code: def test_does_not_flag_correct_examples(filter_by):
      labels = data["labels"]
      pred_probs = data["pred_probs"]
      pred_labels = np.argmax(pred_probs, axis=1)
  
      label_issues_mask = filter.find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          filter_by=filter_by,
      )
  
      matching_mask = (
          labels == pred_labels
      )  # mask specifying whether label == prediction
      assert any(label_issues_mask[matching_mask]) == False
  
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 40 for calling a global function with example usage: test_does_not_flag_correct_examples(filter_by) without return types