input: Write a python function to classify an item using GaussianNB from sklearn.naive_bayes.
  assert classify_item('An iPhone', ['Mobile Devices', 'Computers', 'Appliances'], X, y) == 'Mobile Devices'
  assert classify_item('A Laptop', ['Mobile Devices', 'Computers', 'Appliances'], X, y) == 'Computers'
  assert classify_item('A Refrigerator', ['Mobile Devices', 'Computers', 'Appliances'], X, y) == 'Appliances'
output: from sklearn.naive_bayes import GaussianNB
  from sklearn.feature_extraction.text import CountVectorizer
  
  def classify_item(item, categories, X, y):
      vectorizer = CountVectorizer()
      X_vectors = vectorizer.fit_transform(X)
      clf = GaussianNB()
      clf.fit(X_vectors.toarray(), y)
      item_vector = vectorizer.transform([item]).toarray()
      category_index = clf.predict(item_vector)[0]
      return categories[category_index]