code: def get_uncertainty(mask_preds: Tensor, labels: Tensor) -> Tensor:
      """Estimate uncertainty based on pred logits.
  
      We estimate uncertainty as L1 distance between 0.0 and the logits
      prediction in 'mask_preds' for the foreground class in `classes`.
  
      Args:
          mask_preds (Tensor): mask predication logits, shape (num_rois,
              num_classes, mask_height, mask_width).
  
          labels (Tensor): Either predicted or ground truth label for
              each predicted mask, of length num_rois.
  
      Returns:
          scores (Tensor): Uncertainty scores with the most uncertain
              locations having the highest uncertainty score,
              shape (num_rois, 1, mask_height, mask_width)
      """
      if mask_preds.shape[1] == 1:
          gt_class_logits = mask_preds.clone()
      else:
          inds = torch.arange(
              mask_preds.shape[0], device=mask_preds.device
          )
          gt_class_logits = mask_preds[inds, labels].unsqueeze(1)
      return -torch.abs(gt_class_logits)
  
imports: import torch
  from mmcv.ops import point_sample
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: get_uncertainty(mask_preds, labels) and returns: unknown