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.
      The function takes a list of sample texts (samples) and their corresponding author labels
      (labels) as input. It trains a model using TF-IDF vectorization and Gaussian Naive Bayes
      classifier, and then evaluates the model's accuracy.
  
      >>> identify_author(["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.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
  
      # Vectorize the text samples using TF-IDF
      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=0.5, random_state=42)
  
      # Train a Gaussian Naive Bayes classifier
      model = GaussianNB()
      model.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = model.predict(X_test)
  
      # Calculate and return the accuracy of the model
      return accuracy_score(y_test, y_pred)