How to use the normality.encoding.DEFAULT_ENCODING function in normality

def stringify(value, encoding_default=DEFAULT_ENCODING, encoding=None):
    """Brute-force convert a given object to a string.

    This will attempt an increasingly mean set of conversions to make a given
    object into a unicode string. It is guaranteed to either return unicode or
    None, if all conversions failed (or the value is indeed empty).
    """
    if value is None:
        return None

    if not isinstance(value, six.text_type):
        if isinstance(value, (date, datetime)):
            return value.isoformat()
        elif isinstance(value, (float, Decimal)):
            return Decimal(value).to_eng_string()
        elif isinstance(value, six.binary_type):
            if encoding is None:

def sanitize_text(text, encoding=DEFAULT_ENCODING):
    text = stringify(text, encoding_default=encoding)
    if text is not None:
        try:
            text = compose_nfc(text)
        except (SystemError, Exception) as ex:
            log.warning("Cannot NFC text: %s", ex)
            return None
        text = remove_unsafe_chars(text)
        text = text.encode(DEFAULT_ENCODING, "replace")
        return text.decode(DEFAULT_ENCODING, "replace")

def sanitize_text(text, encoding=DEFAULT_ENCODING):
    text = stringify(text, encoding_default=encoding)
    if text is not None:
        try:
            text = compose_nfc(text)
        except (SystemError, Exception) as ex:
            log.warning("Cannot NFC text: %s", ex)
            return None
        text = remove_unsafe_chars(text)
        text = text.encode(DEFAULT_ENCODING, "replace")
        return text.decode(DEFAULT_ENCODING, "replace")

def normalize(text, lowercase=True, collapse=True, latinize=False, ascii=False,
              encoding_default=DEFAULT_ENCODING, encoding=None,
              replace_categories=UNICODE_CATEGORIES):
    """The main normalization function for text.

    This will take a string and apply a set of transformations to it so
    that it can be processed more easily afterwards. Arguments:

    * ``lowercase``: not very mysterious.
    * ``collapse``: replace multiple whitespace-like characters with a
      single whitespace. This is especially useful with category replacement
      which can lead to a lot of whitespace.
    * ``decompose``: apply a unicode normalization (NFKD) to separate
      simple characters and their diacritics.
    * ``replace_categories``: This will perform a replacement of whole
      classes of unicode characters (e.g. symbols, marks, numbers) with a
      given character. It is used to replace any non-text elements of the
      input string.

def sanitize_text(text, encoding=DEFAULT_ENCODING):
    text = stringify(text, encoding_default=encoding)
    if text is not None:
        try:
            text = compose_nfc(text)
        except (SystemError, Exception) as ex:
            log.warning("Cannot NFC text: %s", ex)
            return None
        text = remove_unsafe_chars(text)
        text = text.encode(DEFAULT_ENCODING, "replace")
        return text.decode(DEFAULT_ENCODING, "replace")