code: def test_fit_and_predict(
      gbsg2_scaled, minlip_model_factory, solver, expected_iters
  ):
      x, y = gbsg2_scaled
      m = minlip_model_factory(solver, x, y)
  
      assert (1, x.shape[0]) == m.coef_.shape
  
      assert m.n_iter_ > expected_iters
  
      p = m.predict(x)
      assert_cindex_almost_equal(
          y["cens"],
          y["time"],
          p,
          (0.5990741854033906, 79720, 53352, 0, 42),
      )
  
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 TestMinlipBreastCancer obj's underlying member method code with example usage: obj.test_fit_and_predict(gbsg2_scaled, minlip_model_factory, solver, expected_iters) without return types