code: def test_scikit_metrics():
      iris = datasets.load_iris()
      X = iris.data
      y = iris.target
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.4, random_state=2
      )
  
      clf = DecisionTreeClassifier(max_depth=1, random_state=1)
      training_acc, test_acc = plot_learning_curves(
          X_train,
          y_train,
          X_test,
          y_test,
          clf,
          scoring="accuracy",
          suppress_plot=True,
      )
  
      desired1 = np.array(
          [0.22, 0.22, 0.22, 0.31, 0.31, 0.3, 0.33, 0.32, 0.33, 0.32]
      )
      desired2 = np.array(
          [0.45, 0.45, 0.35, 0.35, 0.45, 0.43, 0.35, 0.35, 0.35, 0.35]
      )
      np.testing.assert_almost_equal(
          training_acc, 1 - desired1, decimal=2
      )
      np.testing.assert_almost_equal(test_acc, 1 - desired2, decimal=2)
  
imports: import numpy
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from mlxtend.plotting import plot_learning_curves
label_desc: Use image node_id 2 for calling a global function with example usage: test_scikit_metrics() without return types