input: def categorize_dataset(training_data: list) -> str:
      """ Categorizes a given dataset of records using a Support Vector Machine (SVM) classifier.
      The dataset should be a list of dictionaries where each dictionary contains 'label' 
      and 'features' keys. 'label' is a string representing the category, and 'features'
      is a list of numerical values representing the features. The function returns the 
      category predicted for a new set of features [1, 0, 1].
      
      >>> training_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]}, 
      ... ]
      >>> categorize_dataset(training_data)
      'banana'
      """
      import numpy as np
      from sklearn.svm import SVC 
  
      # Feature extraction 
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['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 data in training_data:
          X.append(data['features'])
          y.append(data['label'])
  
      # Model 
      clf = SVC()
      clf.fit(X, y) 
  
      # Prediction 
      predict_data = [1, 0, 1]
      predicted_label = clf.predict([predict_data])
  
      return predicted_label[0]