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 _eps(self, shape, dtype):
      xp = self.xp
      if xp is numpy:
          r = xp.random.standard_normal(shape).astype(dtype)
          return xp.copysign(xp.sqrt(xp.abs(r)), r)
      else:
          r = xp.random.standard_normal(shape, dtype)
          self._noise_function(r)
          return 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 3 for calling the FactorizedNoisyLinear obj's underlying member method code with example usage: obj._eps(shape, dtype) and returns: xp, r