code: def test_evaluate(self):
      # create dummy data
      dummy_pred = self._create_dummy_results()
  
      crowdhuman_metric = CrowdHumanMetric(
          ann_file=self.ann_file_path[0],
          outfile_prefix=f"{self.tmp_dir.name}/test",
      )
      crowdhuman_metric.process(
          {},
          [
              dict(
                  pred_instances=dummy_pred,
                  img_id="283554,35288000868e92d4",
                  ori_shape=(1640, 1640),
              )
          ],
      )
      eval_results = crowdhuman_metric.evaluate(size=1)
      target = {
          "crowd_human/mAP": 0.8333,
          "crowd_human/mMR": 0.0,
          "crowd_human/JI": 1.0,
      }
      self.assertDictEqual(eval_results, target)
      self.assertTrue(
          osp.isfile(osp.join(self.tmp_dir.name, "test.json"))
      )
  
file_path: mmdetection/tests/test_evaluation/test_metrics/test_crowdhuman_metric.py
imports: import os.path
  import tempfile
  from unittest import TestCase
  import numpy
  import torch
  from mmdet.evaluation import CrowdHumanMetric
label_desc: Use image node_id 5 for calling the TestCrowdHumanMetric obj's underlying member method code with example usage: obj.test_evaluate() without return types