code: def test_find_label_issues_match_multiprocessing():
      # minimal version of this test was run in test_missing_classes
      # here testing with larger input matrices
  
      # test with ground truth labels:
      n = 5000  # consider replacing this with larger value
      # some past bugs observed only with larger sample-sizes like n=200000
      m = 100
      labels = np.ones(n, dtype=int)
      labels[(n // 2) :] = 0
      pred_probs = np.zeros((n, 4))
      pred_probs[:, 0] = 0.95
      pred_probs[:, 1] = 0.05
      pred_probs[0, 0] = 0.94
      pred_probs[0, 1] = 0.06
      ground_truth = np.ones(n, dtype=bool)
      ground_truth[(n // 2) :] = False
      ground_truth[
          0
      ] = False  # leave one example for min_example_per_class
      # TODO: consider also testing this line without psutil installed
      issues = filter.find_label_issues(labels, pred_probs)
      issues1 = filter.find_label_issues(labels, pred_probs, n_jobs=1)
      issues2 = filter.find_label_issues(labels, pred_probs, n_jobs=2)
      assert all(issues == ground_truth)
      assert all(issues == issues1)
      assert all(issues == issues2)
      issues = filter.find_label_issues(
          labels, pred_probs, filter_by="prune_by_class"
      )
      issues1 = filter.find_label_issues(
          labels, pred_probs, n_jobs=1, filter_by="prune_by_class"
      )
      issues2 = filter.find_label_issues(
          labels, pred_probs, n_jobs=2, filter_by="prune_by_class"
      )
      assert all(issues == ground_truth)
      assert all(issues == issues1)
      assert all(issues == issues2)
  
      # test with random labels
      normalize = np.random.randint(
          low=1, high=100, size=[n, m], dtype=np.uint8
      )
      pred_probs = np.zeros((n, m))
      for i, col in enumerate(normalize):
          pred_probs[i] = col / np.sum(col)
      labels = np.repeat(np.arange(m), n // m)
      issues = filter.find_label_issues(labels, pred_probs)
      issues1 = filter.find_label_issues(labels, pred_probs, n_jobs=1)
      issues2 = filter.find_label_issues(labels, pred_probs, n_jobs=2)
      assert all(issues == issues1)
      assert all(issues == issues2)
      issues = filter.find_label_issues(
          labels, pred_probs, filter_by="prune_by_class"
      )
      issues1 = filter.find_label_issues(
          labels, pred_probs, n_jobs=1, filter_by="prune_by_class"
      )
      issues2 = filter.find_label_issues(
          labels, pred_probs, n_jobs=2, filter_by="prune_by_class"
      )
      assert all(issues == issues1)
      assert all(issues == issues2)
  
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 34 for calling a global function with example usage: test_find_label_issues_match_multiprocessing() without return types