input: Write a function to build a machine learning model using the Support Vector Classifier (SVC) from sklearn to categorize a dataset of records. The function should take a list of dictionaries where each dictionary contains a 'label' (str) and 'features' (list of int). The function should then train the model and return the trained model. Use the provided dataset for training and assert the correctness of the model by predicting the category of a new record.
  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]}, 
  ]).predict([1, 0, 1]) == 'banana'
  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]}, 
  ]).predict([1, 1, 0]) == 'apple'
  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]}, 
  ]).predict([0, 0, 1]) == 'lemon'
  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]}, 
  ]).predict([1, 0, 0]) == 'orange'
  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]}, 
  ]).predict([0, 1, 0]) == 'pear'
output: def categorize_records(records):
      from sklearn.svm import SVC
      import numpy as np
      
      X = np.array([record['features'] for record in records])
      y = np.array([record['label'] for record in records])
      
      model = SVC(kernel='linear')
      model.fit(X, y)
      
      return model