code: def test_linear_svr(reg_train_data, fit_intercept, loss):
      reg_dense, reg_target = reg_train_data
      reg = LinearSVR(
          random_state=0, fit_intercept=fit_intercept, loss=loss
      )
      reg.fit(reg_dense, reg_target)
      assert reg.score(reg_dense, reg_target) > 0.99
  
file_path: lightning/lightning/impl/tests/test_dual_cd.py
imports: import numpy
  import pytest
  from sklearn.metrics.pairwise import linear_kernel
  from sklearn.datasets import make_regression
  from lightning.impl.dual_cd import LinearSVC
  from lightning.impl.dual_cd import LinearSVR
  from lightning.impl.dual_cd_fast import sparse_dot
  from lightning.impl.dataset_fast import get_dataset
label_desc: Use image node_id 7 for calling a global function with example usage: test_linear_svr(reg_train_data, fit_intercept, loss) without return types