code: def __call__(self, data):
      panoptic = data["label"]
      segments_info = data["ann"]
      thing_ids = set(data["thing_ids"])
      height, width = panoptic.shape[0], panoptic.shape[1]
      semantic = (
          np.zeros_like(panoptic, dtype=np.uint8) + self.ignore_index
      )
      center = np.zeros((1, height, width), dtype=np.float32)
      center_pts = []
      offset = np.zeros((2, height, width), dtype=np.float32)
      y_coord, x_coord = np.meshgrid(
          np.arange(height, dtype=np.float32),
          np.arange(width, dtype=np.float32),
          indexing="ij",
      )
      # Generate pixel-wise loss weights
      semantic_weights = np.ones_like(panoptic, dtype=np.uint8)
      # 0: ignore, 1: has instance
      # three conditions for a region to be ignored for instance branches:
      # (1) It is labeled as `ignore_index`
      # (2) It is crowd region (iscrowd=1)
      # (3) (Optional) It is stuff region (for offset branch)
      center_weights = np.zeros_like(panoptic, dtype=np.uint8)
      offset_weights = np.zeros_like(panoptic, dtype=np.uint8)
      for seg in segments_info:
          cat_id = seg["category_id"]
          if not (self.ignore_crowd_in_semantic and seg["iscrowd"]):
              semantic[panoptic == seg["id"]] = cat_id
          if not seg["iscrowd"]:
              # Ignored regions are not in `segments_info`.
              # Handle crowd region.
              center_weights[panoptic == seg["id"]] = 1
              if not self.ignore_stuff_in_offset or cat_id in thing_ids:
                  offset_weights[panoptic == seg["id"]] = 1
          if cat_id in thing_ids:
              # Find instance center
              mask_index = np.where(panoptic == seg["id"])
              if len(mask_index[0]) == 0:
                  # the instance is completely cropped
                  continue
  
              # Find instance area
              ins_area = len(mask_index[0])
              if ins_area < self.small_instance_area:
                  semantic_weights[
                      panoptic == seg["id"]
                  ] = self.small_instance_weight
  
              center_y, center_x = np.mean(mask_index[0]), np.mean(
                  mask_index[1]
              )
              center_pts.append([center_y, center_x])
  
              # Generate center heatmap
              y, x = int(round(center_y)), int(round(center_x))
              sigma = self.sigma
              # upper left
              ul = int(np.round(x - 3 * sigma - 1)), int(
                  np.round(y - 3 * sigma - 1)
              )
              # bottom right
              br = int(np.round(x + 3 * sigma + 2)), int(
                  np.round(y + 3 * sigma + 2)
              )
  
              # Start and end indices in default Gaussian image
              gaussian_x0, gaussian_x1 = (
                  max(0, -ul[0]),
                  min(br[0], width) - ul[0],
              )
              gaussian_y0, gaussian_y1 = (
                  max(0, -ul[1]),
                  min(br[1], height) - ul[1],
              )
  
              # Start and end indices in center heatmap image
              center_x0, center_x1 = max(0, ul[0]), min(br[0], width)
              center_y0, center_y1 = max(0, ul[1]), min(br[1], height)
              center[
                  0, center_y0:center_y1, center_x0:center_x1
              ] = np.maximum(
                  center[0, center_y0:center_y1, center_x0:center_x1],
                  self.g[
                      gaussian_y0:gaussian_y1, gaussian_x0:gaussian_x1
                  ],
              )
              offset[0][mask_index] = center_y - y_coord[mask_index]
              offset[1][mask_index] = center_x - x_coord[mask_index]
  
      center_weights = center_weights[None]
      offset_weights = offset_weights[None]
  
      data["sem_label"] = semantic.astype("long")
      data["center"] = center.astype("float32")
      data["center_points"] = center_pts
      data["offset"] = offset.astype("float32")
      data["sem_seg_weights"] = semantic_weights.astype("float32")
      data["center_weights"] = center_weights.astype("float32")
      data["offset_weights"] = offset_weights.astype("float32")
  
      return data
  
imports: import numpy
  from paddlepanseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the GeneratePanopticDeepLabTrainTargets obj's underlying member method code with example usage: obj.__call__(data) and returns: data