code: def _expectation_gaussian_sum_inducingpoints__sum_inducingpoints(
      p: Union[Gaussian, DiagonalGaussian],
      kern1: kernels.Sum,
      feat1: InducingPoints,
      kern2: kernels.Sum,
      feat2: InducingPoints,
      nghp: None = None,
  ) -> tf.Tensor:
      r"""
      Compute the expectation:
      expectation[n] = <(\Sum_i K1_i_{Z1, x_n}) (\Sum_j K2_j_{x_n, Z2})>_p(x_n)
          - \Sum_i K1_i_{.,.}, \Sum_j K2_j_{.,.} :: Sum kernels
  
      :return: NxM1xM2
      """
      crossexps = []
  
      if (
          kern1 == kern2 and feat1 == feat2
      ):  # avoid duplicate computation by using transposes
          for i, k1 in enumerate(kern1.kernels):
              crossexps.append(
                  expectation(p, (k1, feat1), (k1, feat1), nghp=nghp)
              )
  
              for k2 in kern1.kernels[:i]:
                  eKK = expectation(
                      p, (k1, feat1), (k2, feat2), nghp=nghp
                  )
                  eKK += tf.linalg.adjoint(eKK)
                  crossexps.append(eKK)
      else:
          for k1, k2 in itertools.product(kern1.kernels, kern2.kernels):
              crossexps.append(
                  expectation(p, (k1, feat1), (k2, feat2), nghp=nghp)
              )
  
      return reduce(tf.add, crossexps)
  
file_path: GPflow/gpflow/expectations/sums.py
imports: import itertools
  from functools import reduce
  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 5 for calling a global function with example usage: _expectation_gaussian_sum_inducingpoints__sum_inducingpoints(p, kern1, feat1, kern2, feat2, nghp) and returns: reduce