input: 
  def categorize_dataset(training_data: list[dict], predict_data: list[int]) -> str:
      """ Given a dataset of records, categorize the dataset using machine learning. The dataset
      consists of dictionaries where each dictionary contains a label and its corresponding features.
      Use an SVM classifier to fit the data and then predict the label for a given set of features.
      Example:
      >>> 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]}, 
      ... ], [1, 0, 1])
      'banana'
      """
      import numpy as np
      from sklearn.svm import SVC 
  
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
  
      clf = SVC()
      clf.fit(X, y) 
  
      predicted_label = clf.predict([predict_data])
      return predicted_label[0]
  
output:     import numpy as np
      from sklearn.svm import SVC 
  
      X = []
      y = []
      for data in training_data:
          X.append(data['features'])
          y.append(data['label'])
  
      clf = SVC()
      clf.fit(X, y) 
  
      predicted_label = clf.predict([predict_data])
      return predicted_label[0]