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 = torch.topk(seg_logits, k=2, dim=1)[0]
      return (top2_scores[:, 1] - top2_scores[:, 0]).unsqueeze(1)
  
imports: import torch
  import torch.nn
  from annotator.uniformer.mmcv.cnn import ConvModule, normal_init
  from annotator.uniformer.mmcv.ops import point_sample
  from annotator.uniformer.mmseg.models.builder import HEADS
  from annotator.uniformer.mmseg.ops import resize
  from ..losses import accuracy
  from .cascade_decode_head import BaseCascadeDecodeHead
label_desc: Use image node_id 1 for calling a global function with example usage: calculate_uncertainty(seg_logits) and returns: unknown