input: 
  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 classify_article_author(samples, labels):
      """Develop a machine learning model in Python to identify the author of a given article.
      The function will load data, build a model using TfidfVectorizer and GaussianNB, train the model,
      and evaluate its performance.
      >>> samples = [
      ...   "sample 1 from AuthorA",
      ...   "sample 2 from AuthorA",
      ...   "sample 1 from AuthorB",
      ...   "sample 2 from AuthorB"
      ... ]
      >>> labels = [
      ...   "AuthorA",
      ...   "AuthorA",
      ...   "AuthorB",
      ...   "AuthorB"
      ... ]
      >>> classify_article_author(samples, labels)
      model score: 1.0
      """
      # build the model
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      # evalulate the model
      score = model.score(X_test, y_test)
      print(f'model score: {score}')
  
output:     return score