code: def test_evaluate(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"dtype": dtype, "device": self.device}
          # Without weights.
          tf = ExpectationPosteriorTransform(n_w=3)
          Y = torch.rand(3, 6, 2, **tkwargs)
          self.assertTrue(
              torch.allclose(
                  tf.evaluate(Y), Y.view(3, 2, 3, 2).mean(dim=-2)
              )
          )
          # With weights - weights intentionally doesn't use tkwargs.
          weights = torch.tensor([[1.0, 2.0], [2.0, 1.0]])
          tf = ExpectationPosteriorTransform(n_w=2, weights=weights)
          expected = (Y.view(3, 3, 2, 2) * weights.to(Y)).sum(
              dim=-2
          ) / 3.0
          self.assertAllClose(tf.evaluate(Y), expected)
  
imports: import itertools
  import warnings
  from typing import Optional
  import torch
  from botorch import settings
  from botorch.acquisition import LearnedObjective
  from botorch.acquisition.objective import ConstrainedMCObjective, DEFAULT_NUM_PREF_SAMPLES, ExpectationPosteriorTransform, GenericMCObjective, IdentityMCObjective, LEARNED_OBJECTIVE_PREF_MODEL_MIXED_DTYPE_WARN, LinearMCObjective, MCAcquisitionObjective, PosteriorTransform, ScalarizedPosteriorTransform
  from botorch.exceptions.errors import UnsupportedError
  from botorch.exceptions.warnings import _get_single_precision_warning, InputDataWarning
  from botorch.models.deterministic import PosteriorMeanModel
  from botorch.models.pairwise_gp import PairwiseGP
  from botorch.models.transforms.input import Normalize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.utils import apply_constraints
  from botorch.utils.testing import _get_test_posterior, BotorchTestCase
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator.operators.dense_linear_operator import to_linear_operator
  from torch import Tensor
label_desc: Use image node_id 2 for calling the TestExpectationPosteriorTransform obj's underlying member method code with example usage: obj.test_evaluate() without return types