code: def test_issue_158():
      # ref: https://github.com/cleanlab/cleanlab/issues/158
      pred_probs = np.array(
          [
              [0.27916167, 0.14589103, 0.29264585, 0.28230144],
              [0.13429196, 0.12536383, 0.47943979, 0.26090442],
              [0.41348584, 0.13463275, 0.25845595, 0.19342546],
              [0.27753469, 0.12295569, 0.33125886, 0.26825075],
              [0.11649856, 0.11219034, 0.51857122, 0.25273988],
              [0.38010026, 0.25572261, 0.13305410, 0.23112304],
              [0.31583755, 0.13630690, 0.29246806, 0.25538750],
              [0.30240076, 0.16925207, 0.26499082, 0.26335636],
              [0.44524505, 0.27410085, 0.08305069, 0.19760341],
              [0.22903975, 0.07783631, 0.38035414, 0.31276980],
              [0.25071560, 0.12072900, 0.32551729, 0.30303812],
              [0.43809229, 0.14401381, 0.20839300, 0.20950090],
              [0.20749181, 0.11883556, 0.38402152, 0.28965111],
              [0.43840254, 0.13538447, 0.24518806, 0.18102493],
              [0.28504779, 0.10309750, 0.34258602, 0.26926868],
              [0.38425408, 0.29168969, 0.15181255, 0.17224368],
              [0.19339907, 0.10804265, 0.37570368, 0.32285460],
              [0.21509781, 0.07190167, 0.38914722, 0.32385330],
              [0.27040334, 0.13037840, 0.32842320, 0.27079507],
              [0.40590210, 0.16713560, 0.24889193, 0.17807036],
          ]
      )
      labels = np.array(
          [3, 3, 1, 3, 0, 2, 2, 2, 0, 2, 2, 1, 0, 0, 0, 0, 2, 1, 3, 3]
      )
  
      cj = count.compute_confident_joint(
          labels, pred_probs, calibrate=False
      )
      # should be no zeros on the diagonal
      assert np.all(cj.diagonal() != 0)
  
      py, noise_matrix, inv_noise_matrix = count.estimate_latent(
          cj, labels
      )
      # no nans anywhere
      assert not np.any(np.isnan(py))
      assert not np.any(np.isnan(noise_matrix))
      assert not np.any(np.isnan(inv_noise_matrix))
  
imports: import cleanlab.multilabel_classification.dataset
  from cleanlab import count, filter
  from cleanlab.count import get_confident_thresholds, estimate_py_and_noise_matrices_from_probabilities
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab.internal.util import value_counts
  from cleanlab.internal.multilabel_utils import int2onehot
  from cleanlab.experimental.label_issues_batched import find_label_issues_batched
  import numpy
  import scipy
  import pytest
  from sklearn.multioutput import MultiOutputClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import cross_val_predict
  from tempfile import mkdtemp
  import os.path
label_desc: Use image node_id 32 for calling a global function with example usage: test_issue_158() without return types