code: def test_regression_squared_loss(reg_train_data):
      X, y = reg_train_data
      reg = SGDRegressor(
          loss="squared",
          penalty="l2",
          learning_rate="constant",
          eta0=1e-2,
          random_state=0,
      )
  
      reg.fit(X, y)
      pred = reg.predict(X)
      np.testing.assert_almost_equal(np.mean((pred - y) ** 2), 4.749, 3)
  
file_path: lightning/lightning/impl/tests/test_sgd.py
imports: import numpy
  import pytest
  from sklearn.datasets import make_regression
  from lightning.impl.datasets.samples_generator import make_nn_regression
  from lightning.impl.sgd import SGDClassifier
  from lightning.impl.sgd import SGDRegressor
  from lightning.impl.tests.utils import check_predict_proba
label_desc: Use image node_id 9 for calling a global function with example usage: test_regression_squared_loss(reg_train_data) without return types