code: def _seaborn_viz_importance(importance_values, idx, cols):
      """Plot feature importances.
  
      Args:
          importance_values: The importances
          idx: The sorted indices
          cols: The columns
  
      Returns:
          fig: The figure
      """
      plt.figure(
          figsize=(
              get_option("display.matplotlib.fig_width"),
              get_option("display.matplotlib.fig_height"),
          )
      )
      plt.xlabel("Permutation Importance Value")
      plt.ylabel("Features")
  
      fig = sns.barplot(
          y=cols[idx], x=importance_values[idx], palette="Blues_d"
      ).set_title("Feature Importance")
      return fig
  
file_path: data-describe/data_describe/core/importance.py
imports: from typing import Optional
  import numpy
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.inspection import permutation_importance
  import seaborn
  import matplotlib.pyplot
  from data_describe.config._config import get_option
  from data_describe.misc.preprocessing import preprocess
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 3 for calling a global function with example usage: _seaborn_viz_importance(importance_values, idx, cols) and returns: fig