def forward(self,
                pred,
                target,
                weight=None,
                mask=None,
                avg_factor=None,
                reduction_override=None):
        """Forward function.

        Args:
            pred (Tensor): The prediction.
            target (Tensor): The learning target of the prediction in gaussian
                distribution.
            weight (Tensor, optional): The weight of loss for each
                prediction. Defaults to None.
            mask (Tensor): The valid mask. Defaults to None.
            avg_factor (int, optional): Average factor that is used to average
                the loss. Defaults to None.
            reduction_override (str, optional): The reduction method used to
                override the original reduction method of the loss.
                Defaults to None.
        """
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        loss_reg = self.loss_weight * center_focal_loss(
            pred,
            target,
            weight,
            mask=mask,
            reduction=reduction,
            avg_factor=avg_factor)
        return loss_reg
Forward function.

        Args:
            pred (Tensor): The prediction.
            target (Tensor): The learning target of the prediction in gaussian
                distribution.
            weight (Tensor, optional): The weight of loss for each
                prediction. Defaults to None.
            mask (Tensor): The valid mask. Defaults to None.
            avg_factor (int, optional): Average factor that is used to average
                the loss. Defaults to None.
            reduction_override (str, optional): The reduction method used to
                override the original reduction method of the loss.
                Defaults to None.
        
ppdet/modeling/losses/keypoint_loss.py