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 author_identification_model() -> Pipeline:
      """ Build and return a machine learning model using a pipeline that first 
      vectorizes the text using TfidfVectorizer and then uses GaussianNB as the 
      classifier to identify the author of a given article.
      
      Example:
      >>> author_model = author_identification_model()
      >>> X_train, X_test, y_train, y_test = train_test_split(\
      ["sample 1 from AuthorA", "sample 1 from AuthorB"], ["AuthorA", "AuthorB"], test_size=0.5)
      >>> author_model.fit(X_train, y_train)
      >>> author_model.score(X_test, y_test)
      1.0
      """
      
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      
      return model
  
output:     model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      
      return model