code: def rnd_reward_fn(
      predictor_output: networks_lib.NetworkOutput,
      target_output: networks_lib.NetworkOutput,
      original_reward: jnp.ndarray,
      intrinsic_reward_coefficient: float = 1.0,
      extrinsic_reward_coefficient: float = 0.0,
  ) -> jnp.ndarray:
      intrinsic_reward = jnp.mean(
          jnp.square(predictor_output - target_output), axis=-1
      )
      return (
          intrinsic_reward_coefficient * intrinsic_reward
          + extrinsic_reward_coefficient * original_reward
      )
  
file_path: acme/acme/agents/jax/rnd/networks.py
imports: import dataclasses
  import functools
  from typing import Callable, Generic, Tuple, TypeVar
  from acme import specs
  from acme import types
  from acme.jax import networks
  from acme.jax import utils
  import haiku
  import jax.numpy
label_desc: Use image node_id 1 for calling a global function with example usage: rnd_reward_fn(predictor_output, target_output, original_reward, intrinsic_reward_coefficient, extrinsic_reward_coefficient) and returns: unknown