code: def test_missing_class():
      labels = np.array(
          [
              [1, np.NaN, 2],
              [1, 1, 2],
              [2, 2, 1],
              [np.NaN, 2, 2],
              [np.NaN, 2, 1],
              [np.NaN, 2, 2],
          ]
      )
  
      pred_probs = np.array(
          [
              [0.4, 0.4, 0.2],
              [0.3, 0.6, 0.1],
              [0.05, 0.2, 0.75],
              [0.1, 0.4, 0.5],
              [0.2, 0.4, 0.4],
              [0.2, 0.4, 0.4],
          ]
      )
  
      # test default case
      consensus_label = get_majority_vote_label(labels)
      consensus_label = get_majority_vote_label(labels, pred_probs)
      multiannotator_dict = get_label_quality_multiannotator(
          labels, pred_probs
      )
  
      # test other consensus and quality methods
      multiannotator_dict = get_label_quality_multiannotator(
          labels, pred_probs, quality_method="agreement"
      )
      multiannotator_dict = get_label_quality_multiannotator(
          labels, pred_probs, consensus_method="majority_vote"
      )
  
imports: import numpy
  import pytest
  from copy import deepcopy
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab import count
  from cleanlab.multiannotator import get_label_quality_multiannotator, get_label_quality_multiannotator_ensemble, get_active_learning_scores, get_active_learning_scores_ensemble, get_majority_vote_label, convert_long_to_wide_dataset
  from cleanlab.internal.multiannotator_utils import format_multiannotator_labels
  import pandas
  from sklearn.linear_model import LogisticRegression
label_desc: Use image node_id 11 for calling a global function with example usage: test_missing_class() without return types