code: def inference_mot(
      model: nn.Module, img: np.ndarray, frame_id: int, video_len: int
  ) -> SampleList:
      """Inference image(s) with the mot model.
  
      Args:
          model (nn.Module): The loaded mot model.
          img (np.ndarray): Loaded image.
          frame_id (int): frame id.
          video_len (int): demo video length
      Returns:
          SampleList: The tracking data samples.
      """
      cfg = model.cfg
      data = dict(
          img=[img.astype(np.float32)],
          frame_id=[frame_id],
          ori_shape=[img.shape[:2]],
          img_id=[frame_id + 1],
          ori_video_length=[video_len],
      )
  
      test_pipeline = build_test_pipeline(cfg)
      data = test_pipeline(data)
  
      if not next(model.parameters()).is_cuda:
          for m in model.modules():
              assert not isinstance(
                  m, RoIPool
              ), "CPU inference with RoIPool is not supported currently."
  
      # forward the model
      with torch.no_grad():
          data = default_collate([data])
          result = model.test_step(data)[0]
      return result
  
imports: import copy
  import warnings
  from pathlib import Path
  from typing import Optional, Sequence, Union
  import numpy
  import torch
  import torch.nn
  from mmcv.ops import RoIPool
  from mmcv.transforms import Compose
  from mmengine.config import Config
  from mmengine.dataset import default_collate
  from mmengine.model.utils import revert_sync_batchnorm
  from mmengine.registry import init_default_scope
  from mmengine.runner import load_checkpoint
  from mmdet.registry import DATASETS
  from mmdet.utils import ConfigType
  from ..evaluation import get_classes
  from ..registry import MODELS
  from ..structures import DetDataSample, SampleList
  from ..utils import get_test_pipeline_cfg
label_desc: Use image node_id 4 for calling a global function with example usage: inference_mot(model, img, frame_id, video_len) and returns: result