.\n\n"         "If 'sigma' is a single value, an isotropic filter at this scale is "         "applied (i.e., each dimension is filtered in the same way). "         "If 'sigma' is a tuple or list of values, the amount of smoothing "         "will be different for each spatial dimension.\n"         "The optional 'sigma_d' (single, tuple, or list) denotes the resolution standard deviation "         "per axis, the optional 'step_size' (single, tuple, or list) the distance between two adjacent "         "pixels for each dimension. "         "The length of the tuples or lists must be equal to the "         "number of spatial dimensions.\n\n"                 "'window_size' and 'roi' have the same meaning as in :func:`gaussianSmoothing`.\n\n"         "For details see gaussianGradientMultiArray_ and ConvolutionOptions_ in the vigra C++ documentation.\n");      def("gaussianGradient",         registerConverters(&pythonGaussianGradientND<float,0>),         (arg("volume"), arg("sigma"), arg("out")=python::object(),           arg("sigma_d")=0.0, arg("step_size")=0.0, arg("window_size")=0.0, arg("roi")=python::object()),         "Likewise for a 0D scalar volume.\n");      