code: def test_value_predict_type_categorical(self, y_type):
      clf = DAGClassifier(alpha=0.1)
      X, y = (
          pd.DataFrame(np.random.normal(size=(100, 2))),
          pd.Series(np.zeros(shape=(100,), dtype=y_type)),
      )
      y[X[0] < -0.1] = y_type(1)
      y[X[0] > 0.1] = y_type(2)
      clf.fit(X, y)
  
      y_pred = clf.predict(X)
      assert isinstance(y_pred[0], y_type)
      y_pred_proba = clf.predict_proba(X)
      assert isinstance(y_pred_proba[0, 0], np.float64)
      assert len(y_pred_proba.shape) == 2
  
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 9 for calling the TestDAGClassifier obj's underlying member method code with example usage: obj.test_value_predict_type_categorical(y_type) without return types