input: 
  def identify_author(samples: List[str], labels: List[str]) -> float:
      """ Given a list of text samples and their respective author labels,
      create a machine learning model to identify the author of a given article.
      The model should be trained and tested using a given test size.
      >>> identify_author(
          ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"],
          ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      )
      0.5
      """
  
      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)
  
      return model.score(X_test, y_test)
  
output:     from sklearn.pipeline import Pipeline
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split