code: def add_tensormask_config(cfg):
      """
      Add config for TensorMask.
      """
      cfg.MODEL.TENSOR_MASK = CN()
  
      # Anchor parameters
      cfg.MODEL.TENSOR_MASK.IN_FEATURES = [
          "p2",
          "p3",
          "p4",
          "p5",
          "p6",
          "p7",
      ]
  
      # Convolutions to use in the towers
      cfg.MODEL.TENSOR_MASK.NUM_CONVS = 4
  
      # Number of foreground classes.
      cfg.MODEL.TENSOR_MASK.NUM_CLASSES = 80
      # Channel size for the classification tower
      cfg.MODEL.TENSOR_MASK.CLS_CHANNELS = 256
  
      cfg.MODEL.TENSOR_MASK.SCORE_THRESH_TEST = 0.05
      # Only the top (1000 * #levels) candidate boxes across all levels are
      # considered jointly during test (to improve speed)
      cfg.MODEL.TENSOR_MASK.TOPK_CANDIDATES_TEST = 6000
      cfg.MODEL.TENSOR_MASK.NMS_THRESH_TEST = 0.5
  
      # Box parameters
      # Channel size for the box tower
      cfg.MODEL.TENSOR_MASK.BBOX_CHANNELS = 128
      # Weights on (dx, dy, dw, dh)
      cfg.MODEL.TENSOR_MASK.BBOX_REG_WEIGHTS = (1.5, 1.5, 0.75, 0.75)
  
      # Loss parameters
      cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_GAMMA = 3.0
      cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_ALPHA = 0.3
  
      # Mask parameters
      # Channel size for the mask tower
      cfg.MODEL.TENSOR_MASK.MASK_CHANNELS = 128
      # Mask loss weight
      cfg.MODEL.TENSOR_MASK.MASK_LOSS_WEIGHT = 2.0
      # weight on positive pixels within the mask
      cfg.MODEL.TENSOR_MASK.POSITIVE_WEIGHT = 1.5
      # Whether to predict in the aligned representation
      cfg.MODEL.TENSOR_MASK.ALIGNED_ON = False
      # Whether to use the bipyramid architecture
      cfg.MODEL.TENSOR_MASK.BIPYRAMID_ON = False
  
label_desc: Use image node_id 1 for calling a global function with example usage: add_tensormask_config(cfg) without return types