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 the number of samples.
  
  Returns a tuple of (num_examples, num_episodes) based on the data split.
docstr_tok: ["Return","the","number","of","samples",".","Returns","a","tuple","of","(","num_examples",",","num_episodes",")","based","on","the","data","split","."]
code: def get_num_samples(self, opt: Opt) -> Tuple[int, int]:
      """
      Return the number of samples.
  
      Returns a tuple of (num_examples, num_episodes) based on the data split.
      """
      return len(self.data_files) * 1000, len(self.data_files) * 1000
  
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 6 for calling the AbstractLMChunkTeacher obj's underlying member method code with example usage: obj.get_num_samples(opt) and returns: unknown,  unknown