model.py
def forward(self, x):
        """
        Note: noise will be updated if x is not volatile
        """
        normal_y = nn.functional.linear(x, self.weight, self.bias)
        if self.training:
            # update the noise once per update
            self.sample_noise()

        noisy_weight = self.noisy_weight * self.noise
        noisy_bias = self.noisy_bias * self.out_noise
        noisy_y = nn.functional.linear(x, noisy_weight, noisy_bias)
        return noisy_y + normal_y

        Note: noise will be updated if x is not volatile
        
noise will be updated if x is not volatile
noise will be updated if x is not volatile
def forward(self, x):
        normal_y = nn.functional.linear(x, self.weight, self.bias)
        if self.training:
            self.sample_noise()
        noisy_weight = self.noisy_weight * self.noise
        noisy_bias = self.noisy_bias * self.out_noise
        noisy_y = nn.functional.linear(x, noisy_weight, noisy_bias)
        return noisy_y + normal_y
def forward ( self , x ) : normal_y = nn . functional . linear ( x , self . weight , self . bias ) if self . training : self . sample_noise ( ) noisy_weight = self . noisy_weight * self . noise noisy_bias = self . noisy_bias * self . out_noise noisy_y = nn . functional . linear ( x , noisy_weight , noisy_bias ) return noisy_y + normal_y
Note: noise will be updated if x is not volatile
Note : noise will be updated if x is not volatile
"""
        Note: noise will be updated if x is not volatile
        """ # update the noise once per update
self
x