code: def test_model_params(torch):
      """Test training params."""
      (X, Y, Z) = Bin2d, Bin1d, Bin1d
      mitigator = get_instance(
          torch=torch,
          tensorflow=not torch,
          fake_mixin=False,
          fake_training=True,
          predictor_model=[10, "ReLU"],
          adversary_model=[3, "ReLU"],
          warm_start=True,
          shuffle=True,
          learning_rate=0.1,
          epochs=3,
          batch_size=3,
          max_iter=10,
          random_state=1,
          progress_updates=0.0000001,
          callbacks=[lambda *args: False, lambda *args: False],
      )
      mitigator.fit(X, Y, sensitive_features=Z)
  
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 2 for calling a global function with example usage: test_model_params(torch) without return types