code: def test_gaussian_updates(self):
      for seed, model in enumerate(
          chain.from_iterable(self.models.values())
      ):
          with torch.random.fork_rng():
              torch.manual_seed(seed)
              self._test_gaussian_updates(model)
  
imports: from __future__ import annotations
  from collections import defaultdict
  from copy import deepcopy
  from itertools import chain
  from unittest.mock import patch
  import torch
  from botorch.models import SingleTaskGP, SingleTaskVariationalGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.sampling.pathwise import draw_kernel_feature_paths, gaussian_update, GeneralizedLinearPath, KernelEvaluationMap
  from botorch.sampling.pathwise.utils import get_train_inputs, get_train_targets
  from botorch.utils.context_managers import delattr_ctx
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import BernoulliLikelihood
  from linear_operator.operators import ZeroLinearOperator
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from torch import Size
  from torch.nn.functional import pad
label_desc: Use image node_id 2 for calling the TestPathwiseUpdates obj's underlying member method code with example usage: obj.test_gaussian_updates() without return types