class_docstr: CQL Builder.
class_docstr_tok: ["CQL","Builder","."]
code: def make_learner(
      self,
      random_key: networks_lib.PRNGKey,
      networks: cql_networks.CQLNetworks,
      dataset: Iterator[types.Transition],
      logger_fn: loggers.LoggerFactory,
      environment_spec: specs.EnvironmentSpec,
      *,
      counter: Optional[counting.Counter] = None,
  ) -> core.Learner:
      del environment_spec
  
      return learning.CQLLearner(
          batch_size=self._config.batch_size,
          networks=networks,
          random_key=random_key,
          demonstrations=dataset,
          policy_optimizer=optax.adam(
              self._config.policy_learning_rate
          ),
          critic_optimizer=optax.adam(
              self._config.critic_learning_rate
          ),
          tau=self._config.tau,
          fixed_cql_coefficient=self._config.fixed_cql_coefficient,
          cql_lagrange_threshold=self._config.cql_lagrange_threshold,
          cql_num_samples=self._config.cql_num_samples,
          num_sgd_steps_per_step=self._config.num_sgd_steps_per_step,
          reward_scale=self._config.reward_scale,
          discount=self._config.discount,
          fixed_entropy_coefficient=self._config.fixed_entropy_coefficient,
          target_entropy=self._config.target_entropy,
          num_bc_iters=self._config.num_bc_iters,
          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.cql import config
  from acme.agents.jax.cql import learning
  from acme.agents.jax.cql import networks
  from acme.jax import networks
  from acme.jax import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  import optax
label_desc: Use image node_id 2 for calling the CQLBuilder obj's underlying member method code with example usage: obj.make_learner(random_key, networks, dataset, logger_fn, environment_spec) and returns: learning