input: from typing import List
  
  def identify_author(samples: List[str], labels: List[str]) -> float:
      """
      Develop a machine learning model using TfidfVectorizer and GaussianNB to identify the author of given articles.
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      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
  
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(samples).toarray()
      y = labels
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = GaussianNB()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)