code: def _expectation_gaussian_kernel_inducingvariables__meanfunction(
      p: Union[Gaussian, MarkovGaussian],
      kernel: kernels.Kernel,
      inducing_variable: InducingVariables,
      mean: mfn.MeanFunction,
      _: None,
      nghp: None = None,
  ) -> tf.Tensor:
      """
      Compute the expectation:
      expectation[n] = <K_{Z, x_n} m(x_n)>_p(x_n)
      or the equivalent for MarkovGaussian
  
      :return: NxMxQ
      """
      return tf.linalg.adjoint(
          expectation(p, mean, (kernel, inducing_variable), nghp=nghp)
      )
  
file_path: GPflow/gpflow/expectations/misc.py
imports: from typing import Optional, Type, Union, cast
  import tensorflow
  from check_shapes import check_shapes
  from ..None import kernels
  from ..None import mean_functions
  from ..inducing_variables import InducingPoints, InducingVariables
  from ..probability_distributions import DiagonalGaussian, Gaussian, MarkovGaussian
  from .None import dispatch
  from .expectations import ExpectationObject, PackedExpectationObject, expectation
label_desc: Use image node_id 2 for calling a global function with example usage: _expectation_gaussian_kernel_inducingvariables__meanfunction(p, kernel, inducing_variable, mean, _, nghp) and returns: tf