input: def identify_author_of_article(samples: List[str], labels: List[str]) -> float:
      """
      Develop a machine learning model in Python to identify the author of a given article.
      The model uses a pipeline with TF-IDF vectorizer and Gaussian Naive Bayes classifier.
      It returns the accuracy score of the model on the test set.
      
      Example:
      >>> identify_author_of_article(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      0.5
      """
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 make_pipeline
      from sklearn.metrics import accuracy_score
  
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.5, random_state=42)
  
      model = make_pipeline(TfidfVectorizer(), GaussianNB())
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)