code: def lovasz_hinge_flat(logits, labels):
      r"""
      Binary Lovasz hinge loss.
  
      Args:
          logits (Tensor): Shape is [P], logits at each prediction (between -\infty and +\infty).
          labels (Tensor): Shape is [P], binary ground truth labels (0 or 1).
      """
      if len(labels) == 0:
          # only void pixels, the gradients should be 0
          return logits.sum() * 0.0
      signs = 2.0 * labels - 1.0
      signs.stop_gradient = True
      errors = 1.0 - logits * signs
      if hasattr(paddle, "_legacy_C_ops"):
          errors_sorted, perm = paddle._legacy_C_ops.argsort(
              errors, "axis", 0, "descending", True
          )
      else:
          errors_sorted, perm = paddle._C_ops.argsort(
              errors, "axis", 0, "descending", True
          )
      errors_sorted.stop_gradient = False
      gt_sorted = paddle.gather(labels, perm)
      grad = lovasz_grad(gt_sorted)
      grad.stop_gradient = True
      loss = paddle.sum(F.relu(errors_sorted) * grad)
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 3 for calling a global function with example usage: lovasz_hinge_flat(logits, labels) and returns: loss, unknown