input: 
  def naive_bayes_classifier() -> None:
      """ Construct a naive bayes classifier using Python to classify emails as spam or not spam.
      >>> naive_bayes_classifier()
      # Create a Gaussian Classifier
      model = GaussianNB()
      # Train the model using the training sets
      model.fit(X_train, y_train)
      # Predict Output
      y_pred = model.predict(X_test)
      """
  
output:     from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split
      import numpy as np
  
      # Example data
      emails = np.array([
          [1, 0, 1, 0],  # email 1 features
          [0, 1, 0, 1],  # email 2 features
          [1, 1, 1, 0],  # email 3 features
          [0, 0, 0, 1]   # email 4 features
      ])
      labels = np.array([0, 1, 0, 1])  # 0: not spam, 1: spam
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(emails, labels, test_size=0.5, random_state=42)
  
      # Create a Gaussian Classifier
      model = GaussianNB()
  
      # Train the model using the training sets
      model.fit(X_train, y_train)
  
      # Predict Output
      y_pred = model.predict(X_test)