class_docstr: A GenericActor that uses observation normalization.
docstr: Initializes a feed forward actor.
  
  Args:
    actor: actor core.
    normalization_fns: Function that are used for normalizing observations.
    random_key: Random key.
    variable_client: The variable client to get policy and observation
      normalization parameters from. The variable client should be defined to
      provide [policy_params, obs_norm_params].
    adder: An adder to add experiences to.
    jit: Whether or not to jit the ActorCore and normalization functions.
    backend: Which backend to use when jitting.
    per_episode_update: if True, updates variable client params once at the
      beginning of each episode
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code: def __init__(
      self,
      actor: actor_core.ActorCore[actor_core.State, actor_core.Extras],
      normalization_fns: NormalizationFns,
      random_key: network_lib.PRNGKey,
      variable_client: Optional[variable_utils.VariableClient],
      adder: Optional[adders.Adder] = None,
      jit: bool = True,
      backend: Optional[str] = "cpu",
      per_episode_update: bool = False,
  ):
      """Initializes a feed forward actor.
  
      Args:
        actor: actor core.
        normalization_fns: Function that are used for normalizing observations.
        random_key: Random key.
        variable_client: The variable client to get policy and observation
          normalization parameters from. The variable client should be defined to
          provide [policy_params, obs_norm_params].
        adder: An adder to add experiences to.
        jit: Whether or not to jit the ActorCore and normalization functions.
        backend: Which backend to use when jitting.
        per_episode_update: if True, updates variable client params once at the
          beginning of each episode
      """
      super().__init__(
          actor,
          random_key,
          variable_client,
          adder,
          jit,
          backend,
          per_episode_update,
      )
      if jit:
          self._apply_normalization = jax.jit(
              normalization_fns.normalize, backend=backend
          )
      else:
          self._apply_normalization = normalization_fns.normalize
  
imports: import dataclasses
  from typing import Any, Callable, Generic, NamedTuple, Optional
  from acme import adders
  from acme import types
  from acme.agents.jax import actor_core
  from acme.agents.jax import actors
  from acme.jax import networks
  from acme.jax import running_statistics
  from acme.jax import utils
  from acme.jax import variable_utils
  import jax
  import jax.numpy
label_desc: Use image node_id 1 to create a new NormalizedGenericActor object from inherited base classes: actors.GenericActor, Generic with example: obj = NormalizedGenericActor(actor, normalization_fns, random_key, variable_client, adder, jit, backend, per_episode_update)