class_docstr: Base class for teachers in all 3 rounds in ANLI tasks.
  
  ``RoundBaseTeacher`` derives anli_round (the round index of ANLI task which consists of 3 rounds NLI tasks with
  increasing difficulty. (See https://arxiv.org/abs/1910.14599 for more information)  from ``opt['task']``.
  ``anli_round`` is used to set the correct path to the downloaded data file for thaue specified round.
  
  ``RoundBaseTeacher`` also parses the requested dialog format(text format w/ or w/o special tokens 'Hypothesis',
  'Premise') and number of classes (3 classes entailment, neutral, contradiction or 2 classes contradiction, not
  contradiction) desired for particular training purposes.
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code: def __init__(self, opt, shared=None):
      opt = copy.deepcopy(opt)
      self.anli_round = (
          opt["task"].split(":")[1]
          if len(opt["task"].split(":")) > 1
          else None
      )
      self.anli_round = self.anli_round.upper()
      if self.anli_round not in ANLI_ROUNDS:
          raise KeyError(f"Undefined task round: {self.anli_round}.")
  
      data_path = _path(opt, self.anli_round)
      opt["datafile"] = data_path
      self.dialog_format = opt.get("dialog_format", False)
      self.binary_classes = opt.get("binary_classes", False)
      self.id = opt["task"].upper()
      super().__init__(opt, shared)
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import copy
  import json
  import os
  from parlai.utils.io import PathManager
  from parlai.core.teachers import DialogTeacher, MultiTaskTeacher
  from parlai.tasks.multinli.agents import convert_to_dialogData, BICLASS_LABELS, MULTINLI_LABELS
  from .build import build
label_desc: Use image node_id 2 to create a new RoundBaseTeacher object from inherited base classes: DialogTeacher with example: obj = RoundBaseTeacher(opt, shared)