class_docstr: Evaluation metrics for MOT Challenge.
  
  Args:
      metric (str | list[str]): Metrics to be evaluated. Options are
          'HOTA', 'CLEAR', 'Identity'.
          Defaults to ['HOTA', 'CLEAR', 'Identity'].
      outfile_prefix (str, optional): Path to save the formatted results.
          Defaults to None.
      track_iou_thr (float): IoU threshold for tracking evaluation.
          Defaults to 0.5.
      benchmark (str): Benchmark to be evaluated. Defaults to 'MOT17'.
      format_only (bool): If True, only formatting the results to the
          official format and not performing evaluation. Defaults to False.
      postprocess_tracklet_cfg (List[dict], optional): configs for tracklets
          postprocessing methods. `InterpolateTracklets` is supported.
          Defaults to []
          - InterpolateTracklets:
              - min_num_frames (int, optional): The minimum length of a
                  track that will be interpolated. Defaults to 5.
              - max_num_frames (int, optional): The maximum disconnected
                  length in a track. Defaults to 20.
              - use_gsi (bool, optional): Whether to use the GSI (Gaussian-
                  smoothed interpolation) method. Defaults to False.
              - smooth_tau (int, optional): smoothing parameter in GSI.
                  Defaults to 10.
      collect_device (str): Device name used for collecting results from
          different ranks during distributed training. Must be 'cpu' or
          'gpu'. Defaults to 'cpu'.
      prefix (str, optional): The prefix that will be added in the metric
          names to disambiguate homonymous metrics of different evaluators.
          If prefix is not provided in the argument, self.default_prefix
          will be used instead. Default: None
  Returns:
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docstr: Compute the metrics from processed results.
  
  Args:
      results (list): The processed results of each batch.
          Defaults to None.
  
  Returns:
      dict: The computed metrics. The keys are the names of the metrics,
      and the values are corresponding results.
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code: def compute_metrics(self, results: list = None) -> dict:
      """Compute the metrics from processed results.
  
      Args:
          results (list): The processed results of each batch.
              Defaults to None.
  
      Returns:
          dict: The computed metrics. The keys are the names of the metrics,
          and the values are corresponding results.
      """
      logger: MMLogger = MMLogger.get_current_instance()
  
      # NOTICE: don't access `self.results` from the method.
      eval_results = dict()
  
      if self.format_only:
          return eval_results
  
      eval_config = trackeval.Evaluator.get_default_eval_config()
  
      # need to split out the tracker name
      # caused by the implementation of TrackEval
      pred_dir_tmp = self.pred_dir.rsplit(osp.sep, 1)[0]
      dataset_config = self.get_dataset_cfg(self.gt_dir, pred_dir_tmp)
  
      evaluator = trackeval.Evaluator(eval_config)
      dataset = [trackeval.datasets.MotChallenge2DBox(dataset_config)]
      metrics = [
          getattr(trackeval.metrics, metric)(
              dict(METRICS=[metric], THRESHOLD=0.5)
          )
          for metric in self.metrics
      ]
      output_res, _ = evaluator.evaluate(dataset, metrics)
      output_res = output_res["MotChallenge2DBox"][self.TRACKER][
          "COMBINED_SEQ"
      ]["pedestrian"]
  
      if "HOTA" in self.metrics:
          logger.info("Evaluating HOTA Metrics...")
          eval_results["HOTA"] = np.average(output_res["HOTA"]["HOTA"])
          eval_results["AssA"] = np.average(output_res["HOTA"]["AssA"])
          eval_results["DetA"] = np.average(output_res["HOTA"]["DetA"])
  
      if "CLEAR" in self.metrics:
          logger.info("Evaluating CLEAR Metrics...")
          eval_results["MOTA"] = np.average(output_res["CLEAR"]["MOTA"])
          eval_results["MOTP"] = np.average(output_res["CLEAR"]["MOTP"])
          eval_results["IDSW"] = np.average(output_res["CLEAR"]["IDSW"])
          eval_results["TP"] = np.average(output_res["CLEAR"]["CLR_TP"])
          eval_results["FP"] = np.average(output_res["CLEAR"]["CLR_FP"])
          eval_results["FN"] = np.average(output_res["CLEAR"]["CLR_FN"])
          eval_results["Frag"] = np.average(output_res["CLEAR"]["Frag"])
          eval_results["MT"] = np.average(output_res["CLEAR"]["MT"])
          eval_results["ML"] = np.average(output_res["CLEAR"]["ML"])
  
      if "Identity" in self.metrics:
          logger.info("Evaluating Identity Metrics...")
          eval_results["IDF1"] = np.average(
              output_res["Identity"]["IDF1"]
          )
          eval_results["IDTP"] = np.average(
              output_res["Identity"]["IDTP"]
          )
          eval_results["IDFN"] = np.average(
              output_res["Identity"]["IDFN"]
          )
          eval_results["IDFP"] = np.average(
              output_res["Identity"]["IDFP"]
          )
          eval_results["IDP"] = np.average(
              output_res["Identity"]["IDP"]
          )
          eval_results["IDR"] = np.average(
              output_res["Identity"]["IDR"]
          )
  
      return eval_results
  
imports: import os
  import os.path
  import shutil
  import tempfile
  from collections import defaultdict
  from typing import List, Optional, Union
  import numpy
  import torch
  from mmengine.dist import all_gather_object, barrier, broadcast, broadcast_object_list, get_dist_info, is_main_process
  from mmengine.logging import MMLogger
  from mmdet.registry import METRICS, TASK_UTILS
  from .base_video_metric import BaseVideoMetric
label_desc: Use image node_id 9 for calling the MOTChallengeMetric obj's underlying member method code with example usage: obj.compute_metrics(results) and returns: eval_results, eval_results