class_docstr: A unittest class for testing Spatial attack.
class_docstr_tok: ["A","unittest","class","for","testing","Spatial","attack","."]
code: def test_5_failure_feature_vectors(self):
      attack_params = {
          "max_translation": 10.0,
          "num_translations": 3,
          "max_rotation": 30.0,
          "num_rotations": 3,
      }
      classifier = get_tabular_classifier_kr()
      attack = SpatialTransformation(
          classifier=classifier, verbose=False
      )
      attack.set_params(**attack_params)
      data = np.random.rand(10, 4)
  
      # Assert that value error is raised for feature vectors
      with self.assertRaises(ValueError) as context:
          attack.generate(data)
  
      self.assertIn("Feature vectors detected.", str(context.exception))
  
file_path: adversarial-robustness-toolbox/tests/attacks/test_spatial_transformation.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import keras.backend
  import numpy
  from art.attacks.evasion.spatial_transformation import SpatialTransformation
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from tests.utils import TestBase
  from tests.utils import get_image_classifier_tf, get_image_classifier_kr
  from tests.utils import get_image_classifier_pt, get_tabular_classifier_kr
  from tests.attacks.utils import backend_test_classifier_type_check_fail
label_desc: Use image node_id 5 for calling the TestSpatialTransformation obj's underlying member method code with example usage: obj.test_5_failure_feature_vectors() without return types