code: def test_unbiased_sampling_error(
      self, generate_regression_data, n_estimators
  ):
      crforest = self.prepare_model(n_estimators=n_estimators)
      (
          X_train,
          X_test,
          y_train,
          y_test,
          treatment_train,
          treatment_test,
      ) = self.prepare_data(generate_regression_data)
      crforest.fit(X=X_train, treatment=treatment_train, y=y_train)
      crforest_test_var = crforest.calculate_error(
          X_train=X_train, X_test=X_test
      )
      assert (crforest_test_var > 0).all()
      assert crforest_test_var.shape[0] == y_test.shape[0]
  
imports: import multiprocessing
  from abc import abstractmethod
  import pandas
  import pytest
  from sklearn.model_selection import train_test_split
  from causalml.inference.tree import CausalTreeRegressor, CausalRandomForestRegressor
  from causalml.metrics import ape
  from causalml.metrics import qini_score
  from .const import RANDOM_SEED, ERROR_THRESHOLD
label_desc: Use image node_id 4 for calling the TestCausalRandomForestRegressor obj's underlying member method code with example usage: obj.test_unbiased_sampling_error(generate_regression_data, n_estimators) without return types