code: def test_integrated_gradient(self):
      inputs = {"sentence": "It was the ending that I hated"}
      archive = load_archive(
          self.FIXTURES_ROOT
          / "basic_classifier"
          / "serialization"
          / "model.tar.gz"
      )
      predictor = Predictor.from_archive(archive, "text_classifier")
  
      interpreter = IntegratedGradient(predictor)
      interpretation = interpreter.saliency_interpret_from_json(inputs)
      assert interpretation is not None
      assert "instance_1" in interpretation
      assert "grad_input_1" in interpretation["instance_1"]
      grad_input_1 = interpretation["instance_1"]["grad_input_1"]
      assert len(grad_input_1) == 7  # 7 words in input
  
      # two interpretations should be identical for integrated gradients
      repeat_interpretation = interpreter.saliency_interpret_from_json(
          inputs
      )
      repeat_grad_input_1 = repeat_interpretation["instance_1"][
          "grad_input_1"
      ]
      for grad, repeat_grad in zip(grad_input_1, repeat_grad_input_1):
          assert grad == approx(repeat_grad)
  
file_path: allennlp/tests/interpret/integrated_gradient_test.py
imports: from pytest import approx, raises
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.models.archival import load_archive
  from allennlp.interpret.saliency_interpreters import IntegratedGradient
  from allennlp.predictors import Predictor, TextClassifierPredictor
  from allennlp.data.dataset_readers import TextClassificationJsonReader
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.common.testing.interpret_test import FakeModelForTestingInterpret, FakePredictorForTestingInterpret
label_desc: Use image node_id 1 for calling the TestIntegratedGradient obj's underlying member method code with example usage: obj.test_integrated_gradient() without return types