code: def test_cl(data):
      cl = CleanLearning(
          clf=LogisticRegression(
              multi_class="auto", solver="lbfgs", random_state=SEED
          )
      )
      X_train_og = deepcopy(data["X_train"])
      cl.fit(data["X_train"], data["labels"])
      score = cl.score(data["X_test"], data["true_labels_test"])
      print(score)
      # ensure data has not been altered:
      if isinstance(X_train_og, np.ndarray):
          assert (data["X_train"] == X_train_og).all()
      elif isinstance(X_train_og, pd.DataFrame):
          assert data["X_train"].equals(X_train_og)
  
imports: from copy import deepcopy
  import warnings
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator
  from sklearn.model_selection import GridSearchCV
  import scipy
  import pytest
  import numpy
  import pandas
  from cleanlab.classification import CleanLearning
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix
  from cleanlab.count import compute_confident_joint, estimate_cv_predicted_probabilities, get_confident_thresholds
  from cleanlab.filter import find_label_issues
label_desc: Use image node_id 4 for calling a global function with example usage: test_cl(data) without return types