input: Write a python function to train a Multinomial Naive Bayes classifier using CountVectorizer and evaluate its accuracy on test data.
  assert train_and_evaluate(X_train, y_train, X_test, y_test) > 0.8
  assert train_and_evaluate(X_train_sample, y_train_sample, X_test_sample, y_test_sample) > 0.75
output: def train_and_evaluate(X_train, y_train, X_test, y_test):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      from sklearn.metrics import accuracy_score
  
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)