input: 
  def identify_author(samples: List[str], labels: List[str]) -> float:
      """Develop a machine learning model in Python to identify the author of a given article.
      The model uses a pipeline with TfidfVectorizer and GaussianNB.
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"],
                         ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      1.0
      """
  
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
  
      # Create a pipeline with TfidfVectorizer and GaussianNB
      pipeline = make_pipeline(TfidfVectorizer(), GaussianNB())
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.5, random_state=42)
  
      # Fit the pipeline on the training data
      pipeline.fit(X_train, y_train)
  
      # Predict the labels for the test data
      y_pred = pipeline.predict(X_test)
  
      # Calculate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy