input: from typing import List, Dict
  
  def categorize_records(records: List[Dict[str, any]]) -> str:
      """ Categorizes a dataset of records using a Support Vector Machine (SVM).
      Each record contains a label and a list of features. The function trains an SVM on the given dataset
      and predicts the category of a new record with features [1, 0, 1].
      
      Example:
      >>> 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]}, 
      ... ])
      'banana'
      """
output:     from sklearn.svm import SVC
      from sklearn.preprocessing import LabelEncoder
  
      # Extract labels and features
      labels = [record['label'] for record in records]
      features = [record['features'] for record in records]
  
      # Encode labels
      label_encoder = LabelEncoder()
      encoded_labels = label_encoder.fit_transform(labels)
  
      # Train SVM
      svm = SVC(kernel='linear')
      svm.fit(features, encoded_labels)
  
      # Predict category for new record
      new_record_features = [1, 0, 1]
      predicted_category_encoded = svm.predict([new_record_features])
  
      # Decode the predicted category
      predicted_category = label_encoder.inverse_transform(predicted_category_encoded)
  
      return predicted_category[0]