code: def _expectation_gaussian_linear_inducingpoints(
      p: Gaussian,
      kernel: kernels.Linear,
      inducing_variable: InducingPoints,
      _: None,
      __: None,
      nghp: None = None,
  ) -> tf.Tensor:
      """
      Compute the expectation:
      <K_{X, Z}>_p(X)
          - K_{.,.} :: Linear kernel
  
      :return: NxM
      """
      # use only active dimensions
      Z, Xmu = kernel.slice(inducing_variable.Z, p.mu)
  
      return tf.linalg.matmul(
          Xmu, Z * kernel.variance, transpose_b=True
      )
  
file_path: GPflow/gpflow/expectations/linears.py
imports: from typing import Type, Union
  import tensorflow
  from check_shapes import check_shapes
  from ..None import kernels
  from ..None import mean_functions
  from ..inducing_variables import InducingPoints
  from ..probability_distributions import DiagonalGaussian, Gaussian, MarkovGaussian
  from .None import dispatch
  from .expectations import expectation
label_desc: Use image node_id 2 for calling a global function with example usage: _expectation_gaussian_linear_inducingpoints(p, kernel, inducing_variable, _, __, nghp) and returns: tf