code: def test_pair_wise_loss_predictions(self, X, label, gc, dc):
      workspace.FeedBlob("X", X)
      workspace.FeedBlob("label", label)
      new_label = np.array([label[1], label[0]])
      new_x = np.array([X[1], X[0]])
      workspace.FeedBlob("new_x", new_x)
      workspace.FeedBlob("new_label", new_label)
      net = core.Net("net")
      net.PairWiseLoss(["X", "label"], ["output"])
      net.PairWiseLoss(["new_x", "new_label"], ["new_output"])
      plan = core.Plan("predict_data")
      plan.AddStep(
          core.execution_step("predict_data", [net], num_iter=1)
      )
      workspace.RunPlan(plan)
      output = workspace.FetchBlob("output")
      new_output = workspace.FetchBlob("new_output")
      sign = 1 if label[0] > label[1] else -1
      if label[0] == label[1]:
          self.assertEqual(np.asscalar(output), 0)
          return
  
      self.assertAlmostEqual(
          np.asscalar(output),
          np.asscalar(np.log(1 + np.exp(sign * (X[1] - X[0])))),
          delta=1e-4,
      )
      # check swapping row order doesn't alter overall loss
      self.assertAlmostEqual(output, new_output)
  
file_path: pytorch/caffe2/python/operator_test/rank_loss_operator_test.py
imports: from caffe2.python import core, workspace
  from hypothesis import given
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  import hypothesis.strategies
  import numpy
label_desc: Use image node_id 1 for calling the TestPairWiseLossOps obj's underlying member method code with example usage: obj.test_pair_wise_loss_predictions(X, label, gc, dc) without return types