code: def run(cls, job, eval=True):
      """Run a test"""
      if job == "fit":
          lr, _ = est.get_learner(cls, True, False)
          lr.dtype = np.float64
      else:
          lr = run(cls, "fit", False)
  
      data = Data(cls, True, False, True)
      X, y = data.get_data((25, 4), 3)
  
      if job in ["fit", "transform"]:
          (F, wf), _ = data.ground_truth(X, y, data.indexer.partitions)
      else:
          _, (F, wf) = data.ground_truth(X, y, data.indexer.partitions)
  
      args = {"fit": [X, y], "transform": [X], "predict": [X]}[job]
  
      P = _run(lr, job, *args, return_preds=True)
      if not eval:
          return lr
  
      np.testing.assert_array_equal(P, F)
  
      if job in ["fit", "transform"]:
          lrs = lr.sublearners
      else:
          lrs = lr.learner
  
      w = [obj.estimator.coef_ for obj in lrs]
      np.testing.assert_array_equal(w, wf)
  
imports: import numpy
  from mlens.testing import Data, EstimatorContainer
  from mlens.parallel import Group, Learner, Pipeline, run
  from mlens.utils.dummy import OLS, Scale
  from mlens.externals.sklearn.base import clone
label_desc: Use image node_id 6 for calling a global function with example usage: run(cls, job, eval) and returns: lr