code: def map_task_to_id(self, train_subset):
      src_lang_set, tgt_lang_set, domain_set = set(), set(), set()
      for split in train_subset.split(","):
          seq = split.split("_")
          assert (
              len(seq) == 4
          ), "subset {} should be in the format of train_src_tgt_domain".format(
              split
          )
          _, src_lang, tgt_lang, domain = seq
          src_lang_set.add(src_lang)
          tgt_lang_set.add(tgt_lang)
          domain_set.add(domain)
      src_langs = sorted(src_lang_set)
      tgt_langs = sorted(tgt_lang_set)
      domains = sorted(domain_set)
      self.src_lang_map = {
          src_lang: i for (i, src_lang) in enumerate(src_langs)
      }
      self.tgt_lang_map = {
          tgt_lang: i for (i, tgt_lang) in enumerate(tgt_langs)
      }
      self.domain_map = {
          domain: i for (i, domain) in enumerate(domains)
      }
      if self.task_type == "lang":
          self.encoder_tasks = len(self.src_lang_map)
          self.decoder_tasks = len(self.tgt_lang_map)
      elif self.task_type == "domain":
          self.encoder_tasks = len(self.domain_map)
          self.decoder_tasks = len(self.domain_map)
  
file_path: fairseq/examples/attention_head_selection/src/speech_to_text_head_selection.py
imports: import torch
  from fairseq.optim.amp_optimizer import AMPOptimizer
  from fairseq.tasks import register_task
  from fairseq.tasks.speech_to_text import SpeechToTextTask
  from .data.speech_to_text_dataset_with_domain import SpeechToTextDatasetCreatorWithDomain
  from .loss.attention_head_selection import HeadSelectionLoss
label_desc: Use image node_id 3 for calling the SpeechToTextHeadSelectionTask obj's underlying member method code with example usage: obj.map_task_to_id(train_subset) without return types