code: def test_model_init(torch):
      """Test model init from list."""
      (X, Y, Z) = Bin2d, Bin1d, Bin1d
      mitigator = get_instance(
          torch=torch,
          tensorflow=not torch,
          fake_mixin=False,
          fake_training=True,
          predictor_model=[
              10,
              "Sigmoid",
              "Softmax",
              "ReLU",
              "Leaky_ReLU",
          ],
      )
      mitigator.fit(X, Y, sensitive_features=Z)
      layers = mitigator.backendEngine_.predictor_model.layers_
      if hasattr(layers, "layers"):
          layers = layers.layers
      assert not hasattr(layers[0], "a") or layers[0].a == cols
      assert layers[0].b == 10
      assert layers[1].__name__ == "Sigmoid"
      assert layers[2].__name__ == "Softmax"
      assert layers[3].__name__ == "ReLU"
      assert layers[4].__name__.replace("_", "") == "LeakyReLU"
      assert not hasattr(layers[0], "a") or layers[5].a == 10
      assert layers[5].b == 1
      assert layers[6].__name__.lower() == "sigmoid"
      assert len(layers) == 7
  
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 1 for calling a global function with example usage: test_model_init(torch) without return types