docstr: Parameters
  -----------
  objects : array_like of GroupFairnessRegression objects
      Additional objects to plot (default is `None`).
  type : str, optional
      Type of the plot. Default is `'fairness_check'`.
      When the type of plot is specified, user may provide additional
      keyword arguments (`**kwargs`) which will be used in creating
      plot of certain type. Below there is list of types:
  
      - fairness_check:
          fairness_check plot visualizes the fairness_check method
          for one or more GroupFairnessClassification objects.
          It accepts following keyword arguments:
           'epsilon' - which denotes the decision
                       boundary (like in `fairness_check` method)
  
      - density:
          density plot visualizes the output of the model for each
          subgroup in form of violin plots with boxplots on top of them.
          It does not accept additional keyword arguments.
  title : str, optional
      Title of the plot (default depends on the `type` attribute).
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code: def plot(
      self,
      objects=None,
      type="fairness_check",
      title=None,
      show=True,
      **kwargs,
  ):
      """
      Parameters
      -----------
      objects : array_like of GroupFairnessRegression objects
          Additional objects to plot (default is `None`).
      type : str, optional
          Type of the plot. Default is `'fairness_check'`.
          When the type of plot is specified, user may provide additional
          keyword arguments (`**kwargs`) which will be used in creating
          plot of certain type. Below there is list of types:
  
          - fairness_check:
              fairness_check plot visualizes the fairness_check method
              for one or more GroupFairnessClassification objects.
              It accepts following keyword arguments:
               'epsilon' - which denotes the decision
                           boundary (like in `fairness_check` method)
  
          - density:
              density plot visualizes the output of the model for each
              subgroup in form of violin plots with boxplots on top of them.
              It does not accept additional keyword arguments.
      title : str, optional
          Title of the plot (default depends on the `type` attribute).
  
      """
  
      other_objects = None
      if objects is not None:
          other_objects = []
          if not isinstance(objects, (list, tuple)):
              objects = [objects]
          for obj in objects:
              _global_checks.global_check_object_class(
                  obj, self.__class__
              )
              other_objects.append(obj)
          basic_checks.check_other_fairness_objects(self, other_objects)
  
      if type == "density":
          fig = plot.plot_density(
              self, other_objects, title=title, **kwargs
          )
  
      elif type == "fairness_check":
          fig = plot.plot_fairness_check_reg(
              self, other_objects=other_objects, title=title, **kwargs
          )
  
      else:
          raise ParameterCheckError(
              f"plot type {type} not supported, try other types."
          )
  
      if show:
          fig.show(config=_theme.get_default_config())
      else:
          return fig
  
file_path: DALEX/python/dalex/dalex/fairness/_group_fairness/object.py
imports: from .None import checks, plot, utils
  from .._basics import checks
  from .._basics._base_objects import _FairnessObject
  from .._basics.exceptions import ParameterCheckError
  from ...None import _global_checks, _theme
label_desc: Use image node_id 3 for calling the GroupFairnessRegression obj's underlying member method code with example usage: obj.plot(objects, type, title, show) and returns: fig