class_docstr: This helper makes it easy to read from variables being set with a prior and
  writes values back to the same variables.
  
  Example::
  
      model = ...  # Create a GPflow model
      hmc_helper = SamplingHelper(model.log_posterior_density, model.trainable_parameters)
  
      target_log_prob_fn = hmc_helper.target_log_prob_fn
      current_state = hmc_helper.current_state
  
      hmc = tfp.mcmc.HamiltonianMonteCarlo(target_log_prob_fn=target_log_prob_fn, ...)
      adaptive_hmc = tfp.mcmc.SimpleStepSizeAdaptation(hmc, ...)
  
      @tf.function
      def run_chain_fn():
          return mcmc.sample_chain(
              num_samples, num_burnin_steps, current_state, kernel=adaptive_hmc)
  
      hmc_samples = run_chain_fn()
      parameter_samples = hmc_helper.convert_to_constrained_values(hmc_samples)
docstr: Return the current state of the unconstrained variables, used in HMC.
docstr_tok: ["Return","the","current","state","of","the","unconstrained","variables",",","used","in","HMC","."]
code: def current_state(self) -> Sequence[tf.Variable]:
      """Return the current state of the unconstrained variables, used in HMC."""
  
      return self._variables
  
file_path: GPflow/gpflow/optimizers/mcmc.py
imports: from typing import Callable, Optional, Sequence, Tuple
  import tensorflow
  from gpflow.base import Parameter
label_desc: Use image node_id 2 for calling the SamplingHelper obj's underlying member method code with example usage: obj.current_state() and returns: self