input: 
  def classify_emails(df: pd.DataFrame, test_size: float, random_state: int) -> float:
      """ Classify emails from the Enron Spam Dataset as either spam or non-spam using
      a bag of words model and a linear SVM classifier. Return the precision score of the model.
      >>> classify_emails(pd.DataFrame({'EmailText': ['win money', 'meeting tomorrow'], 'Label': ['spam', 'ham']}), 0.5, 42)
      1.0
      """
      # Load label and data
      X = df['EmailText']
      y = df['Label']
      
      # Split data into train-test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
      
      # Create bag of words model
      count_vectorizer = CountVectorizer()
      X_train_counts = count_vectorizer.fit_transform(X_train)
      
      # Train the model
      clf = svm.SVC(kernel='linear')
      clf.fit(X_train_counts, y_train)
      
      # Test the model
      X_test_counts = count_vectorizer.transform(X_test)
      predictions = clf.predict(X_test_counts)
      score = precision_score(y_test, predictions, average='binary')
      
      return score
  
output:     from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn import svm
      from sklearn.metrics import precision_score