input: from typing import List, Dict
  import numpy as np
  from sklearn.svm import SVC 
  
  def categorize_dataset(training_data: List[Dict[str, List[int]]]) -> str:
      """ Categorize a dataset of records using a Support Vector Machine (SVM).
      Given a list of dictionaries where each dictionary contains a 'label' and 'features',
      the function extracts the features and labels, trains an SVM model on this data,
      and then predicts the category of a new record with features [1, 0, 1].
      >>> 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]}, 
      ... ])
      'banana'
      """
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
      
      clf = SVC()
      clf.fit(X, y) 
      
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]
output:     X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
      
      clf = SVC()
      clf.fit(X, y) 
      
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
      
      return predicted_label[0]