code: def gaussian(mean, ln_var, **kwargs):
      """gaussian(mean, ln_var, *, eps=None, return_eps=False)
  
      Gaussian sampling function.
  
      This function takes a mean :math:`\\mu` and the logarithm of a variance
      :math:`\\log(\\sigma^2)` as inputs and outputs a sample drawn from a
      Gaussian distribution :math:`N(\\mu, \\sigma)`.
  
      The inputs must have the same shape.
  
      Args:
          mean (:class:`~chainer.Variable` or :ref:`ndarray`):
              Input variable representing the mean :math:`\\mu`.
          ln_var (:class:`~chainer.Variable` or :ref:`ndarray`):
              Input variable representing the logarithm of a variance
              :math:`\\log(\\sigma^2)`.
          eps (:ref:`ndarray` or None):
              The eps value to be used.
              You do not have to specify this value, unless you need to make
              results deterministic.
              If ``eps`` is not specified or set to ``None``, an eps value will
              be generated randomly.
              The shape and dtype must be the same as ``ln_var`` and should be
              on the same device.
          return_eps (bool):
              If ``True``, the eps value used in this function is returned
              together with the output variable.
              The returned eps can later be reused by passing it to the ``eps``
              argument.
  
      Returns:
          ~chainer.Variable or tuple:
              When ``return_eps`` is ``False`` (default), returns the output
              variable with the shape of ``mean`` and/or ``ln_var``.
              When ``True``, returns the tuple of the output variable and eps
              (:ref:`ndarray`).
              The eps will be on the same device as the input (``ln_var``).
  
      """
      eps = None
      return_eps = False
      if kwargs:
          eps, return_eps = argument.parse_kwargs(
              kwargs, ("eps", eps), ("return_eps", return_eps)
          )
  
      func = Gaussian(eps)
      out = func.apply((mean, ln_var))[0]
      if return_eps:
          return out, func.eps
      return out
  
file_path: chainer/chainer/functions/noise/gaussian.py
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling a global function with example usage: gaussian(mean, ln_var) and returns: out, out,  func