input: from typing import List
  
  def classify_sentiment(text: str, classifier) -> str:
      """ Classify the given text into 'positive' or 'negative' sentiment using the provided classifier.
      >>> positiveText = "This is a great movie!"
      >>> negativeText = "This movie was terrible"
      >>> import nltk
      >>> nltk.download('punkt')
      >>> from nltk.classify import NaiveBayesClassifier
      >>> def label_words(words): 
      ...     return dict([(word, True) for word in words]) 
      >>> positive_tokens = nltk.word_tokenize(positiveText) 
      >>> negative_tokens = nltk.word_tokenize(negativeText)
      >>> positive_data = [(label_words(positive_tokens), 'positive')] 
      >>> negative_data = [(label_words(negative_tokens), 'negative')]
      >>> train_data = positive_data + negative_data 
      >>> classifier = NaiveBayesClassifier.train(train_data) 
      >>> classify_sentiment("This movie was ok", classifier)
      'positive'
      """
output:     words = nltk.word_tokenize(text)
      features = label_words(words)
      sentiment = classifier.classify(features)
      return sentiment