code: def test_class_params_logic():
      features = data["X_test"]
      pred_probs = data["pred_probs"]
  
      # TESTING: params dict is a copy
      params_dict = {"k": 10, "t": 5}
      OOD = OutOfDistribution(params=params_dict)
  
      OOD.fit(features=features)
      ood_params = OOD.params
      params_dict = params_dict.update({"k": 20})
      assert ood_params == OOD.params
  
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 4 for calling a global function with example usage: test_class_params_logic() without return types