def METHOD_NAME(y: List[Dict[str, np.ndarray]], height: int, width: int) -> List[Dict[str, np.ndarray]]:
    """
    :param y: Target values of format `List[Dict[Tensor]]`, one for each input image. The fields of the Dict are as
              follows:
              - boxes (FloatTensor[N, 4]): the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                                               0 <= y1 < y2 <= H in image scale.
              - labels (Int64Tensor[N]): the labels for each image
              - scores (Tensor[N]): the scores or each prediction.
    :param height: Height of images in pixels.
    :param width: Width if images in pixels.
    :return: Target values of format `List[Dict[Tensor]]`, one for each input image. The fields of the Dict are as
             follows:
             - boxes (FloatTensor[N, 4]): the boxes in [y1, x1, y2, x2] format, with 0 <= x1 < x2 <= W and
                                          0 <= y1 < y2 <= H in scale [0, 1].
             - labels (Int64Tensor[N]): the labels for each image
             - scores (Tensor[N]): the scores or each prediction.
    """
    for i, _ in enumerate(y):
        y[i]["boxes"][:, 0] /= width
        y[i]["boxes"][:, 1] /= height
        y[i]["boxes"][:, 2] /= width
        y[i]["boxes"][:, 3] /= height
        y[i]["boxes"] = y[i]["boxes"][:, [1, 0, 3, 2]]
        y[i]["labels"] = y[i]["labels"] - 1
    return y