code: def find_issues_with_predictions(
      predictions: np.ndarray,
      y: np.ndarray,
      threshold: float,
      **kwargs,
  ) -> pd.DataFrame:
      """Find label issues in a regression dataset based on predictions.
      This uses a threshold to determine if an example has a label issue
      based on the quality score.
  
      Parameters
      ----------
      predictions :
          The predictions from a regression model.
  
      y :
          The given labels.
  
      threshold :
          The threshold to use to determine if an example has a label issue. It is a multiplier
          of the median label quality score that sets the absolute threshold.
  
      **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 the same as the predictions parameter.
      """
      _accepted_kwargs = ["method"]
      _kwargs = {k: kwargs.get(k) for k in _accepted_kwargs}
      _kwargs = {k: v for k, v in _kwargs.items() if v is not None}
      quality_scores = get_label_quality_scores(
          labels=y, predictions=predictions, **_kwargs
      )
  
      median_score = np.median(quality_scores)
      is_label_issue_mask = quality_scores < median_score * threshold
  
      issues = pd.DataFrame(
          {
              "is_label_issue": is_label_issue_mask,
              "label_score": quality_scores,
              "given_label": y,
              "predicted_label": predictions,
          }
      )
      return issues
  
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 1 for calling a global function with example usage: find_issues_with_predictions(predictions, y, threshold) and returns: issues