code: def hpo(n_workers=16):
      experiment = create_experiment(
          name="dask",
          max_trials=1000,
          max_broken=5,
          space={
              "C": "loguniform(1e-6, 1e6, precision=None)",
              "gamma": "loguniform(1e-8, 1e8, precision=None)",
              "tol": "loguniform(1e-4, 1e-1, precision=None)",
              "class_weight": "choices([None, 'balanced'])",
          },
          algorithm={"random": {"seed": 1}},
      )
  
      with experiment.tmp_executor("dask", n_workers=n_workers):
          experiment.workon(main, n_workers=n_workers // 2)
  
      experiment.plot.regret().show()
      experiment.plot.partial_dependencies(
          params=["C", "gamma", "tol"]
      ).show()
  
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 2 for calling a global function with example usage: hpo(n_workers) without return types