code: def test_fake_models(torch3):
      """Test various data types and see if it is interpreted correctly."""
      for (X, Y, Z), (
          X_type,
          Y_type,
          Z_type,
      ) in generate_data_combinations():
          mitigator = get_instance(
              fake_training=True, torch=torch3, tensorflow=not torch3
          )
  
          mitigator.fit(X, Y, sensitive_features=Z)
          assert isinstance(
              mitigator.backendEngine_.predictor_loss,
              KeywordToClass(Y_type),
          )
          assert isinstance(
              mitigator.backendEngine_.adversary_loss,
              KeywordToClass(Z_type),
          )
  
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 15 for calling a global function with example usage: test_fake_models(torch3) without return types