input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should use TF-IDF vectorization and a Gaussian Naive Bayes classifier, train the model on provided samples, and return the model score.
  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 author_identification_model(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 author_identification_model([
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) > 0
  assert author_identification_model([
    "This is an article by AuthorX",
    "Another piece by AuthorX",
    "A short story by AuthorY",
    "Another short story by AuthorY"
  ], ["AuthorX", "AuthorX", "AuthorY", "AuthorY"]) > 0
  assert author_identification_model([], []) is None
output: def author_identification_model(samples, labels):
      if not samples:
          return None
      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