class_docstr: Program definition for multi-objective MPO.
  
  This agent distinguishes itself from the distributed MPO agent in two ways:
  - Allowing for one or more objectives (see `acme/agents/tf/mompo/learning.py`
      for details on what form this sequence of objectives should take)
  - Optionally using a distributional critic (state-action value approximator)
      as in DMPO. In other words, the critic network can output either scalar
      Q-values or a DiscreteValuedDistribution.
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code: def coordinator(
      self, counter: counting.Counter, max_actor_steps: int
  ):
      return lp_utils.StepsLimiter(counter, max_actor_steps)
  
imports: from typing import Callable, Dict, Optional, Sequence
  import acme
  from acme import datasets
  from acme import specs
  from acme.adders import reverb
  from acme.agents.tf import actors
  from acme.agents.tf.mompo import learning
  from acme.tf import losses
  from acme.tf import networks
  from acme.tf import savers
  from acme.tf import utils
  from acme.tf import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  from acme.utils import lp_utils
  import dm_env
  import launchpad
  import reverb
  import sonnet
  import tensorflow
label_desc: Use image node_id 4 for calling the DistributedMultiObjectiveMPO obj's underlying member method code with example usage: obj.coordinator(counter, max_actor_steps) and returns: lp_utils