code: def test_cl_default_clf():
      cl = CleanLearning()  # default clf is LogisticRegression
      X_train_og = deepcopy(HIGH_DIM_DATA["X_train"])
      cl.fit(HIGH_DIM_DATA["X_train"], HIGH_DIM_DATA["labels_train"])
  
      # assert result has the correct length
      result = cl.predict(HIGH_DIM_DATA["X_test"])
      assert len(result) == len(HIGH_DIM_DATA["X_test"])
  
      result = cl.predict(X=HIGH_DIM_DATA["X_test"])
      assert len(result) == len(HIGH_DIM_DATA["X_test"])
  
      # assert pred_proba has the right dimensions (N x K),
      # where K = 10 (number of classes) as specified in make_high_dim_data()
      pred_proba = cl.predict_proba(HIGH_DIM_DATA["X_test"])
      assert pred_proba.shape == (len(HIGH_DIM_DATA["X_test"]), 10)
  
      pred_proba = cl.predict_proba(X=HIGH_DIM_DATA["X_test"])
      assert pred_proba.shape == (len(HIGH_DIM_DATA["X_test"]), 10)
  
      score = cl.score(
          HIGH_DIM_DATA["X_test"], HIGH_DIM_DATA["labels_test"]
      )
  
      cl.find_label_issues(
          HIGH_DIM_DATA["X_train"], HIGH_DIM_DATA["labels_train"]
      )
  
      # ensure data has not been altered:
      assert (HIGH_DIM_DATA["X_train"] == X_train_og).all()
  
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 5 for calling a global function with example usage: test_cl_default_clf() without return types