input: 
  def author_identification_model():
      """Create and evaluate a machine learning model to identify the author of a given article.
      >>> 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)
      >>> score = model.score(X_test, y_test)
      >>> print(f'model score: {score}')
      model score: 1.0
      """
  
output:     from sklearn.pipeline import Pipeline
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split
  
      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)
      score = model.score(X_test, y_test)
      print(f'model score: {score}')