How to use the torchgeometry.color.Normalize function in torchgeometry

def test_smoke(self):
        mean = [0.5]
        std = [0.1]
        repr = 'Normalize(mean=[0.5], std=[0.1])'
        assert str(color.Normalize(mean, std)) == repr

def test_normalize(self):

        # prepare input data
        data = torch.ones(1, 2, 2)
        mean = torch.tensor([0.5])
        std = torch.tensor([2.0])

        # expected output
        expected = torch.tensor([0.25]).repeat(1, 2, 2).view_as(data)

        f = color.Normalize(mean, std)
        assert_allclose(f(data), expected)

def test_gradcheck(self):

        # prepare input data
        data = torch.ones(2, 3, 1, 1)
        data += 2
        mean = torch.tensor([0.5, 1.0, 2.0]).double()
        std = torch.tensor([2., 2., 2.]).double()

        data = utils.tensor_to_gradcheck_var(data)  # to var

        assert gradcheck(color.Normalize(mean, std), (data,),
                         raise_exception=True)

def test_batch_normalize(self):

        # prepare input data
        data = torch.ones(2, 3, 1, 1)
        data += 2

        mean = torch.tensor([0.5, 1.0, 2.0]).repeat(2, 1)
        std = torch.tensor([2.0, 2.0, 2.0]).repeat(2, 1)

        # expected output
        expected = torch.tensor([1.25, 1, 0.5]).repeat(2, 1, 1).view_as(data)

        f = color.Normalize(mean, std)
        assert_allclose(f(data), expected)

def test_broadcast_normalize(self):

        # prepare input data
        data = torch.ones(2, 3, 1, 1)
        data += 2

        mean = torch.tensor([0.5, 1.0, 2.0])
        std = torch.tensor([2.0, 2.0, 2.0])

        # expected output
        expected = torch.tensor([1.25, 1, 0.5]).repeat(2, 1, 1).view_as(data)

        f = color.Normalize(mean, std)
        assert_allclose(f(data), expected)