input: Write a python function to classify a sentence as positive or negative using a Naive Bayes classifier from the NLTK library.
  assert classify_sentence("The new iPhone has a great design.") == "positive"
  assert classify_sentence("The old phone has a horrible design.") == "negative"
  assert classify_sentence("The new tablet is amazing build.") == "positive"
output: def classify_sentence(sentence):
      import nltk
      from nltk.corpus import movie_reviews
      from nltk.classify import NaiveBayesClassifier
      from nltk.classify.util import accuracy
      from nltk.tokenize import word_tokenize
  
      # Prepare the dataset
      nltk.download('movie_reviews')
      nltk.download('punkt')
      
      def extract_features(words):
          return {word: True for word in words}
      
      pos_reviews = [(extract_features(movie_reviews.words(fileid)), 'positive') 
                     for fileid in movie_reviews.fileids('pos')]
      neg_reviews = [(extract_features(movie_reviews.words(fileid)), 'negative') 
                     for fileid in movie_reviews.fileids('neg')]
      
      # Split the data into training and test sets
      train_set = pos_reviews[:800] + neg_reviews[:800]
      test_set = pos_reviews[800:] + neg_reviews[800:]
      
      # Train the Naive Bayes classifier
      classifier = NaiveBayesClassifier.train(train_set)
      
      # Tokenize the input sentence
      tokens = word_tokenize(sentence)
      
      # Classify the sentence
      features = extract_features(tokens)
      return classifier.classify(features)