class_docstr: Epsilon-greedy Exploration class that produces exploration actions.
  
  When given a Model's output and a current epsilon value (based on some
  Schedule), it produces a random action (if rand(1) < eps) or
  uses the model-computed one (if rand(1) >= eps).
code: def get_state(self, sess: Optional["tf.Session"] = None):
      if sess:
          return sess.run(self._tf_state_op)
      eps = self.epsilon_schedule(self.last_timestep)
      return {
          "cur_epsilon": convert_to_numpy(eps)
          if self.framework != "tf"
          else eps,
          "last_timestep": convert_to_numpy(self.last_timestep)
          if self.framework != "tf"
          else self.last_timestep,
      }
  
imports: import gymnasium
  import numpy
  import tree
  import random
  from typing import Union, Optional
  from ray.rllib.models.torch.torch_action_dist import TorchMultiActionDistribution
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.exploration.exploration import Exploration, TensorType
  from ray.rllib.utils.framework import try_import_tf, try_import_torch, get_variable
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.schedules import Schedule, PiecewiseSchedule
  from ray.rllib.utils.torch_utils import FLOAT_MIN
label_desc: Use image node_id 5 for calling the EpsilonGreedy obj's underlying member method code with example usage: obj.get_state(sess) and returns: dict, sess