code: def test_feature_importances(self, hidden_layer_units):
      reg = DAGRegressor(hidden_layer_units=hidden_layer_units)
      X, y = (
          pd.DataFrame(np.random.normal(size=(100, 1))),
          pd.Series(np.random.normal(size=(100,))),
      )
      X["true_feat"] = y * -3
      reg.fit(X, y)
      assert isinstance(reg.feature_importances_, np.ndarray)
      coef_ = pd.Series(reg.feature_importances_, index=X.columns)
      # assert that the sign of the coefficient is positive for both nonlinear and linear cases
      assert coef_["true_feat"] > 0
  
imports: import os
  import IPython
  import networkx
  import numpy
  import pandas
  import pytest
  import torch
  from sklearn.exceptions import NotFittedError
  from sklearn.gaussian_process.kernels import RBF
  from sklearn.model_selection import KFold, cross_val_score
  from causalnex.structure import data_generators
  from causalnex.structure.pytorch import DAGClassifier, DAGRegressor
label_desc: Use image node_id 4 for calling the TestDAGRegressor obj's underlying member method code with example usage: obj.test_feature_importances(hidden_layer_units) without return types