input: Write a function to develop a machine learning model in Python to identify the author of a given article. Use a pipeline that incorporates TfidfVectorizer and GaussianNB, then train and evaluate the model. If the data is not enough or missing, return "Insufficient Data".
  
  def identify_author(samples, labels):
      if len(samples) < 1 or len(labels) < 1:
          return "Insufficient Data"
      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 f'model score: {score}'
  
  assert identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) != "Insufficient Data"
  assert identify_author([], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) == "Insufficient Data"
  assert identify_author(["sample 1 from AuthorA"], []) == "Insufficient Data"
output: def identify_author(samples, labels):
      if len(samples) < 1 or len(labels) < 1:
          return "Insufficient Data"
      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 f'model score: {score}'