code: def test_nonlinear_performance(self, standardize):
      torch.manual_seed(42)
      np.random.seed(42)
      sm = dg.generate_structure(num_nodes=5, degree=3)
      sm.threshold_till_dag()
      data = dg.generate_continuous_dataframe(
          sm,
          n_samples=200,
          intercept=True,
          seed=42,
          noise_scale=0.1,
          kernel=RBF(1),
      )
      node = 1
      y = data.iloc[:, node]
      X = data.drop(node, axis=1)
  
      reg = DAGRegressor(
          alpha=0.0,
          fit_intercept=True,
          dependent_target=True,
          hidden_layer_units=[0],
          standardize=standardize,
      )
      linear_score = cross_val_score(
          reg, X, y, cv=KFold(n_splits=3, shuffle=True, random_state=42)
      ).mean()
  
      reg = DAGRegressor(
          alpha=0.1,
          fit_intercept=True,
          dependent_target=True,
          hidden_layer_units=[2],
          standardize=standardize,
      )
      small_nl_score = cross_val_score(
          reg, X, y, cv=KFold(n_splits=3, shuffle=True, random_state=42)
      ).mean()
  
      assert small_nl_score > linear_score or np.isclose(
          small_nl_score, linear_score, atol=1e-5
      )
  
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 6 for calling the TestDAGRegressor obj's underlying member method code with example usage: obj.test_nonlinear_performance(standardize) without return types