class_docstr: BC Builder.
class_docstr_tok: ["BC","Builder","."]
code: def make_learner(
      self,
      random_key: networks_lib.PRNGKey,
      networks: bc_networks.BCNetworks,
      dataset: Iterator[types.Transition],
      logger_fn: loggers.LoggerFactory,
      environment_spec: specs.EnvironmentSpec,
      *,
      counter: Optional[counting.Counter] = None,
  ) -> core.Learner:
      del environment_spec
  
      return learning.BCLearner(
          networks=networks,
          random_key=random_key,
          loss_fn=self._loss_fn,
          optimizer=optax.adam(
              learning_rate=self._config.learning_rate
          ),
          prefetching_iterator=utils.sharded_prefetch(dataset),
          num_sgd_steps_per_step=self._config.num_sgd_steps_per_step,
          loss_has_aux=self._loss_has_aux,
          logger=logger_fn("learner"),
          counter=counter,
      )
  
imports: from typing import Iterator, Optional
  from acme import core
  from acme import specs
  from acme import types
  from acme.agents.jax import actor_core
  from acme.agents.jax import actors
  from acme.agents.jax import builders
  from acme.agents.jax.bc import config
  from acme.agents.jax.bc import learning
  from acme.agents.jax.bc import losses
  from acme.agents.jax.bc import networks
  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
label_desc: Use image node_id 2 for calling the BCBuilder obj's underlying member method code with example usage: obj.make_learner(random_key, networks, dataset, logger_fn, environment_spec) and returns: learning