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

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

        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 28, 3, 1)),
            ('prelu1', nn.PReLU(28)),
            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv2', nn.Conv2d(28, 48, 3, 1)),
            ('prelu2', nn.PReLU(48)),
            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv3', nn.Conv2d(48, 64, 2, 1)),
            ('prelu3', nn.PReLU(64)),

            ('flatten', Flatten()),
            ('conv4', nn.Linear(576, 128)),
            ('prelu4', nn.PReLU(128))
        ]))

        self.conv5_1 = nn.Linear(128, 2)
        self.conv5_2 = nn.Linear(128, 4)

        weights = np.load('mtcnn/weights/rnet.npy', allow_pickle=True)[()]
        # print('Finished loading RNet model!')
        for n, p in self.named_parameters():
            p.data = torch.FloatTensor(weights[n])

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

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

        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 28, 3, 1)),
            ('prelu1', nn.PReLU(28)),
            ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv2', nn.Conv2d(28, 48, 3, 1)),
            ('prelu2', nn.PReLU(48)),
            ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)),

            ('conv3', nn.Conv2d(48, 64, 2, 1)),
            ('prelu3', nn.PReLU(64)),

            ('flatten', Flatten()),
            ('conv4', nn.Linear(576, 128)),
            ('prelu4', nn.PReLU(128))
        ]))

        self.conv5_1 = nn.Linear(128, 2)
        self.conv5_2 = nn.Linear(128, 4)

        weights = np.load('mtcnn/weights/rnet.npy', allow_pickle=True)[()]
        for n, p in self.named_parameters():
            p.data = torch.FloatTensor(weights[n])

('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)),

            ('flatten', Flatten()),
            ('conv5', nn.Linear(1152, 256)),
            ('drop5', nn.Dropout(0.25)),
            ('prelu5', nn.PReLU(256)),
        ]))

        self.conv6_1 = nn.Linear(256, 2)
        self.conv6_2 = nn.Linear(256, 4)
        self.conv6_3 = nn.Linear(256, 10)

        weights = np.load('mtcnn/weights/onet.npy')[()]
        for n, p in self.named_parameters():
            p.data = torch.FloatTensor(weights[n])

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

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

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

('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)),

            ('flatten', Flatten()),
            ('conv5', nn.Linear(1152, 256)),
            ('drop5', nn.Dropout(0.25)),
            ('prelu5', nn.PReLU(256)),
        ]))

        self.conv6_1 = nn.Linear(256, 2)
        self.conv6_2 = nn.Linear(256, 4)
        self.conv6_3 = nn.Linear(256, 10)

        weights = np.load('mtcnn/weights/onet.npy', allow_pickle=True)[()]
        # print('Finished loading ONet model!')
        for n, p in self.named_parameters():
            p.data = torch.FloatTensor(weights[n])