class_docstr: MBOP Builder.
  
  This builder uses ensemble regressor learners for the world model, policy
  prior and the n-step return models with fixed learning rates. The ensembles
  and the learning rate are configured in the config.
docstr: See base class.
docstr_tok: ["See","base","class","."]
code: def make_policy(
      self,
      networks: mbop_networks.MBOPNetworks,
      environment_spec: specs.EnvironmentSpec,
      evaluation: bool,
  ) -> acting.ActorCore:
      """See base class."""
      return acting.make_ensemble_actor_core(
          networks,
          self._config.mppi_config,
          environment_spec,
          self._mean_std,
          use_round_robin=not evaluation,
      )
  
imports: import functools
  from typing import Iterator, Optional
  from acme import core
  from acme import specs
  from acme import types
  from acme.agents.jax import builders
  from acme.agents.jax.mbop import acting
  from acme.agents.jax.mbop import config
  from acme.agents.jax.mbop import learning
  from acme.agents.jax.mbop import losses
  from acme.agents.jax.mbop import networks
  from acme.jax import networks
  from acme.jax import running_statistics
  from acme.utils import counting
  from acme.utils import loggers
  import optax
label_desc: Use image node_id 4 for calling the MBOPBuilder obj's underlying member method code with example usage: obj.make_policy(networks, environment_spec, evaluation) and returns: acting