code: def dimN_data(N):
      size = [100] + [3 for _ in range(N - 1)]
      X = np.random.normal(size=size)
      labels = np.random.randint(0, 4, size=100)
      # ensure that every class is represented
      labels[0:10] = 0
      labels[11:20] = 1
      labels[21:30] = 2
      labels[31:40] = 3
      return 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 28 for calling a global function with example usage: dimN_data(N) and returns: X,  labels