input: Write a function to identify the author of a given article using a machine learning model. The function should take a list of article texts and their corresponding authors as input, train a model using TF-IDF vectorization and Naive Bayes classification, and return the accuracy score of the model when tested on a subset of the data.
  
  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, labels):
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
      score = model.score(X_test, y_test)
      return score
  
  assert identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) > 0.0
  assert identify_author(["article text 1", "article text 2"], ["AuthorC", "AuthorC"]) > 0.0
  assert identify_author([], []) == 0.0
  assert identify_author(["single article"], ["UnknownAuthor"]) > 0.0
output: def identify_author(samples, labels):
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
      score = model.score(X_test, y_test)
      return score