code: def train(self, config: Configuration, seed: int = 0) -> float:
      x1 = config["x0"]
      x2 = config["x1"]
  
      cost = 100.0 * (x2 - x1**2.0) ** 2.0 + (1 - x1) ** 2.0
      return cost
  
file_path: SMAC3/examples/1_basics/4_callback.py
imports: from __future__ import annotations
  from ConfigSpace import Configuration, ConfigurationSpace, Float
  import smac
  from smac import Callback
  from smac import HyperparameterOptimizationFacade
  from smac import Scenario
  from smac.runhistory import TrialInfo, TrialValue
label_desc: Use image node_id 2 for calling the Rosenbrock2D obj's underlying member method code with example usage: obj.train(config, seed) and returns: cost