code: def svm_model_factory():
      def create_and_fit_model(solver, x, y, **kwargs):
          params = {"solver": solver, "alpha": 2}
          params.update(kwargs)
          m = HingeLossSurvivalSVM(**params)
          return m.fit(x, y)
  
      return create_and_fit_model
  
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 8 for calling a global function with example usage: svm_model_factory() and returns: create_and_fit_model, m