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 log_prob(self, x):
      x = chainer.as_variable(x)
      logp = (
          self._log_alpha
          + self.alpha * self._log_scale
          - (self.alpha + 1) * exponential.log(x)
      )
      xp = logp.xp
      return where.where(
          utils.force_array(x.data >= self.scale.data),
          logp,
          xp.array(-xp.inf, logp.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 10 for calling the Pareto obj's underlying member method code with example usage: obj.log_prob(x) and returns: where