code: def _test_model(self, config_path, inference_func, batch=1):
      model = model_zoo.get(config_path, trained=True)
      image = get_sample_coco_image()
      inputs = tuple(image.clone() for _ in range(batch))
  
      wrapper = TracingAdapter(model, inputs, inference_func)
      wrapper.eval()
      with torch.no_grad():
          # trace with smaller images, and the trace must still work
          trace_inputs = tuple(
              nn.functional.interpolate(
                  image, scale_factor=random.uniform(0.5, 0.7)
              )
              for _ in range(batch)
          )
          traced_model = torch.jit.trace(wrapper, trace_inputs)
  
      testing_devices = self._get_device_casting_test_cases(model)
      # save and load back the model in order to show traceback of TorchScript
      with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
          basename = "model"
          jitfile = f"{d}/{basename}.jit"
          torch.jit.save(traced_model, jitfile)
          traced_model = torch.jit.load(jitfile)
  
          if any(
              device and "cuda" in device for device in testing_devices
          ):
              self._check_torchscript_no_hardcoded_device(
                  jitfile, d, "cuda"
              )
  
      for device in testing_devices:
          print(
              f"Testing casting to {device} for inference (traced on {model.device}) ..."
          )
          with torch.no_grad():
              outputs = inference_func(
                  copy.deepcopy(model).to(device), *inputs
              )
              traced_outputs = wrapper.outputs_schema(
                  traced_model.to(device)(*inputs)
              )
          if batch > 1:
              for output, traced_output in zip(outputs, traced_outputs):
                  assert_instances_allclose(
                      output, traced_output, size_as_tensor=True
                  )
          else:
              assert_instances_allclose(
                  outputs, traced_outputs, size_as_tensor=True
              )
  
imports: import copy
  import glob
  import json
  import os
  import random
  import tempfile
  import unittest
  import zipfile
  import torch
  from torch import Tensor, nn
  from detectron2 import model_zoo
  from detectron2.config import get_cfg
  from detectron2.config.instantiate import dump_dataclass, instantiate
  from detectron2.export import dump_torchscript_IR, scripting_with_instances
  from detectron2.export.flatten import TracingAdapter, flatten_to_tuple
  from detectron2.export.torchscript_patch import patch_builtin_len
  from detectron2.layers import ShapeSpec
  from detectron2.modeling import build_backbone
  from detectron2.modeling.postprocessing import detector_postprocess
  from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead
  from detectron2.structures import Boxes, Instances
  from detectron2.utils.env import TORCH_VERSION
  from detectron2.utils.testing import assert_instances_allclose, convert_scripted_instances, get_sample_coco_image, random_boxes, reload_script_model, skipIfOnCPUCI
label_desc: Use image node_id 8 for calling the TestTracing obj's underlying member method code with example usage: obj._test_model(config_path, inference_func, batch) without return types