code: def test_initialize_q_batch_nonneg(self):
      for dtype in (torch.float, torch.double):
          # basic test
          X = torch.rand(5, 3, 4, device=self.device, dtype=dtype)
          Y = torch.rand(5, device=self.device, dtype=dtype)
          ics = initialize_q_batch_nonneg(X=X, Y=Y, n=2)
          self.assertEqual(ics.shape, torch.Size([2, 3, 4]))
          self.assertEqual(ics.device, X.device)
          self.assertEqual(ics.dtype, X.dtype)
          # ensure nothing happens if we want all samples
          ics = initialize_q_batch_nonneg(X=X, Y=Y, n=5)
          self.assertTrue(torch.equal(X, ics))
          # make sure things work with constant inputs
          Y = torch.ones(5, device=self.device, dtype=dtype)
          ics = initialize_q_batch_nonneg(X=X, Y=Y, n=2)
          self.assertEqual(ics.shape, torch.Size([2, 3, 4]))
          self.assertEqual(ics.device, X.device)
          self.assertEqual(ics.dtype, X.dtype)
          # ensure raises correct warning
          Y = torch.zeros(5, device=self.device, dtype=dtype)
          with warnings.catch_warnings(
              record=True
          ) as w, settings.debug(True):
              ics = initialize_q_batch_nonneg(X=X, Y=Y, n=2)
              self.assertEqual(len(w), 1)
              self.assertTrue(
                  issubclass(
                      w[-1].category, BadInitialCandidatesWarning
                  )
              )
          self.assertEqual(ics.shape, torch.Size([2, 3, 4]))
          with self.assertRaises(RuntimeError):
              initialize_q_batch_nonneg(X=X, Y=Y, n=10)
          # test less than `n` positive acquisition values
          Y = torch.arange(5, device=self.device, dtype=dtype) - 3
          ics = initialize_q_batch_nonneg(X=X, Y=Y, n=2)
          self.assertEqual(ics.shape, torch.Size([2, 3, 4]))
          self.assertEqual(ics.device, X.device)
          self.assertEqual(ics.dtype, X.dtype)
          # check that we chose the point with the positive acquisition value
          self.assertTrue(
              torch.equal(ics[0], X[-1]) or torch.equal(ics[1], X[-1])
          )
          # test less than `n` alpha_pos values
          Y = torch.arange(5, device=self.device, dtype=dtype)
          ics = initialize_q_batch_nonneg(X=X, Y=Y, n=2, alpha=1.0)
          self.assertEqual(ics.shape, torch.Size([2, 3, 4]))
          self.assertEqual(ics.device, X.device)
          self.assertEqual(ics.dtype, X.dtype)
  
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 1 for calling the TestInitializeQBatch obj's underlying member method code with example usage: obj.test_initialize_q_batch_nonneg() without return types