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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def mom(self, K, **kws):
 """ Raw statistical moments. Creates non-centralized raw moments from the random variable. If analytical options can not be utilized, Monte Carlo integration will be used. Args: K (numpy.ndarray):
 Index of the raw moments. k.shape must be compatible with distribution shape. Sampling scheme when performing Monte Carlo rule (str):
 rule for estimating the moment if the analytical method fails. composite (numpy.ndarray):
 If provided, composit quadrature will be used. Ignored in the case if gaussian=True. If int provided, determines number of even domain splits. If array of ints, determines number of even domain splits along each axis. If array of arrays/floats, determines location of splits. antithetic (numpy.ndarray):
 List of bool. Represents the axes to mirror using antithetic variable during MCI. Returns: (numpy.ndarray):
 Shapes are related through the identity ``k.shape == dist.shape+k.shape``. """

        K = numpy.asarray(K, dtype=int)
        shape = K.shape
        dim = len(self)

        if dim > 1:
            shape = shape[1:]

        size = int(K.size/dim)
        K = K.reshape(dim, size)

        cache = {}
        out = [evaluation.evaluate_moment(self, kdata, cache) for kdata in K.T]
        out = numpy.array(out)
        return out.reshape(shape)