def __call__(self, results):
        """
        Performs normalization operations.
        Args:
            imgs: Numpy array.
        return:
            np_imgs: Numpy array after normalization.
        """

        if self.inplace:  # default is False
            n = len(results['imgs'])
            h, w, c = results['imgs'][0].shape
            norm_imgs = np.empty((n, h, w, c), dtype=np.float32)
            for i, img in enumerate(results['imgs']):
                norm_imgs[i] = img

            for img in norm_imgs:  # [n,h,w,c]
                mean = np.float64(self.mean.reshape(1, -1))  # [1, 3]
                stdinv = 1 / np.float64(self.std.reshape(1, -1))  # [1, 3]
                cv2.subtract(img, mean, img)
                cv2.multiply(img, stdinv, img)
        else:
            imgs = results['imgs']
            norm_imgs = imgs / 255.0
            norm_imgs -= self.mean
            norm_imgs /= self.std
            if 'backend' in results and results['backend'] == 'pyav':
                norm_imgs = paddle.to_tensor(norm_imgs, dtype=paddle.float32)
        results['imgs'] = norm_imgs
        return results

        Performs normalization operations.
        Args:
            imgs: Numpy array.
        return:
            np_imgs: Numpy array after normalization.
        
deploy/pipeline/pphuman/video_action_preprocess.py