code: def test_backbone(self):
      MMDetBackbone(
          backbone=dict(
              type="DetectoRS_ResNet",
              conv_cfg=dict(type="ConvAWS"),
              sac=dict(type="SAC", use_deform=True),
              stage_with_sac=(False, True, True, True),
              depth=50,
              num_stages=4,
              out_indices=(0, 1, 2, 3),
              frozen_stages=1,
              norm_cfg=dict(type="BN", requires_grad=True),
              norm_eval=True,
              style="pytorch",
          ),
          neck=dict(
              type="FPN",
              in_channels=[256, 512, 1024, 2048],
              out_channels=256,
              num_outs=5,
          ),
          # skip pretrained model for tests
          # pretrained_backbone="torchvision://resnet50",
          output_shapes=[
              ShapeSpec(channels=256, stride=s)
              for s in [4, 8, 16, 32, 64]
          ],
          output_names=["p2", "p3", "p4", "p5", "p6"],
      )
  
imports: import unittest
  from detectron2.layers import ShapeSpec
  from detectron2.modeling.mmdet_wrapper import MMDetBackbone, MMDetDetector
label_desc: Use image node_id 1 for calling the TestMMDetWrapper obj's underlying member method code with example usage: obj.test_backbone() without return types