code: def Kuu_matern12_fourierfeatures1d(
      inducing_variable, kernel, jitter=None
  ):
      a, b, ms = (lambda u: (u.a, u.b, u.ms))(inducing_variable)
      omegas = 2.0 * np.pi * ms / (b - a)
  
      # Cosine block:
      lamb = 1.0 / kernel.lengthscales
      two_or_four = to_default_float(tf.where(omegas == 0, 2.0, 4.0))
      d_cos = (
          (b - a)
          * (tf.square(lamb) + tf.square(omegas))
          / lamb
          / kernel.variance
          / two_or_four
      )  # eq. (111)
      v_cos = tf.ones_like(d_cos) / tf.sqrt(
          kernel.variance
      )  # eq. (110)
      cosine_block = LowRank(
          Diag(d_cos, is_positive_definite=True), v_cos[:, None]
      )
  
      # Sine block:
      omegas = omegas[
          tf.not_equal(omegas, 0)
      ]  # the sine block does not include omega=0
      d_sin = (
          (b - a)
          * (tf.square(lamb) + tf.square(omegas))
          / lamb
          / kernel.variance
          / 4.0
      )  # eq. (113)
      sine_block = Diag(d_sin, is_positive_definite=True)
  
      return BlockDiag([cosine_block, sine_block])
  
imports: import numpy
  import tensorflow
  from check_shapes import Shape
  import gpflow
  from gpflow import covariances
  from gpflow import kullback_leiblers
  from gpflow.base import TensorLike
  from gpflow.ci_utils import reduce_in_tests
  from gpflow.inducing_variables import InducingVariables
  from gpflow.utilities import to_default_float
  import matplotlib.pyplot
  import gpflow.posteriors
label_desc: Use image node_id 1 for calling a global function with example usage: Kuu_matern12_fourierfeatures1d(inducing_variable, kernel, jitter) and returns: BlockDiag