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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 train_phrases(paths, out='data/bigram_model.phrases', tokenizer=word_tokenize, **kwargs):
 """ Train a bigram phrase model on a list of files. """

    n = 0
    for path in paths:
        print('Counting lines for {0}...'.format(path))
        n += sum(1 for line in open(path, 'r'))
    print('Processing {0} lines...'.format(n))

    # Change to use less memory. Default is 40m.
    kwargs = {
        'max_vocab_size': 40000000,
        'threshold': 8.
    }.update(kwargs)

    print('Training bigrams...')
    bigram = Phrases(_phrase_doc_stream(paths, n, tokenizer=word_tokenize), **kwargs)

    print('Saving...')
    bigram.save(out)