code: def test_get_video_feat_extractor(
      model_name, image_modality, attention, num_classes
  ):
      (
          feature_network,
          class_feature_dim,
          domain_feature_dim,
      ) = get_video_feat_extractor(
          model_name, image_modality, attention, num_classes
      )
  
      assert isinstance(feature_network, dict)
  
      if image_modality == "joint":
          assert isinstance(feature_network["rgb"], torch.nn.Module)
          assert isinstance(feature_network["flow"], torch.nn.Module)
      elif image_modality == "rgb":
          assert isinstance(feature_network["rgb"], torch.nn.Module)
          assert feature_network["flow"] is None
      else:
          assert feature_network["rgb"] is None
          assert isinstance(feature_network["flow"], torch.nn.Module)
  
      if model_name == "I3D":
          assert domain_feature_dim == 1024
          assert (
              class_feature_dim == 2048
              if image_modality == "joint"
              else class_feature_dim == 1024
          )
  
      else:
          assert domain_feature_dim == 512
          assert (
              class_feature_dim == 1024
              if image_modality == "joint"
              else class_feature_dim == 512
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_get_video_feat_extractor(model_name, image_modality, attention, num_classes) without return types