code: def test_real_datasets(dataset_name):
      print("\n" + dataset_name.capitalize() + "\n")
      class_names = eval(dataset_name)
      (
          pred_probs,
          labels,
      ) = _get_pred_probs_labels_from_labelerrors_datasets(dataset_name)
      # if this runs without issue no all four datasets, the test passes
      _ = health_summary(
          pred_probs=pred_probs,
          labels=labels,
          class_names=class_names,
          verbose=dataset_name
          != "mnist",  # test out verbose=False on one of the datasets.
      )
  
file_path: cleanlab/tests/test_dataset.py
imports: import requests
  import pytest
  import hypothesis.extra.numpy
  import hypothesis.strategies
  import io
  import numpy
  from hypothesis import given, settings
  from cleanlab.dataset import health_summary, find_overlapping_classes, rank_classes_by_label_quality, overall_label_health_score
  from cleanlab.count import estimate_joint, num_label_issues, compute_confident_joint
label_desc: Use image node_id 2 for calling a global function with example usage: test_real_datasets(dataset_name) without return types