def forward(
        self, positive_score: torch.Tensor, negative_score: torch.Tensor
    ) -> torch.Tensor:
        """Forward propagation method for the logistic loss.

        Args:
            positive_score: Tensor containing predictions for known positive items.
            negative_score: Tensor containing predictions for sampled negative items.

        Returns:
            computed loss
        """
        self._assert_equal_size(positive_score, negative_score)

        positives_loss = 1.0 - torch.sigmoid(positive_score)
        negatives_loss = torch.sigmoid(negative_score)

        loss = positives_loss + negatives_loss

        return loss.mean()
Forward propagation method for the logistic loss.

        Args:
            positive_score: Tensor containing predictions for known positive items.
            negative_score: Tensor containing predictions for sampled negative items.

        Returns:
            computed loss
        
catalyst/contrib/losses/recsys.py