code: def test_predict_2(
      minlip_model_factory, toy_data_standardized, solver
  ):
      (x, y), x_test = toy_data_standardized
  
      y = y.copy()
      y["time"] = np.arange(1, 7)
  
      m = minlip_model_factory(solver, x, y, pairs="next")
  
      p = m.predict(x_test)
      expected = np.array(
          [1.368221203557392, -0.476483331099142, -1.009079072163642]
      )
      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 4 for calling the TestToyMinlipSurvivalAnalysis obj's underlying member method code with example usage: obj.test_predict_2(minlip_model_factory, toy_data_standardized, solver) without return types