code: def test_compute_confident_joint():
      cj = count.compute_confident_joint(
          labels=data["labels"],
          pred_probs=data["pred_probs"],
      )
      # Check that confident joint doesn't overcount number of examples.
      assert np.sum(cj) <= data["n"]
      # Check that confident joint is correct shape
      assert np.shape(cj) == (data["m"], data["m"])
  
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 12 for calling a global function with example usage: test_compute_confident_joint() without return types