code: def plot_feature_shap_values(
      cls, feature_shap_values, class_id, X_train=None
  ):
      feature_name = [n for n, _ in feature_shap_values]
      shap_values = np.array(
          [v for _, v in feature_shap_values]
      ).transpose()
      import shap
  
      shap.summary_plot(
          shap_values[class_id, ::], X_train, feature_names=feature_name
      )
  
file_path: contextual-ai/xai/plots/data_stats_notebook_plots.py
imports: from collections import Counter
  import matplotlib.pyplot
  import pandas
  import numpy
  import seaborn
  from IPython.display import display, Markdown
label_desc: Use image node_id 7 for calling the NotebookPlots obj's underlying member method code with example usage: obj.plot_feature_shap_values(cls, feature_shap_values, class_id, X_train) without return types