class_docstr: MPO loss with decoupled KL constraints as in (Abdolmaleki et al., 2018).
  
  This implementation of the MPO loss includes the following features, as
  options:
  - Satisfying the KL-constraint on a per-dimension basis (on by default);
  - Penalizing actions that fall outside of [-1, 1] (on by default) as a
      special case of multi-objective MPO (MO-MPO; Abdolmaleki et al., 2020).
  For best results on the control suite, keep both of these on.
  
  (Abdolmaleki et al., 2018): https://arxiv.org/pdf/1812.02256.pdf
  (Abdolmaleki et al., 2020): https://arxiv.org/pdf/2005.07513.pdf
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code: def per_dim_constraining(self):
      return self._per_dim_constraining
  
file_path: acme/acme/jax/losses/mpo.py
imports: from typing import NamedTuple, Optional, Tuple, Union
  import jax
  import jax.numpy
  import tensorflow_probability
label_desc: Use image node_id 2 for calling the MPO obj's underlying member method code with example usage: obj.per_dim_constraining() and returns: self