class_docstr: DQN Builder.
class_docstr_tok: ["DQN","Builder","."]
code: def make_actor(
      self,
      random_key: networks_lib.PRNGKey,
      policy: dqn_actor.DQNPolicy,
      environment_spec: Optional[specs.EnvironmentSpec],
      variable_source: Optional[core.VariableSource] = None,
      adder: Optional[adders.Adder] = None,
  ) -> core.Actor:
      del environment_spec
      assert variable_source is not None
      # Inference happens on CPU, so it's better to move variables there too.
      variable_client = variable_utils.VariableClient(
          variable_source, "", device="cpu"
      )
      return actors.GenericActor(
          actor=policy,
          random_key=random_key,
          variable_client=variable_client,
          adder=adder,
          backend=self._actor_backend,
      )
  
imports: from typing import Iterator, List, Optional, Sequence
  from acme import adders
  from acme import core
  from acme import specs
  from acme.adders import reverb
  from acme.agents.jax import actors
  from acme.agents.jax import builders
  from acme.agents.jax.dqn import actor
  from acme.agents.jax.dqn import config
  from acme.agents.jax.dqn import learning_lib
  from acme.agents.jax.dqn import networks
  from acme.datasets import reverb
  from acme.jax import networks
  from acme.jax import utils
  from acme.jax import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  import jax
  import optax
  import reverb
  from reverb import rate_limiters
label_desc: Use image node_id 3 for calling the DQNBuilder obj's underlying member method code with example usage: obj.make_actor(random_key, policy, environment_spec, variable_source, adder) and returns: actors