code: def normalize_unicode_text(text: str) -> str:
      """
      TODO @xueyang: Apply NFC form may be too aggressive since it would ignore some accented characters that do not exist
        in predefined German alphabet (nemo.collections.common.tokenizers.text_to_speech.ipa_lexicon.IPA_CHARACTER_SETS),
        such as 'é'. This is not expected. A better solution is to add an extra normalization with NFD to discard the
        diacritics and consider 'é' and 'e' produce similar pronunciations.
  
      Note that the tokenizer needs to run `unicodedata.normalize("NFC", x)` before calling `encode` function,
      especially for the characters that have diacritics, such as 'ö' in the German alphabet. 'ö' can be encoded as
      b'\xc3\xb6' (one char) as well as b'o\xcc\x88' (two chars). Without the normalization of composing two chars
      together and without a complete predefined set of diacritics, when the tokenizer reads the input sentence
      char-by-char, it would skip the combining diaeresis b'\xcc\x88', resulting in indistinguishable pronunciations
      for 'ö' and 'o'.
  
      Args:
          text (str): the original input sentence.
  
      Returns:
          NFC normalized sentence (str).
      """
      # normalize word with NFC form
      if not unicodedata.is_normalized("NFC", text):
          text = unicodedata.normalize("NFC", text)
  
      return text
  
file_path: NeMo/nemo/collections/common/tokenizers/text_to_speech/tokenizer_utils.py
imports: import re
  import unicodedata
  from builtins import str
  from typing import List, Tuple
label_desc: Use image node_id 3 for calling a global function with example usage: normalize_unicode_text(text) and returns: text