code: def _expectation_gaussian_sqe(
      p: Gaussian,
      kernel: kernels.SquaredExponential,
      _: None,
      __: None,
      ___: None,
      nghp: None = None,
  ) -> tf.Tensor:
      """
      Compute the expectation:
      <diag(K_{X, X})>_p(X)
          - K_{.,.} :: RBF kernel
  
      :return: N
      """
      return kernel(p.mu, full_cov=False)
  
file_path: GPflow/gpflow/expectations/squared_exponentials.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 ..utilities.ops import square_distance
  from .None import dispatch
  from .expectations import expectation
label_desc: Use image node_id 1 for calling a global function with example usage: _expectation_gaussian_sqe(p, kernel, _, __, ___, nghp) and returns: kernel