code: def _get_predictions_demo():
      instances = [
          {
              "bbox_label": 0,
              "bbox": [0, 0, 100, 100],
              "ignore_flag": 0,
              "instance_id": 1,
              "mot_conf": 1.0,
              "category_id": 1,
              "visibility": 1.0,
          },
          {
              "bbox_label": 0,
              "bbox": [0, 0, 100, 100],
              "ignore_flag": 0,
              "instance_id": 2,
              "mot_conf": 1.0,
              "category_id": 1,
              "visibility": 1.0,
          },
      ]
      instances_2 = copy.deepcopy(instances)
      sep = os.sep
      pred_instances_data = dict(
          bboxes=torch.tensor(
              [
                  [0, 0, 100, 100],
                  [0, 0, 100, 40],
              ]
          ),
          instances_id=torch.tensor([1, 2]),
          scores=torch.tensor([1.0, 1.0]),
      )
      pred_instances_data_2 = copy.deepcopy(pred_instances_data)
      pred_instances = InstanceData(**pred_instances_data)
      pred_instances_2 = InstanceData(**pred_instances_data_2)
      img_data_sample = DetDataSample()
      img_data_sample.pred_track_instances = pred_instances
      img_data_sample.instances = instances
      img_data_sample.set_metainfo(
          dict(
              frame_id=0,
              ori_video_length=2,
              video_length=2,
              img_id=1,
              img_path=f"xxx{sep}MOT17-09-DPM{sep}img1{sep}000001.jpg",
          )
      )
      img_data_sample_2 = DetDataSample()
      img_data_sample_2.pred_track_instances = pred_instances_2
      img_data_sample_2.instances = instances_2
      img_data_sample_2.set_metainfo(
          dict(
              frame_id=1,
              ori_video_length=2,
              video_length=2,
              img_id=2,
              img_path=f"xxx{sep}MOT17-09-DPM{sep}img1{sep}000002.jpg",
          )
      )
      track_data_sample = TrackDataSample()
      track_data_sample.video_data_samples = [
          img_data_sample,
          img_data_sample_2,
      ]
      # [TrackDataSample]
      predictions = []
      if isinstance(track_data_sample, BaseDataElement):
          predictions.append(track_data_sample.to_dict())
      return predictions
  
file_path: mmdetection/tests/test_evaluation/test_metrics/test_mot_challenge_metrics.py
imports: import copy
  import os
  import tempfile
  from unittest import TestCase
  import torch
  from mmengine.structures import BaseDataElement, InstanceData
  from mmdet.evaluation import MOTChallengeMetric
  from mmdet.structures import DetDataSample, TrackDataSample
label_desc: Use image node_id 3 for calling the TestMOTChallengeMetric obj's underlying member method code with example usage: obj._get_predictions_demo() and returns: predictions