docstr: Prepare for a prediction plot.
  
  - Update `self.n_classes` according to `model` and `n_classes`.
  - Assign `self.target` with a list of pred columns.
  - Assign `self.df` with a DataFrame containing feature and prediction columns.
docstr_tok: ["Prepare","for","a","prediction","plot",".","-","Update","`","self.n_classes","`","according","to","`","model","`","and","`","n_classes","`",".","-","Assign","`","self.target","`","with","a","list","of","pred","columns",".","-","Assign","`","self.df","`","with","a","DataFrame","containing","feature","and","prediction","columns","."]
code: def prepare_predict_plot(
      self, model, df, pred_func, n_classes, predict_kwds, chunk_size
  ):
      """
      Prepare for a prediction plot.
  
      - Update `self.n_classes` according to `model` and `n_classes`.
      - Assign `self.target` with a list of pred columns.
      - Assign `self.df` with a DataFrame containing feature and prediction columns.
      """
      self.n_classes, pred_func, from_model = _check_model(
          model, n_classes, pred_func
      )
      preds = _calc_preds(
          model, df, pred_func, from_model, predict_kwds, chunk_size
      )
  
      df = df[self.feature_cols]
      pred_cols = ["pred"]
      if self.n_classes == 0:
          df["pred"] = preds
      elif self.n_classes == 2:
          df["pred"] = preds[:, 1]
      else:
          pred_cols = [
              f"pred_{class_idx}" for class_idx in range(self.n_classes)
          ]
          for idx, col in enumerate(pred_cols):
              df[col] = preds[:, idx]
  
      self.target = pred_cols
      self.df = 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 3 for calling the _BaseInfoPlot obj's underlying member method code with example usage: obj.prepare_predict_plot(model, df, pred_func, n_classes, predict_kwds, chunk_size) without return types