input: Write a function to develop a machine learning model in Python to identify the author of a given article using a TfidfVectorizer and Gaussian Naive Bayes classifier. Return the model score after evaluating it on a test set. If no samples or labels are provided, return None.
  samples = [
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ]
  labels = [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ]
  
  model = Pipeline([
    ('tfidf', TfidfVectorizer()),
    ('clf', GaussianNB())
  ])
  
  X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
  model.fit(X_train, y_train)
  
  assert identify_author([
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ], [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ]) != None
  
  assert identify_author([], []) == None
output: def identify_author(samples, labels):
      if not samples or not labels:
          return None
      
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
      
      return model.score(X_test, y_test)