class_docstr: Logatithm Normal Distribution.
  
  The probability density function of the distribution is expressed as
  
  .. math::
      p(x;\mu,\sigma) = \frac{1}{\sqrt{2\pi\sigma^2}x}
          \exp\left(-\frac{(\log{x}-\mu)^2}{2\sigma^2}\right)
  
  Args:
      mu(:class:`~chainer.Variable` or :ref:`ndarray`): Parameter of
          distribution :math:`\mu`.
      sigma(:class:`~chainer.Variable` or :ref:`ndarray`): Parameter of
          distribution :math:`\sigma`.
code: def sample_n(self, n):
      xp = backend.get_array_module(self.mu)
      if xp is cuda.cupy:
          eps = xp.random.standard_normal(
              (n,) + self.mu.shape, dtype=self.mu.dtype
          )
      else:
          eps = xp.random.standard_normal((n,) + self.mu.shape).astype(
              self.mu.dtype
          )
  
      noise = self.sigma * eps
      noise += self.mu
  
      return exponential.exp(noise)
  
imports: import math
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import distribution
  from chainer.functions.math import exponential
  from chainer.utils import cache
label_desc: Use image node_id 11 for calling the LogNormal obj's underlying member method code with example usage: obj.sample_n(n) and returns: exponential