code: def __init__(self, args, lang_pairs, langs, dicts, sampling_method):
      super().__init__()
      self.args = args
      self.seed = args.seed
      self.lang_pairs = lang_pairs
      self.extra_lang_pairs = (
          list(
              {
                  p
                  for _, v in args.extra_lang_pairs.items()
                  for p in v.split(",")
              }
          )
          if args.extra_lang_pairs
          else []
      )
      self.src_langs = {
          p.split("-")[0]
          for p in args.lang_pairs + self.extra_lang_pairs
      }
      self.tgt_langs = {
          p.split("-")[1]
          for p in args.lang_pairs + self.extra_lang_pairs
      }
      self.langs = langs
      self.dicts = dicts
      self.lang_dict = self.create_lang_dictionary(self.langs)
      self.sampling_method = sampling_method
      self.sampling_scheduler = None
      self._has_sharded_data = False
      self._num_shards_dict = {}
      self._training_data_sizes = defaultdict(lambda: {})
  
imports: import itertools
  import json
  import logging
  import math
  import os
  from collections import OrderedDict, defaultdict
  from argparse import ArgumentError
  from fairseq import utils
  from fairseq.data import AppendTokenDataset, ConcatDataset, Dictionary, LanguagePairDataset, PrependTokenDataset, SampledMultiDataset, SampledMultiEpochDataset, StripTokenDataset, TransformEosLangPairDataset, TruncateDataset, data_utils, indexed_dataset
  from fairseq.data.multilingual.multilingual_utils import EncoderLangtok, LangTokSpec, LangTokStyle, augment_dictionary, get_lang_tok
  from fairseq.data.multilingual.sampled_multi_dataset import CollateFormat
  from fairseq.file_io import PathManager
  from fairseq.utils import FileContentsAction, csv_str_list, eval_str_dict
label_desc: Use image node_id 1 to create a new MultilingualDatasetManager object from inherited base classes: object with example: obj = MultilingualDatasetManager(args, lang_pairs, langs, dicts, sampling_method)