code: def test_dataloader_fit_evaluate(self):
      metric_name = "rmse"
      modelBuilder = PytorchModelBuilder(
          model_creator=model_creator_pytorch,
          optimizer_creator=optimizer_creator,
          loss_creator=loss_creator,
      )
      model = modelBuilder.build(
          config={
              "lr": 1e-2,
              "batch_size": 32,
              "train_size": 500,
              "valid_size": 100,
              "shuffle": True,
          }
      )
      val_result = model.fit_eval(
          data=train_dataloader_creator,
          validation_data=valid_dataloader_creator,
          metric=metric_name,
          epochs=20,
      )
      assert model.config["train_size"] == 500
      assert model.config["valid_size"] == 100
      assert model.config["shuffle"] is True
      assert val_result.get(metric_name)
  
imports: from unittest import TestCase
  from zoo.orca.automl.model.base_pytorch_model import PytorchModelBuilder
  from torch.utils.data import Dataset, DataLoader
  import pytest
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 5 for calling the TestBasePytorchModel obj's underlying member method code with example usage: obj.test_dataloader_fit_evaluate() without return types