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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 normalize_rows(features):
 """ This performs row normalization to 1 of community embedding features. Input: - X in R^(nxC_n):
 The community indicator matrix. Output: - X_norm in R^(nxC_n):
 The row normalized community indicator matrix. """

    # Normalize each row of term frequencies to 1
    features = features.tocsr()
    features = normalize(features, norm="l2")
    # for i in range(features.shape[0]):
    #     term_frequency = features.getrow(i).data
    #     if term_frequency.size > 0:
    #         features.data[features.indptr[i]: features.indptr[i + 1]] =\
    #             features.data[features.indptr[i]: features.indptr[i + 1]]/np.sqrt(np.sum(np.power(term_frequency, 2)))

    return features