text: """
  Example usage of BenchmarkEvaluator,
  which evaluates the given architecture in NASBench-101
  
  """
  from nord.neural_nets import BenchmarkEvaluator, NeuralDescriptor
  
  # Instantiate the evaluator
  evaluator = BenchmarkEvaluator()
  # Instantiate a descriptor
  d = NeuralDescriptor()
  
  # See the available layers (ops)
  # for NASBench-101
  layers = evaluator.get_available_ops()
  print(layers)
  
  # Add NASBench-101 Layers connected
  # sequentially
  d.add_layer('input', None, 'in')
  d.add_layer_sequential(layers[0], None, 'layer_1')
  d.add_layer_sequential(layers[2], None, 'layer_2')
  d.add_layer_sequential('output', None, 'out')
  
  
  # Add Connections
  d.connect_layers('layer_1', 'out')
  
  # Get the validation accuracy and training time
  val_acc, train_time = evaluator.descriptor_evaluate(
      d, acc='validation_accuracy')
  
  # Get the test accuracy and training time
  test_acc, train_time = evaluator.descriptor_evaluate(d, acc='test_accuracy')
  
  print(d)
  print('Train time:', train_time)
  print('Validation accuracy:', val_acc)
  print('Test accuracy:', test_acc)
  