input: 
  def identify_author(samples: List[str], labels: List[str], test_size: float = 0.3) -> float:
      """Develop a machine learning model to identify the author of a given article.
      Given a list of article samples and their corresponding author labels, the function
      builds a model to predict the author of an article and evaluates its accuracy.
      Example:
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], 
                          ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      0.5
      """
      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
  
      # 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=test_size)
      model.fit(X_train, y_train)
  
      # evalulate the model
      return model.score(X_test, y_test)
  
output:     # evalulate the model
      return model.score(X_test, y_test)