code: def lovasz_hinge_flat(logits, labels):
      """Binary Lovasz hinge loss.
  
      Args:
          logits (torch.Tensor): [P], logits at each prediction
              (between -infty and +infty).
          labels (torch.Tensor): [P], binary ground truth labels (0 or 1).
  
      Returns:
          torch.Tensor: The calculated loss.
      """
      if len(labels) == 0:
          # only void pixels, the gradients should be 0
          return logits.sum() * 0.0
      signs = 2.0 * labels.float() - 1.0
      errors = 1.0 - logits * signs
      errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
      perm = perm.data
      gt_sorted = labels[perm]
      grad = lovasz_grad(gt_sorted)
      loss = torch.dot(F.relu(errors_sorted), grad)
      return loss
  
file_path: lavis/lavis/common/annotator/uniformer/mmseg/models/losses/lovasz_loss.py
imports: import annotator.uniformer.mmcv
  import torch
  import torch.nn
  import torch.nn.functional
  from ..builder import LOSSES
  from .utils import get_class_weight, weight_reduce_loss
label_desc: Use image node_id 4 for calling a global function with example usage: lovasz_hinge_flat(logits, labels) and returns: loss, unknown