code: def markov_gauss() -> MarkovGaussian:
      cov_params: AnyNDArray = (
          rng.randn(num_data + 1, D_in, 2 * D_in) / 2.0
      )  # (N+1)xDx2D
      Xcov: AnyNDArray = cov_params @ np.transpose(
          cov_params, (0, 2, 1)
      )  # (N+1)xDxD
      Xcross = cov_params[:-1] @ np.transpose(
          cov_params[1:], (0, 2, 1)
      )  # NxDxD
      Xcross = np.concatenate(
          (Xcross, np.zeros((1, D_in, D_in))), 0
      )  # (N+1)xDxD
      Xcov = np.stack([Xcov, Xcross])  # 2x(N+1)xDxD
      return MarkovGaussian(Xmu_markov, ctt(Xcov))
  
imports: from typing import Any, Callable, Iterable, Mapping, Optional, Sequence
  import numpy
  import pytest
  import tensorflow
  from numpy.testing import assert_allclose
  from tensorflow import convert_to_tensor
  import gpflow
  import gpflow.inducing_variables
  import gpflow.kernels
  from gpflow import mean_functions
  from gpflow.base import AnyNDArray
  from gpflow.config import default_float
  from gpflow.expectations import expectation, quadrature_expectation
  from gpflow.probability_distributions import DiagonalGaussian, Gaussian, MarkovGaussian, ProbabilityDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: markov_gauss() and returns: MarkovGaussian