code: def _eval_seq_jde(
      self,
      dataloader,
      save_dir=None,
      show_image=False,
      frame_rate=30,
      draw_threshold=0,
  ):
      if save_dir:
          if not os.path.exists(save_dir):
              os.makedirs(save_dir)
      tracker = self.model.tracker
      tracker.max_time_lost = int(
          frame_rate / 30.0 * tracker.track_buffer
      )
  
      timer = MOTTimer()
      frame_id = 0
      self.status["mode"] = "track"
      self.model.eval()
      results = defaultdict(
          list
      )  # support single class and multi classes
  
      for step_id, data in enumerate(tqdm(dataloader)):
          self.status["step_id"] = step_id
          # forward
          timer.tic()
          pred_dets, pred_embs = self.model(data)
  
          pred_dets, pred_embs = pred_dets.numpy(), pred_embs.numpy()
          online_targets_dict = self.model.tracker.update(
              pred_dets, pred_embs
          )
          online_tlwhs = defaultdict(list)
          online_scores = defaultdict(list)
          online_ids = defaultdict(list)
          for cls_id in range(self.cfg.num_classes):
              online_targets = online_targets_dict[cls_id]
              for t in online_targets:
                  tlwh = t.tlwh
                  tid = t.track_id
                  tscore = t.score
                  if tlwh[2] * tlwh[3] <= tracker.min_box_area:
                      continue
                  if (
                      tracker.vertical_ratio > 0
                      and tlwh[2] / tlwh[3] > tracker.vertical_ratio
                  ):
                      continue
                  online_tlwhs[cls_id].append(tlwh)
                  online_ids[cls_id].append(tid)
                  online_scores[cls_id].append(tscore)
              # save results
              results[cls_id].append(
                  (
                      frame_id + 1,
                      online_tlwhs[cls_id],
                      online_scores[cls_id],
                      online_ids[cls_id],
                  )
              )
  
          timer.toc()
          save_vis_results(
              data,
              frame_id,
              online_ids,
              online_tlwhs,
              online_scores,
              timer.average_time,
              show_image,
              save_dir,
              self.cfg.num_classes,
              self.ids2names,
          )
          frame_id += 1
  
      return results, frame_id, timer.average_time, timer.calls
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import glob
  import re
  import paddle
  import paddle.nn
  import numpy
  from tqdm import tqdm
  from collections import defaultdict
  from ppdet.core.workspace import create
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
  from ppdet.modeling.mot.utils import Detection, get_crops, scale_coords, clip_box
  from ppdet.modeling.mot.utils import MOTTimer, load_det_results, write_mot_results, save_vis_results
  from ppdet.modeling.mot.tracker import JDETracker, CenterTracker
  from ppdet.modeling.mot.tracker import DeepSORTTracker, OCSORTTracker, BOTSORTTracker
  from ppdet.modeling.architectures import YOLOX
  from ppdet.metrics import Metric, MOTMetric, KITTIMOTMetric, MCMOTMetric
  from ppdet.data.source.category import get_categories
  import ppdet.utils.stats
  from .callbacks import Callback, ComposeCallback
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 10 for calling the Tracker obj's underlying member method code with example usage: obj._eval_seq_jde(dataloader, save_dir, show_image, frame_rate, draw_threshold) and returns: results,  frame_id,  timer,  timer