code: def create_inducing(
      data: XYData, rng: np.random.Generator
  ) -> InducingPointsLike:
      n = min(data.N // 2, 200)
      Z = Halton(data.D, scramble=False).random(n)
      lower = np.min(data.X, axis=0)
      upper = np.max(data.X, axis=0)
      Z = Z * (upper - lower) + lower
      return InducingPoints(Z)
  
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 2 for calling a global function with example usage: create_inducing(data, rng) and returns: InducingPoints