code: def test_fit_alpha_2(
      minlip_model_factory, toy_data, solver, expected_iters
  ):
      x, y = toy_data
      m = minlip_model_factory(solver, x, y, alpha=2.0)
  
      assert m.n_iter_ > expected_iters
      assert (1, 6) == m.coef_.shape
      assert 1 == m.coef0
      expected_coef = np.array(
          [
              [
                  -0.011728003147,
                  0.011728002895,
                  0.000000000252,
                  -0.017524801335,
                  0.017524801335,
                  0.0,
              ]
          ]
      )
      assert_array_almost_equal(m.coef_, expected_coef)
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pytest
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.preprocessing import scale
  from sksurv.column import encode_categorical
  from sksurv.datasets import load_gbsg2
  from sksurv.exceptions import NoComparablePairException
  from sksurv.svm._minlip import create_difference_matrix
  from sksurv.svm.minlip import HingeLossSurvivalSVM, MinlipSurvivalAnalysis
  from sksurv.testing import FixtureParameterFactory, assert_cindex_almost_equal
  from sksurv.util import Surv
label_desc: Use image node_id 1 for calling the TestToyMinlipSurvivalAnalysis obj's underlying member method code with example usage: obj.test_fit_alpha_2(minlip_model_factory, toy_data, solver, expected_iters) without return types