code: def _get_test_posterior(
      shape, device, dtype, interleaved=True, lazy=False
  ):
      mean = torch.rand(shape, device=device, dtype=dtype)
      n_covar = shape[-2:].numel()
      diag = torch.rand(shape, device=device, dtype=dtype)
      diag = diag.view(*diag.shape[:-2], n_covar)
      a = torch.rand(
          *shape[:-2], n_covar, n_covar, device=device, dtype=dtype
      )
      covar = a @ a.transpose(-1, -2) + torch.diag_embed(diag)
      if lazy:
          covar = DenseLinearOperator(covar)
      if shape[-1] == 1:
          mvn = MultivariateNormal(mean.squeeze(-1), covar)
      else:
          mvn = MultitaskMultivariateNormal(
              mean, covar, interleaved=interleaved
          )
      return GPyTorchPosterior(mvn)
  
imports: import itertools
  from copy import deepcopy
  import torch
  from botorch.models.transforms.outcome import Bilog, ChainedOutcomeTransform, Log, OutcomeTransform, Power, Standardize
  from botorch.models.transforms.utils import norm_to_lognorm_mean, norm_to_lognorm_variance
  from botorch.posteriors import GPyTorchPosterior, TransformedPosterior
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator.operators import BlockDiagLinearOperator, DenseLinearOperator, DiagLinearOperator
label_desc: Use image node_id 1 for calling a global function with example usage: _get_test_posterior(shape, device, dtype, interleaved, lazy) and returns: GPyTorchPosterior