input: Write a function to develop a machine learning model in Python to identify the author of a given article. The model should be built using TfidfVectorizer and GaussianNB, and evaluated using a train-test split. Return the score of the model's performance on the test set. If the input data is empty, return 0.0.
  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([], []) == 0.0
output: def identify_author(texts, labels):
      if not texts:
          return 0.0
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split
      from sklearn.pipeline import make_pipeline
  
      X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2, random_state=42)
      model = make_pipeline(TfidfVectorizer(), GaussianNB())
      model.fit(X_train, y_train)
      return model.score(X_test, y_test)