code: def plot_co2_model_prediction(
      ax: Axes, model: gpflow.models.GPModel, start: float, stop: float
  ) -> None:
      Xplot = np.linspace(start, stop, 200)[:, None]
      idx_plot = (start < Xco2) & (Xco2 < stop)
  
      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.plot(Xco2[idx_plot], Yco2[idx_plot], "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/parameters_and_their_optimisation.pct.py
imports: import os
  import warnings
  from typing import Optional
  import matplotlib.pyplot
  import numpy
  import pandas
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes
  from matplotlib.axes import Axes
  import gpflow
  from gpflow.ci_utils import is_continuous_integration
label_desc: Use image node_id 2 for calling a global function with example usage: plot_co2_model_prediction(ax, model, start, stop) without return types