docs: 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