How to use the memcnn.models.additive.AdditiveBlockFunction2.apply function in memcnn

def forward(self, x):
        args = [x, self.Fm, self.Gm] + [w for w in self.Fm.parameters()] + [w for w in self.Gm.parameters()]

        if self.implementation_fwd == 0:
            out = AdditiveBlockFunction.apply(*args)
        elif self.implementation_fwd == 1:
            out = AdditiveBlockFunction2.apply(*args)
        elif self.implementation_fwd == -1:
            warnings.warn('Using direct non-memory saving implementation.', NonMemorySavingWarning)
            x1, x2 = torch.chunk(x, 2, dim=1)
            x1, x2 = x1.contiguous(), x2.contiguous()
            fmd = self.Fm.forward(x2)
            y1 = x1 + fmd
            gmd = self.Gm.forward(y1)
            y2 = x2 + gmd
            out = torch.cat([y1, y2], dim=1)
        else:
            raise NotImplementedError("Selected implementation ({}) not implemented..."
                                      .format(self.implementation_fwd))

        return out