input: 
  from typing import List
  
  def identify_author(samples: List[str], labels: List[str], test_size: float) -> float:
      """ Develop a machine learning model using a pipeline that includes TfidfVectorizer 
      and GaussianNB to identify the author of given articles. The function returns the accuracy
      score of the model on the test set.
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"], 0.3)
      0.0
      """
  
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=test_size, random_state=42)
  
      pipeline = make_pipeline(TfidfVectorizer(), GaussianNB())
      pipeline.fit(X_train, y_train)
  
      y_pred = pipeline.predict(X_test)
      return accuracy_score(y_test, y_pred)