code: 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
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from scipy.optimize import linear_sum_assignment
  from paddlepanseg.cvlibs import manager, build_info_dict
  from paddlepanseg.core.runner import PanSegRunner
label_desc: Use image node_id 5 for calling a global function with example usage: sigmoid_ce_loss(inputs, targets, num_masks) and returns: unknown