code: def plot_gp(m, Xnew, name=""):
      Fmean, Fvar = m.predict_f(Xnew[:, None])
      Fmean = Fmean.numpy().squeeze()
      Fvar = Fvar.numpy().squeeze()
      (p,) = plt.plot(Xnew, Fmean, label=name)
      plt.fill_between(
          Xnew,
          Fmean - 2 * np.sqrt(Fvar),
          Fmean + 2 * np.sqrt(Fvar),
          alpha=0.3,
          color=p.get_color(),
      )
  
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 9 for calling a global function with example usage: plot_gp(m, Xnew, name) without return types