input: Write a function to develop a machine learning model in Python that can identify the author of a given article. The function should use a pipeline that includes TF-IDF vectorization and a Gaussian Naive Bayes classifier. 
  
  assert develop_author_identification_model(["sample 1 from AuthorA", "sample 2 from AuthorB"], ["AuthorA", "AuthorB"])
  assert develop_author_identification_model(["sample 1 from AuthorB", "sample 2 from AuthorA"], ["AuthorB", "AuthorA"])
  assert develop_author_identification_model(["sample 1 from AuthorC", "sample 2 from AuthorC"], ["AuthorC", "AuthorC"])
  assert develop_author_identification_model([], [])
  assert develop_author_identification_model(["sample 1 from AuthorD"], ["AuthorD"])
output: def develop_author_identification_model(articles, authors):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
      from sklearn.preprocessing import LabelEncoder
  
      if not articles or not authors:
          return None
  
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(articles).toarray()
      label_encoder = LabelEncoder()
      y = label_encoder.fit_transform(authors)
  
      model = make_pipeline(GaussianNB())
      model.fit(X, y)
      return model