def sigmoid_ce_loss(inputs, targets, num_masks):
    """
    Args:
        inputs (Tensor): A float tensor of arbitrary shape.
            The predictions for each example.
        targets (Tensor): A float tensor with the same shape as inputs. Stores the binary
            classification label for each element in inputs
            (0 for the negative class and 1 for the positive class).
    
    Returns:
        Loss tensor
    """
    loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')

    return loss.mean(1).sum() / num_masks

    Args:
        inputs (Tensor): A float tensor of arbitrary shape.
            The predictions for each example.
        targets (Tensor): A float tensor with the same shape as inputs. Stores the binary
            classification label for each element in inputs
            (0 for the negative class and 1 for the positive class).
    
    Returns:
        Loss tensor
    
contrib/PanopticSeg/paddlepanseg/runners/maskformer_runner.py