teachers","for","extracting","data","from","world","log","files",",","generated","via","`","generate_lm_data.py","`"]
docstr: Return a list of chunk IDs (integer).
  
  Given the datatype (train/test/valid), return the list of chunk IDs that
  correspond to that split.
docstr_tok: ["Return","a","list","of","chunk","IDs","(","integer",")",".","Given","the","datatype","(","train\/test\/valid",")",",","return","the","list","of","chunk","IDs","that","correspond","to","that","split","."]
code: def get_fold_chunks(self, opt: Opt) -> List[int]:
      """
      Return a list of chunk IDs (integer).
  
      Given the datatype (train/test/valid), return the list of chunk IDs that
      correspond to that split.
      """
      return list(range(len(self.data_files)))
  
imports: from abc import ABC, abstractmethod
  import os
  from typing import Tuple, List, Optional, Any, Dict
  import random
  from parlai.core.message import Message
  from parlai.core.metrics import F1Metric, BleuMetric, RougeMetric, AverageMetric, ExactMatchMetric, normalize_answer
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.teachers import ChunkTeacher, ChunkOutput
  import parlai.tasks.wizard_of_internet.constants
  from parlai.utils.conversations import Conversations
  from parlai.utils.data import DatatypeHelper
  import parlai.utils.logging
  from parlai.tasks.wizard_of_wikipedia.agents import TOKEN_KNOWLEDGE, TOKEN_END_KNOWLEDGE
  from projects.seeker.utils import extract_entities, remove_possible_title_from_text
label_desc: Use image node_id 7 for calling the AbstractLMChunkTeacher obj's underlying member method code with example usage: obj.get_fold_chunks(opt) and returns: list