def _log_joint(current_target_log_prob, current_momentum):
  """Log-joint probability given a state's log-probability and momentum."""
  momentum_log_prob = -sum(
      [tf.reduce_sum(input_tensor=0.5 * (m**2.)) for m in current_momentum])
  return current_target_log_prob + momentum_log_prob
def _log_joint(current_target_log_prob, current_momentum):
  
  momentum_log_prob = -sum(
      [tf.reduce_sum(input_tensor=0.5 * (m**2.)) for m in current_momentum])
  return current_target_log_prob + momentum_log_prob
def _log_joint ( current_target_log_prob , current_momentum ) : momentum_log_prob = - sum ( [ tf . reduce_sum ( input_tensor = 0.5 * ( m ** 2. ) ) for m in current_momentum ] ) return current_target_log_prob + momentum_log_prob
Log-joint probability given a state's log-probability and momentum.
Log - joint probability given a state s log - probability and momentum .