code: def rnd_update_step(
      state: RNDTrainingState,
      transitions: types.Transition,
      loss_fn: RNDLoss,
      optimizer: optax.GradientTransformation,
  ) -> Tuple[RNDTrainingState, Dict[str, jnp.ndarray]]:
      """Run an update steps on the given transitions.
  
      Args:
        state: The learner state.
        transitions: Transitions to update on.
        loss_fn: The loss function.
        optimizer: The optimizer of the predictor network.
  
      Returns:
        A new state and metrics.
      """
      loss, grads = jax.value_and_grad(loss_fn)(
          state.params, state.target_params, transitions=transitions
      )
  
      update, optimizer_state = optimizer.update(
          grads, state.optimizer_state
      )
      params = optax.apply_updates(state.params, update)
  
      new_state = RNDTrainingState(
          optimizer_state=optimizer_state,
          params=params,
          target_params=state.target_params,
          steps=state.steps + 1,
      )
      return new_state, {"rnd_loss": loss}
  
imports: import functools
  import time
  from typing import Any, Callable, Dict, Iterator, List, NamedTuple, Optional, Tuple
  import acme
  from acme import types
  from acme.agents.jax.rnd import networks
  from acme.jax import networks
  from acme.jax import utils
  from acme.utils import counting
  from acme.utils import loggers
  from acme.utils import reverb_utils
  import jax
  import jax.numpy
  import optax
  import reverb
label_desc: Use image node_id 1 for calling a global function with example usage: rnd_update_step(state, transitions, loss_fn, optimizer) and returns: new_state,  dict