code: def print_metrics(y_truth, y_pred):
      print(f"Accuracy = {accuracy_score(y_truth, y_pred):0.5f}")
      print(f"Precision = {precision_score(y_truth, y_pred):0.5f}")
      print(f"Recall = {recall_score(y_truth, y_pred):0.5f}")
      print(f"F1 = {f1_score(y_truth, y_pred):0.5f}")
      print(
          f'F1 Weighted = {f1_score(y_truth, y_pred, average="weighted"):0.5f}'
      )
      print()
  
imports: import warnings
  import pickle
  from time import time
  import numpy
  import pandas
  import shap
  from aix360.algorithms.rbm import BooleanRuleCG, BRCGExplainer, FeatureBinarizer, FeatureBinarizerFromTrees, GLRMExplainer, LogisticRuleRegression
  from aix360.datasets import HELOCDataset, MEPSDataset
  from aix360.datasets.heloc_dataset import nan_preprocessing
  from pandas import DataFrame
  from sklearn.datasets import load_breast_cancer
  from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 8 for calling a global function with example usage: print_metrics(y_truth, y_pred) without return types