code: def create_batch(
      batch_size,
      candidate_num,
      candidate_dim,
      device,
      learning_method,
      diverse_input=False,
  ):
      # fake state, we only use candidates
      state = torch.zeros(batch_size, 1)
      if diverse_input:
          # city coordinates are spread in [0, 4]
          candidates = torch.randint(
              5, (batch_size, candidate_num, candidate_dim)
          ).float()
      else:
          # every training data has the same nodes as the input cities
          global FIX_CANDIDATES
          if FIX_CANDIDATES is None or FIX_CANDIDATES.shape != (
              batch_size,
              candidate_num,
              candidate_dim,
          ):
              candidates = torch.randint(
                  5, (batch_size, candidate_num, candidate_dim)
              ).float()
              candidates[1:] = candidates[0]
              FIX_CANDIDATES = candidates
          else:
              candidates = FIX_CANDIDATES
  
      batch_dict = {
          "state": state,
          "candidates": candidates,
          "device": device,
      }
      if learning_method == OFF_POLICY:
          # using data from a uniform sampling policy
          action = torch.stack(
              [torch.randperm(candidate_num) for _ in range(batch_size)]
          )
          propensity = torch.full(
              (batch_size, 1), 1.0 / math.factorial(candidate_num)
          )
          ranked_cities = gather(candidates, action)
          reward = compute_reward(ranked_cities)
          batch_dict["action"] = action
          batch_dict["logged_propensities"] = propensity
          batch_dict["slate_reward"] = -reward
  
      batch = rlt.PreprocessedRankingInput.from_input(**batch_dict)
      logger.info("Generate one batch")
      return batch
  
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 4 for calling a global function with example usage: create_batch(batch_size, candidate_num, candidate_dim, device, learning_method, diverse_input) and returns: batch