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 get_exploration_action(
      self,
      *,
      action_distribution: ActionDistribution,
      timestep: Union[int, TensorType],
      explore: bool = True,
  ):
      # Adds IID Gaussian noise for exploration, TD3-style.
      if self.framework == "torch":
          return self._get_torch_exploration_action(
              action_distribution, explore, timestep
          )
      else:
          return self._get_tf_exploration_action_op(
              action_distribution, explore, 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 2 for calling the GaussianNoise obj's underlying member method code with example usage: obj.get_exploration_action() and returns: self, self