code: def calculate_uncertainty(logits):
      """
      We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
          foreground class in `classes`.
  
      Args:
          logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or
              class-agnostic, where R is the total number of predicted masks in all images and C is
              the number of foreground classes. The values are logits.
  
      Returns:
          scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
              the most uncertain locations having the highest uncertainty score.
      """
      assert logits.shape[1] == 1
      gt_class_logits = logits.clone()
      return -(paddle.abs(gt_class_logits))
  
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 7 for calling a global function with example usage: calculate_uncertainty(logits) and returns: unknown