code: def Kuu_sqexp_multiscale(
      inducing_variable: Multiscale,
      kernel: SquaredExponential,
      *,
      jitter: float = 0.0,
  ) -> tf.Tensor:
      Zmu, Zlen = kernel.slice(
          inducing_variable.Z, inducing_variable.scales
      )
      idlengthscales2 = tf.square(kernel.lengthscales + Zlen)
      sc = tf.sqrt(
          idlengthscales2[None, ...]
          + idlengthscales2[:, None, ...]
          - kernel.lengthscales**2
      )
      d = inducing_variable._cust_square_dist(Zmu, Zmu, sc)
      Kzz = (
          kernel.variance
          * tf.exp(-d / 2)
          * tf.reduce_prod(kernel.lengthscales / sc, 2)
      )
      Kzz += jitter * tf.eye(
          inducing_variable.num_inducing, dtype=Kzz.dtype
      )
      return Kzz
  
file_path: GPflow/gpflow/covariances/kuus.py
imports: import tensorflow
  from check_shapes import check_shapes
  from ..config import default_float
  from ..inducing_variables import InducingPatches, InducingPoints, Multiscale
  from ..kernels import Convolutional, Kernel, SquaredExponential
  from .dispatch import Kuu
label_desc: Use image node_id 2 for calling a global function with example usage: Kuu_sqexp_multiscale(inducing_variable, kernel) and returns: Kzz