input: Write a function to develop a machine learning model in Python to identify the author of a given article using the given samples and labels. Return the model score after evaluating the model. The function should use TfidfVectorizer and GaussianNB in a pipeline.
  assert identify_author([
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ], [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ])>=0.5
  assert identify_author([], [])==0.0
output: def identify_author(samples, labels):
      if not samples:
          return 0.0
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
      from sklearn.model_selection import cross_val_score
      
      model = make_pipeline(TfidfVectorizer(), GaussianNB())
      scores = cross_val_score(model, samples, labels, cv=2)
      return scores.mean()