code: def _next_state_reward(
      self, state: State, action: Action
  ) -> StateReward:
      value = state.value
      assert isinstance(
          value, tuple
      ), f"got type {type(value)} instead of tuple"
      # pyre-fixme[23]: Unable to unpack single value, 2 were expected.
      (x, y) = value
      assert isinstance(x, int) and isinstance(
          y, int
      ), "Gridworld expects states to be Tuple[int, int]"
      if state.value in self.walls or state.value == self.goal:
          return StateReward(State((x, y), state.is_terminal), 0.0)
      if action.value == 0:
          to_pos, reward, is_end = self._transit((x, y), (x + 1, y))
      elif action.value == 1:
          to_pos, reward, is_end = self._transit((x, y), (x, y + 1))
      elif action.value == 2:
          to_pos, reward, is_end = self._transit((x, y), (x - 1, y))
      else:
          to_pos, reward, is_end = self._transit((x, y), (x, y - 1))
      return StateReward(State(to_pos, is_end), reward)
  
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 9 for calling the GridWorld obj's underlying member method code with example usage: obj._next_state_reward(state, action) and returns: StateReward, StateReward