class_docstr: Mock object that implements dummy methods and feeds through specified outputs
class_docstr_tok: ["Mock","object","that","implements","dummy","methods","and","feeds","through","specified","outputs"]
code: def posterior(
      self,
      X: Tensor,
      observation_noise: bool = False,
      posterior_transform: Optional[PosteriorTransform] = None,
  ) -> MockPosterior:
      m_shape = X.shape[:-1]
      r_shape = list(X.shape[:-2]) + [1, 1]
      mvn = MultivariateNormal(
          mean=torch.zeros(m_shape, dtype=X.dtype, device=X.device),
          covariance_matrix=torch.eye(
              m_shape[-1], dtype=X.dtype, device=X.device
          ).repeat(r_shape),
      )
      if self.num_outputs > 1:
          mvn = mvn = MultitaskMultivariateNormal.from_independent_mvns(
              mvns=[mvn] * self.num_outputs
          )
      posterior = GPyTorchPosterior(mvn)
      if posterior_transform is not None:
          return posterior_transform(posterior)
      return posterior
  
imports: from typing import Callable, Optional
  from unittest import mock
  import torch
  from botorch.acquisition.cost_aware import InverseCostWeightedUtility
  from botorch.acquisition.max_value_entropy_search import _sample_max_value_Gumbel, _sample_max_value_Thompson, qLowerBoundMaxValueEntropy, qMaxValueEntropy, qMultiFidelityLowerBoundMaxValueEntropy, qMultiFidelityMaxValueEntropy
  from botorch.acquisition.objective import PosteriorTransform, ScalarizedPosteriorTransform
  from botorch.exceptions.errors import UnsupportedError
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from torch import Tensor
label_desc: Use image node_id 2 for calling the MESMockModel obj's underlying member method code with example usage: obj.posterior(X, observation_noise, posterior_transform) and returns: posterior, posterior_transform