input: 
  def identify_author(samples: List[str], labels: List[str]) -> Pipeline:
      """Develop a machine learning model in Python to identify the author of a given article.
      The model uses TF-IDF for word embedding and Gaussian Naive Bayes for classification.
      The function returns the trained model.
      
      Example:
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> model = identify_author(samples, labels)
      >>> model_score = model.score(["sample 1 from AuthorA"], ["AuthorA"])
      >>> model_score
      1.0
      """
  
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import Pipeline
      from sklearn.preprocessing import LabelEncoder
  
      # Encode labels
      label_encoder = LabelEncoder()
      encoded_labels = label_encoder.fit_transform(labels)
  
      # Create pipeline
      pipeline = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('gnb', GaussianNB())
      ])
  
      # Fit the model
      pipeline.fit(samples, encoded_labels)
  
      return pipeline