code: def test_imagelab_issues_checked(
      self,
      image_dataset,
      pred_probs,
      features,
      capsys,
      num_imagelab_issues,
      num_datalab_issues,
  ):
      datalab = Datalab(
          data=image_dataset,
          label_name=LABEL_NAME,
          image_key=IMAGE_NAME,
      )
      datalab.find_issues(pred_probs=pred_probs, features=features)
      captured = capsys.readouterr()
      assert (
          "Finding dark, light, low_information, odd_aspect_ratio, odd_size, grayscale, blurry images"
          in captured.out
      )
      # unable to check for non iid as feature space is too small, skipping it in interest of time
      assert (
          "Failed to check for these issue types: [NonIIDIssueManager]"
          in captured.out
      )
      assert len(datalab.issues) == len(image_dataset)
  
      # add up imagelab + datalab issues
      assert (
          len(datalab.issues.columns)
          == (num_imagelab_issues + num_datalab_issues) * 2
      )
      assert (
          len(datalab.issue_summary)
          == num_imagelab_issues + num_datalab_issues
      )
  
      all_keys = IMAGELAB_ISSUE_TYPES + [
          "statistics",
          "label",
          "outlier",
          "near_duplicate",
          "class_imbalance",
          "null"
          # "non_iid",
      ]
  
      assert set(all_keys) == set(datalab.info.keys())
      datalab.report()
      captured = capsys.readouterr()
  
      for issue_type in IMAGELAB_ISSUE_TYPES:
          assert issue_type in captured.out
  
      df = pd.DataFrame(
          {
              "issue_type": [
                  "dark",
                  "light",
                  "low_information",
                  "odd_aspect_ratio",
                  "odd_size",
                  "grayscale",
                  "blurry",
                  "label",
                  "outlier",
                  "near_duplicate",
                  "class_imbalance",
                  "null",
              ],
              "num_issues": [1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
          }
      )
      expected_count = df.sort_values(by="issue_type")[
          "num_issues"
      ].tolist()
      count = datalab.issue_summary.sort_values(by="issue_type")[
          "num_issues"
      ].tolist()
      assert count == expected_count
      assert (
          datalab.issue_summary["num_issues"].sum()
          == df["num_issues"].sum()
      )
  
file_path: cleanlab/tests/datalab/test_cleanvision_integration.py
imports: import matplotlib.pyplot
  import numpy
  import pytest
  import pandas
  from cleanlab import Datalab
  import cleanlab.datalab.internal.adapter.imagelab
label_desc: Use image node_id 6 for calling the TestCleanvisionIntegration obj's underlying member method code with example usage: obj.test_imagelab_issues_checked(image_dataset, pred_probs, features, capsys, num_imagelab_issues, num_datalab_issues) without return types