code: def seqs_overall_metrics(summary_df, verbose=False):
      """
      Calculate overall metrics for multiple sequences
      """
      add_col = [
          "num_frames",
          "num_matches",
          "num_switches",
          "num_transfer",
          "num_ascend",
          "num_migrate",
          "num_false_positives",
          "num_misses",
          "num_detections",
          "num_objects",
          "num_predictions",
          "num_unique_objects",
          "mostly_tracked",
          "partially_tracked",
          "mostly_lost",
          "num_fragmentations",
          "idfp",
          "idfn",
          "idtp",
      ]
      calc_col = [
          "motp",
          "mota",
          "precision",
          "recall",
          "idp",
          "idr",
          "idf1",
      ]
      calc_df = summary_df.copy()
  
      overall_dic = {}
      for col in add_col:
          overall_dic[col] = calc_df[col].sum()
  
      for col in calc_col:
          overall_dic[col] = getattr(
              MCMOTMetricOverall, col + "_overall"
          )(calc_df, overall_dic)
  
      overall_df = pd.DataFrame(overall_dic, index=["overall_calc"])
      calc_df = pd.concat([calc_df, overall_df])
  
      if verbose:
          mh = mm.metrics.create()
          str_calc_df = mm.io.render_summary(
              calc_df, formatters=mh.formatters, namemap=NAME_MAP
          )
          print(str_calc_df)
  
      return calc_df
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import copy
  import sys
  import math
  from collections import defaultdict
  import numpy
  import pandas
  from .metrics import Metric
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 2 for calling a global function with example usage: seqs_overall_metrics(summary_df, verbose) and returns: calc_df