How to use the torchgeometry.augmentation.Grayscale function in torchgeometry

def test_rgb_to_grayscale(self):
        channels, height, width = 3, 4, 5
        img = torch.ones(channels, height, width)
        assert taug.Grayscale()(img).shape == (1, height, width)

def test_smoke_batch(self):
        batch_size, height, width, channels = 2, 4, 5, 3 
        img = np.ones((batch_size, height, width, channels))

        transforms = nn.Sequential(
            taug.ToTensor(),
            taug.Grayscale(),
        )
        assert transforms(img).shape == (batch_size, 1, height, width)

def test_smoke_batch(self):
        batch_size, height, width, channels = 2, 4, 5, 3 
        img = np.ones((batch_size, height, width, channels))

        transforms = torchvision.transforms.Compose([
            taug.ToTensor(),
            taug.Grayscale(),
        ])
        assert transforms(img).shape == (batch_size, 1, height, width)

def test_smoke(self):
        height, width, channels = 4, 5, 3 
        img = np.ones((height, width, channels))

        transforms = torchvision.transforms.Compose([
            taug.ToTensor(),
            taug.Grayscale(),
        ])
        assert transforms(img).shape == (1, height, width)

def test_smoke(self):
        assert str(taug.Grayscale()) == 'Grayscale()'