docstr: The plot function for `TargetPlot` and `PredictPlot`.
  
  Parameters
  ----------
  which_classes : list of int, optional
      List of class indices to plot. If None, all classes will be plotted.
      Default is None.
  show_percentile : bool, optional
      If True, percentiles are shown in the plot. Default is False.
  figsize : tuple or None, optional
      The figure size for matplotlib or plotly figure. If None, the default
      figure size is used. Default is None.
  dpi : int, optional
      The resolution of the plot, measured in dots per inch. Only applicable when
      `engine` is 'matplotlib'. Default is 300.
  ncols : int, optional
      The number of columns of subplots in the figure. Default is 2.
  plot_params : dict or None, optional
      Custom plot parameters that control the style and aesthetics of the plot.
      Default is None.
  engine : {'matplotlib', 'plotly'}, optional
      The plotting engine to use. Default is plotly.
  template : str, optional
      The template to use for plotly plots. Only applicable when `engine` is
      'plotly'. Reference: https://plotly.com/python/templates/ Default is
      plotly_white.
  
  Returns
  -------
  matplotlib.figure.Figure or plotly.graph_objects.Figure
      A Matplotlib or Plotly figure object depending on the plot engine being
      used.
  dict of matplotlib.axes.Axes or None
      A dictionary of Matplotlib axes objects. The keys are the names of the
      axes. The values are the axes objects. If `engine` is 'ploltly', it is
      None.
  pd.DataFrame
      A DataFrame that contains the summary statistics of target (for target
      plot) or predict (for predict plot) values for each feature bucket.
docstr_tok: ["The","plot","function","for","`","TargetPlot","`","and","`","PredictPlot","`",".","Parameters","--","--","--","--","--","which_classes",":","list","of","int",",","optional","List","of","class","indices","to","plot",".","If","None",",","all","classes","will","be","plotted",".","Default","is","None",".","show_percentile",":","bool",",","optional","If","True",",","percentiles","are","shown","in","the","plot",".","Default","is","False",".","figsize",":","tuple","or","None",",","optional","The","figure","size","for","matplotlib","or","plotly","figure",".","If","None",",","the","default","figure","size","is","used",".","Default","is","None",".","dpi",":","int",",","optional","The","resolution","of","the","plot",",","measured","in","dots","per","inch",".","Only","applicable","when","`","engine","`","is","'matplotlib","'",".","Default","is","300.","ncols",":","int",",","optional","The","number","of","columns","of","subplots","in","the","figure",".","Default","is","2.","plot_params",":","dict","or","None",",","optional","Custom","plot","parameters","that","control","the","style","and","aesthetics","of","the","plot",".","Default","is","None",".","engine",":","{","'matplotlib","'",",","'plotly","'","}",",","optional","The","plotting","engine","to","use",".","Default","is","plotly",".","template",":","str",",","optional","The","template","to","use","for","plotly","plots",".","Only","applicable","when","`","engine","`","is","'plotly","'",".","Reference",":","https",":","\/\/plotly.com\/python\/templates\/","Default","is","plotly_white",".","Returns","--","--","--","-","matplotlib.figure.Figure","or","plotly.graph_objects.Figure","A","Matplotlib","or","Plotly","figure","object","depending","on","the","plot","engine","being","used",".","dict","of","matplotlib.axes.Axes","or","None","A","dictionary","of","Matplotlib","axes","objects",".","The","keys","are","the","names","of","the","axes",".","The","values","are","the","axes","objects",".","If","`","engine","`","is","'ploltly","'",",","it","is","None",".","pd.DataFrame","A","DataFrame","that","contains","the","summary","statistics","of","target","(","for","target","plot",")","or","predict","(","for","predict","plot",")","values","for","each","feature","bucket","."]
code: def plot(
      self,
      which_classes=None,
      show_percentile=False,
      figsize=None,
      dpi=300,
      ncols=2,
      plot_params=None,
      engine="plotly",
      template="plotly_white",
  ):
      """
      The plot function for `TargetPlot` and `PredictPlot`.
  
      Parameters
      ----------
      which_classes : list of int, optional
          List of class indices to plot. If None, all classes will be plotted.
          Default is None.
      show_percentile : bool, optional
          If True, percentiles are shown in the plot. Default is False.
      figsize : tuple or None, optional
          The figure size for matplotlib or plotly figure. If None, the default
          figure size is used. Default is None.
      dpi : int, optional
          The resolution of the plot, measured in dots per inch. Only applicable when
          `engine` is 'matplotlib'. Default is 300.
      ncols : int, optional
          The number of columns of subplots in the figure. Default is 2.
      plot_params : dict or None, optional
          Custom plot parameters that control the style and aesthetics of the plot.
          Default is None.
      engine : {'matplotlib', 'plotly'}, optional
          The plotting engine to use. Default is plotly.
      template : str, optional
          The template to use for plotly plots. Only applicable when `engine` is
          'plotly'. Reference: https://plotly.com/python/templates/ Default is
          plotly_white.
  
      Returns
      -------
      matplotlib.figure.Figure or plotly.graph_objects.Figure
          A Matplotlib or Plotly figure object depending on the plot engine being
          used.
      dict of matplotlib.axes.Axes or None
          A dictionary of Matplotlib axes objects. The keys are the names of the
          axes. The values are the axes objects. If `engine` is 'ploltly', it is
          None.
      pd.DataFrame
          A DataFrame that contains the summary statistics of target (for target
          plot) or predict (for predict plot) values for each feature bucket.
      """
      return self._plot(
          which_classes,
          show_percentile,
          figsize,
          dpi,
          ncols,
          plot_params,
          engine,
          template,
          num_bins=self.feature_info.num_bins,
      )
  
imports: from .info_plot_utils import TargetPlotEngine, PredictPlotEngine, InteractInfoPlotEngine
  from .utils import _make_list, _check_target, _q1, _q2, _q3, FeatureInfo, _check_dataset, _check_model, _calc_preds, _check_classes, _expand_params_for_interact
  import pandas
  from itertools import product
label_desc: Use image node_id 4 for calling the _InfoPlot obj's underlying member method code with example usage: obj.plot(which_classes, show_percentile, figsize, dpi, ncols, plot_params, engine, template) and returns: self