input: Write a function to extract all verbs from a given sentence and return them as a list. If no verbs are found, return an empty list. The function should use the Natural Language Toolkit (nltk) for tokenization and part-of-speech tagging.
  import nltk
  
  def extract_verbs(sentence):
      tokens = nltk.word_tokenize(sentence)
      tags = nltk.pos_tag(tokens)
      verbs = [word for word, pos in tags if pos.startswith('V')]
      return verbs
  
  # Test cases
  assert extract_verbs("The cat jumped over the moon.") == ['jumped']
  assert extract_verbs("She is running fast.") == ['is', 'running']
  assert extract_verbs("The sun shines brightly.") == ['shines']
  assert extract_verbs("He ate an apple.") == ['ate']
  assert extract_verbs("It was a dark and stormy night.") == ['was', 'stormy']
  assert extract_verbs("The quick brown fox.") == []
  assert extract_verbs("") == []
output: import nltk
  
  def extract_verbs(sentence):
      tokens = nltk.word_tokenize(sentence)
      tags = nltk.pos_tag(tokens)
      verbs = [word for word, pos in tags if pos.startswith('V')]
      return verbs