code: def test_model_equalized_odds_model_setup():
      """Test model initialization with equalized_odds."""
      (X, Y, Z) = Bin2d, Bin1d, Bin1d
      mitigator = get_instance(
          torch=True,
          tensorflow=False,
          fake_mixin=False,
          fake_training=True,
          predictor_model=[10, "ReLU"],
          adversary_model=[3, "ReLU"],
          constraints="equalized_odds",
      )
      mitigator.fit(X, Y, sensitive_features=Z)
      assert (
          mitigator.backendEngine_.adversary_model.layers_.layers[0].a
          == 2
      )
  
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 4 for calling a global function with example usage: test_model_equalized_odds_model_setup() without return types