code: def test_gen_candidates(
      self,
      gen_candidates=gen_candidates_scipy,
      options=None,
      timeout_sec=None,
  ):
      options = options or {}
      options = {**options, "maxiter": options.get("maxiter", 5)}
      for double in (True, False):
          self._setUp(double=double)
          acqfs = [
              qExpectedImprovement(self.model, best_f=self.f_best),
              qKnowledgeGradient(
                  self.model, num_fantasies=4, current_value=self.f_best
              ),
          ]
          for acqf in acqfs:
              ics = self.initial_conditions
              if isinstance(acqf, qKnowledgeGradient):
                  ics = ics.repeat(5, 1)
  
              kwargs = {
                  "initial_conditions": ics,
                  "acquisition_function": acqf,
                  "lower_bounds": 0,
                  "upper_bounds": 1,
                  "options": options or {},
                  "timeout_sec": timeout_sec,
              }
              if gen_candidates is gen_candidates_torch:
                  kwargs["callback"] = mock.MagicMock()
              candidates, _ = gen_candidates(**kwargs)
  
              if isinstance(acqf, qKnowledgeGradient):
                  candidates = acqf.extract_candidates(candidates)
              if gen_candidates is gen_candidates_torch:
                  self.assertTrue(kwargs["callback"].call_count > 0)
  
              self.assertTrue(-EPS <= candidates <= 1 + EPS)
  
imports: import math
  import warnings
  from unittest import mock
  import torch
  from botorch.acquisition import qExpectedImprovement, qKnowledgeGradient
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.generation.gen import gen_candidates_scipy, gen_candidates_torch, get_best_candidates
  from botorch.models import SingleTaskGP
  from botorch.utils.testing import BotorchTestCase, MockAcquisitionFunction
  from gpytorch import settings
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from scipy.optimize import OptimizeResult
label_desc: Use image node_id 1 for calling the TestGenCandidates obj's underlying member method code with example usage: obj.test_gen_candidates(gen_candidates, options, timeout_sec) without return types