(
        self,
        discriminator: Discriminator,
        real_samples: Tensor,
        fake_samples: Tensor,
        depth: int,
        alpha: float,
        labels: Optional[Tensor] = None,
    )
calculate the discriminator loss using the following data
        Args:
            discriminator: the Discriminator used by the GAN
            real_samples: real batch of samples
            fake_samples: fake batch of samples
            depth: resolution log 2 of the images under consideration
            alpha: alpha value of the fader
            labels: optional in case of the conditional discriminator

        Returns: computed discriminator loss
calculate the discriminator loss using the following data
        Args:
            discriminator: the Discriminator used by the GAN
            real_samples: real batch of samples
            fake_samples: fake batch of samples
            depth: resolution log 2 of the images under consideration
            alpha: alpha value of the fader
            labels: optional in case of the conditional discriminator
calculate the discriminator loss using the following data Args : discriminator : the Discriminator used by the GAN real_samples : real batch of samples fake_samples : fake batch of samples depth : resolution log 2 of the images under consideration alpha : alpha value of the fader labels : optional in case of the conditional discriminator
def dis_loss(
        self,
        discriminator: Discriminator,
        real_samples: Tensor,
        fake_samples: Tensor,
        depth: int,
        alpha: float,
        labels: Optional[Tensor] = None,
    ) -> Tensor:
        """
        calculate the discriminator loss using the following data
        Args:
            discriminator: the Discriminator used by the GAN
            real_samples: real batch of samples
            fake_samples: fake batch of samples
            depth: resolution log 2 of the images under consideration
            alpha: alpha value of the fader
            labels: optional in case of the conditional discriminator

        Returns: computed discriminator loss
        """
        raise NotImplementedError("dis_loss method has not been implemented")
def dis_loss ( self , discriminator : Discriminator , real_samples : Tensor , fake_samples : Tensor , depth : int , alpha : float , labels : Optional [ Tensor ] = None , ) -> Tensor : raise NotImplementedError ( "dis_loss method has not been implemented" )