code: def test_num_label_issues_different_estimation_types():
      # these numbers are hardcoded as data[] does not create a difference in both functions
      y = np.array([0, 1, 1, 1, 1, 0, 0, 1, 0])
      pred_probs = np.array(
          [
              [0.7110397298505661, 0.2889602701494339],
              [0.6367131487519773, 0.36328685124802274],
              [0.7571834730987641, 0.24281652690123584],
              [0.6394163729473307, 0.3605836270526695],
              [0.5853684039196656, 0.4146315960803345],
              [0.6675968116482668, 0.33240318835173316],
              [0.7240647829106976, 0.2759352170893023],
              [0.740474240697777, 0.25952575930222266],
              [0.7148252196621883, 0.28517478033781196],
          ]
      )
  
      n3 = count.num_label_issues(
          labels=y,
          pred_probs=pred_probs,
          estimation_method="off_diagonal_calibrated",
      )
  
      n2 = count.num_label_issues(
          labels=y,
          pred_probs=pred_probs,
          estimation_method="off_diagonal",
      )
  
      f2 = filter.find_label_issues(
          labels=y,
          pred_probs=pred_probs,
          filter_by="confident_learning",
      )
  
      assert np.sum(f2) == n2
      assert n3 != n2
  
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 27 for calling a global function with example usage: test_num_label_issues_different_estimation_types() without return types