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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 augmented_tf_idf(attribute_matrix):
 """ Performs augmented TF-IDF normalization on a bag-of-words vector representation of data. Augmented TF-IDF introduced in: Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval (Vol. 1, p. 6). Cambridge: Cambridge university press. Input: - attribute_matrix: A bag-of-words vector representation in SciPy sparse matrix format. Output: - attribute_matrix: The same matrix after augmented tf-idf normalization. """

    number_of_documents = attribute_matrix.shape[0]

    max_term_frequencies = np.ones(number_of_documents, dtype=np.float64)
    idf_array = np.ones(attribute_matrix.shape[1], dtype=np.float64)

    # Calculate inverse document frequency
    attribute_matrix = attribute_matrix.tocsc()
    for j in range(attribute_matrix.shape[1]):
        document_frequency = attribute_matrix.getcol(j).data.size
        if document_frequency > 1:
            idf_array[j] = np.log(number_of_documents/document_frequency)

    # Calculate maximum term frequencies for a user
    attribute_matrix = attribute_matrix.tocsr()
    for i in range(attribute_matrix.shape[0]):
        max_term_frequency = attribute_matrix.getrow(i).data
        if max_term_frequency.size > 0:
            max_term_frequency = max_term_frequency.max()
            if max_term_frequency > 0.0:
                max_term_frequencies[i] = max_term_frequency

    # Do augmented tf-idf normalization
    attribute_matrix = attribute_matrix.tocoo()
    attribute_matrix.data = 0.5 + np.divide(0.5*attribute_matrix.data, np.multiply((max_term_frequencies[attribute_matrix.row]), (idf_array[attribute_matrix.col])))
    attribute_matrix = attribute_matrix.tocsr()

    return attribute_matrix