class_docstr: A recurrent network for use with IMPALA.
  
  See https://arxiv.org/pdf/1802.01561.pdf for more information.
code: def __call__(
      self, inputs: observation_action_reward.OAR, state: hk.LSTMState
  ) -> Any:
      embeddings = self._embed(inputs)  # [B?, D+A+1]
      embeddings, new_state = self._core(embeddings, state)
      logits, value = self._head(
          embeddings
      )  # logits: [B?, A], value: [B?, 1]
  
      return (logits, value), new_state
  
imports: from typing import Any, Optional, Sequence, Tuple
  from acme.jax.networks import base
  from acme.jax.networks import duelling
  from acme.jax.networks import embedding
  from acme.jax.networks import policy_value
  from acme.jax.networks import resnet
  from acme.wrappers import observation_action_reward
  import haiku
  import jax
  import jax.numpy
label_desc: Use image node_id 2 for calling the DeepIMPALAAtariNetwork obj's underlying member method code with example usage: obj.__call__(inputs, state) and returns: logits,  value,  new_state