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 __init__(self, mu_link, sigma_scale=0.4):
      super(FactorizedNoisyLinear, self).__init__()
      self._kernel = None
      self.out_size = mu_link.out_size
      self.nobias = not (
          "/b" in [name for name, _ in mu_link.namedparams()]
      )
  
      W_data = mu_link.W.array
      in_size = None if W_data is None else W_data.shape[1]
      device_id = mu_link._device_id
  
      with self.init_scope():
          self.mu = L.Linear(
              in_size,
              self.out_size,
              self.nobias,
              initialW=LeCunUniform(1 / numpy.sqrt(3)),
          )
  
          self.sigma = L.Linear(
              in_size,
              self.out_size,
              self.nobias,
              initialW=VarianceScalingConstant(sigma_scale),
              initial_bias=VarianceScalingConstant(sigma_scale),
          )
  
      if device_id is not None:
          self.to_gpu(device_id)
  
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 1 to create a new FactorizedNoisyLinear object from inherited base classes: chainer with example: obj = FactorizedNoisyLinear(mu_link, sigma_scale)