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 __init__(self, eps=None):
      # When ``eps`` is set to None, per-instance noise that is generated
      # once during its first forward pass and then reused in subsequent
      # calls.
      self.eps = eps
  
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 to create a new Gaussian object from inherited base classes: function_node with example: obj = Gaussian(eps)