code: def feature_importances(
      model=None,
      feature_names=None,
      title="Feature Importance",
      max_num_features=50,
  ):
      """Log a plot depicting the relative importance of each feature for a classifier's decisions.
  
      Should only be called with a fitted classifer (otherwise an error is thrown).
      Only works with classifiers that have a feature_importances_ attribute, like trees.
  
      Arguments:
          model: (clf) Takes in a fitted classifier.
          feature_names: (list) Names for features. Makes plots easier to read by
                                replacing feature indexes with corresponding names.
  
      Returns:
          None: To see plots, go to your W&B run page then expand the 'media' tab
                under 'auto visualizations'.
  
      Example:
      ```python
      wandb.sklearn.plot_feature_importances(model, ["width", "height", "length"])
      ```
      """
      not_missing = utils.test_missing(model=model)
      correct_types = utils.test_types(model=model)
      model_fitted = utils.test_fitted(model)
  
      if not_missing and correct_types and model_fitted:
          feature_importance_chart = calculate.feature_importances(
              model, feature_names
          )
          wandb.log({"feature_importances": feature_importance_chart})
  
file_path: wandb/wandb/sklearn/plot/classifier.py
imports: from warnings import simplefilter
  import numpy
  from sklearn import naive_bayes
  import wandb
  import wandb.plots
  from wandb.sklearn import calculate, utils
  from .None import shared
label_desc: Use image node_id 5 for calling a global function with example usage: feature_importances(model, feature_names, title, max_num_features) without return types