code: def find_issues_with_features(
      features: np.ndarray,
      y: np.ndarray,
      cl: CleanLearning,
      **kwargs,
  ) -> pd.DataFrame:
      """Find label issues in a regression dataset based on features.
      This delegates the work to the CleanLearning.find_label_issues method.
  
      Parameters
      ----------
      features :
          The numerical features from a regression dataset.
  
      y :
          The given labels.
  
      **kwargs :
          Various keyword arguments.
  
      Returns
      -------
      issues :
          A dataframe of the issues. It contains the following columns:
          - is_label_issue : bool
              True if the example has a label issue.
          - label_score : float
              The quality score of the label.
          - given_label : float
              The given label. It is the same as the y parameter.
          - predicted_label : float
              The predicted label. It is determined by the CleanLearning.find_label_issues method.
      """
      _accepted_kwargs = [
          "uncertainty",
          "coarse_search_range",
          "fine_search_size",
          "save_space",
          "model_kwargs",
      ]
      _kwargs = {
          k: v
          for k, v in kwargs.items()
          if k in _accepted_kwargs and v is not None
      }
      return cl.find_label_issues(X=features, y=y, **_kwargs)
  
file_path: cleanlab/cleanlab/datalab/internal/issue_manager/regression/label.py
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional
  import numpy
  import pandas
  from cleanlab.regression.learn import CleanLearning
  from cleanlab.datalab.internal.issue_manager import IssueManager
  from cleanlab.regression.rank import get_label_quality_scores
label_desc: Use image node_id 2 for calling a global function with example usage: find_issues_with_features(features, y, cl) and returns: cl