docstr: Creates a SVM based on a configuration and evaluates it on the
  iris-dataset using cross-validation.
docstr_tok: ["Creates","a","SVM","based","on","a","configuration","and","evaluates","it","on","the","iris-dataset","using","cross-validation","."]
code: def train(self, config: Configuration, seed: int = 0) -> float:
      """Creates a SVM based on a configuration and evaluates it on the
      iris-dataset using cross-validation."""
      config_dict = config.get_dictionary()
      if "gamma" in config:
          config_dict["gamma"] = (
              config_dict["gamma_value"]
              if config_dict["gamma"] == "value"
              else "auto"
          )
          config_dict.pop("gamma_value", None)
  
      classifier = svm.SVC(**config_dict, random_state=seed)
      scores = cross_val_score(classifier, iris.data, iris.target, cv=5)
      cost = 1 - np.mean(scores)
  
      return cost
  
file_path: SMAC3/examples/1_basics/2_svm_cv.py
imports: import numpy
  from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Float, Integer
  from ConfigSpace.conditions import InCondition
  from sklearn import datasets, svm
  from sklearn.model_selection import cross_val_score
  from smac import HyperparameterOptimizationFacade, Scenario
label_desc: Use image node_id 2 for calling the SVM obj's underlying member method code with example usage: obj.train(config, seed) and returns: cost