class_docstr: A recurrent network for use with IMPALA.
  
  See https://arxiv.org/pdf/1802.01561.pdf for more information.
docstr: Efficient unroll that applies embeddings, MLP, & convnet in one pass.
docstr_tok: ["Efficient","unroll","that","applies","embeddings",",","MLP",",","&","convnet","in","one","pass","."]
code: def unroll(
      self, inputs: observation_action_reward.OAR, state: hk.LSTMState
  ) -> Any:
      """Efficient unroll that applies embeddings, MLP, & convnet in one pass."""
      embeddings = self._embed(inputs)
      embeddings, new_states = hk.static_unroll(
          self._core, embeddings, state
      )
      logits, values = self._head(embeddings)
  
      return (logits, values), new_states
  
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 4 for calling the DeepIMPALAAtariNetwork obj's underlying member method code with example usage: obj.unroll(inputs, state) and returns: logits,  values,  new_states