How to use the mtcnn.models.ONet function in mtcnn

def __init__(self, net='mtcnn', type='cuda'):
        cudnn.benchmark = True
        self.net = net
        self.device = torch.device(type)
        self.pnet = PNet().to(self.device)
        self.rnet = RNet().to(self.device)
        self.onet = ONet().to(self.device)

def __init__(self):
        super(ONet, self).__init__()

        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 32, 3, 1)),
            ('prelu1', nn.PReLU(32)),
            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv2', nn.Conv2d(32, 64, 3, 1)),
            ('prelu2', nn.PReLU(64)),
            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv3', nn.Conv2d(64, 64, 3, 1)),
            ('prelu3', nn.PReLU(64)),
            ('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)),

            ('conv4', nn.Conv2d(64, 128, 2, 1)),
            ('prelu4', nn.PReLU(128)),

def __init__(self):
        super(ONet, self).__init__()

        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 32, 3, 1)),
            ('prelu1', nn.PReLU(32)),
            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv2', nn.Conv2d(32, 64, 3, 1)),
            ('prelu2', nn.PReLU(64)),
            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv3', nn.Conv2d(64, 64, 3, 1)),
            ('prelu3', nn.PReLU(64)),
            ('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)),

            ('conv4', nn.Conv2d(64, 128, 2, 1)),
            ('prelu4', nn.PReLU(128)),

def __init__(self):
        super(ONet, self).__init__()

        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 32, 3, 1)),
            ('prelu1', nn.PReLU(32)),
            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv2', nn.Conv2d(32, 64, 3, 1)),
            ('prelu2', nn.PReLU(64)),
            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv3', nn.Conv2d(64, 64, 3, 1)),
            ('prelu3', nn.PReLU(64)),
            ('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)),

            ('conv4', nn.Conv2d(64, 128, 2, 1)),
            ('prelu4', nn.PReLU(128)),

"""
    Arguments:
        image: an instance of PIL.Image.
        min_face_size: a float number.
        thresholds: a list of length 3.
        nms_thresholds: a list of length 3.
    Returns:
        two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
        bounding boxes and facial landmarks.
    """

    with torch.no_grad():
        # LOAD MODELS
        pnet = PNet()
        rnet = RNet()
        onet = ONet()
        onet.eval()

        # BUILD AN IMAGE PYRAMID
        width, height = image.size
        min_length = min(height, width)

        min_detection_size = 12
        factor = 0.707  # sqrt(0.5)

        # scales for scaling the image
        scales = []

        # scales the image so that
        # minimum size that we can detect equals to
        # minimum face size that we want to detect
        m = min_detection_size / min_face_size

def __init__(self, net='mtcnn', type='cuda'):
        cudnn.benchmark = True
        self.net = net
        self.device = torch.device(type)
        self.pnet = PNet().to(self.device)
        self.rnet = RNet().to(self.device)
        self.onet = ONet().to(self.device)

"""
    Arguments:
        image: an instance of PIL.Image.
        min_face_size: a float number.
        thresholds: a list of length 3.
        nms_thresholds: a list of length 3.
    Returns:
        two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
        bounding boxes and facial landmarks.
    """

    with torch.no_grad():
        # LOAD MODELS
        pnet = PNet().to(device)
        rnet = RNet().to(device)
        onet = ONet().to(device)
        onet.eval()

        # BUILD AN IMAGE PYRAMID
        width, height = image.size
        min_length = min(height, width)

        min_detection_size = 12
        factor = 0.707  # sqrt(0.5)

        # scales for scaling the image
        scales = []

        # scales the image so that
        # minimum size that we can detect equals to
        # minimum face size that we want to detect
        m = min_detection_size / min_face_size

def __init__(self):
        super(ONet, self).__init__()

        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 32, 3, 1)),
            ('prelu1', nn.PReLU(32)),
            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv2', nn.Conv2d(32, 64, 3, 1)),
            ('prelu2', nn.PReLU(64)),
            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv3', nn.Conv2d(64, 64, 3, 1)),
            ('prelu3', nn.PReLU(64)),
            ('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)),

            ('conv4', nn.Conv2d(64, 128, 2, 1)),
            ('prelu4', nn.PReLU(128)),