code: def main():
      data = datasets.load_digits()
      X = normalize(data.data)
      y = data.target
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.4
      )
  
      clf = NaiveBayes()
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
  
      accuracy = accuracy_score(y_test, y_pred)
  
      print("Accuracy:", accuracy)
  
      # Reduce dimension to two using PCA and plot the results
      Plot().plot_in_2d(
          X_test,
          y_pred,
          title="Naive Bayes",
          accuracy=accuracy,
          legend_labels=data.target_names,
      )
  
imports: from __future__ import division, print_function
  from sklearn import datasets
  import numpy
  from mlfromscratch.utils import train_test_split, normalize, accuracy_score, Plot
  from mlfromscratch.supervised_learning import NaiveBayes
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types