code: def test_toy_hinge_fit_and_predict(
      svm_model_factory, toy_data_standardized, solver, pairs
  ):
      (x, y), x_test = toy_data_standardized
      m = svm_model_factory(solver, x, y, pairs=pairs)
  
      assert (1, 6) == m.coef_.shape
      assert 1 == m.coef0
      expected_coef = np.array(
          [
              [
                  -1.893832101337,
                  1.083653895940,
                  0.810178205398,
                  -2.0,
                  2.0,
                  0.0,
              ]
          ]
      )
      assert_array_almost_equal(m.coef_, expected_coef, decimal=5)
  
      p = m.predict(x)
      assert_cindex_almost_equal(
          y["status"], y["time"], p, (1.0, 11, 0, 0, 0)
      )
  
      p = m.predict(x_test)
      expected = np.array([2.8571060045, -1.2661069033, -2.3044907774])
      assert_array_almost_equal(expected, p, decimal=5)
  
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 9 for calling a global function with example usage: test_toy_hinge_fit_and_predict(svm_model_factory, toy_data_standardized, solver, pairs) without return types