class_docstr: A JAX implementation of the DQN agent.
class_docstr_tok: ["A","JAX","implementation","of","the","DQN","agent","."]
code: def _build_networks_and_optimizer(self):
      self._rng, rng = jax.random.split(self._rng)
      state = self.preprocess_fn(self.state)
      self.online_params = self.network_def.init(rng, x=state)
      self.optimizer = create_optimizer(self._optimizer_name)
      self.optimizer_state = self.optimizer.init(self.online_params)
      self.target_network_params = self.online_params
  
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 the JaxDQNAgent obj's underlying member method code with example usage: obj._build_networks_and_optimizer() without return types