code: def train_with_bc(
      make_demonstrations: Callable[[int], Iterator[types.Transition]],
      networks: bc_networks.BCNetworks,
      loss: losses.BCLoss,
      num_steps: int = 100000,
  ) -> networks_lib.Params:
      """Trains the given network with BC and returns the params.
  
      Args:
        make_demonstrations: A function (batch_size) -> iterator with demonstrations
          to be imitated.
        networks: Network taking (params, obs, is_training, key) as input
        loss: BC loss to use.
        num_steps: number of training steps
  
      Returns:
        The trained network params.
      """
      demonstration_iterator = make_demonstrations(256)
      prefetching_iterator = utils.sharded_prefetch(
          demonstration_iterator,
          buffer_size=2,
          num_threads=jax.local_device_count(),
      )
  
      learner = learning.BCLearner(
          networks=networks,
          random_key=jax.random.PRNGKey(0),
          loss_fn=loss,
          prefetching_iterator=prefetching_iterator,
          optimizer=optax.adam(1e-4),
          num_sgd_steps_per_step=1,
      )
  
      # Train the agent
      for _ in range(num_steps):
          learner.step()
  
      return learner.get_variables(["policy"])[0]
  
file_path: acme/acme/agents/jax/bc/pretraining.py
imports: from typing import Callable, Iterator
  from acme import types
  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
  import jax
  import optax
label_desc: Use image node_id 1 for calling a global function with example usage: train_with_bc(make_demonstrations, networks, loss, num_steps) and returns: learner