class_docstr: Manages label issues in a Datalab for regression tasks.
  
  Parameters
  ----------
  datalab :
      A Datalab instance.
  
  clean_learning_kwargs :
      Keyword arguments to pass to the :py:meth:`regression.learn.CleanLearning <cleanlab.regression.learn.CleanLearning>` constructor.
  
  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. Only used if
      predictions are provided to `~RegressionLabelIssueManager.find_issues`, not if
      features are provided. Default is 0.1.
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docstr: Find label issues in the datalab.
  
  .. admonition:: Priority Order for finding issues:
  
      1. Custom Model: Requires `features` to be passed to this method. Used if a model is set up in the constructor.
      2. Predictions: Uses `predictions` if provided and no model is set up in the constructor.
      3. Default Model: Defaults to a standard model using `features` if no model or predictions are provided.
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code: def find_issues(
      self,
      features: Optional[np.ndarray] = None,
      predictions: Optional[np.ndarray] = None,
      **kwargs,
  ) -> None:
      """Find label issues in the datalab.
  
      .. admonition:: Priority Order for finding issues:
  
          1. Custom Model: Requires `features` to be passed to this method. Used if a model is set up in the constructor.
          2. Predictions: Uses `predictions` if provided and no model is set up in the constructor.
          3. Default Model: Defaults to a standard model using `features` if no model or predictions are provided.
      """
      if features is None and predictions is None:
          raise ValueError(
              "Regression requires numerical `features` or `predictions` "
              "to be passed in as an argument to `find_issues`."
          )
      if features is None and self._uses_custom_model:
          raise ValueError(
              "Regression requires numerical `features` to be passed in as an argument to `find_issues` "
              "when using a custom model."
          )
      # If features are provided and either a custom model is used or no predictions are provided
      use_features = features is not None and (
          self._uses_custom_model or predictions is None
      )
      if use_features:
          assert (
              features is not None
          )  # mypy won't narrow the type for some reason
          self.issues = find_issues_with_features(
              features=features,
              y=self.datalab.labels,
              cl=self.cl,
              **kwargs,  # function sanitizes kwargs
          )
          self.issues.rename(
              columns={"label_quality": self.issue_score_key},
              inplace=True,
          )
  
      # Otherwise, if predictions are provided, process them
      else:
          assert (
              predictions is not None
          )  # mypy won't narrow the type for some reason
          self.issues = find_issues_with_predictions(
              predictions=predictions,
              y=self.datalab.labels,
              **{
                  **kwargs,
                  **{"threshold": self.threshold},
              },  # function sanitizes kwargs
          )
  
      # Get a summarized dataframe of the label issues
      self.summary = self.make_summary(
          score=self.issues[self.issue_score_key].mean()
      )
  
      # Collect info about the label issues
      self.info = self.collect_info(issues=self.issues)
  
      # Drop columns from issues that are in the info
      self.issues = self.issues.drop(
          columns=["given_label", "predicted_label"]
      )
  
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 the RegressionLabelIssueManager obj's underlying member method code with example usage: obj.find_issues(features, predictions) without return types