code: def mom(self, K, **kws):
          
          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)
docs: 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``.