input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should use TfidfVectorizer and GaussianNB in a Pipeline and evaluate the model's performance on a test set. Return the score of the model.
  def identify_author(samples, labels, test_size=0.3):
      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=test_size)
      model.fit(X_train, y_train)
  
      # evalulate the model
      score = model.score(X_test, y_test)
      return score
  
  assert identify_author([
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ], [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ]) == 1.0
  assert identify_author([
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB",
    "sample 1 from AuthorC"
  ], [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB",
    "AuthorC"
  ]) == 1.0
  assert identify_author([
    "sample 1 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorA",
    "sample 2 from AuthorB"
  ], [
    "AuthorA",
    "AuthorB",
    "AuthorA",
    "AuthorB"
  ]) == 1.0
output: def identify_author(samples, labels, test_size=0.3):
      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
  
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
      
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=test_size)
      model.fit(X_train, y_train)
  
      score = model.score(X_test, y_test)
      return score