input: from typing import List, Tuple
  
  def identify_author(samples: List[str], labels: List[str], test_size: float) -> float:
      """ Train a machine learning model to identify the author of a given article.
      Input will be a list of article samples and a corresponding list of author labels.
      The model is trained using a combination of TF-IDF vectorization and Gaussian Naive Bayes.
      The test_size parameter determines what proportion of the data will be used for testing.
      The function returns the accuracy score of the model on the test data.
  
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> test_size = 0.3
      >>> identify_author(samples, labels, test_size)
      1.0
      """
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import GaussianNB
      from sklearn.metrics import accuracy_score
  
      # Vectorize the text data
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(samples).toarray()
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=test_size, random_state=42)
  
      # Train the Gaussian Naive Bayes model
      model = GaussianNB()
      model.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test)
  
      # Calculate and return the accuracy score
      return accuracy_score(y_test, y_pred)