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 sample_n(self, n):
      xp = chainer.backend.get_array_module(self.scale)
      if xp is cuda.cupy:
          eps = xp.random.pareto(
              self.alpha.data,
              (n,) + self.batch_shape,
              dtype=self.alpha.dtype,
          )
      else:
          eps = xp.random.pareto(
              self.alpha.data, (n,) + self.batch_shape
          ).astype(self.alpha.dtype)
  
      noise = self.scale * (eps + 1)
      return noise
  
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 13 for calling the Pareto obj's underlying member method code with example usage: obj.sample_n(n) and returns: noise