code: def calculate_uncertainty(seg_logits):
      """
      Estimate uncertainty based on seg logits.
      For each location of the prediction ``seg_logits`` we estimate
      uncertainty as the difference between top first and top second
      predicted logits.
  
      Args:
          seg_logits (Tensor): Semantic segmentation logits,
              shape (batch_size, num_classes, height, width).
      Returns:
          scores (Tensor): T uncertainty scores with the most uncertain
              locations having the highest uncertainty score, shape (
              batch_size, 1, height, width)
      """
  
      top2_scores = paddle.topk(seg_logits, k=2, axis=1)[0]
      return paddle.unsqueeze(
          top2_scores[:, 1] - top2_scores[:, 0], axis=1
      )
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling a global function with example usage: calculate_uncertainty(seg_logits) and returns: paddle