input: 
  def categorize_data(dataset: List[Dict[str, Union[str, List[int]]]]) -> str:
      """
      Given a dataset of records with features and labels, this function uses a Support Vector Classifier (SVC) 
      to categorize a new data point based on its features.
  
      >>> categorize_data([
      ... {"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'
      """
  
output:     from sklearn.svm import SVC
      from sklearn.preprocessing import LabelEncoder
  
      # Extract features and labels
      X = [entry["features"] for entry in dataset]
      y = [entry["label"] for entry in dataset]
  
      # Encode labels
      le = LabelEncoder()
      y_encoded = le.fit_transform(y)
  
      # Train the SVC model
      svc = SVC()
      svc.fit(X, y_encoded)
  
      # Predict the label for a new data point
      new_data_point = [0, 1, 1]
      predicted_label_encoded = svc.predict([new_data_point])
  
      # Decode the predicted label
      predicted_label = le.inverse_transform(predicted_label_encoded)
  
      return predicted_label[0]