code: def __init__(
      self,
      gridworld: GridWorld,
      action_space: ActionSpace,
      epsilon: float = 0.1,
      max_horizon: int = -1,
  ):
      super().__init__(max_horizon)
      self._gridworld = gridworld
      self.action_space = action_space
      self.epsilon = epsilon
      self.noise_prob = epsilon / (len(action_space) - 1)
  
imports: import logging
  import random
  from typing import Iterable, Optional, Sequence, Tuple
  import numpy
  import torch
  from reagent.ope.estimators.sequential_estimators import DMEstimator, DoublyRobustEstimator, EpsilonGreedyRLPolicy, IPSEstimator, MAGICEstimator, NeuralDualDICE, RewardProbability, RLEstimatorInput, State, StateDistribution, StateReward, ValueFunction
  from reagent.ope.estimators.types import Action, ActionSpace
  from reagent.ope.test.envs import Environment, PolicyLogGenerator
  from reagent.ope.trainers.rl_tabular_trainers import DPTrainer, DPValueFunction, EstimatedStateValueFunction, TabularPolicy
label_desc: Use image node_id 1 to create a new NoiseGridWorldModel object from inherited base classes: Environment with example: obj = NoiseGridWorldModel(gridworld, action_space, epsilon, max_horizon)