code: def _gaussian_update_ApproximateGP(
      model: ApproximateGP,
      likelihood: Union[Likelihood, NoneType],
      **kwargs: Any,
  ) -> GeneralizedLinearPath:
      return GaussianUpdate(model, model.variational_strategy, **kwargs)
  
imports: from __future__ import annotations
  from typing import Any, Callable, Optional, Union
  import torch
  from botorch.models.approximate_gp import ApproximateGPyTorchModel
  from botorch.models.transforms.input import InputTransform
  from botorch.sampling.pathwise.features import KernelEvaluationMap
  from botorch.sampling.pathwise.paths import GeneralizedLinearPath, SamplePath
  from botorch.sampling.pathwise.utils import get_input_transform, get_train_inputs, get_train_targets, TInputTransform
  from botorch.utils.dispatcher import Dispatcher
  from botorch.utils.types import DEFAULT, NoneType
  from gpytorch.kernels.kernel import Kernel
  from gpytorch.likelihoods import _GaussianLikelihoodBase, Likelihood
  from gpytorch.models import ApproximateGP, ExactGP, GP
  from gpytorch.variational import VariationalStrategy
  from linear_operator.operators import LinearOperator, SumLinearOperator, ZeroLinearOperator
  from torch import Tensor
label_desc: Use image node_id 5 for calling a global function with example usage: _gaussian_update_ApproximateGP(model, likelihood) and returns: GaussianUpdate