How to use the torchgan.losses.functional.minimax_generator_loss function in torchgan

def forward(self, real_inputs, gen_outputs, dgz):
        return self.recon_weight * F.mse_loss(real_inputs, gen_outputs) +\
            self.gen_weight + minimax_generator_loss(dgz, reduction=self.reduction)

def forward(self, dgz):
        r"""Computes the loss for the given input.

        Args:
            dgz (torch.Tensor) : Output of the Discriminator with generated data. It must have the
                                 dimensions (N, \*) where \* means any number of additional
                                 dimensions.

        Returns:
            scalar if reduction is applied else Tensor with dimensions (N, \*).
        """
        return minimax_generator_loss(dgz, self.nonsaturating, self.reduction)