code: def test_find_label_issues_same_value():
      f1 = filter.find_label_issues(
          labels=data["labels"],
          pred_probs=data["pred_probs"],
          filter_by="confident_learning",
      )
  
      f2 = filter.find_label_issues(
          labels=data["labels"],
          pred_probs=data["pred_probs"],
          filter_by="low_self_confidence",
      )
  
      f3 = filter.find_label_issues(
          labels=data["labels"],
          pred_probs=data["pred_probs"],
          filter_by="low_normalized_margin",
      )
  
      assert np.sum(f1) == np.sum(f2)
      assert np.sum(f2) == np.sum(f3)
  
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 28 for calling a global function with example usage: test_find_label_issues_same_value() without return types