code: def create_train_and_test_batches(
      batch_size,
      candidate_num,
      candidate_dim,
      device,
      num_train_batches,
      learning_method,
      diverse_input,
  ):
      train_batches = [
          create_batch(
              batch_size,
              candidate_num,
              candidate_dim,
              device,
              learning_method,
              diverse_input=diverse_input,
          )
          for _ in range(num_train_batches)
      ]
  
      if diverse_input:
          test_batch = create_batch(
              batch_size,
              candidate_num,
              candidate_dim,
              device,
              learning_method,
              diverse_input=diverse_input,
          )
      else:
          test_batch = train_batches[0]
  
      return train_batches, test_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 5 for calling a global function with example usage: create_train_and_test_batches(batch_size, candidate_num, candidate_dim, device, num_train_batches, learning_method, diverse_input) and returns: train_batches,  test_batch