class_docstr: A recurrent network for use with R2D2.
  
  See https://openreview.net/forum?id=r1lyTjAqYX for more information.
class_docstr_tok: ["A","recurrent","network","for","use","with","R2D2",".","See","https",":","\/\/openreview.net\/forum","?","id=r1lyTjAqYX","for","more","information","."]
code: def __call__(
      self,
      inputs: observation_action_reward.OAR,
      state: base.State,
  ) -> Tuple[QValues, base.State]:
      embeddings = self._embed(inputs)
      embeddings, new_state = self._core(embeddings, state)
      action_values = self._head(embeddings)  # [B, A]
  
      return action_values, new_state
  
imports: from typing import Optional, Tuple
  from acme.tf.networks import base
  from acme.tf.networks import duelling
  from acme.tf.networks import embedding
  from acme.tf.networks import policy_value
  from acme.tf.networks import recurrence
  from acme.tf.networks import vision
  from acme.wrappers import observation_action_reward
  import sonnet
  import tensorflow
label_desc: Use image node_id 2 for calling the R2D2AtariNetwork obj's underlying member method code with example usage: obj.__call__(inputs, state) and returns: action_values,  new_state