code: def test_trainers(
      training_data, trainer, class1_accuracy, class2_accuracy
  ):
      predictors, sparse_predictors, response = training_data
      if "sparse" in trainer.__name__:
          predictors = sparse_predictors
      cv = cross_validate_trainer(
          trainer(), predictors, response, folds=10
      )
      assert cv.class1_accuracy == pytest.approx(class1_accuracy)
      assert cv.class2_accuracy == pytest.approx(class2_accuracy)
  
      decision_function = trainer().train(predictors, response)
      assert decision_function(predictors[2]) < 0
      assert decision_function(predictors[3]) > 0
      if "linear" in trainer.__name__:
          assert len(decision_function.weights) == 3
  
imports: from __future__ import division
  import pytest
  from random import Random
  from dlib import vectors, vector, sparse_vectors, sparse_vector, pair, array, cross_validate_trainer, svm_c_trainer_radial_basis, svm_c_trainer_sparse_radial_basis, svm_c_trainer_histogram_intersection, svm_c_trainer_sparse_histogram_intersection, svm_c_trainer_linear, svm_c_trainer_sparse_linear, rvm_trainer_radial_basis, rvm_trainer_sparse_radial_basis, rvm_trainer_histogram_intersection, rvm_trainer_sparse_histogram_intersection, rvm_trainer_linear, rvm_trainer_sparse_linear
label_desc: Use image node_id 2 for calling a global function with example usage: test_trainers(training_data, trainer, class1_accuracy, class2_accuracy) without return types