code: def _expectation_gaussian_sqe_inducingpoints__sqe_inducingpoints(
      p: Union[Gaussian, DiagonalGaussian],
      kern1: kernels.SquaredExponential,
      feat1: InducingPoints,
      kern2: kernels.SquaredExponential,
      feat2: InducingPoints,
      nghp: None = None,
  ) -> tf.Tensor:
      """
      Compute the expectation:
      expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
          - Ka_{.,.}, Kb_{.,.} :: RBF kernels
      Ka and Kb as well as Z1 and Z2 can differ from each other, but this is supported
      only if the Gaussian p is Diagonal (p.cov NxD) and Ka, Kb have disjoint active_dims
      in which case the joint expectations simplify into a product of expectations
  
      :return: NxMxM
      """
      if kern1.on_separate_dims(kern2) and isinstance(
          p, DiagonalGaussian
      ):  # no joint expectations required
          eKxz1 = expectation(p, (kern1, feat1))
          eKxz2 = expectation(p, (kern2, feat2))
          return eKxz1[:, :, None] * eKxz2[:, None, :]
  
      if feat1 != feat2 or kern1 != kern2:
          raise NotImplementedError(
              "The expectation over two kernels has only an "
              "analytical implementation if both kernels are equal."
          )
  
      kernel = kern1
      inducing_variable = feat1
  
      # use only active dimensions
      Xcov = kernel.slice_cov(
          tf.linalg.diag(p.cov)
          if isinstance(p, DiagonalGaussian)
          else p.cov
      )
      Z, Xmu = kernel.slice(inducing_variable.Z, p.mu)
  
      N = tf.shape(Xmu)[0]
      D = tf.shape(Xmu)[1]
  
      squared_lengthscales = kernel.lengthscales**2
      if not kernel.ard:
          zero_lengthscales = tf.zeros(
              (D,), dtype=squared_lengthscales.dtype
          )
          squared_lengthscales = (
              squared_lengthscales + zero_lengthscales
          )
  
      sqrt_det_L = tf.reduce_prod(0.5 * squared_lengthscales) ** 0.5
      C = tf.linalg.cholesky(
          0.5 * tf.linalg.diag(squared_lengthscales) + Xcov
      )  # NxDxD
      dets = sqrt_det_L / tf.exp(
          tf.reduce_sum(tf.math.log(tf.linalg.diag_part(C)), axis=1)
      )  # N
  
      C_inv_mu = tf.linalg.triangular_solve(
          C, tf.expand_dims(Xmu, 2), lower=True
      )  # NxDx1
      C_inv_z = tf.linalg.triangular_solve(
          C,
          tf.tile(tf.expand_dims(0.5 * tf.transpose(Z), 0), [N, 1, 1]),
          lower=True,
      )  # NxDxM
      mu_CC_inv_mu = tf.expand_dims(
          tf.reduce_sum(tf.square(C_inv_mu), 1), 2
      )  # Nx1x1
      z_CC_inv_z = tf.reduce_sum(tf.square(C_inv_z), 1)  # NxM
      zm_CC_inv_zn = tf.linalg.matmul(
          C_inv_z, C_inv_z, transpose_a=True
      )  # NxMxM
      two_z_CC_inv_mu = (
          2
          * tf.linalg.matmul(C_inv_z, C_inv_mu, transpose_a=True)[
              :, :, 0
          ]
      )  # NxM
      # NxMxM
      exponent_mahalanobis = (
          mu_CC_inv_mu
          + tf.expand_dims(z_CC_inv_z, 1)
          + tf.expand_dims(z_CC_inv_z, 2)
          + 2 * zm_CC_inv_zn
          - tf.expand_dims(two_z_CC_inv_mu, 2)
          - tf.expand_dims(two_z_CC_inv_mu, 1)
      )
      exponent_mahalanobis = tf.exp(
          -0.5 * exponent_mahalanobis
      )  # NxMxM
  
      # Compute sqrt(self(Z)) explicitly to prevent automatic gradient from
      # being NaN sometimes, see pull request #615
      kernel_sqrt = tf.exp(
          -0.25 * square_distance(Z / kernel.lengthscales, None)
      )
      return (
          kernel.variance**2
          * kernel_sqrt
          * tf.reshape(dets, [N, 1, 1])
          * exponent_mahalanobis
      )
  
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 5 for calling a global function with example usage: _expectation_gaussian_sqe_inducingpoints__sqe_inducingpoints(p, kern1, feat1, kern2, feat2, nghp) and returns: unknown, unknown