class_docstr: Gaussian sampling function.
  
  .. note::
  
      In forward calculation, this function takes a mean and the logarithm of
      a variance as inputs, and draws a sample from a Gaussian distribution
      accordingly.
class_docstr_tok: ["Gaussian","sampling","function",".","..","note",":",":","In","forward","calculation",",","this","function","takes","a","mean","and","the","logarithm","of","a","variance","as","inputs",",","and","draws","a","sample","from","a","Gaussian","distribution","accordingly","."]
code: def forward_cpu(self, inputs):
      self.retain_inputs((1,))
  
      mean, ln_var = inputs
      if self.eps is None:
          self.eps = numpy.random.standard_normal(ln_var.shape).astype(
              mean.dtype, copy=False
          )
  
      self.noise = numpy.exp(ln_var * mean.dtype.type(0.5)) * self.eps
      return (utils.force_array(mean + self.noise),)
  
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 3 for calling the Gaussian obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: utils