How to use the chaospy.distributions.approximation.approximate_moment function in chaospy

def _mom(self, x, dist, cache):
        return approximation.approximate_moment(self, x)

def _mom(self, x, dist, cache):
        return approximation.approximate_moment(self, x)

if cache_key(distribution) in cache:
            return cache[cache_key(distribution)]

    from .. import baseclass
    try:
        parameters = load_parameters(
            distribution, "_mom", parameters, cache, cache_key)
        out = distribution._mom(k_data, **parameters)

    except baseclass.StochasticallyDependentError:

        logger.info(
            "Distribution %s has stochastic dependencies; "
            "Approximating moments with quadrature.", distribution)
        from .. import approximation
        out = approximation.approximate_moment(distribution, k_data)

    if isinstance(out, numpy.ndarray):
        out = out.item()

    cache[cache_key(distribution)] = out

    return out

def _mom(self, x, dist, cache):
        return approximation.approximate_moment(self, x)

def _mom(self, x, dist, cache):
        return approximation.approximate_moment(self, x)

def _mom(self, x, dist, cache):
        return approximation.approximate_moment(self, x)

def _mom(self, x, dist, cache):
        return approximation.approximate_moment(self, x)