code: def run_chain_fn_unconstrained():
      return tfp.mcmc.sample_chain(
          num_results=num_samples,
          num_burnin_steps=num_burnin_steps,
          current_state=hmc_helper.current_state,
          kernel=adaptive_hmc,
          trace_fn=lambda _, pkr: pkr.inner_results.is_accepted,
      )
  
file_path: GPflow/doc/sphinx/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 8 for calling a global function with example usage: run_chain_fn_unconstrained() and returns: tfp