input: def identify_author(samples: List[str], labels: List[str]) -> float:
      """ Identify the author of a given article using a machine learning model.
      The function builds and trains a Pipeline that includes TfidfVectorizer and GaussianNB.
      After training, it evaluates the model and returns the score.
      >>> samples = [
              "sample 1 from AuthorA",
              "sample 2 from AuthorA",
              "sample 1 from AuthorB",
              "sample 2 from AuthorB"
          ]
      >>> labels = [
              "AuthorA",
              "AuthorA",
              "AuthorB",
              "AuthorB"
          ]
      >>> identify_author(samples, labels)
      0.5
      """
output:     from sklearn.pipeline import Pipeline
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import cross_val_score
  
      pipeline = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  
      scores = cross_val_score(pipeline, samples, labels, cv=2)
      return scores.mean()