code: def summary_metrics(
      model=None, X=None, y=None, X_test=None, y_test=None
  ):
      """Logs a chart depicting summary metrics for a model.
  
      Should only be called with a fitted model (otherwise an error is thrown).
  
      Arguments:
          model: (clf or reg) Takes in a fitted regressor or classifier.
          X: (arr) Training set features.
          y: (arr) Training set labels.
          X_test: (arr) Test set features.
          y_test: (arr) Test set labels.
  
      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_summary_metrics(model, X_train, y_train, X_test, y_test)
      ```
      """
      not_missing = utils.test_missing(
          model=model, X=X, y=y, X_test=X_test, y_test=y_test
      )
      correct_types = utils.test_types(
          model=model, X=X, y=y, X_test=X_test, y_test=y_test
      )
      model_fitted = utils.test_fitted(model)
  
      if not_missing and correct_types and model_fitted:
          metrics_chart = calculate.summary_metrics(
              model, X, y, X_test, y_test
          )
          wandb.log({"summary_metrics": metrics_chart})
  
imports: from warnings import simplefilter
  import numpy
  import wandb
  from wandb.sklearn import calculate, utils
label_desc: Use image node_id 1 for calling a global function with example usage: summary_metrics(model, X, y, X_test, y_test) without return types