def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):
    """
    Args:
        inputs: A float tensor of arbitrary shape.
                The predictions for each example.
        targets: 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, torch.ones_like(inputs), reduction="none"
    )
    neg = F.binary_cross_entropy_with_logits(
        inputs, torch.zeros_like(inputs), reduction="none"
    )

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

    return loss / hw

    Args:
        inputs: A float tensor of arbitrary shape.
                The predictions for each example.
        targets: 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
    
modeling/modules/matcher.py