code: def test__cv_estimate():
      df_train = pd.DataFrame(
          dict(
              x=[1, 1, 2, 2, 3, 3],
              y=[1.0, 1, 2, 2, 3, 3],
          )
      )
  
      np.random.seed(123)
      y_pred, models = _cv_estimate(
          model=LinearRegression(),
          train_data=df_train,
          features=["x"],
          y="y",
          n_splits=3,
      )
  
      assert (df_train["y"] == y_pred).all()
      assert np.all([isinstance(m, LinearRegression) for m in models])
  
file_path: fklearn/tests/causal/cate_learning/test_double_machine_learning.py
imports: from collections import Counter
  import pandas
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from fklearn.causal.cate_learning.double_machine_learning import non_parametric_double_ml_learner, _cv_estimate
  from sklearn.linear_model import LinearRegression
label_desc: Use image node_id 1 for calling a global function with example usage: test__cv_estimate() without return types