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 __init__(self, scale, alpha):
      super(Pareto, self).__init__()
      self.__scale = scale
      self.__alpha = alpha
  
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 1 to create a new Pareto object from inherited base classes: distribution with example: obj = Pareto(scale, alpha)