def METHOD_NAME(self, axis=0, scaler_arg=('standard', StandardScaler())):
    scaler_name, scaler = scaler_arg
    dims, length = 4, 2
    original = numpy.arange(length ** dims, dtype='float32').reshape([length] * dims)
    expected = numpy.empty(original.shape)
    for i in range(original.shape[0]):
        # Swap the axes before and after so we can test all the dimensions
        # using the same operator ([i, :, :, :]).
        original = numpy.swapaxes(original, 0, axis)
        expected = numpy.swapaxes(expected, 0, axis)
        tmp = scaler.fit_transform(original[i, :, :, :].reshape(-1, 1))
        expected[i, :, :, :] = tmp.reshape([1] + [length] * (dims - 1))
        original = numpy.swapaxes(original, 0, axis)
        expected = numpy.swapaxes(expected, 0, axis)
    result = TensorNormalize(scaler=scaler_name, axis=axis)(original)
    self.assertAlmostEqualVector(result.flatten(), expected.flatten(), 1e-6)