econml/grf/classes.py [339:368]:
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    def __init__(self,
                 n_estimators=100, *,
                 criterion="mse",
                 max_depth=None,
                 min_samples_split=10,
                 min_samples_leaf=5,
                 min_weight_fraction_leaf=0.,
                 min_var_fraction_leaf=None,
                 min_var_leaf_on_val=False,
                 max_features="auto",
                 min_impurity_decrease=0.,
                 max_samples=.45,
                 min_balancedness_tol=.45,
                 honest=True,
                 inference=True,
                 fit_intercept=True,
                 subforest_size=4,
                 n_jobs=-1,
                 random_state=None,
                 verbose=0,
                 warm_start=False):
        super().__init__(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth,
                         min_samples_split=min_samples_split,
                         min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf,
                         min_var_fraction_leaf=min_var_fraction_leaf, min_var_leaf_on_val=min_var_leaf_on_val,
                         max_features=max_features, min_impurity_decrease=min_impurity_decrease,
                         max_samples=max_samples, min_balancedness_tol=min_balancedness_tol,
                         honest=honest, inference=inference, fit_intercept=fit_intercept,
                         subforest_size=subforest_size, n_jobs=n_jobs, random_state=random_state, verbose=verbose,
                         warm_start=warm_start)
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econml/grf/classes.py [669:698]:
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    def __init__(self,
                 n_estimators=100, *,
                 criterion="mse",
                 max_depth=None,
                 min_samples_split=10,
                 min_samples_leaf=5,
                 min_weight_fraction_leaf=0.,
                 min_var_fraction_leaf=None,
                 min_var_leaf_on_val=False,
                 max_features="auto",
                 min_impurity_decrease=0.,
                 max_samples=.45,
                 min_balancedness_tol=.45,
                 honest=True,
                 inference=True,
                 fit_intercept=True,
                 subforest_size=4,
                 n_jobs=-1,
                 random_state=None,
                 verbose=0,
                 warm_start=False):
        super().__init__(n_estimators=n_estimators, criterion=criterion, max_depth=max_depth,
                         min_samples_split=min_samples_split,
                         min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf,
                         min_var_fraction_leaf=min_var_fraction_leaf, min_var_leaf_on_val=min_var_leaf_on_val,
                         max_features=max_features, min_impurity_decrease=min_impurity_decrease,
                         max_samples=max_samples, min_balancedness_tol=min_balancedness_tol,
                         honest=honest, inference=inference, fit_intercept=fit_intercept,
                         subforest_size=subforest_size, n_jobs=n_jobs, random_state=random_state, verbose=verbose,
                         warm_start=warm_start)
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