code: def _expectation_gaussian__kernel_inducingpoints(
      p: Gaussian,
      identity_mean: mfn.Identity,
      _: None,
      kernel: kernels.Kernel,
      inducing_variable: InducingPoints,
      nghp: None = None,
  ) -> tf.Tensor:
      """
      This prevents infinite recursion for kernels that don't have specific
      implementations of _expectation(p, identity_mean, None, kernel, inducing_variable).
      Recursion can arise because Identity is a subclass of Linear mean function
      so _expectation(p, linear_mean, none, kernel, inducing_variable) would call itself.
      More specific signatures (e.g. (p, identity_mean, None, RBF, inducing_variable)) will
      be found and used whenever available
      """
      raise NotImplementedError
  
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 5 for calling a global function with example usage: _expectation_gaussian__kernel_inducingpoints(p, identity_mean, _, kernel, inducing_variable, nghp) without return types