class_docstr: This class contains tests for the find_issues method with a CleanLearning
  object that behaves deterministically. This is useful to run a "regression"-test on
  the results computed by the find_issues method.
  The test dataset is a random toy regression dataset with 5 features and 100 samples.
  The ground truth is a linear function of the first feature plus a bias defined in the
  class attribute BIAS.
  The ground truth is used to emulate a perfect model and compute the expected score
  for the label issue detection. The gaussian noise contributes to lower label quality
  scores.
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code: def test_find_issues_with_features(
      self, regression_dataset, issue_manager
  ):
      X, _, _ = regression_dataset
      issue_manager.find_issues(features=X)
      summary = issue_manager.summary
      assert np.isclose(summary["score"], 0.262423, atol=1e-5)
  
imports: import numpy
  import pandas
  import pytest
  from cleanlab import Datalab
  from cleanlab.datalab.internal.issue_manager.regression.label import RegressionLabelIssueManager
label_desc: Use image node_id 3 for calling the TestRegressionLabelIssueManagerIntegration obj's underlying member method code with example usage: obj.test_find_issues_with_features(regression_dataset, issue_manager) without return types