code: def __init__(
      self, action_space: ActionSpace, epsilon: float = 0.0, device=None
  ) -> None:
      super().__init__(action_space, device)
      self._epsilon = epsilon
      as_size = len(action_space)
      self._exploitation_prob = 1.0 - epsilon
      self._exploration_prob = epsilon / len(action_space)
      self._uniform_probs: List[float] = as_size * [1.0 / as_size]
      self._state_space = {}
  
imports: import pickle
  from functools import reduce
  from typing import List, Mapping, Sequence
  import torch
  from reagent.ope.estimators.sequential_estimators import Model, RLPolicy, State, ValueFunction
  from reagent.ope.estimators.types import Action, ActionDistribution, ActionSpace
  from reagent.ope.test.envs import Environment, PolicyLogGenerator
  from reagent.ope.utils import RunningAverage
label_desc: Use image node_id 1 to create a new TabularPolicy object from inherited base classes: RLPolicy with example: obj = TabularPolicy(action_space, epsilon, device)