input: Write a python function to train a logistic regression model to classify the sentiment of movie reviews, and make predictions on new reviews.
  assert classify_reviews(['I really enjoyed this movie. It was a great story and the acting was superb!', 'This movie was terrible! The story was boring and the acting was terrible.'], [1, 0], ['The movie was amazing!']) == [1]
  assert classify_reviews(['I really enjoyed this movie. It was a great story and the acting was superb!', 'This movie was terrible! The story was boring and the acting was terrible.'], [1, 0], ['The movie was not good at all.']) == [0]
  assert classify_reviews(['Best movie ever!','Worst movie ever!'], [1, 0], ['It was an excellent film!']) == [1]
output: def classify_reviews(train_reviews, train_labels, test_reviews):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.linear_model import LogisticRegression
      
      # Convert the text data into feature vectors
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(train_reviews)
      X_test = vectorizer.transform(test_reviews)
      
      # Train a logistic regression model
      model = LogisticRegression()
      model.fit(X_train, train_labels)
      
      # Make predictions on the new reviews
      predictions = model.predict(X_test)
      
      return predictions.tolist()