class_docstr: Pareto Distribution.
  
  .. math::
      f(x) = \alpha x_m^{\alpha}(x)^{-(\alpha+1)},
  
  Args:
      scale(:class:`~chainer.Variable` or :ref:`ndarray`): Parameter of
          distribution :math:`x_m`.
      alpha(:class:`~chainer.Variable` or :ref:`ndarray`): Parameter of
          distribution :math:`\alpha`.
code: def mean(self):
      mean = self.alpha * self.scale / (self.alpha - 1)
      xp = mean.xp
      return where.where(
          self.alpha.data > 1, mean, xp.array(xp.inf, mean.dtype)
      )
  
imports: import chainer
  from chainer.backends import cuda
  from chainer import distribution
  from chainer.functions.array import where
  from chainer.functions.math import exponential
  from chainer import utils
  from chainer.utils import cache
label_desc: Use image node_id 11 for calling the Pareto obj's underlying member method code with example usage: obj.mean() and returns: where