input: from typing import List, Tuple
  
  def identify_author(samples: List[str], labels: List[str]) -> Tuple[float, Pipeline]:
      """Develop a machine learning model using a pipeline comprising TfidfVectorizer and GaussianNB
      to identify the author of a given article.
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"],
                          ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      (0.5, Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', GaussianNB())]))
      """
      # 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)
      return score, model
output:     from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import Pipeline