input: 
  def categorize_dataset(data_records):
      """
      Given a list of dictionaries where each dictionary contains 'label' (string)
      and 'features' (list of integers), returns the predicted label for a new
      record with features [1, 0, 1] using a Support Vector Classifier (SVC).
  
      >>> categorize_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]}, 
      ... ])
      'lemon'
      """
      import numpy as np
      from sklearn.svm import SVC
  
      # Feature extraction
      X = []
      y = []
      for record in data_records:
          X.append(record['features'])
          y.append(record['label'])
  
      # Model
      clf = SVC()
      clf.fit(X, y)
  
      # Prediction
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]
  
output:     import numpy as np
      from sklearn.svm import SVC
  
      # Feature extraction
      X = []
      y = []
      for record in data_records:
          X.append(record['features'])
          y.append(record['label'])
  
      # Model
      clf = SVC()
      clf.fit(X, y)
  
      # Prediction
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]