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 identify_author(samples: List[str], labels: List[str]) -> float:
      """Identifies the author of a given article using a machine learning model.
      The model uses a Pipeline that includes TF-IDF vectorization and Gaussian Naive Bayes classification.
      The function returns the accuracy score of the model on a test set split of 30%.
      
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> identify_author(samples, labels)
      1.0
      """
output:     pipeline = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3, random_state=42)
      
      pipeline.fit(X_train, y_train)
      
      accuracy = pipeline.score(X_test, y_test)
      
      return accuracy