code: def expand_cov(q_sqrt: tf.Tensor, W: tf.Tensor) -> tf.Tensor:
      """
      :param q_sqrt: cholesky of covariance matrices
      :param W: mixing matrix
      :return: cholesky of the expanded covariance matrix
      """
      q_cov = np.matmul(
          q_sqrt, q_sqrt.transpose([0, 2, 1])
      )  # [L, M, M]
      q_cov_expanded = scipy.linalg.block_diag(*q_cov)  # [LM, LM]
      q_sqrt_expanded = np.linalg.cholesky(q_cov_expanded)  # [LM, LM]
      return q_sqrt_expanded[None, ...]
  
imports: from typing import Callable, List, Sequence, Tuple, cast
  import numpy
  import pytest
  import scipy
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import ShapeChecker, check_shapes
  import gpflow
  import gpflow.inducing_variables.multioutput
  import gpflow.kernels.multioutput
  from gpflow import set_trainable
  from gpflow.base import AnyNDArray, RegressionData
  from gpflow.conditionals import sample_conditional
  from gpflow.conditionals.util import fully_correlated_conditional, fully_correlated_conditional_repeat, independent_interdomain_conditional, sample_mvn
  from gpflow.config import default_float, default_jitter
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import SquaredExponential
  from gpflow.likelihoods import Gaussian
  from gpflow.models import SVGP
label_desc: Use image node_id 4 for calling a global function with example usage: expand_cov(q_sqrt, W) and returns: q_sqrt_expanded