code: def rnd_loss(
      predictor_params: networks_lib.Params,
      target_params: networks_lib.Params,
      transitions: types.Transition,
      networks: rnd_networks.RNDNetworks,
  ) -> float:
      """The Random Network Distillation loss.
  
      See https://arxiv.org/pdf/1810.12894.pdf A.2
  
      Args:
        predictor_params: Parameters of the predictor
        target_params: Parameters of the target
        transitions: Transitions to compute the loss on.
        networks: RND networks
  
      Returns:
        The MSE loss as a float.
      """
      target_output = networks.target.apply(
          target_params, transitions.observation, transitions.action
      )
      predictor_output = networks.predictor.apply(
          predictor_params, transitions.observation, transitions.action
      )
      return jnp.mean(jnp.square(target_output - predictor_output))
  
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 2 for calling a global function with example usage: rnd_loss(predictor_params, target_params, transitions, networks) and returns: jnp