def METHOD_NAME(self, input: Input) -> Dict[str, Any]:
    img = LoadImage.convert_to_ndarray(input)[:, :, ::-1]
    mean = np.array([0.408, 0.447, 0.470],
                    dtype=np.float32).reshape(1, 1, 3)
    std = np.array([0.289, 0.274, 0.278],
                   dtype=np.float32).reshape(1, 1, 3)
    height, width = img.shape[0:2]
    inp_height, inp_width = 512, 512
    c = np.array([width / 2., height / 2.], dtype=np.float32)
    s = max(height, width) * 1.0
    trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height])
    resized_image = cv2.resize(img, (width, height))
    inp_image = cv2.warpAffine(
        resized_image,
        trans_input, (inp_width, inp_height),
        flags=cv2.INTER_LINEAR)
    inp_image = ((inp_image / 255. - mean) / std).astype(np.float32)
    images = inp_image.transpose(2, 0, 1).reshape(1, 3, inp_height,
                                                  inp_width)
    images = torch.from_numpy(images).to(self.device)
    meta = {
        'c': c,
        's': s,
        'input_height': inp_height,
        'input_width': inp_width,
        'out_height': inp_height // 4,
        'out_width': inp_width // 4
    }
    result = {'img': images, 'meta': meta}
    return result