def decode(self, simcc_x: np.ndarray,
               simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        """Decode keypoint coordinates from SimCC representations. The decoded
        coordinates are in the input image space.

        Args:
            encoded (Tuple[np.ndarray, np.ndarray]): SimCC labels for x-axis
                and y-axis
            simcc_x (np.ndarray): SimCC label for x-axis
            simcc_y (np.ndarray): SimCC label for y-axis

        Returns:
            tuple:
            - keypoints (np.ndarray): Decoded coordinates in shape (N, K, D)
            - socres (np.ndarray): The keypoint scores in shape (N, K).
                It usually represents the confidence of the keypoint prediction
        """

        keypoints, scores = get_simcc_maximum(simcc_x, simcc_y)

        # Unsqueeze the instance dimension for single-instance results
        if keypoints.ndim == 2:
            keypoints = keypoints[None, :]
            scores = scores[None, :]

        if self.use_dark:
            x_blur = int((self.sigma[0] * 20 - 7) // 3)
            y_blur = int((self.sigma[1] * 20 - 7) // 3)
            x_blur -= int((x_blur % 2) == 0)
            y_blur -= int((y_blur % 2) == 0)
            keypoints[:, :, 0] = refine_simcc_dark(keypoints[:, :, 0], simcc_x,
                                                   x_blur)
            keypoints[:, :, 1] = refine_simcc_dark(keypoints[:, :, 1], simcc_y,
                                                   y_blur)

        keypoints /= self.simcc_split_ratio

        return keypoints, scores
Decode keypoint coordinates from SimCC representations. The decoded
        coordinates are in the input image space.

        Args:
            encoded (Tuple[np.ndarray, np.ndarray]): SimCC labels for x-axis
                and y-axis
            simcc_x (np.ndarray): SimCC label for x-axis
            simcc_y (np.ndarray): SimCC label for y-axis

        Returns:
            tuple:
            - keypoints (np.ndarray): Decoded coordinates in shape (N, K, D)
            - socres (np.ndarray): The keypoint scores in shape (N, K).
                It usually represents the confidence of the keypoint prediction
        
mmpose/mmpose/codecs/simcc_label.py