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

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

        Args:
            interpolate (torch.Tensor) : It must have the dimensions (N, \*) where
                                         \* means any number of additional dimensions.
            d_interpolate (torch.Tensor) : Output of the ``discriminator`` with ``interpolate``
                                           as the input. 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 dragan_gradient_penalty(
            interpolate, d_interpolate, self.k, self.reduction
        )