code: def __init__(
      self,
      size: Tuple[int, int],
      start: Tuple[int, int],
      goal: Tuple[int, int],
      max_horizon: int = -1,
      walls: Iterable[Tuple[int, int]] = (),
      use_taxicab_reward: bool = False,
  ):
      super().__init__(max_horizon)
      self.size = size
      self.start = start
      self.goal = goal
      self.walls = set(walls)
      self.use_taxicab_reward = use_taxicab_reward
      self.reset()
  
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 GridWorld object from inherited base classes: Environment with example: obj = GridWorld(size, start, goal, max_horizon, walls, use_taxicab_reward)