code: 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
  
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 6 for calling a global function with example usage: batch_sigmoid_ce_loss(inputs, targets) and returns: unknown