input: 
  def classify_emails_as_spam_or_non_spam():
      """
      Classify emails from the Enron Spam Dataset as either spam or non-spam using a Bag of Words model and an SVM classifier.
      >>> classify_emails_as_spam_or_non_spam()
      'Precision score of the model: 0.95'
      """
  
output:     
      import os
      import numpy as np
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import precision_score
  
      # Load the dataset
      def load_data(path):
          emails = []
          labels = []
          for label in ['spam', 'ham']:
              dir_path = os.path.join(path, label)
              for filename in os.listdir(dir_path):
                  with open(os.path.join(dir_path, filename), 'r', encoding='latin-1') as file:
                      emails.append(file.read())
                      labels.append(label)
          return emails, labels
  
      # Path to the Enron Spam Dataset
      dataset_path = 'path_to_enron_dataset'
      emails, labels = load_data(dataset_path)
  
      # Convert text data to a matrix of token counts
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(emails)
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
  
      # Train an SVM classifier
      classifier = SVC(kernel='linear')
      classifier.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = classifier.predict(X_test)
  
      # Calculate the precision score
      precision = precision_score(y_test, y_pred, pos_label='spam')
  
      return f'Precision score of the model: {precision:.2f}'
  