code: def loss_with_changed_parameter(model, parameter, value: float):
      original = parameter.numpy()
      parameter.assign(value)
      loss = model.training_loss()
      parameter.assign(original)
      return loss.numpy()
  
file_path: GPflow/notebooks/theory/cglb.pct.py
imports: import matplotlib.pyplot
  import numpy
  import tensorflow
  from cglb import load_snelson_data, plot_prediction
  from gpflow.kernels import SquaredExponential
  from gpflow.models import CGLB, GPR, SGPR
  from gpflow.optimizers import Scipy
label_desc: Use image node_id 1 for calling a global function with example usage: loss_with_changed_parameter(model, parameter, value) and returns: loss