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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def lossy_tokenize(text, lang, include_punctuation=False, external_wordlist=False):
 """ Get a list of tokens for this text, with largely the same results and options as `tokenize`, but aggressively normalize some text in a lossy way that's good for counting word frequencies. In particular: - Any sequence of 2 or more adjacent digits, possibly with intervening punctuation such as a decimal point, will replace each digit with '0' so that frequencies for numbers don't have to be counted separately. This is similar to but not quite identical to the word2vec Google News data, which replaces digits with '#' in tokens with more than one digit. - In Chinese, unless Traditional Chinese is specifically requested using 'zh-Hant', all characters will be converted to Simplified Chinese. """

    global _simplify_chinese

    info = get_language_info(lang)
    tokens = tokenize(text, lang, include_punctuation, external_wordlist)

    if info['lookup_transliteration'] == 'zh-Hans':
        from wordfreq.chinese import simplify_chinese as _simplify_chinese
        tokens = [_simplify_chinese(token) for token in tokens]

    return [smash_numbers(token) for token in tokens]