code: def test_find_issues_missing_classes():
      labels = np.array([0, 0, 2, 2])
      pred_probs = np.array(
          [
              [0.9, 0.0, 0.1, 0.0],
              [0.8, 0.0, 0.2, 0.0],
              [0.1, 0.0, 0.9, 0.0],
              [0.95, 0.0, 0.05, 0.0],
          ]
      )
      issues_df = CleanLearning(
          find_label_issues_kwargs={"min_examples_per_class": 0}
      ).find_label_issues(labels=labels, pred_probs=pred_probs)
      issues = issues_df["is_label_issue"].values
      assert np.all(issues == np.array([False, False, False, True]))
      # Check results match without these missing classes present in pred_probs:
      pred_probs2 = pred_probs[:, list(sorted(np.unique(labels)))]
      labels2 = np.array([0, 0, 1, 1])
      issues_df2 = CleanLearning(
          find_label_issues_kwargs={"min_examples_per_class": 0}
      ).find_label_issues(labels=labels2, pred_probs=pred_probs2)
      assert all(issues_df2["is_label_issue"].values == issues)
  
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 34 for calling a global function with example usage: test_find_issues_missing_classes() without return types