code: def test_bayesian_gplvm_1d() -> None:
      Q = 1
      kernel = gpflow.kernels.SquaredExponential()
      inducing_variable = np.linspace(0, 1, Data.M)[:, None]
      m = gpflow.models.BayesianGPLVM(
          Data.Y,
          np.zeros((Data.N, Q)),
          np.ones((Data.N, Q)),
          kernel,
          inducing_variable=inducing_variable,
      )
      assert m.inducing_variable.num_inducing == Data.M
  
      elbo_initial = m.elbo()
      opt = gpflow.optimizers.Scipy()
      opt.minimize(
          m.training_loss,
          m.trainable_variables,
          options=dict(maxiter=2),
      )
      assert m.elbo() > elbo_initial
  
file_path: GPflow/tests/gpflow/models/test_gplvm.py
imports: from typing import Optional
  import numpy
  import pytest
  import gpflow
  from gpflow.utilities.ops import pca_reduce
label_desc: Use image node_id 2 for calling a global function with example usage: test_bayesian_gplvm_1d() without return types