code: def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
      np.random.seed(seed)
      X1 = np.random.randn(N).astype(np.float32)
      X2 = np.random.randn(N).astype(np.float32)
      Y = np.random.choice([-1, 1], size=N).astype(np.int32)
      op = core.CreateOperator(
          "MarginRankingCriterion",
          ["X1", "X2", "Y"],
          ["loss"],
          margin=margin,
      )
  
      def ref_cec(X1, X2, Y):
          result = np.maximum(-Y * (X1 - X2) + margin, 0)
          return (result,)
  
      inputs = [X1, X2, Y]
      # This checks the op implementation against a reference function in
      # python.
      self.assertReferenceChecks(gc, op, inputs, ref_cec)
      # This checks the op implementation over multiple device options (e.g.
      # CPU and CUDA). [0] means that the 0-th output is checked.
      self.assertDeviceChecks(dc, op, inputs, [0])
  
      # Make singular points less sensitive
      X1[np.abs(margin - Y * (X1 - X2)) < 0.1] += 0.1
      X2[np.abs(margin - Y * (X1 - X2)) < 0.1] -= 0.1
  
      # Check dX1
      self.assertGradientChecks(gc, op, inputs, 0, [0])
      # Check dX2
      self.assertGradientChecks(gc, op, inputs, 1, [0])
  
file_path: pytorch/caffe2/python/operator_test/margin_ranking_criterion_op_test.py
imports: from caffe2.python import core
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  from hypothesis import given, settings
  import hypothesis.strategies
  import numpy
label_desc: Use image node_id 1 for calling the TestMarginRankingCriterion obj's underlying member method code with example usage: obj.test_margin_ranking_criterion(N, seed, margin, gc, dc) and returns: result