code: def plot_model(model: gpflow.models.GPModel) -> None:
      X, Y = model.data
      opt = gpflow.optimizers.Scipy()
      opt.minimize(model.training_loss, model.trainable_variables)
      gpflow.utilities.print_summary(model, "notebook")
  
      Xplot = np.linspace(0.0, 1.0, 200)[:, None]
  
      y_mean, y_var = model.predict_y(Xplot, full_cov=False)
      y_lower = y_mean - 1.96 * np.sqrt(y_var)
      y_upper = y_mean + 1.96 * np.sqrt(y_var)
  
      _, ax = plt.subplots(nrows=1, ncols=1)
      ax.plot(X, Y, "kx", mew=2)
      (mean_line,) = ax.plot(Xplot, y_mean, "-")
      color = mean_line.get_color()
      ax.plot(Xplot, y_lower, lw=0.1, color=color)
      ax.plot(Xplot, y_upper, lw=0.1, color=color)
      ax.fill_between(
          Xplot[:, 0],
          y_lower[:, 0],
          y_upper[:, 0],
          color=color,
          alpha=0.1,
      )
  
file_path: GPflow/doc/sphinx/notebooks/getting_started/classification_and_other_data_distributions.pct.py
imports: import os
  import warnings
  from typing import Sequence
  import matplotlib.pyplot
  import numpy
  import gpflow
label_desc: Use image node_id 1 for calling a global function with example usage: plot_model(model) without return types