input: Write a function to develop and train a machine learning model in Python that identifies the author of a given article. Use the TfidfVectorizer and GaussianNB from sklearn and ensure the model is evaluated.
  
  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(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}')
    return model
  
  assert identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) is not None
output: 
  def identify_author(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}')
    return model
  