input: Write a function named `categorized_dataset` that takes a list of records and categorizes each record based on its features using a Support Vector Machine (SVM) algorithm. Return the predicted category for a new set of features. If the dataset is empty, return None.
  
  assert categorized_dataset([
   {"label": "apple", "features": [1, 1, 0]}, 
   {"label": "banana", "features": [0, 1, 1]}, 
   {"label": "lemon", "features": [0, 0, 1]}, 
   {"label": "orange", "features": [1, 0, 0]}, 
   {"label": "pear", "features": [0, 1, 0]}, 
  ]) == "banana"
  assert categorized_dataset([]) == None
output: 
  def categorized_dataset(records):
      if not records:
          return None
      
      from sklearn.svm import SVC
      from sklearn.preprocessing import LabelEncoder
      
      X = [record["features"] for record in records]
      y = [record["label"] for record in records]
      
      le = LabelEncoder()
      y = le.fit_transform(y)
      
      svm = SVC()
      svm.fit(X, y)
      
      new_features = [0, 1, 1]
      predicted_category = svm.predict([new_features])
      
      return le.inverse_transform(predicted_category)[0]
  