def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
        """
        Draw semantic segmentation predictions/labels.

        Args:
            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
                Each value is the integer label of the pixel.
            area_threshold (int): segments with less than `area_threshold` are not drawn.
            alpha (float): the larger it is, the more opaque the segmentations are.

        Returns:
            output (VisImage): image object with visualizations.
        """
        if isinstance(sem_seg, torch.Tensor):
            sem_seg = sem_seg.numpy()
        labels, areas = np.unique(sem_seg, return_counts=True)
        sorted_idxs = np.argsort(-areas).tolist()
        labels = labels[sorted_idxs]
        for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
            try:
                mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
            except (AttributeError, IndexError):
                mask_color = None

            binary_mask = (sem_seg == label).astype(np.uint8)
            text = self.metadata.stuff_classes[label]
            self.draw_binary_mask(
                binary_mask,
                color=mask_color,
                edge_color=_OFF_WHITE,
                text=text,
                alpha=alpha,
                area_threshold=area_threshold,
            )
        return self.output

        Draw semantic segmentation predictions/labels.

        Args:
            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
                Each value is the integer label of the pixel.
            area_threshold (int): segments with less than `area_threshold` are not drawn.
            alpha (float): the larger it is, the more opaque the segmentations are.

        Returns:
            output (VisImage): image object with visualizations.
        
video_chat_with_ChatGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py