code: def test_get_consensus_label():
      labels = data["labels"]
  
      # getting consensus labels without pred_probs
      consensus_label = get_majority_vote_label(labels)
  
      # making synthetic data to test tiebreaks of get_consensus_label
      # also testing passing labels as np.ndarray
      labels_tiebreaks = np.array(
          [
              [1, 2, 0],
              [1, 1, 0],
              [1, 0, 0],
              [2, 2, 2],
              [1, 2, 0],
              [1, 2, 0],
          ]
      )
      pred_probs_tiebreaks = np.array(
          [
              [0.4, 0.4, 0.2],
              [0.3, 0.6, 0.1],
              [0.75, 0.2, 0.05],
              [0.1, 0.4, 0.5],
              [0.2, 0.4, 0.4],
              [0.2, 0.4, 0.4],
          ]
      )
      consensus_label = get_majority_vote_label(
          labels_tiebreaks, pred_probs_tiebreaks
      )
  
      # more tiebreak testing (without pred_probs + non-overlapping annotators)
      labels_tiebreaks = np.array(
          [
              [1, np.NaN, np.NaN, 2, np.NaN],
              [np.NaN, 1, 0, np.NaN, np.NaN],
              [np.NaN, np.NaN, 0, np.NaN, np.NaN],
              [np.NaN, 2, np.NaN, np.NaN, np.NaN],
              [2, np.NaN, 0, 2, np.NaN],
              [np.NaN, np.NaN, np.NaN, 2, 1],
          ]
      )
      consensus_label = get_majority_vote_label(labels_tiebreaks)
      assert all(consensus_label == np.array([1, 1, 0, 2, 2, 1]))
  
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 13 for calling a global function with example usage: test_get_consensus_label() without return types