class_docstr: BVE Builder.
code: def make_learner(
      self,
      random_key: jax_types.PRNGKey,
      networks: bve_networks.BVENetworks,
      dataset: Iterator[utils.PrefetchingSplit],
      logger_fn: loggers.LoggerFactory,
      environment_spec: specs.EnvironmentSpec,
      counter: Optional[counting.Counter] = None,
  ) -> core.Learner:
      del environment_spec
  
      loss_fn = losses.BVELoss(
          discount=self._config.discount,
          max_abs_reward=self._config.max_abs_reward,
          huber_loss_parameter=self._config.huber_loss_parameter,
      )
  
      return learning_lib.SGDLearner(
          network=networks.policy_network,
          random_key=random_key,
          optimizer=optax.adam(
              self._config.learning_rate, eps=self._config.adam_eps
          ),
          target_update_period=self._config.target_update_period,
          data_iterator=dataset,
          loss_fn=loss_fn,
          counter=counter,
          num_sgd_steps_per_step=self._config.num_sgd_steps_per_step,
          logger=logger_fn("learner"),
      )
  
imports: from typing import Iterator, Optional
  from acme import core
  from acme import specs
  from acme.agents.jax import actor_core
  from acme.agents.jax import actors
  from acme.agents.jax import builders
  from acme.agents.jax.bve import losses
  from acme.agents.jax.bve import networks
  from acme.agents.jax.dqn import learning_lib
  from acme.jax import networks
  from acme.jax import types
  from acme.jax import utils
  from acme.jax import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  import haiku
  import optax
label_desc: Use image node_id 2 for calling the BVEBuilder obj's underlying member method code with example usage: obj.make_learner(random_key, networks, dataset, logger_fn, environment_spec, counter) and returns: learning_lib