code: def test_class_wrong_info_assert_valid_inputs():
      features = data["X_train"]
      pred_probs = data["pred_probs"]
  
      OOD = OutOfDistribution()
  
      # TESTING: _assert_valid_inputs() asserts correct errors in fit
      try:
          OOD.fit()
      except Exception as e:
          assert "Not enough information to compute scores" in str(e)
          with pytest.raises(ValueError) as e:
              OOD.fit()
      try:
          OOD.fit(features=features, pred_probs=pred_probs)
      except Exception as e:
          assert (
              "Cannot fit to OOD Estimator to both features and pred_probs"
              in str(e)
          )
          with pytest.raises(ValueError) as e:
              OOD.fit(features=features, pred_probs=pred_probs)
  
      OOD = OutOfDistribution()
  
      features_flat = np.ravel(features)
      features_extra_dim = features[np.newaxis]
      try:
          OOD.fit(features=features_flat)
      except Exception as e:
          assert "array needs to be of shape (N, M)" in str(e)
          with pytest.raises(ValueError) as e:
              OOD.fit(features=features_flat)
      try:
          OOD.fit(features=features_extra_dim)
      except Exception as e:
          assert "array needs to be of shape (N, M)" in str(e)
          with pytest.raises(ValueError) as e:
              OOD.fit(features=features_extra_dim)
  
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 2 for calling a global function with example usage: test_class_wrong_info_assert_valid_inputs() without return types