How to use the clustergrammer2.clustergrammer_fun.proc_df_labels.main function in clustergrammer2

def load_tsv_to_net(net, file_buffer, filename=None):
  lines = file_buffer.getvalue().split('\n')
  num_labels = categories.check_categories(lines)

  row_arr = list(range(num_labels['row']))
  col_arr = list(range(num_labels['col']))

  # use header if there are col categories
  if len(col_arr) > 1:
    df = pd.read_table(file_buffer, index_col=row_arr,
                                  header=col_arr)
  else:
    df = pd.read_table(file_buffer, index_col=row_arr)

  df = proc_df_labels.main(df)

  net.df_to_dat(df, True)
  net.dat['filename'] = filename

for inst_sig in sigs:
    inst_sig_name = inst_sig['col_name']
    col_data = inst_sig['data']

    for inst_row_data in col_data:
      inst_row = inst_row_data['row_name']
      inst_value = inst_row_data['val']

      row_index = all_rows.index(inst_row)
      col_index = all_sigs.index(inst_sig_name)

      net.dat['mat'][row_index, col_index] = inst_value

  tmp_df = net.dat_to_df()
  tmp_df = proc_df_labels.main(tmp_df)

  real_net.df_to_dat(tmp_df)