code: def plot_null_feature_importance_distributions(
      null_distributions: Mapping[int, List[float]], ax=None
  ) -> None:
      if ax is None:
          _, ax = plt.subplots(1, 1)
      df = pd.DataFrame(null_distributions)
      df = (
          pd.DataFrame(df.unstack())
          .reset_index()
          .drop("level_1", axis=1)
      )
      df.columns = ["variable", "p"]
      sns.boxplot(x="variable", y="p", data=df, ax=ax)
      ax.set_title("Null Feature Importance Distribution")
      return ax
  
file_path: imodels/imodels/experimental/bartpy/diagnostics/features.py
imports: from collections import Counter
  from typing import List, Mapping, Union, Optional
  import numpy
  import pandas
  import seaborn
  from matplotlib import pyplot
  from ..runner import run_models
  from ..sklearnmodel import SklearnModel
label_desc: Use image node_id 4 for calling a global function with example usage: plot_null_feature_importance_distributions(null_distributions, ax) and returns: ax