class_docstr: An exploration that adds white noise to continuous actions.
  
  If explore=True, returns actions plus scale (annealed over time) x
  Gaussian noise. Also, some completely random period is possible at the
  beginning.
  
  If explore=False, returns the deterministic action.
class_docstr_tok: ["An","exploration","that","adds","white","noise","to","continuous","actions",".","If","explore=True",",","returns","actions","plus","scale","(","annealed","over","time",")","x","Gaussian","noise",".","Also",",","some","completely","random","period","is","possible","at","the","beginning",".","If","explore=False",",","returns","the","deterministic","action","."]
code: def set_state(
      self, state: dict, sess: Optional["tf.Session"] = None
  ) -> None:
      if self.framework == "tf":
          self.last_timestep.load(state["last_timestep"], session=sess)
      elif isinstance(self.last_timestep, int):
          self.last_timestep = state["last_timestep"]
      else:
          self.last_timestep.assign(state["last_timestep"])
  
imports: from gymnasium.spaces import Space
  import numpy
  from typing import Union, Optional
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.exploration.exploration import Exploration
  from ray.rllib.utils.exploration.random import Random
  from ray.rllib.utils.framework import try_import_tf, try_import_torch, get_variable, TensorType
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.schedules import Schedule
  from ray.rllib.utils.schedules.piecewise_schedule import PiecewiseSchedule
  from ray.rllib.utils.tf_utils import zero_logps_from_actions
label_desc: Use image node_id 6 for calling the GaussianNoise obj's underlying member method code with example usage: obj.set_state(state, sess) without return types