code: def training_data():
      r = Random(0)
      predictors = vectors()
      sparse_predictors = sparse_vectors()
      response = array()
      for i in range(30):
          for c in [-1, 1]:
              response.append(c)
              values = [r.random() + c * 0.5 for _ in range(3)]
              predictors.append(vector(values))
              sp = sparse_vector()
              for i, v in enumerate(values):
                  sp.append(pair(i, v))
              sparse_predictors.append(sp)
      return predictors, sparse_predictors, response
  
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 1 for calling a global function with example usage: training_data() and returns: predictors,  sparse_predictors,  response