code: def _expectation(p, kern, feat, none1, none2, nghp=None):
      
      with params_as_tensors_for(kern, feat):
          # use only active dimensions
          Z, Xmu = kern._slice(feat.Z, p.mu)
  
          return tf.matmul(Xmu, Z * kern.variance, transpose_b=True)
docs: Compute the expectation:
      <K_{X, Z}>_p(X)
          - K_{.,.} :: Linear kernel
  
      :return: NxM