code: def test_forward(self):
      in_channels = 16
      num_filters = (8, 8)
      num_kernels = (4, 4)
      feat = torch.rand(1, 16, 4, 4)
      ct_resnet_neck = CTResNetNeck(
          in_channels=in_channels,
          num_deconv_filters=num_filters,
          num_deconv_kernels=num_kernels,
          use_dcn=False,
      )
  
      # feat must be list or tuple
      with self.assertRaises(AssertionError):
          ct_resnet_neck(feat)
  
      out_feat = ct_resnet_neck([feat])[0]
      self.assertEqual(out_feat.shape, (1, num_filters[-1], 16, 16))
  
      if torch.cuda.is_available():
          # test dcn
          ct_resnet_neck = CTResNetNeck(
              in_channels=in_channels,
              num_deconv_filters=num_filters,
              num_deconv_kernels=num_kernels,
          )
          ct_resnet_neck = ct_resnet_neck.cuda()
          feat = feat.cuda()
          out_feat = ct_resnet_neck([feat])[0]
          self.assertEqual(out_feat.shape, (1, num_filters[-1], 16, 16))
  
imports: import unittest
  import torch
  from mmdet.models.necks import CTResNetNeck
label_desc: Use image node_id 2 for calling the TestCTResNetNeck obj's underlying member method code with example usage: obj.test_forward() without return types