code: def test_wrong_info_get_ood_predictions_scores():
      # Test calling function with not enough information to calculate ood scores
      try:
          outlier._get_ood_predictions_scores(
              pred_probs=data["pred_probs"],
              labels=None,
              adjust_pred_probs=True,  # this should throw ValueError because knn=None and features=None
          )
      except Exception as e:
          assert (
              "Cannot calculate adjust_pred_probs without labels"
              in str(e)
          )
          with pytest.raises(ValueError) as e:
              outlier._get_ood_predictions_scores(
                  pred_probs=data["pred_probs"],
                  labels=None,
                  adjust_pred_probs=True,  # this should throw ValueError because knn=None and features=None
              )
  
      # Test calling function with not enough information to calculate ood scores
      try:
          outlier._get_ood_predictions_scores(
              pred_probs=data["pred_probs"],
              adjust_pred_probs=True,  # this should throw ValueError because knn=None and features=None
          )
      except Exception as e:
          assert (
              "Cannot calculate adjust_pred_probs without labels"
              in str(e)
          )
          with pytest.raises(ValueError) as e:
              outlier._get_ood_predictions_scores(
                  pred_probs=data["pred_probs"],
                  adjust_pred_probs=True,  # this should throw ValueError because not enough data provided
              )
  
      # Test calling function with not a real method
      try:
          outlier._get_ood_predictions_scores(
              pred_probs=data["pred_probs"],
              labels=data["labels"],
              adjust_pred_probs=True,
              method="not_a_real_method",  # this should throw ValueError because method not real method
          )
      except Exception as e:
          assert "not a valid OOD scoring" in str(e)
          with pytest.raises(ValueError) as e:
              outlier._get_ood_predictions_scores(
                  pred_probs=data["pred_probs"],
                  labels=data["labels"],
                  adjust_pred_probs=True,
                  method="not_a_real_method",  # this should throw ValueError because method not real method
              )
  
      # Test calling function with too much information to calculate ood scores
      outlier._get_ood_predictions_scores(
          pred_probs=data["pred_probs"],
          labels=data["labels"],
          adjust_pred_probs=False,  # this should user warning because provided info is not used
      )
  
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 10 for calling a global function with example usage: test_wrong_info_get_ood_predictions_scores() without return types