code: def test_fitted_svm_score(optimization):
      """Test SVM detector score method.
  
      Test SVM detector that has been fitted on reference data but has not had a threshold
      inferred can still score data using the predict method. Test that it does not raise an error
      but does not return `threshold`, `p_value` and `is_outlier` values.
      """
      svm_detector = SVM(
          n_components=10,
          backend="pytorch",
          kernel=GaussianRBF(torch.tensor(2)),
          optimization=optimization,
          nu=0.1,
      )
      x_ref = np.random.randn(100, 2)
      svm_detector.fit(x_ref)
      x = np.array([[0, 10], [0.1, 0]])
      scores = svm_detector.score(x)
  
      y = svm_detector.predict(x)
      y = y["data"]
      assert y["instance_score"][0] > -0.01
      assert y["instance_score"][1] < -0.8
      assert all(y["instance_score"] == scores)
      assert not y["threshold_inferred"]
      assert y["threshold"] is None
      assert y["is_outlier"] is None
      assert y["p_value"] is None
  
file_path: alibi-detect/alibi_detect/od/tests/test__svm/test__svm.py
imports: import pytest
  import numpy
  import torch
  from alibi_detect.od._svm import SVM
  from alibi_detect.exceptions import NotFittedError
  from alibi_detect.utils.pytorch import GaussianRBF
  from sklearn.datasets import make_moons
label_desc: Use image node_id 5 for calling a global function with example usage: test_fitted_svm_score(optimization) without return types