def forward()

in models_all.py [0:0]

• 16 lines of code
• 9 McCabe index (conditional complexity)

    def forward(self, x, noise=None):
        if noise is None and self.noise is None:
            if self.use_random_initial_noise:
                noise = torch.randn(
                    x.size(0), 1, x.size(2), x.size(3), device=x.device, dtype=x.dtype
                )
            else:
                noise = torch.zeros(
                    x.size(0), 1, x.size(2), x.size(3), device=x.device, dtype=x.dtype
                )
            # set noise to initial noise values without generating random noise
            # at every inference step.
            if self.randomize_noise is False:
                self.noise = noise
        elif noise is None:
            # here is a little trick: if you get all the noiselayers and set each
            # modules .noise attribute, you can have pre-defined noise.
            # Very useful for analysis
            noise = self.noise
        x = x + self.weight.view(1, -1, 1, 1) * noise
        return x