code: def test_files_to_torch(files_data, torch_data):
      converted_data = files_to_torch(files_data)
      assert converted_data[0][0].shape == torch_data[0][0].shape
  
      resized_converted_data = files_to_numpy(
          files_data,
          resize=256,
          crop=224,
          mean=[0.1, 0.1, 0.1],
          std=[0.1, 0.1, 0.1],
      )
      assert resized_converted_data[0][0].shape[1] == 224
      assert resized_converted_data[0][0].shape[2] == 224
  
file_path: traintool/tests/image_classification/test_preprocessing.py
imports: import comet_ml
  import pytest
  import numpy
  import torch
  from conftest import create_dataset, create_image
  from traintool.image_classification.preprocessing import recognize_data_format, torch_to_numpy, numpy_to_torch, files_to_numpy, files_to_torch, load_image, recognize_image_format, get_num_classes
label_desc: Use image node_id 12 for calling a global function with example usage: test_files_to_torch(files_data, torch_data) without return types