code: def test_coef_categorical(self, hidden_layer_units):
      torch.manual_seed(42)
      np.random.seed(42)
  
      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.coef_, np.ndarray)
      assert clf.coef_.shape == (3, 2)
      coef_ = pd.DataFrame(clf.coef_, columns=X.columns)
      # second category is made likely by negative X
      assert coef_.iloc[1, 0] < 0 or np.isclose(
          coef_.iloc[1, 0], 0, atol=1e5
      )
      # third category is made likely by positive X
      assert coef_.iloc[2, 0] > 0 or np.isclose(
          coef_.iloc[2, 0], 0, atol=1e5
      )
  
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 5 for calling the TestDAGClassifier obj's underlying member method code with example usage: obj.test_coef_categorical(hidden_layer_units) without return types