code: def _expectation_gaussian_sqe_inducingpoints__linear_inducingpoints(
      p: Union[Gaussian, DiagonalGaussian],
      sqexp_kern: kernels.SquaredExponential,
      feat1: InducingPoints,
      lin_kern: kernels.Linear,
      feat2: InducingPoints,
      nghp: None = None,
  ) -> tf.Tensor:
      """
      Compute the expectation:
      expectation[n] = <Ka_{Z1, x_n} Kb_{x_n, Z2}>_p(x_n)
          - K_lin_{.,.} :: SqExp kernel
          - K_sqexp_{.,.} :: Linear kernel
      Different Z1 and Z2 are handled if p is diagonal and K_lin and K_sqexp have disjoint
      active_dims, in which case the joint expectations simplify into a product of expectations
  
      :return: NxM1xM2
      """
      if sqexp_kern.on_separate_dims(lin_kern) and isinstance(
          p, DiagonalGaussian
      ):  # no joint expectations required
          eKxz1 = expectation(p, (sqexp_kern, feat1))
          eKxz2 = expectation(p, (lin_kern, feat2))
          return eKxz1[:, :, None] * eKxz2[:, None, :]
  
      if feat1 != feat2:
          raise NotImplementedError(
              "inducing_variables have to be the same for both kernels."
          )
  
      if sqexp_kern.active_dims != lin_kern.active_dims:
          raise NotImplementedError(
              "active_dims have to be the same for both kernels."
          )
  
      # use only active dimensions
      Xcov = sqexp_kern.slice_cov(
          tf.linalg.diag(p.cov)
          if isinstance(p, DiagonalGaussian)
          else p.cov
      )
      Z, Xmu = sqexp_kern.slice(feat1.Z, p.mu)
  
      N = tf.shape(Xmu)[0]
      D = tf.shape(Xmu)[1]
  
      def take_with_ard(value: TensorType) -> TensorType:
          if not sqexp_kern.ard:
              return tf.zeros((D,), dtype=value.dtype) + value
          return value
  
      lin_kern_variances = take_with_ard(lin_kern.variance)
      sqexp_kern_lengthscales = take_with_ard(sqexp_kern.lengthscales)
  
      chol_L_plus_Xcov = tf.linalg.cholesky(
          tf.linalg.diag(sqexp_kern_lengthscales**2) + Xcov
      )  # NxDxD
  
      Z_transpose = tf.transpose(Z)
      all_diffs = Z_transpose - tf.expand_dims(Xmu, 2)  # NxDxM
      exponent_mahalanobis = tf.linalg.triangular_solve(
          chol_L_plus_Xcov, all_diffs, lower=True
      )  # NxDxM
      exponent_mahalanobis = tf.reduce_sum(
          tf.square(exponent_mahalanobis), 1
      )  # NxM
      exponent_mahalanobis = tf.exp(-0.5 * exponent_mahalanobis)  # NxM
  
      sqrt_det_L = tf.reduce_prod(sqexp_kern_lengthscales)
      sqrt_det_L_plus_Xcov = tf.exp(
          tf.reduce_sum(
              tf.math.log(tf.linalg.diag_part(chol_L_plus_Xcov)), axis=1
          )
      )
      determinants = sqrt_det_L / sqrt_det_L_plus_Xcov  # N
      eKxz_sqexp = sqexp_kern.variance * (
          determinants[:, None] * exponent_mahalanobis
      )  ## NxM <- End RBF eKxz code
  
      tiled_Z = tf.tile(
          tf.expand_dims(Z_transpose, 0), (N, 1, 1)
      )  # NxDxM
      z_L_inv_Xcov = tf.linalg.matmul(
          tiled_Z,
          Xcov / sqexp_kern_lengthscales[:, None] ** 2.0,
          transpose_a=True,
      )  # NxMxD
  
      cross_eKzxKxz = tf.linalg.cholesky_solve(
          chol_L_plus_Xcov,
          cast(
              tf.Tensor,
              (lin_kern_variances * sqexp_kern_lengthscales**2.0),
          )[..., None]
          * tiled_Z,
      )  # NxDxM
  
      cross_eKzxKxz = tf.linalg.matmul(
          (z_L_inv_Xcov + Xmu[:, None, :]) * eKxz_sqexp[..., None],
          cross_eKzxKxz,
      )  # NxMxM
      return cross_eKzxKxz
  
file_path: GPflow/gpflow/expectations/cross_kernels.py
imports: from typing import Union, cast
  import tensorflow
  from check_shapes import check_shapes
  from ..None import kernels
  from ..base import TensorType
  from ..inducing_variables import InducingPoints
  from ..probability_distributions import DiagonalGaussian, Gaussian
  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_inducingpoints__linear_inducingpoints(p, sqexp_kern, feat1, lin_kern, feat2, nghp) and returns: cross_eKzxKxz, unknown, value, unknown