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 backward(self, indexes, grad_outputs):
      (ln_var,) = self.get_retained_inputs()
      (gy,) = grad_outputs
  
      ret = []
      if 0 in indexes:
          ret.append(gy)
      if 1 in indexes:
          noise = chainer.functions.exp(ln_var * 0.5) * self.eps
          ret.append(gy * noise * 0.5)
      return ret
  
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 5 for calling the Gaussian obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ret