class_docstr: A feed-forward actor.
  
  An actor based on a feed-forward policy which takes non-batched observations
  and outputs non-batched actions. It also allows adding experiences to replay
  and updating the weights from the policy on the learner.
class_docstr_tok: ["A","feed-forward","actor",".","An","actor","based","on","a","feed-forward","policy","which","takes","non-batched","observations","and","outputs","non-batched","actions",".","It","also","allows","adding","experiences","to","replay","and","updating","the","weights","from","the","policy","on","the","learner","."]
code: def observe_first(self, timestep: dm_env.TimeStep):
      if self._adder:
          self._adder.add_first(timestep)
  
imports: from typing import Optional, Tuple
  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 4 for calling the FeedForwardActor obj's underlying member method code with example usage: obj.observe_first(timestep) without return types