def forward()

in quant/utils/moving_average.py [0:0]

• 11 lines of code
• 3 McCabe index (conditional complexity)

    def forward(self, x: torch.Tensor) -> torch.Tensor:  # type: ignore
        """Return the current moving average, given a vector x."""
        if self.training:
            with torch.no_grad():
                if self.num_batches_tracked.item() > 0:  # type: ignore
                    old = self.momentum * self.moving_average  # type: ignore
                    new = (torch.ones_like(self.momentum) - self.momentum) * x  # type: ignore
                    self.moving_average.copy_(old + new)  # type: ignore
                else:
                    self.moving_average.copy_(x)  # type: ignore
                self.num_batches_tracked += 1  # type: ignore

        return self.moving_average  # type: ignore