input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should load sample data, build a pipeline using TfidfVectorizer and GaussianNB for classification, train the model, and evaluate its performance. The function should return the model score as a float. If the input data is empty, the function should return None.
  
  assert develop_author_model(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) != None
  assert develop_author_model([], []) == None
output: def develop_author_model(texts, authors):
      if not texts:
          return None
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
      
      X_train, X_test, y_train, y_test = train_test_split(texts, authors, test_size=0.2, random_state=42)
      
      pipeline = make_pipeline(TfidfVectorizer(), GaussianNB())
      pipeline.fit(X_train, y_train)
      
      y_pred = pipeline.predict(X_test)
      return accuracy_score(y_test, y_pred)