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

def forward(self, dx, dgz):
        return minimax_discriminator_loss(dx, dgz)

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

        Args:
            dx (torch.Tensor) : Output of the Discriminator with real data. It must have the
                                dimensions (N, \*) where \* means any number of additional
                                dimensions.
            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_discriminator_loss(
            dx, dgz, label_smoothing=self.label_smoothing, reduction=self.reduction
        )