code: def test_train_and_embed(self):
      n_subfolders = 3
      n_samples_per_subfolder = 3
      n_samples = n_subfolders * n_samples_per_subfolder
  
      # embed, no overwrites
      dataset_dir, _, _ = self.create_dataset(
          n_subfolders, n_samples_per_subfolder
      )
  
      # train, one overwrite
      embeddings, labels, filenames = train_model_and_embed_images(
          input_dir=dataset_dir,
          trainer={"max_epochs": 1},
          loader={"num_workers": 0},
      )
      self.assertEqual(len(embeddings), n_samples)
      self.assertEqual(len(labels), n_samples)
      self.assertEqual(len(filenames), n_samples)
      self.assertIsInstance(embeddings[0], np.ndarray)
      self.assertIsInstance(
          int(labels[0]), int
      )  # see if casting to int works
      self.assertIsInstance(filenames[0], str)
  
file_path: lightly/tests/core/test_Core.py
imports: import os
  import re
  import shutil
  import tempfile
  import unittest
  import numpy
  import pytest
  import torchvision
  from lightly.core import train_model_and_embed_images
label_desc: Use image node_id 3 for calling the TestCore obj's underlying member method code with example usage: obj.test_train_and_embed() without return types