input: def identify_author(samples: List[str], labels: List[str]) -> float:
      """Develop a machine learning model in Python to identify the author of a given article.
      Given a list of article samples and their corresponding authors, the function will
      train a model using TF-IDF vectorizer and Gaussian Naive Bayes classifier. It will then
      evaluate the model on a test set and return the accuracy score.
      >>> identify_author(
          ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"],
          ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      )
      1.0
      >>> identify_author(
          ["article 1 from AuthorC", "article 2 from AuthorC", "article 1 from AuthorD", "article 2 from AuthorD"],
          ["AuthorC", "AuthorC", "AuthorD", "AuthorD"]
      )
      1.0
      """
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.2, random_state=42)
  
      # Vectorize the text data using TF-IDF
      vectorizer = TfidfVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train).toarray()
      X_test_vec = vectorizer.transform(X_test).toarray()
  
      # Train a Gaussian Naive Bayes classifier
      classifier = GaussianNB()
      classifier.fit(X_train_vec, y_train)
  
      # Predict on the test set
      y_pred = classifier.predict(X_test_vec)
  
      # Calculate and return the accuracy score
      return accuracy_score(y_test, y_pred)