input: 
  def identify_author(samples: List[str], labels: List[str], test_size: float = 0.3) -> float:
      """Develop a machine learning model in Python to identify the author of a given article.
      The function uses TfidfVectorizer for text feature extraction and Gaussian Naive Bayes
      for classification. The model is trained on the provided samples and evaluated on a 
      portion of the data (specified by test_size). The function returns the accuracy score 
      of the model.
      >>> identify_author(["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
      
      # Load data
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=test_size)
      
      # Build the model
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      
      # Train the model
      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
      
      # Load data
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=test_size)
      
      # Build the model
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
      
      # Train the model
      model.fit(X_train, y_train)
      
      # Evaluate the model
      score = model.score(X_test, y_test)
      
      return score