def METHOD_NAME(art_warning, fix_get_mnist_subset):
    try:
        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
        x_train_mnist = torch.from_numpy(x_train_mnist)
        y_train_mnist = torch.from_numpy(y_train_mnist)
        nb_samples = 3
        eot = EoTImageRotationPyTorch(
            nb_samples=nb_samples, angles=(45.0, 45.0), clip_values=(0.0, 1.0), label_type="classification"
        )
        x_eot, y_eot = eot.forward(x=x_train_mnist, y=y_train_mnist)
        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
        x_eot_expected = np.array(
            [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.07058824,
                0.3137255,
                0.6117647,
                0.05490196,
                0.0,
                0.54509807,
                0.04313726,
                0.13725491,
                0.31764707,
                0.9411765,
                0.1764706,
                0.0627451,
                0.3647059,
                0.0,
                0.99215686,
                0.99215686,
                0.98039216,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
            ]
        )
        np.testing.assert_almost_equal(x_eot.numpy()[0, 0, 14, :], x_eot_expected)
    except ARTTestException as e:
        art_warning(e)