code: def test_sol_to_tensors(self):
      input_param = discrete_input_param()
      sampled_sol = {
          "choice1": torch.tensor([0, 1, 2]),
          "choice2": torch.tensor([1, 2, 0]),
          "choice3": torch.tensor([0, 1, 0]),
          "choice4": torch.tensor([4, 3, 2]),
          "choice5": torch.tensor([1, 2, 3]),
      }
      tensor = torch.FloatTensor(
          [
              [
                  1,
                  0,
                  0,
                  0,
                  0,
                  1,
                  0,
                  0,
                  1,
                  0,
                  0,
                  0,
                  0,
                  0,
                  1,
                  0,
                  1,
                  0,
                  0,
                  0,
              ],
              [
                  0,
                  1,
                  0,
                  0,
                  0,
                  0,
                  1,
                  0,
                  0,
                  1,
                  0,
                  0,
                  0,
                  1,
                  0,
                  0,
                  0,
                  1,
                  0,
                  0,
              ],
              [
                  0,
                  0,
                  1,
                  0,
                  1,
                  0,
                  0,
                  0,
                  1,
                  0,
                  0,
                  0,
                  1,
                  0,
                  0,
                  0,
                  0,
                  0,
                  1,
                  0,
              ],
          ]
      )
      sampled_tensor = sol_to_tensors(sampled_sol, input_param)
      self.assertTrue(torch.all(tensor == sampled_tensor))
  
imports: import random
  import unittest
  from collections import defaultdict
  from typing import Dict
  import nevergrad
  import numpy
  import torch
  import torch.nn
  from reagent.lite.optimizer import BayesianByBackpropOptimizer, BayesianMLPEnsemblerOptimizer, BayesianOptimizerBase, GREEDY_TEMP, GumbelSoftmaxOptimizer, NeverGradOptimizer, PolicyGradientOptimizer, QLearningOptimizer, RandomSearchOptimizer, sol_to_tensors
label_desc: Use image node_id 12 for calling the TestComboOptimizer obj's underlying member method code with example usage: obj.test_sol_to_tensors() without return types