code: def dependence(
      shap_values,
      X_vald,
      model_file_path,
      learner_name,
      file_postfix="",
  ):
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          fig = plt.figure(figsize=(14, 7))
          plots_cnt = np.min([9, X_vald.shape[1]])
          cols_cnt = 3
          rows_cnt = 3
          if plots_cnt < 4:
              rows_cnt = 1
          elif plots_cnt < 7:
              rows_cnt = 2
          for i in range(plots_cnt):
              ax = fig.add_subplot(rows_cnt, cols_cnt, i + 1)
              shap.dependence_plot(
                  f"rank({i})",
                  shap_values,
                  X_vald,
                  show=False,
                  title=f"Importance #{i+1}",
                  ax=ax,
              )
  
          fig.tight_layout(pad=2.0)
          fig.savefig(
              os.path.join(
                  model_file_path,
                  f"{learner_name}_shap_dependence{file_postfix}.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 6 for calling the PlotSHAP obj's underlying member method code with example usage: obj.dependence(shap_values, X_vald, model_file_path, learner_name, file_postfix) without return types