code: def test_rank_hinge_loss(self):
      def rank_hinge_loss(**kwargs):
          if isinstance(kwargs, dict) and "batch" in kwargs:
              batch = kwargs["batch"]
  
          def _rank_hinge_loss(y_true, y_pred):
              y_pred = y_pred + y_true - y_true
              margin = 1.0
              pos = merge(
                  [y_pred.slice(0, i, 1) for i in range(0, batch, 2)],
                  mode="concat",
                  concat_axis=0,
              )
              neg = merge(
                  [y_pred.slice(0, i, 1) for i in range(1, batch, 2)],
                  mode="concat",
                  concat_axis=0,
              )
              loss = maximum(margin + neg - pos, 0.0)
              return loss
  
          return _rank_hinge_loss
  
      def keras_rank_hinge_loss(y_true, y_pred):
          margin = 1.0
          y_pos = klayers.Lambda(
              lambda a: a[::2, :], output_shape=(1,)
          )(y_pred)
          y_neg = klayers.Lambda(
              lambda a: a[1::2, :], output_shape=(1,)
          )(y_pred)
          loss = KK.maximum(0.0, margin + y_neg - y_pos)
          return KK.mean(loss)
  
      batch = 32
      self.compareLossWithKeras(
          keras_rank_hinge_loss,
          CustomLoss(rank_hinge_loss(batch=batch), [1]),
          [batch, 1],
          sizeAverageKerasLoss=False,
      )
  
file_path: analytics-zoo/pyzoo/test/zoo/pipeline/autograd/test_loss.py
imports: import keras.backend
  import keras.layers
  import keras.objectives
  import pytest
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.pipeline.api.autograd import *
  from zoo.pipeline.api.keras.models import *
  from zoo.pipeline.api.keras.layers import *
label_desc: Use image node_id 3 for calling the TestLoss obj's underlying member method code with example usage: obj.test_rank_hinge_loss() and returns: _rank_hinge_loss, KK, loss