code: def test_given_categorical_data_when_evaluating_is_categorical_then_returns_expected_result():
      assert is_categorical(np.array(["A", "B", "A"]))
      assert is_categorical(np.array([True, False, False]))
      assert is_categorical(
          pd.DataFrame({"X": [True, False, False]}).to_numpy()
      )
      assert not is_categorical(np.array([1, 2, 3]))
  
imports: import random
  import networkx
  import numpy
  import pandas
  import pytest
  from _pytest.python_api import approx
  from dowhy import gcm
  from dowhy.gcm.util.general import apply_catboost_encoding, apply_one_hot_encoding, auto_apply_encoders, auto_fit_encoders, fit_catboost_encoders, fit_one_hot_encoders, has_categorical, is_categorical, is_discrete, set_random_seed, setdiff2d, shape_into_2d
label_desc: Use image node_id 2 for calling a global function with example usage: test_given_categorical_data_when_evaluating_is_categorical_then_returns_expected_result() without return types