code: def train_and_infer(self, idx=0):
      results = []
      if not os.path.exists(os.path.join(self.data_dir, "MedNIST")):
          # skip test if no MedNIST dataset
          return results
  
      set_determinism(seed=0)
      losses, best_metric, best_metric_epoch = run_training_test(
          self.data_dir,
          self.train_x,
          self.train_y,
          self.val_x,
          self.val_y,
          device=self.device,
      )
      infer_metric = run_inference_test(
          self.data_dir, self.test_x, self.test_y, device=self.device
      )
  
      print(f"integration_classification_2d {losses}")
      print("best metric", best_metric)
      print("infer metric", infer_metric)
      # check training properties
      self.assertTrue(
          test_integration_value(
              TASK, key="losses", data=losses, rtol=1e-2
          )
      )
      self.assertTrue(
          test_integration_value(
              TASK, key="best_metric", data=best_metric, rtol=1e-4
          )
      )
      np.testing.assert_allclose(best_metric_epoch, 4)
      model_file = os.path.join(self.data_dir, "best_metric_model.pth")
      self.assertTrue(os.path.exists(model_file))
      # check inference properties
      self.assertTrue(
          test_integration_value(
              TASK,
              key="infer_prop",
              data=np.asarray(infer_metric),
              rtol=1,
          )
      )
      results.extend(losses)
      results.append(best_metric)
      results.extend(infer_metric)
      return results
  
imports: from __future__ import annotations
  import os
  import unittest
  import warnings
  import numpy
  import torch
  from torch.utils.data import DataLoader
  import monai
  from monai.apps import download_and_extract
  from monai.data import decollate_batch
  from monai.metrics import ROCAUCMetric
  from monai.networks import eval_mode
  from monai.networks.nets import DenseNet121
  from monai.transforms import Activations, AsDiscrete, Compose, EnsureChannelFirst, LoadImage, RandFlip, RandRotate, RandZoom, ScaleIntensity, Transpose
  from monai.utils import set_determinism
  from tests.testing_data.integration_answers import test_integration_value
  from tests.utils import DistTestCase, TimedCall, skip_if_downloading_fails, skip_if_quick, testing_data_config
label_desc: Use image node_id 3 for calling the IntegrationClassification2D obj's underlying member method code with example usage: obj.train_and_infer(idx) and returns: results, results