code: def test_intercept_categorical(self, fit_intercept):
      model, y = DAGClassifier, np.random.randint(3, size=(100,))
      m = model(fit_intercept=fit_intercept)
      X = np.random.normal(size=(100, 2))
      X, y = pd.DataFrame(X), pd.Series(y)
      m.fit(X, y)
      # intercept should return zero when fit_intercept == False
      for intercept in m.intercept_:
          assert (intercept == 0) is not fit_intercept
      assert isinstance(m.intercept_, np.ndarray)
      assert len(m.intercept_) == 3
  
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 3 for calling the TestDAGClassifier obj's underlying member method code with example usage: obj.test_intercept_categorical(fit_intercept) without return types