code: def test_class_wrong_info_fit_ood():
      features = data["X_test"]
      pred_probs = data["pred_probs"]
      labels = data["labels"]
  
      # TESTING: wrong param in params dict
      try:
          OOD = OutOfDistribution(params={"strange_param": -1})
      except Exception as e:
          assert "strange_param" in str(e)
          with pytest.raises(ValueError) as e:
              OOD = OutOfDistribution(params={"strange_param": -1})
  
      #### SCORE wrong info
  
      # TESTING: calling score before any fitting
      OOD = OutOfDistribution()
      try:
          OOD.score(features=features)
      except Exception as e:
          assert (
              "OOD estimator needs to be fit on features first"
              in str(e)
          )
          with pytest.raises(ValueError) as e:
              OOD.score(features=features)
  
      try:
          OOD.score(pred_probs=pred_probs)
      except Exception as e:
          assert (
              "OOD estimator needs to be fit on pred_probs first"
              in str(e)
          )
          with pytest.raises(ValueError) as e:
              OOD.score(pred_probs=pred_probs)
  
      # TESTING: calling scoring with opposite fitting
      OOD_outlier = OutOfDistribution()
      OOD_outlier.fit(features=features)
      try:
          OOD_outlier.score(pred_probs=pred_probs)
      except Exception as e:
          assert (
              "OOD estimator needs to be fit on pred_probs first"
              in str(e)
          )
          with pytest.raises(ValueError) as e:
              OOD_outlier.score(pred_probs=pred_probs)
  
      OOD_ood = OutOfDistribution()
      OOD_ood.fit(pred_probs=pred_probs, labels=labels)
      try:
          OOD_ood.score(features=features)
      except Exception as e:
          assert (
              "OOD estimator needs to be fit on features first"
              in str(e)
          )
          with pytest.raises(ValueError) as e:
              OOD_ood.score(features=features)
  
imports: import numpy
  import pandas
  import pytest
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab import count, outlier
  from cleanlab.count import get_confident_thresholds
  from cleanlab.outlier import OutOfDistribution
  from cleanlab.internal.label_quality_utils import get_normalized_entropy
  from sklearn.neighbors import NearestNeighbors
  from sklearn.linear_model import LogisticRegression
label_desc: Use image node_id 3 for calling a global function with example usage: test_class_wrong_info_fit_ood() without return types