How to use the clustergrammer2.clustergrammer_fun.run_filter.df_filter_col_sum function in clustergrammer2

'''
    Filter a network's rows or columns based on the sum across rows or columns.
    '''

    if axis is None:
      axis = inst_rc
      print('warning inst_rc argument will be deprecated, please use axis')

      if inst_rc is None:
        print('please provide axis argument')

    inst_df = self.dat_to_df()
    if axis == 'row':
      inst_df = run_filter.df_filter_row_sum(inst_df, threshold, take_abs)
    elif axis == 'col':
      inst_df = run_filter.df_filter_col_sum(inst_df, threshold, take_abs)
    self.df_to_dat(inst_df)

check_keep_num = -1

    if check_keep_num < len(rows_sorted):

      tmp_net = deepcopy(Network())

      if inst_keep != 'all':

        keep_rows = rows_sorted[0:inst_keep]

        tmp_df['mat'] = tmp_df['mat'].loc[keep_rows]

        if 'mat_orig' in tmp_df:
          tmp_df['mat_orig'] = tmp_df['mat_orig'].loc[keep_rows]

        tmp_df = run_filter.df_filter_col_sum(tmp_df, 0.001)
        tmp_net.df_to_dat(tmp_df)

      else:
        tmp_net.df_to_dat(tmp_df)

      try:
        try:
          calc_clust.cluster_row_and_col(tmp_net, dist_type, run_clustering=True)
        except:
          calc_clust.cluster_row_and_col(tmp_net, dist_type, run_clustering=False)

        # add view
        inst_view = {}
        inst_view['N_row_' + rank_type] = inst_keep
        inst_view['dist'] = 'cos'
        inst_view['nodes'] = {}