input: Write a function to develop a machine learning model in Python that identifies the author of a given article using TF-IDF and Gaussian Naive Bayes. If the model is successfully trained and predicts the author correctly, return True. If not, return False. If the input list of articles is empty, return False.
  
  assert identify_author_model(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])==True
  assert identify_author_model([], ["AuthorA", "AuthorB", "AuthorB"])==False
  assert identify_author_model(["sample 1 from AuthorC"], [])==False
  assert identify_author_model(["sample 1 from AuthorC"], ["AuthorD"])==False
  assert identify_author_model([], [])==False
output: def identify_author_model(articles, authors):
      if not articles or not authors:
          return False
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(articles).toarray()
      y = authors
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
  
      model = GaussianNB()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred) == 1.0