class_docstr: Linear layer in Factorized Noisy Network
  
  Args:
      mu_link (L.Linear): Linear link that computes mean of output.
      sigma_scale (float): The hyperparameter sigma_0 in the original paper.
          Scaling factor of the initial weights of noise-scaling parameters.
class_docstr_tok: ["Linear","layer","in","Factorized","Noisy","Network","Args",":","mu_link","(","L.Linear",")",":","Linear","link","that","computes","mean","of","output",".","sigma_scale","(","float",")",":","The","hyperparameter","sigma_0","in","the","original","paper",".","Scaling","factor","of","the","initial","weights","of","noise-scaling","parameters","."]
code: def _noise_function(self, r):
      if self._kernel is None:
          self._kernel = cuda.elementwise(
              "",
              "T r",
              """r = copysignf(sqrtf(fabsf(r)), r);""",
              "noise_func",
          )
      self._kernel(r)
  
file_path: chainerrl/chainerrl/links/noisy_linear.py
imports: import chainer
  from chainer import cuda
  import chainer.functions
  from chainer.initializers import LeCunUniform
  import chainer.links
  import numpy
  from chainerrl.functions import muladd
  from chainerrl.initializers import VarianceScalingConstant
label_desc: Use image node_id 2 for calling the FactorizedNoisyLinear obj's underlying member method code with example usage: obj._noise_function(r) without return types