code: def marginal_samples(samples, parameters, y_axis_label):
      fig, axes = plt.subplots(
          1,
          len(param_to_name),
          figsize=(15, 3),
          constrained_layout=True,
      )
      for ax, val, param in zip(axes, samples, parameters):
          ax.hist(np.stack(val).flatten(), bins=20)
          ax.set_title(param_to_name[param])
      fig.suptitle(y_axis_label)
      plt.show()
  
file_path: GPflow/notebooks/advanced/mcmc.pct.py
imports: import matplotlib.pyplot
  import numpy
  import tensorflow
  import tensorflow_probability
  from multiclass_classification import colors, plot_from_samples
  from tensorflow_probability import distributions
  import gpflow
  from gpflow import set_trainable
  from gpflow.ci_utils import reduce_in_tests
label_desc: Use image node_id 4 for calling a global function with example usage: marginal_samples(samples, parameters, y_axis_label) without return types