input: Write a function to develop a machine learning model in Python that identifies the author of a given article. The function should use TfidfVectorizer and GaussianNB, train the model on provided samples, and print the model's score on a test set.
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.naive_bayes import GaussianNB
  from sklearn.pipeline import Pipeline
  from sklearn.model_selection import train_test_split
  
  def identify_author_of_article(samples, labels):
      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)
      score = model.score(X_test, y_test)
      print(f'model score: {score}')
  
  samples = [
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ]
  labels = [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ]
  
  identify_author_of_article(samples, labels)
output: 
  def identify_author_of_article(samples, labels):
      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)
      score = model.score(X_test, y_test)
      print(f'model score: {score}')
  