code: def main(C, gamma, tol, class_weight):
      diabetes = datasets.load_diabetes()
  
      X = diabetes.data
      y = diabetes.target
  
      model = SVC(
          kernel="rbf",
          C=C,
          gamma=gamma,
          tol=tol,
          class_weight=class_weight,
      )
  
      # Single metric evaluation using cross_validate
      with joblib.parallel_backend("dask"):
          cv_results = cross_validate(model, X, y, cv=5)
  
      accuracy = numpy.mean(cv_results["test_score"])
      error_rate = 1 - accuracy
  
      return [
          {
              "name": "test_error_rate",
              "type": "objective",
              "value": error_rate,
          }
      ]
  
file_path: orion/examples/dask_example.py
imports: import joblib
  import numpy
  from sklearn import datasets
  from sklearn.model_selection import cross_validate
  from sklearn.svm import SVC
  from orion.client import create_experiment
label_desc: Use image node_id 1 for calling a global function with example usage: main(C, gamma, tol, class_weight) and returns: list