def batch_sigmoid_ce_loss(inputs, targets):
    """
    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
    """
    hw = inputs.shape[1]

    pos = F.binary_cross_entropy_with_logits(
        inputs, paddle.ones_like(inputs), reduction='none')
    neg = F.binary_cross_entropy_with_logits(
        inputs, paddle.zeros_like(inputs), reduction='none')

    loss = paddle.einsum('nc,mc->nm', pos, targets) + paddle.einsum(
        'nc,mc->nm', neg, (1 - targets))

    return loss / hw

    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