text: import numpy as np
  import torch
  from botorch.test_functions.multi_fidelity import AugmentedHartmann
  
  VOCS = {
      'name': 'AugmentedHartmann',
      'description': 'Augmented hartmann multi-fideldity optimization problem, provided by botorch, '
                     'see https://botorch.org/tutorials/multi_fidelity_bo',
      'variables': {
          'x1': [0, 1.0],
          'x2': [0, 1.0],
          'x3': [0, 1.0],
          'x4': [0, 1.0],
          'x5': [0, 1.0],
          'x6': [0, 1.0],
          'cost': [0, 1.0]
      },
      'objectives': {
          'y1': 'MINIMIZE',
  
      },
      'constraints': {},
      'constants': {}
  }
  
  
  # labeled version
  def evaluate(inputs, extra_option='abc', **params):
      x = [inputs[f'x{ele}'] for ele in range(1, 7)]
      x += [inputs['cost']]
      problem = AugmentedHartmann(negate=False)
      objective = problem(torch.tensor(x).reshape(1, -1)).numpy()
      outputs = {'y1': objective[0]}
  
      return outputs
  
  
  if __name__ == '__main__':
      input = {'x1': 0.208,
               'x2': 0.164,
               'x3': 0.514,
               'x4': 0.280,
               'x5': 0.301,
               'x6': 0.664,
               'cost': 1.000}
      print(evaluate(input))
  