code: def get_gradients(
      online_params, apply_fn, target_params, batch, cumulative_gamma
  ):
      """Calculates the gradiens and the loss."""
  
      def q_target(state):
          return apply_fn(target_params, state)
  
      q_vals = jax.vmap(q_target)(batch["next_state"]).q_values
      q_vals = jnp.squeeze(q_vals)
      replay_next_qt_max = jnp.max(q_vals, 1)
      # Calculate the Bellman target value.
      #   Q_t = R_t + \gamma^N * Q'_t+1
      # where,
      #   Q'_t+1 = \argmax_a Q(S_t+1, a)
      #          (or) 0 if S_t is a terminal state,
      # and
      #   N is the update horizon (by default, N=1).
      target = jax.lax.stop_gradient(
          batch["reward"]
          + cumulative_gamma
          * replay_next_qt_max
          * (1.0 - batch["terminal"])
      )
      grad_fn = jax.value_and_grad(loss_fn)
      loss, grad = grad_fn(
          online_params,
          target=target,
          state=batch["state"],
          action=batch["action"],
          apply_fn=apply_fn,
      )
      return loss, grad
  
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 2 for calling a global function with example usage: get_gradients(online_params, apply_fn, target_params, batch, cumulative_gamma) and returns: loss,  grad, apply_fn