input: def identify_author_machine_learning_model(samples: List[str], labels: List[str], test_size: float = 0.3) -> float:
      """ Develops a machine learning model in Python to identify the author of a given article.
      The model uses TF-IDF vectorization and a Gaussian Naive Bayes classifier. The accuracy of
      the model on a test set is returned.
      >>> identify_author_machine_learning_model(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      1.0
      """
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import Pipeline
      from sklearn.model_selection import train_test_split
  
      # build the model
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=test_size)
      model.fit(X_train, y_train)
  
      # evaluate the model
      score = model.score(X_test, y_test)
      return score
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import Pipeline
      from sklearn.model_selection import train_test_split
  
      # build the model
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=test_size)
      model.fit(X_train, y_train)
  
      # evaluate the model
      score = model.score(X_test, y_test)
      return score