docstr: Prepare the feature information.
  
  - Update `self.df` with feature bucket information.
  - Assign `self.count_df` with the count of each feature bucket.
  - Assign `self.summary_df` with the summary statistics for each feature bucket.
docstr_tok: ["Prepare","the","feature","information",".","-","Update","`","self.df","`","with","feature","bucket","information",".","-","Assign","`","self.count_df","`","with","the","count","of","each","feature","bucket",".","-","Assign","`","self.summary_df","`","with","the","summary","statistics","for","each","feature","bucket","."]
code: def prepare_feature(self):
      """
      Prepare the feature information.
  
      - Update `self.df` with feature bucket information.
      - Assign `self.count_df` with the count of each feature bucket.
      - Assign `self.summary_df` with the summary statistics for each feature bucket.
      """
      (
          self.df,
          self.count_df,
          self.summary_df,
      ) = self.feature_info.prepare(self.df)
  
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 2 for calling the _InfoPlot obj's underlying member method code with example usage: obj.prepare_feature() without return types