input: from typing import List
  
  
  def classify_sentiment(reviews: List[str], threshold=1.0) -> List[str]:
      """ Classifies the sentiment of the given movie reviews using NLP.
      The function returns a list of sentiment classifications which correspond
      one-to-one with the input list of reviews. The classifications are based 
      on the sentiment's intensity score given by Vader, where scores greater than 
      the threshold result in "positive" sentiment and scores less than or equal 
      to the threshold result in "negative" sentiment.
      >>> classify_sentiment(['The movie was terrible. It felt like it would never end!'], 1.0)
      ['negative']
      >>> classify_sentiment(['Great graphics, loved the acting!', 'Hated the villain.'], 1.0)
      ['positive', 'negative']
      """
      from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      results = []
      
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['compound'] > threshold:
              results.append('positive')
          else:
              results.append('negative')
      
      return results
output:     from nltk.sentiment import vader
  
      sid = vader.SentimentIntensityAnalyzer()
      results = []
      
      for review in reviews:
          scores = sid.polarity_scores(review)
          if scores['compound'] > threshold:
              results.append('positive')
          else:
              results.append('negative')
      
      return results