code: def synthetic_data(num: int, rng: np.random.RandomState):
      X = rng.rand(num, 1)
      Y = (
          np.sin(12 * X)
          + 0.66 * np.cos(25 * X)
          + rng.randn(num, 1) * 0.1
          + 3
      )
      return X, Y
  
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 1 for calling a global function with example usage: synthetic_data(num, rng) and returns: X,  Y