code: def get_coupled_kl_constraints(out, params, per_dimension):
      (
          kl_mean,
          kl_covariance,
      ) = _decoupled_multivariate_normal_kl_divergence(
          out["target_pi_params"]["mean"],
          out["target_pi_params"]["stddev"],
          out["pi_params"]["mean"],
          out["pi_params"]["stddev"],
          per_dimension=per_dimension,
      )
      alpha_mean = params["mpo"]["alpha_mean"] * jnp.ones_like(kl_mean)
      return [
          (
              kl_mean + kl_covariance,
              mpo_ops.LagrangePenalty(
                  alpha=alpha_mean,
                  epsilon=_EPSILON_MEAN_BOUND
                  + _EPSILON_COVARIANCE_BOUND,
                  per_dimension=per_dimension,
              ),
          )
      ]
  
imports: import functools
  import math
  from typing import Tuple
  from absl.testing import absltest
  from absl.testing import parameterized
  import chex
  import haiku
  import jax
  import jax.numpy
  import numpy
  import optax
  from rlax._src import distributions
  from rlax._src import mpo_ops
label_desc: Use image node_id 7 for calling a global function with example usage: get_coupled_kl_constraints(out, params, per_dimension) and returns: list