class_docstr: An exploration that simply samples from a distribution.
  
  The sampling can be made deterministic by passing explore=False into
  the call to `get_exploration_action`.
  Also allows for scheduled parameters for the distributions, such as
  lowering stddev, temperature, etc.. over time.
class_docstr_tok: ["An","exploration","that","simply","samples","from","a","distribution",".","The","sampling","can","be","made","deterministic","by","passing","explore=False","into","the","call","to","`","get_exploration_action","`",".","Also","allows","for","scheduled","parameters","for","the","distributions",",","such","as","lowering","stddev",",","temperature",",","etc","..","over","time","."]
code: def get_exploration_action(
      self,
      *,
      action_distribution: ActionDistribution,
      timestep: Optional[Union[int, TensorType]] = None,
      explore: bool = True,
  ):
      if self.framework == "torch":
          return self._get_torch_exploration_action(
              action_distribution, timestep, explore
          )
      else:
          return self._get_tf_exploration_action_op(
              action_distribution, timestep, explore
          )
  
file_path: ray/python/ray/rllib/utils/exploration/stochastic_sampling.py
imports: import functools
  import gymnasium
  import numpy
  from typing import Optional, Union
  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 get_variable, try_import_tf, try_import_torch, TensorType
  from ray.rllib.utils.tf_utils import zero_logps_from_actions
label_desc: Use image node_id 2 for calling the StochasticSampling obj's underlying member method code with example usage: obj.get_exploration_action() and returns: self, self