class_docstr: Create plots for `PDPIsolate`
  
  Methods
  -------
  plot : generate the plot
  plot_matplotlib : plot using matplotlib
  plot_plotly : plot using plotly
class_docstr_tok: ["Create","plots","for","`","PDPIsolate","`","Methods","--","--","--","-","plot",":","generate","the","plot","plot_matplotlib",":","plot","using","matplotlib","plot_plotly",":","plot","using","plotly"]
code: def __init__(
      self,
      plot_obj,
      which_classes,
      plot_params,
  ):
      feature_info = plot_obj.feature_info
      self.feat_name = feature_info.name
      self.feat_type = feature_info.type
      self.is_numeric = self.feat_type == "numeric"
      self.display_columns = feature_info.display_columns
      self.percentile_columns = feature_info.percentile_columns
      self.grids, self.percentiles = [], []
      if self.is_numeric:
          self.grids = [_get_string(v) for v in feature_info.grids]
          self.percentiles = feature_info.percentiles
  
      self.plot_style = _prepare_plot_style(
          self.feat_name,
          len(which_classes),
          plot_params,
          plot_obj.plot_type,
      )
      self.which_classes = which_classes
      self.plot_obj = plot_obj
      self.cmaps = cycle(self.plot_style.line["cmaps"])
  
imports: from .utils import _get_string, _to_rgba
  from .styles import _prepare_plot_style, _axes_modify, _display_percentile, _modify_legend_axes, _get_axes_label
  import pandas
  import numpy
  import matplotlib
  import matplotlib.pyplot
  from matplotlib.gridspec import GridSpecFromSubplotSpec
  import copy
  from mpl_toolkits.axes_grid1.inset_locator import inset_axes
  import plotly.graph_objects
  from sklearn.cluster import MiniBatchKMeans, KMeans
  from itertools import cycle
label_desc: Use image node_id 1 to create a new PDPIsolatePlotEngine object  with example: obj = PDPIsolatePlotEngine(plot_obj, which_classes, plot_params)