code: def lovasz_hinge(
      logits,
      labels,
      classes="present",
      per_image=False,
      class_weight=None,
      reduction="mean",
      avg_factor=None,
      ignore_index=255,
  ):
      """Binary Lovasz hinge loss.
  
      Args:
          logits (torch.Tensor): [B, H, W], logits at each pixel
              (between -infty and +infty).
          labels (torch.Tensor): [B, H, W], binary ground truth masks (0 or 1).
          classes (str | list[int], optional): Placeholder, to be consistent with
              other loss. Default: None.
          per_image (bool, optional): If per_image is True, compute the loss per
              image instead of per batch. Default: False.
          class_weight (list[float], optional): Placeholder, to be consistent
              with other loss. Default: None.
          reduction (str, optional): The method used to reduce the loss. Options
              are "none", "mean" and "sum". This parameter only works when
              per_image is True. Default: 'mean'.
          avg_factor (int, optional): Average factor that is used to average
              the loss. This parameter only works when per_image is True.
              Default: None.
          ignore_index (int | None): The label index to be ignored. Default: 255.
  
      Returns:
          torch.Tensor: The calculated loss.
      """
      if per_image:
          loss = [
              lovasz_hinge_flat(
                  *flatten_binary_logits(
                      logit.unsqueeze(0),
                      label.unsqueeze(0),
                      ignore_index,
                  )
              )
              for logit, label in zip(logits, labels)
          ]
          loss = weight_reduce_loss(
              torch.stack(loss), None, reduction, avg_factor
          )
      else:
          loss = lovasz_hinge_flat(
              *flatten_binary_logits(logits, labels, ignore_index)
          )
      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 5 for calling a global function with example usage: lovasz_hinge(logits, labels, classes, per_image, class_weight, reduction, avg_factor, ignore_index) and returns: loss