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 check_type_forward(self, in_types):
      type_check._argname(in_types, ("mean", "ln_var"))
  
      m_type, v_type = in_types
      type_check.expect(
          m_type.dtype.kind == "f",
          m_type.dtype == v_type.dtype,
          m_type.shape == v_type.shape,
      )
  
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 2 for calling the Gaussian obj's underlying member method code with example usage: obj.check_type_forward(in_types) without return types