code: def forward(
      self, state, candidates, ranked_cities, src_src_mask, tgt_out_idx
  ):
      reward = compute_reward(ranked_cities)
      # negate because we want to minimize
      return -reward
  
imports: import logging
  import math
  import tempfile
  from itertools import permutations
  import pytorch_lightning
  import reagent.core.types
  import torch
  import torch.nn
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.core.parameters_seq2slate import LearningMethod, SimulationParameters
  from reagent.core.torch_utils import gather
  from reagent.model_utils.seq2slate_utils import Seq2SlateOutputArch
  from reagent.models.seq2slate import Seq2SlateMode, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.seq2slate_sim_trainer import Seq2SlateSimulationTrainer
  from reagent.training.ranking.seq2slate_trainer import Seq2SlateTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling the TSPRewardModel obj's underlying member method code with example usage: obj.forward(state, candidates, ranked_cities, src_src_mask, tgt_out_idx) and returns: unknown