code: def train_generic_model(
      model: gpflow.models.GPModel, data: gpflow.base.RegressionData
  ) -> None:
      loss = gpflow.models.training_loss_closure(model, data)
      opt = gpflow.optimizers.Scipy()
      opt.minimize(loss, model.trainable_variables)
  
file_path: GPflow/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 2 for calling a global function with example usage: train_generic_model(model, data) without return types