docstr: The 2-dimensional Rosenbrock function as a toy model.
  The Rosenbrock function is well-known in the optimization community and
  often serves as a toy problem. It can be defined for arbitrary
  dimensions. The minimum is always at x_i = 1 with a function value of
  zero. All input parameters are continuous. The search domain for
  all x's is the interval [-5, 10].
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code: def train(
      self,
      config: Configuration,
      seed: int = 0,
      budget: float = None,
      instance: str = None,
  ) -> float:
      """The 2-dimensional Rosenbrock function as a toy model.
      The Rosenbrock function is well-known in the optimization community and
      often serves as a toy problem. It can be defined for arbitrary
      dimensions. The minimum is always at x_i = 1 with a function value of
      zero. All input parameters are continuous. The search domain for
      all x's is the interval [-5, 10].
      """
      x1 = config["x0"]
      x2 = config["x1"]
  
      cost = 100.0 * (x2 - x1**2.0) ** 2.0 + (1 - x1) ** 2.0
      return cost
  
file_path: SMAC3/tests/fixtures/models.py
imports: from __future__ import annotations
  import warnings
  import numpy
  import pytest
  from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Float
  from sklearn.linear_model import SGDClassifier
  from sklearn.model_selection import StratifiedKFold, cross_val_score
label_desc: Use image node_id 2 for calling the Rosenbrock2D obj's underlying member method code with example usage: obj.train(config, seed, budget, instance) and returns: cost