code: def _setUp(self, double=False, expand=False):
      dtype = torch.double if double else torch.float
      train_x = torch.linspace(
          0, 1, 10, device=self.device, dtype=dtype
      ).unsqueeze(-1)
      train_y = torch.sin(train_x * (2 * math.pi))
      noise = torch.tensor(NOISE, device=self.device, dtype=dtype)
      self.train_x = train_x
      self.train_y = train_y + noise
      if expand:
          self.train_x = self.train_x.expand(-1, 2)
          ics = torch.tensor(
              [[0.5, 1.0]], device=self.device, dtype=dtype
          )
      else:
          ics = torch.tensor([[0.5]], device=self.device, dtype=dtype)
      self.initial_conditions = ics
      self.f_best = self.train_y.max().item()
      model = SingleTaskGP(self.train_x, self.train_y)
      self.model = model.to(device=self.device, dtype=dtype)
      self.mll = ExactMarginalLogLikelihood(
          self.model.likelihood, self.model
      )
      with warnings.catch_warnings():
          self.mll = fit_gpytorch_mll(
              self.mll,
              optimizer_kwargs={"options": {"maxiter": 1}},
              max_attempts=1,
          )
  
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 TestBaseCandidateGeneration obj's underlying member method code with example usage: obj._setUp(double, expand) without return types