code: def test_build_eval_data_loader(
      self, model_sample, train_dataset, training_config_batch_size
  ):
      trainer = BaseTrainer(
          model=model_sample,
          train_dataset=train_dataset,
          training_config=training_config_batch_size,
      )
  
      eval_data_loader = trainer.get_eval_dataloader(train_dataset)
  
      assert issubclass(
          type(eval_data_loader), torch.utils.data.DataLoader
      )
      assert eval_data_loader.dataset == train_dataset
  
      assert (
          eval_data_loader.batch_size
          == trainer.training_config.per_device_eval_batch_size
      )
  
imports: import os
  from copy import deepcopy
  import pytest
  import torch
  from pythae.customexception import ModelError
  from pythae.models import AE, RHVAE, VAE, AEConfig, RHVAEConfig, VAEConfig
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
  from tests.data.custom_architectures import *
label_desc: Use image node_id 3 for calling the Test_DataLoader obj's underlying member method code with example usage: obj.test_build_eval_data_loader(model_sample, train_dataset, training_config_batch_size) without return types