input: Write a function to create a machine learning algorithm in Python that categorizes a dataset of records using a Support Vector Classifier (SVC). The function should take a list of dictionaries where each dictionary contains a 'label' and 'features'. The function should train the SVC model on the given dataset and return the predicted label for a new set of features.
  assert categorize_records([
   {"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]}, 
   ])==[
   'apple',
   'banana',
   'lemon',
   'orange',
   'pear',
   ]
  assert categorize_records([
   {"label": "apple", "features": [1, 1, 0]}, 
   ])==['apple']
  assert categorize_records([
   {"label": "banana", "features": [0, 1, 1]}, 
   {"label": "lemon", "features": [0, 0, 1]}, 
   ])==['banana', 'lemon']
  assert categorize_records([])==[]
output: 
  def categorize_records(dataset):
      if not dataset:
          return []
      from sklearn.svm import SVC
      X = [entry['features'] for entry in dataset]
      y = [entry['label'] for entry in dataset]
      model = SVC(kernel='linear')
      model.fit(X, y)
      return model.predict(X)
  