code: def test_estimate_joint(use_confident_joint):
      dataset = data
      joint = count.estimate_joint(
          labels=dataset["labels"],
          pred_probs=dataset["pred_probs"],
          confident_joint=dataset["cj"]
          if use_confident_joint
          else None,
      )
  
      # Check that joint sums to 1.
      assert abs(np.sum(joint) - 1.0) < 1e-6
  
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 8 for calling a global function with example usage: test_estimate_joint(use_confident_joint) without return types