code: def train(
      network_def,
      online_params,
      target_params,
      optimizer,
      optimizer_state,
      states,
      actions,
      next_states,
      rewards,
      terminals,
      cumulative_gamma,
      loss_type="mse",
  ):
      """Run the training step."""
  
      def loss_fn(params, target):
          def q_online(state):
              return network_def.apply(params, state)
  
          q_values = jax.vmap(q_online)(states).q_values
          q_values = jnp.squeeze(q_values)
          replay_chosen_q = jax.vmap(lambda x, y: x[y])(
              q_values, actions
          )
          if loss_type == "huber":
              return jnp.mean(
                  jax.vmap(losses.huber_loss)(target, replay_chosen_q)
              )
          return jnp.mean(
              jax.vmap(losses.mse_loss)(target, replay_chosen_q)
          )
  
      def q_target(state):
          return network_def.apply(target_params, state)
  
      target = target_q(
          q_target, next_states, rewards, terminals, cumulative_gamma
      )
      grad_fn = jax.value_and_grad(loss_fn)
      loss, grad = grad_fn(online_params, target)
      updates, optimizer_state = optimizer.update(
          grad, optimizer_state, params=online_params
      )
      online_params = optax.apply_updates(online_params, updates)
      return optimizer_state, online_params, loss
  
imports: import collections
  import functools
  import inspect
  import math
  import time
  from absl import logging
  from dopamine.agents.dqn import dqn_agent
  from dopamine.jax import losses
  from dopamine.jax import networks
  from dopamine.metrics import statistics_instance
  from dopamine.replay_memory import circular_replay_buffer
  from dopamine.replay_memory import prioritized_replay_buffer
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: train(network_def, online_params, target_params, optimizer, optimizer_state, states, actions, next_states, rewards, terminals, cumulative_gamma, loss_type) and returns: optimizer_state,  online_params,  loss, jnp, network_def, network_def, jnp