input: Write a function to implement a naive bayes classifier using Python to classify emails as spam or not spam. If the model can accurately classify the emails, return True else return False. If the input data is empty, return True.
  
  assert classify_emails(X_train, y_train, X_test)==True
  assert classify_emails(X_train_imbalanced, y_train_mislabeled, X_test)==False
  assert classify_emails(X_train_none, y_train_none, X_test)==True
output: def classify_emails(X_train, y_train, X_test):
      if not X_train or not y_train:
          return True
      
      from sklearn.naive_bayes import GaussianNB
      from sklearn.metrics import accuracy_score
      
      model = GaussianNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy >= 0.5