code: def loss_fn(params, target, state, action, apply_fn):
      """Calculates the loss."""
  
      def q_online(state):
          return apply_fn(params, state)
  
      q_values = jax.vmap(q_online)(state).q_values
      q_values = jnp.squeeze(q_values)
      replay_chosen_q = jax.vmap(lambda x, y: x[y])(q_values, action)
      loss = jnp.mean(
          jax.vmap(losses.huber_loss)(target, replay_chosen_q)
      )
      # TODO(all) add weight decay. See jax.example_libraries.optimizers.l2_norm
      return loss
  
imports: import functools
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.labs.redo import networks
  from dopamine.labs.redo import weight_recyclers
  from dopamine.metrics import statistics_instance
  import gin
  import jax
  import jax.numpy
  import optax
label_desc: Use image node_id 1 for calling a global function with example usage: loss_fn(params, target, state, action, apply_fn) and returns: loss, apply_fn