How do I install MNN on a Raspberry Pi 3 to make it work?

OS Raspbian

I have already used 3 instructions for installation, 2 of them are official from github. 1 of them did not work at all, and the other 2 as a result of running one ready-made project (for which MNN is installed) give the following error:

AtrributeError: module 'MNN' has no attribute 'Interpreter'.

Although according to the documentation, it is. That is, the library is not installed correctly.

Code snippet where the error occurs:

def inference():
    input_size = [int(v.strip()) for v in args.input_size.split(",")]
    priors = define_img_size(input_size)
    result_path = args.results_path
    imgs_path = args.imgs_path
    if not os.path.exists(result_path):
        os.makedirs(result_path)
    listdir = os.listdir(imgs_path)
    for file_path in listdir:
        img_path = os.path.join(imgs_path, file_path)
        image_ori = cv2.imread(img_path)
        interpreter = MNN.Interpreter(args.model_path)
        session = interpreter.createSession()
        input_tensor = interpreter.getSessionInput(session)
        image = cv2.cvtColor(image_ori, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, tuple(input_size))
        image = image.astype(float)
        image = (image - image_mean) / image_std
        image = image.transpose((2, 0, 1))
        tmp_input = MNN.Tensor((1, 3, input_size[1], input_size[0]), MNN.Halide_Type_Float, image, MNN.Tensor_DimensionType_Caffe)
        input_tensor.copyFrom(tmp_input)
        time_time = time.time()
        interpreter.runSession(session)
        scores = interpreter.getSessionOutput(session, "scores").getData()
        boxes = interpreter.getSessionOutput(session, "boxes").getData()
        boxes = np.expand_dims(np.reshape(boxes, (-1, 4)), axis=0)
        scores = np.expand_dims(np.reshape(scores, (-1, 2)), axis=0)
        print("inference time: {} s".format(round(time.time() - time_time, 4)))
        boxes = box_utils.convert_locations_to_boxes(boxes, priors, center_variance, size_variance)
        boxes = box_utils.center_form_to_corner_form(boxes)
        boxes, labels, probs = predict(image_ori.shape[1], image_ori.shape[0], scores, boxes, args.threshold)
        for i in range(boxes.shape[0]):
            box = boxes[i, :]
            cv2.rectangle(image_ori, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
        cv2.imwrite(os.path.join(result_path, file_path), image_ori)
        print("result_pic is written to {}".format(os.path.join(result_path, file_path)))
        cv2.imshow("UltraFace_mnn_py", image_ori)
        cv2.waitKey(-1)
    cv2.destroyAllWindows()