code: def create_gaussian(
      data: XYData, rng: np.random.Generator
  ) -> Gaussian:
      return Gaussian(variance=rng.gamma(5.0, 0.2, []))
  
file_path: GPflow/benchmark/models.py
imports: import numpy
  from scipy.stats.qmc import Halton
  import benchmark.dataset_api
  from benchmark.dataset_api import XYData
  from benchmark.model_api import REGRESSION, SPARSE, VARIATIONAL, make_model_factory
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import RBF, Kernel
  from gpflow.likelihoods import Gaussian
  from gpflow.models import GPR, SGPR, SVGP, VGP, GPModel
  from gpflow.models.util import InducingPointsLike
label_desc: Use image node_id 3 for calling a global function with example usage: create_gaussian(data, rng) and returns: Gaussian