input: from typing import List, Tuple
  
  
  def classify_author(samples: List[str], labels: List[str]) -> Pipeline:
      """
      Develops and trains a machine learning model using a Naive Bayes
      classifier to identify the author of a given article based on
      the provided samples and labels.
  
      >>> model = classify_author([
      ...   "sample 1 from AuthorA",
      ...   "sample 2 from AuthorA",
      ...   "sample 1 from AuthorB",
      ...   "sample 2 from AuthorB"
      ... ], [
      ...   "AuthorA",
      ...   "AuthorA",
      ...   "AuthorB",
      ...   "AuthorB"
      ... ])
      >>> score = model.score(["sample 3 from AuthorA"], ["AuthorA"])
      >>> print(f'model score: {score}')
      model score: 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=0.3)
      model.fit(X_train, y_train)
  
      return model
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=0.3)
      model.fit(X_train, y_train)
  
      return model