code: def make_ensemble_regressor_learner(
      name: str,
      num_networks: int,
      logger_fn: loggers.LoggerFactory,
      counter: counting.Counter,
      rng_key: jnp.ndarray,
      iterator: Iterator[types.Transition],
      base_network: networks_lib.FeedForwardNetwork,
      loss: mbop_losses.TransitionLoss,
      optimizer: optax.GradientTransformation,
      num_sgd_steps_per_step: int,
  ):
      """Creates an ensemble regressor learner from the base network.
  
      Args:
        name: Name of the learner used for logging and counters.
        num_networks: Number of networks in the ensemble.
        logger_fn: Constructs a logger for a label.
        counter: Parent counter object.
        rng_key: Random key.
        iterator: An iterator of time-batched transitions used to train the
          networks.
        base_network: Base network for the ensemble.
        loss: Training loss to use.
        optimizer: Optax optimizer.
        num_sgd_steps_per_step: Number of gradient updates per step.
  
      Returns:
        An ensemble regressor learner.
      """
      mbop_ensemble = ensemble.make_ensemble(
          base_network, ensemble.apply_all, num_networks
      )
      local_counter = counting.Counter(parent=counter, prefix=name)
      local_logger = (
          logger_fn(name, local_counter.get_steps_key())
          if logger_fn
          else None
      )
  
      def loss_fn(
          networks: bc.BCNetworks,
          params: networks_lib.Params,
          key: jax_types.PRNGKey,
          transitions: types.Transition,
      ) -> jnp.ndarray:
          del key
          return loss(
              functools.partial(networks.policy_network.apply, params),
              transitions,
          )
  
      bc_policy_network = bc.convert_to_bc_network(mbop_ensemble)
      bc_networks = bc.BCNetworks(bc_policy_network)
  
      # This is effectively a regressor learner.
      return bc.BCLearner(
          bc_networks,
          rng_key,
          loss_fn,
          optimizer,
          iterator,
          num_sgd_steps_per_step,
          logger=local_logger,
          counter=local_counter,
      )
  
imports: import dataclasses
  import functools
  import itertools
  import time
  from typing import Any, Callable, Iterator, List, Optional
  from acme import core
  from acme import types
  from acme.agents.jax import bc
  from acme.agents.jax.mbop import ensemble
  from acme.agents.jax.mbop import losses
  from acme.agents.jax.mbop import networks
  from acme.jax import networks
  from acme.jax import types
  from acme.jax import utils
  from acme.utils import counting
  from acme.utils import loggers
  import jax
  import jax.numpy
  import optax
label_desc: Use image node_id 1 for calling a global function with example usage: make_ensemble_regressor_learner(name, num_networks, logger_fn, counter, rng_key, iterator, base_network, loss, optimizer, num_sgd_steps_per_step) and returns: bc, loss