code: def test_dimN(N):
      X, labels = dimN_data(N)
      cl = CleanLearning(clf=ReshapingLogisticRegression())
      # just make sure we don't crash...
      cl.fit(X, labels)
      cl.predict(X)
      cl.predict_proba(X)
      cl.score(X, labels)
  
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 29 for calling a global function with example usage: test_dimN(N) without return types