code: def run_regression(datatype, loss, eps, dims):
      nrows, ncols = dims
      X_train, X_test, y_train, y_test = make_regression_dataset(
          datatype, nrows, ncols
      )
  
      # solving in primal is not supported by sklearn for this loss type.
      skdual = loss == "epsilon_insensitive"
      # limit the max iterations for sklearn to reduce the max test time
      cuit = 10000
      skit = max(10, min(cuit, cuit * 1000 / nrows))
  
      t = time.perf_counter()
      cum = cu.LinearSVR(loss=loss, epsilon=eps, max_iter=cuit)
      cum.fit(X_train, y_train)
      cus = cum.score(X_test, y_test)
      t = max(5, (time.perf_counter() - t) * SKLEARN_TIMEOUT_FACTOR)
  
      # cleanup cuml objects so that we can more easily fork the process
      # and test sklearn
      del cum
      X_train = X_train.get()
      X_test = X_test.get()
      y_train = y_train.get()
      y_test = y_test.get()
      gc.collect()
  
      try:
  
          def run_sklearn():
              skm = sk.LinearSVR(
                  loss=loss, epsilon=eps, max_iter=skit, dual=skdual
              )
              skm.fit(X_train, y_train)
              return skm.score(X_test, y_test)
  
          sks = with_timeout(timeout=t, target=run_sklearn)
          good_enough(cus, sks, nrows)
      except TimeoutError:
          pytest.skip(f"sklearn did not finish within {t} seconds.")
  
imports: import cuml.internals.logger
  import cuml
  import cuml.svm
  import sklearn.svm
  from cuml.testing.utils import unit_param, quality_param, stress_param
  from queue import Empty
  import cuml.model_selection
  import cuml.datasets
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  import gc
  import multiprocessing
  import time
  import math
  from cuml.internals.safe_imports import gpu_only_import
  from cuml.common import input_to_cuml_array
label_desc: Use image node_id 5 for calling a global function with example usage: run_regression(datatype, loss, eps, dims) and returns: skm