code: def test_feature_importances_categorical(self, hidden_layer_units):
      clf = DAGClassifier(
          alpha=0.1, hidden_layer_units=hidden_layer_units
      )
      X, y = (
          pd.DataFrame(np.random.normal(size=(100, 2))),
          pd.Series(np.zeros(shape=(100,), dtype=int)),
      )
      y[X[0] < -0.1] = 1
      y[X[0] > 0.1] = 2
      clf.fit(X, y)
  
      assert isinstance(clf.feature_importances_, np.ndarray)
      assert clf.feature_importances_.shape == (3, 2)
      feature_importances_ = pd.DataFrame(
          clf.feature_importances_, columns=X.columns
      )
      # assert that the sign of the coefficient is positive for both nonlinear and linear cases
      assert feature_importances_.iloc[1, 0] > 0
      assert feature_importances_.iloc[2, 0] > 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 7 for calling the TestDAGClassifier obj's underlying member method code with example usage: obj.test_feature_importances_categorical(hidden_layer_units) without return types