code: def test_not_correct_backend(backend):
      """Test if validate_data properly preprocesses dataframes to ndarray."""
      mitigator = get_instance(
          torch=backend != "torch",
          tensorflow=backend != "tensorflow",
          backend=backend,
      )
      with pytest.raises(RuntimeError):
          mitigator._validate_backend()
  
imports: import sys
  import pytest
  import numpy
  import pandas
  from sklearn.utils import estimator_checks
  from fairlearn.adversarial import AdversarialFairnessClassifier, AdversarialFairnessRegressor
  from fairlearn.adversarial._adversarial_mitigation import _AdversarialFairness
  from fairlearn.adversarial._preprocessor import FloatTransformer
  from fairlearn.adversarial._constants import _TYPE_COMPLIANCE_ERROR
  from .helper import get_instance, generate_data_combinations, cols, Bin2d, Bin1d, Cat, Cont2d, Cont1d, BCE, CCE, MSE, Keyword_CATEGORY, Keyword_BINARY, Keyword_CONTINUOUS, Keyword_CLASSIFICATION, Keyword_AUTO, KeywordToClass
label_desc: Use image node_id 25 for calling a global function with example usage: test_not_correct_backend(backend) without return types