def forward(self,
                pred,
                target,
                valid,
                area,
                weight=None,
                avg_factor=None,
                reduction_override=None,
                **kwargs):
        """Forward function.

        Args:
            pred (Tensor): The prediction.
            target (Tensor): The learning target of the prediction.
            valid (Tensor): The visible flag of the target pose.
            area (Tensor): The area of the target pose.
            weight (Tensor, optional): The weight of loss for each
                prediction. 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. Options are "none", "mean" and "sum".
        """
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        if (weight is not None) and (not paddle.any(weight > 0)) and (
                reduction != 'none'):
            if pred.dim() == weight.dim() + 1:
                weight = weight.unsqueeze(1)
            return (pred * weight).sum()  # 0
        if weight is not None and weight.dim() > 1:
            # TODO: remove this in the future
            # reduce the weight of shape (n, 4) to (n,) to match the
            # iou_loss of shape (n,)
            assert weight.shape == pred.shape
            weight = weight.mean(-1)
        loss = self.loss_weight * oks_loss(
            pred,
            target,
            weight,
            valid=valid,
            area=area,
            linear=self.linear,
            sigmas=self.sigmas,
            eps=self.eps,
            reduction=reduction,
            avg_factor=avg_factor,
            **kwargs)
        return loss
Forward function.

        Args:
            pred (Tensor): The prediction.
            target (Tensor): The learning target of the prediction.
            valid (Tensor): The visible flag of the target pose.
            area (Tensor): The area of the target pose.
            weight (Tensor, optional): The weight of loss for each
                prediction. 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. Options are "none", "mean" and "sum".
        
ppdet/modeling/losses/keypoint_loss.py