class_docstr: A recurrent actor.
class_docstr_tok: ["A","recurrent","actor","."]
code: def __init__(
      self,
      network: snt.RNNCore,
      adder: Optional[adders.Adder] = None,
      variable_client: Optional[
          tf2_variable_utils.VariableClient
      ] = None,
  ):
      # Store these for later use.
      self._adder = adder
      self._variable_client = variable_client
      self._network = network
  
      # TODO(b/152382420): Ideally we would call tf.function(network) instead but
      # this results in an error when using acme RNN snapshots.
      self._policy = tf.function(network.__call__)
  
      self._state = None
      self._prev_state = None
      self._prev_logits = None
  
file_path: acme/acme/agents/tf/impala/acting.py
imports: from typing import Optional
  from acme import adders
  from acme import core
  from acme import types
  from acme.tf import utils
  from acme.tf import variable_utils
  import dm_env
  import sonnet
  import tensorflow
  import tensorflow_probability
label_desc: Use image node_id 1 to create a new IMPALAActor object from inherited base classes: core with example: obj = IMPALAActor(network, adder, variable_client)