class_docstr: Defines an interface for defining the components of an RL agent.
  
  Implementations of this interface contain a complete specification of a
  concrete RL agent. An instance of this class can be used to build an
  RL agent which interacts with the environment either locally or in a
  distributed setup.
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docstr: Creates an instance of the learner.
  
  Args:
    random_key: A key for random number generation.
    networks: struct describing the networks needed by the learner; this can
      be specific to the learner in question.
    dataset: iterator over samples from replay.
    logger_fn: factory providing loggers used for logging progress.
    environment_spec: A container for all relevant environment specs.
    replay_client: client which allows communication with replay. Note that
      this is only intended to be used for updating priorities. Samples should
      be obtained from `dataset`.
    counter: a Counter which allows for recording of counts (learner steps,
      actor steps, etc.) distributed throughout the agent.
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code: def make_learner(
      self,
      random_key: networks_lib.PRNGKey,
      networks: Networks,
      dataset: Iterator[Sample],
      logger_fn: loggers.LoggerFactory,
      environment_spec: specs.EnvironmentSpec,
      replay_client: Optional[reverb.Client] = None,
      counter: Optional[counting.Counter] = None,
  ) -> core.Learner:
      """Creates an instance of the learner.
  
      Args:
        random_key: A key for random number generation.
        networks: struct describing the networks needed by the learner; this can
          be specific to the learner in question.
        dataset: iterator over samples from replay.
        logger_fn: factory providing loggers used for logging progress.
        environment_spec: A container for all relevant environment specs.
        replay_client: client which allows communication with replay. Note that
          this is only intended to be used for updating priorities. Samples should
          be obtained from `dataset`.
        counter: a Counter which allows for recording of counts (learner steps,
          actor steps, etc.) distributed throughout the agent.
      """
  
imports: import abc
  import dataclasses
  from typing import Generic, Iterator, List, Optional
  from acme import adders
  from acme import core
  from acme import specs
  from acme.jax import networks
  from acme.jax import types
  from acme.utils import counting
  from acme.utils import loggers
  import reverb
label_desc: Use image node_id 5 for calling the ActorLearnerBuilder obj's underlying member method code with example usage: obj.make_learner(random_key, networks, dataset, logger_fn, environment_spec, replay_client, counter) without return types