code: def _parse_as_cmu_dict(phoneme_dict_path=None, encoding="latin-1"):
      if phoneme_dict_path is None:
          # this part of code downloads file, but it is not rank zero guarded
          # Try to check if torch distributed is available, if not get global rank zero to download corpora and make
          # all other ranks sleep for a minute
          if (
              torch.distributed.is_available()
              and torch.distributed.is_initialized()
          ):
              group = torch.distributed.group.WORLD
              if is_global_rank_zero():
                  try:
                      nltk.data.find("corpora/cmudict.zip")
                  except LookupError:
                      nltk.download("cmudict", quiet=True)
              torch.distributed.barrier(group=group)
          elif is_global_rank_zero():
              logging.error(
                  f"Torch distributed needs to be initialized before you initialized EnglishG2p. This class is prone to "
                  "data access race conditions. Now downloading corpora from global rank 0. If other ranks pass this "
                  "before rank 0, errors might result."
              )
              try:
                  nltk.data.find("corpora/cmudict.zip")
              except LookupError:
                  nltk.download("cmudict", quiet=True)
          else:
              logging.error(
                  f"Torch distributed needs to be initialized before you initialized EnglishG2p. This class is prone to "
                  "data access race conditions. This process is not rank 0, and now going to sleep for 1 min. If this "
                  "rank wakes from sleep prior to rank 0 finishing downloading, errors might result."
              )
              time.sleep(60)
  
          logging.warning(
              f"English g2p_dict will be used from nltk.corpus.cmudict.dict(), because phoneme_dict_path=None. "
              "Note that nltk.corpus.cmudict.dict() has old version (0.6) of CMUDict. "
              "You can use the latest official version of CMUDict (0.7b) with additional changes from NVIDIA directly from NeMo "
              "using the path scripts/tts_dataset_files/cmudict-0.7b_nv22.10."
          )
  
          return nltk.corpus.cmudict.dict()
  
      _alt_re = re.compile(r"\([0-9]+\)")
      g2p_dict = {}
      with open(phoneme_dict_path, encoding=encoding) as file:
          for line in file:
              if len(line) and (
                  "A" <= line[0] <= "Z" or line[0] == "'"
              ):
                  parts = line.split("  ")
                  word = re.sub(_alt_re, "", parts[0])
                  word = word.lower()
  
                  pronunciation = parts[1].strip().split(" ")
                  if word in g2p_dict:
                      g2p_dict[word].append(pronunciation)
                  else:
                      g2p_dict[word] = [pronunciation]
      return g2p_dict
  
file_path: NeMo/nemo/collections/tts/g2p/models/en_us_arpabet.py
imports: import pathlib
  import random
  import re
  import time
  from typing import Optional
  import nltk
  import torch
  from nemo.collections.common.tokenizers.text_to_speech.tokenizer_utils import english_word_tokenize
  from nemo.collections.tts.g2p.models.base import BaseG2p
  from nemo.utils import logging
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 2 for calling the EnglishG2p obj's underlying member method code with example usage: obj._parse_as_cmu_dict(phoneme_dict_path, encoding) and returns: g2p_dict, nltk