code: def train(
      network_def,
      online_params,
      target_params,
      optimizer,
      optimizer_state,
      states,
      actions,
      next_states,
      rewards,
      terminals,
      loss_weights,
      support,
      cumulative_gamma,
  ):
      """Run a training step."""
  
      def loss_fn(params, target, loss_multipliers):
          def q_online(state):
              return network_def.apply(params, state, support)
  
          logits = jax.vmap(q_online)(states).logits
          # Fetch the logits for its selected action. We use vmap to perform this
          # indexing across the batch.
          chosen_action_logits = jax.vmap(lambda x, y: x[y])(
              logits, actions
          )
          loss = jax.vmap(
              losses.softmax_cross_entropy_loss_with_logits
          )(target, chosen_action_logits)
          mean_loss = jnp.mean(loss_multipliers * loss)
          return mean_loss, loss
  
      def q_target(state):
          return network_def.apply(target_params, state, support)
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      target = target_distribution(
          q_target,
          next_states,
          rewards,
          terminals,
          support,
          cumulative_gamma,
      )
  
      # Get the unweighted loss without taking its mean for updating priorities.
      (mean_loss, loss), grad = grad_fn(
          online_params, target, loss_weights
      )
      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, mean_loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from dopamine.jax import losses
  from dopamine.jax import networks
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.metrics import statistics_instance
  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 1 for calling a global function with example usage: train(network_def, online_params, target_params, optimizer, optimizer_state, states, actions, next_states, rewards, terminals, loss_weights, support, cumulative_gamma) and returns: optimizer_state,  online_params,  loss,  mean_loss, mean_loss,  loss, network_def, network_def