code: def test_train_and_test(self):
      res = self.wrapper.train_and_test_on_datasets(
          self.dataset,
          self.dataset,
          self.optim,
          32,
          5,
          False,
          return_best_weights=False,
      )
      assert len(res) == 5
      res = self.wrapper.train_and_test_on_datasets(
          self.dataset,
          self.dataset,
          self.optim,
          32,
          5,
          False,
          return_best_weights=True,
      )
      assert len(res) == 2
      assert len(res[0]) == 5
      assert isinstance(res[1], dict)
      mock = Mock()
      mock.side_effect = (
          ((np.linspace(0, 50) - 10) / 10) ** 2
      ).tolist()
      self.wrapper.test_on_dataset = mock
      res = self.wrapper.train_and_test_on_datasets(
          self.dataset,
          self.dataset,
          self.optim,
          32,
          50,
          False,
          return_best_weights=True,
          patience=1,
      )
  
      assert len(res) == 2
      assert len(res[0]) < 50
  
      mock = Mock()
      mock.side_effect = (
          ((np.linspace(0, 50) - 10) / 10) ** 2
      ).tolist()
      self.wrapper.test_on_dataset = mock
      res = self.wrapper.train_and_test_on_datasets(
          self.dataset,
          self.dataset,
          self.optim,
          32,
          50,
          False,
          return_best_weights=True,
          patience=1,
          min_epoch_for_es=20,
      )
      assert len(res) == 2
      assert len(res[0]) < 50 and len(res[0]) > 20
  
imports: import math
  import unittest
  from unittest.mock import Mock
  import numpy
  import pytest
  import torch
  import torchmetrics
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  from baal.modelwrapper import ModelWrapper, mc_inference
  from baal.utils.metrics import ClassificationReport
label_desc: Use image node_id 10 for calling the ModelWrapperTest obj's underlying member method code with example usage: obj.test_train_and_test() without return types