input: Write a function to extract all verbs from a given sentence and return them as a list. Make sure to 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
  
  assert extract_verbs("The cat jumped over the moon.") == ['jumped', 'over']
  assert extract_verbs("She is running fast.") == ['is', 'running', 'fast']
  assert extract_verbs("He eats apples daily.") == ['eats']
  assert extract_verbs("Walking and talking are great exercises.") == ['Walking', 'talking', 'are', 'exercises']
  assert extract_verbs("There was no activity.") == ['was']
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