code: def kl_constraint_loss(
      kl: Array,
      penalty: LagrangePenalty,
      projection_operator: ProjectionOperatorFn,
      use_stop_gradient: bool = True,
  ) -> Tuple[Array, Array, Array]:
      """Implements a hard KL constraint.
  
      The optimization proceeds in two phases. First, we optimize the weighting
      term `alpha` keeping the KL constant and then we optimize the KL keeping
      `alpha` constant. Each phase is implemented by appropriately using a
      stop_gradient.
  
      If `bound` - `kl` > 0, then `alpha` is pushed toward `min_alpha`. However
      this also means the policy is free to change more since `kl` < `bound `.
      This eventually leads to `bound` - `kl` < 0 which pressures alpha to get to
      a high positive value. This coordinate ascent results in `kl` staying close
      to `bound`.
  
      Args:
        kl: The kl per example which is being constrained.
        penalty: The dual variable used to impose a penalty and parameters of the
          constraint.
        projection_operator: Function to project dual variables kl constraint alphas
          into the positive range.
        use_stop_gradient: bool indicating whether or not to apply stop gradient.
  
      Returns:
        A `tuple` consisting of three arrays: `kl_loss`, `alpha_loss` and
        `clipped_alpha`. The first two terms represent the losses for the two phases
        of computation. `clipped_alpha` is the clipped lagrangian multiplier `alpha`
        that is learnt.
      """
      chex.assert_type([kl, penalty.alpha, penalty.epsilon], float)
  
      alpha = projection_operator(penalty.alpha)
      alpha_constant = jax.lax.select(
          use_stop_gradient, jax.lax.stop_gradient(alpha), alpha
      )
  
      # First step: Optimize w.r.t. alphas
      alpha_loss = alpha * (
          penalty.epsilon
          - jax.lax.select(
              use_stop_gradient, jax.lax.stop_gradient(kl), kl
          )
      )
  
      # Second step: KL loss.
      kl_loss = alpha_constant * kl
  
      # If kl_loss and alpha_loss are per dimension then at this point sum over
      # dimensions.
      if penalty.per_dimension:
          kl_loss = jnp.sum(kl_loss, axis=-1)
          alpha_loss = jnp.sum(alpha_loss, axis=-1)
  
      return kl_loss, alpha_loss, alpha
  
file_path: rlax/rlax/_src/mpo_ops.py
imports: import functools
  from typing import Callable, NamedTuple, Optional, Sequence, Tuple
  from absl import logging
  import chex
  import jax
  import jax.numpy
  from rlax._src import base
label_desc: Use image node_id 7 for calling a global function with example usage: kl_constraint_loss(kl, penalty, projection_operator, use_stop_gradient) and returns: kl_loss,  alpha_loss,  alpha