code: def test_gen_batch_initial_conditions_fixed_X_fantasies(self):
      bounds = torch.stack([torch.zeros(2), torch.ones(2)])
      mock_acqf = MockAcquisitionFunction()
      mock_acqf.objective = lambda y: y.squeeze(-1)
      for dtype in (torch.float, torch.double):
          bounds = bounds.to(device=self.device, dtype=dtype)
          mock_acqf.X_baseline = (
              bounds  # for testing sample_around_best
          )
          mock_acqf.model = MockModel(MockPosterior(mean=bounds[:, :1]))
          fixed_X_fantasies = torch.rand(
              3, 2, dtype=dtype, device=self.device
          )
          for (
              nonnegative,
              seed,
              init_batch_limit,
              ffs,
              sample_around_best,
          ) in product(
              [True, False],
              [None, 1234],
              [None, 1],
              [None, {0: 0.5}],
              [True, False],
          ):
              with mock.patch.object(
                  MockAcquisitionFunction,
                  "__call__",
                  wraps=mock_acqf.__call__,
              ) as mock_acqf_call:
                  batch_initial_conditions = (
                      gen_batch_initial_conditions(
                          acq_function=mock_acqf,
                          bounds=bounds,
                          q=1,
                          num_restarts=2,
                          raw_samples=10,
                          fixed_features=ffs,
                          options={
                              "nonnegative": nonnegative,
                              "eta": 0.01,
                              "alpha": 0.1,
                              "seed": seed,
                              "init_batch_limit": init_batch_limit,
                              "sample_around_best": sample_around_best,
                          },
                          fixed_X_fantasies=fixed_X_fantasies,
                      )
                  )
                  expected_shape = torch.Size([2, 4, 2])
                  self.assertEqual(
                      batch_initial_conditions.shape, expected_shape
                  )
                  self.assertEqual(
                      batch_initial_conditions.device, bounds.device
                  )
                  self.assertEqual(
                      batch_initial_conditions.dtype, bounds.dtype
                  )
                  self.assertLess(
                      _get_max_violation_of_bounds(
                          batch_initial_conditions, bounds
                      ),
                      1e-6,
                  )
                  batch_shape = (
                      torch.Size([])
                      if init_batch_limit is None
                      else torch.Size([init_batch_limit])
                  )
                  raw_samps = mock_acqf_call.call_args[0][0]
                  batch_shape = (
                      torch.Size([20 if sample_around_best else 10])
                      if init_batch_limit is None
                      else torch.Size([init_batch_limit])
                  )
                  expected_raw_samps_shape = batch_shape + torch.Size(
                      [4, 2]
                  )
                  self.assertEqual(
                      raw_samps.shape, expected_raw_samps_shape
                  )
  
                  if ffs is not None:
                      for idx, val in ffs.items():
                          self.assertTrue(
                              torch.all(
                                  batch_initial_conditions[..., 0, idx]
                                  == val
                              )
                          )
                  self.assertTrue(
                      torch.equal(
                          batch_initial_conditions[:, 1:],
                          fixed_X_fantasies.unsqueeze(0).expand(
                              2, 3, 2
                          ),
                      )
                  )
      # test wrong shape
      msg = (
          "`fixed_X_fantasies` and `bounds` must both have the same trailing"
          " dimension `d`, but have 3 and 2, respectively."
      )
      with self.assertRaisesRegex(BotorchTensorDimensionError, msg):
          gen_batch_initial_conditions(
              acq_function=mock_acqf,
              bounds=bounds,
              q=1,
              num_restarts=2,
              raw_samples=10,
              fixed_X_fantasies=torch.rand(
                  3, 3, dtype=dtype, device=self.device
              ),
          )
  
imports: import warnings
  from contextlib import ExitStack
  from itertools import product
  from random import random
  from typing import Optional
  from unittest import mock
  import torch
  from botorch import settings
  from botorch.acquisition.analytic import PosteriorMean
  from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
  from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement
  from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import qHypervolumeKnowledgeGradient, qMultiFidelityHypervolumeKnowledgeGradient
  from botorch.acquisition.multi_objective.monte_carlo import qNoisyExpectedHypervolumeImprovement
  from botorch.exceptions import BadInitialCandidatesWarning, SamplingWarning
  from botorch.exceptions.errors import BotorchTensorDimensionError, UnsupportedError
  from botorch.exceptions.warnings import BotorchWarning
  from botorch.models import SingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.optim import initialize_q_batch, initialize_q_batch_nonneg
  from botorch.optim.initializers import gen_batch_initial_conditions, gen_one_shot_hvkg_initial_conditions, gen_one_shot_kg_initial_conditions, gen_value_function_initial_conditions, sample_perturbed_subset_dims, sample_points_around_best, sample_q_batches_from_polytope, sample_truncated_normal_perturbations, transform_constraints, transform_inter_point_constraint, transform_intra_point_constraint
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.sampling import draw_sobol_samples, manual_seed
  from botorch.utils.testing import _get_max_violation_of_bounds, _get_max_violation_of_constraints, BotorchTestCase, MockAcquisitionFunction, MockModel, MockPosterior
label_desc: Use image node_id 14 for calling the TestGenBatchInitialCandidates obj's underlying member method code with example usage: obj.test_gen_batch_initial_conditions_fixed_X_fantasies() without return types