code: def test_removing_class_consistent_results():
      # Note that only one label is class 1 (we're going to change it to class 2 later...)
      labels = np.array([0, 0, 0, 0, 1, 2, 2, 2])
      # Third example is a label error
      pred_probs = np.array(
          [
              [0.9, 0.1, 0.0],
              [0.8, 0.1, 0.1],
              [0.1, 0.0, 0.9],
              [0.9, 0.0, 0.1],
              [0.1, 0.3, 0.6],
              [0.1, 0.0, 0.9],
              [0.1, 0.0, 0.9],
              [0.1, 0.0, 0.9],
          ]
      )
      cj_with1 = count.compute_confident_joint(labels, pred_probs)
      issues_with1 = filter.find_label_issues(labels, pred_probs)
  
      labels_no1 = labels = np.array(
          [0, 0, 0, 0, 2, 2, 2, 2]
      )  # change 1 to 2 (class 1 is missing!)
      cj_no1 = count.compute_confident_joint(labels, pred_probs)
      issues_no1 = filter.find_label_issues(labels, pred_probs)
  
      assert np.all(issues_with1 == issues_no1)
      assert np.all(
          cj_with1
          == [
              [3, 0, 1],
              [0, 1, 0],
              [0, 0, 3],
          ]
      )
      # Check that the 1, 1 entry goes away and moves to 2, 2 (since we changed label 1 to 2)
      assert np.all(
          cj_no1
          == [
              [3, 0, 1],
              [0, 0, 0],
              [0, 0, 4],
          ]
      )
  
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 36 for calling a global function with example usage: test_removing_class_consistent_results() without return types