code: def decisions_regression(
      df_preds,
      shap_values,
      expected_value,
      X_vald,
      y_vald,
      model_file_path,
      learner_name,
  ):
      fig = plt.gcf()
      shap.decision_plot(
          expected_value,
          shap_values[df_preds.lp[:10], :],
          X_vald.loc[df_preds.index[:10]],
          show=False,
      )
      fig.tight_layout(pad=2.0)
      fig.savefig(
          os.path.join(
              model_file_path,
              f"{learner_name}_shap_worst_decisions.png",
          )
      )
      plt.close("all")
  
      fig = plt.gcf()
      shap.decision_plot(
          expected_value,
          shap_values[df_preds.lp[-10:], :],
          X_vald.loc[df_preds.index[-10:]],
          show=False,
      )
      fig.tight_layout(pad=2.0)
      fig.savefig(
          os.path.join(
              model_file_path, f"{learner_name}_shap_best_decisions.png"
          )
      )
      plt.close("all")
  
imports: import logging
  import os
  import matplotlib.pyplot
  import numpy
  import pandas
  import shap
  from sklearn.preprocessing import OneHotEncoder
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.utils.config import LOG_LEVEL
  import warnings
label_desc: Use image node_id 8 for calling the PlotSHAP obj's underlying member method code with example usage: obj.decisions_regression(df_preds, shap_values, expected_value, X_vald, y_vald, model_file_path, learner_name) without return types