code: def test_sk_learn_svc_train(setup):
      digits = load_digits()
      param_space = {
          "C": np.logspace(-6, 6, 30),
          "gamma": np.logspace(-8, 8, 30),
          "tol": np.logspace(-4, -1, 30),
          "class_weight": [None, "balanced"],
      }
      model = SVC(kernel="rbf")
      search = RandomizedSearchCV(
          model, param_space, cv=5, n_iter=5, verbose=10
      )
  
      with joblib.parallel_backend("mars", n_parallel=16):
          search.fit(digits.data, digits.target)
  
imports: import numpy
  import joblib
  from sklearn.datasets import load_digits
  from sklearn.model_selection import RandomizedSearchCV
  from sklearn.svm import SVC
  from ..None import register_mars_backend
label_desc: Use image node_id 1 for calling a global function with example usage: test_sk_learn_svc_train(setup) without return types