class_docstr: Builder for SVG0 which constructs individual components of the agent.
class_docstr_tok: ["Builder","for","SVG0","which","constructs","individual","components","of","the","agent","."]
docstr: Create an actor instance.
docstr_tok: ["Create","an","actor","instance","."]
code: def make_actor(
      self,
      policy_network: snt.Module,
      adder: Optional[adders.Adder] = None,
      variable_source: Optional[core.VariableSource] = None,
      deterministic_policy: Optional[bool] = False,
  ):
      """Create an actor instance."""
      if variable_source:
          # Create the variable client responsible for keeping the actor up-to-date.
          variable_client = variable_utils.VariableClient(
              client=variable_source,
              variables={"policy": policy_network.variables},
              update_period=1000,
          )
  
          # Make sure not to use a random policy after checkpoint restoration by
          # assigning variables before running the environment loop.
          variable_client.update_and_wait()
  
      else:
          variable_client = None
  
      # Create the actor which defines how we take actions.
      return acting.SVG0Actor(
          policy_network=policy_network,
          adder=adder,
          variable_client=variable_client,
          deterministic_policy=deterministic_policy,
      )
  
imports: import copy
  import dataclasses
  from typing import Iterator, List, Optional, Tuple
  from acme import adders
  from acme import core
  from acme import datasets
  from acme import specs
  from acme.adders import reverb
  from acme.agents import agent
  from acme.agents.tf.svg0_prior import acting
  from acme.agents.tf.svg0_prior import learning
  from acme.tf import utils
  from acme.tf import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  import reverb
  import sonnet
  import tensorflow
label_desc: Use image node_id 5 for calling the SVG0Builder obj's underlying member method code with example usage: obj.make_actor(policy_network, adder, variable_source, deterministic_policy) and returns: acting