code: def test_from_filepaths_smoke(tmpdir):
      tmpdir = Path(tmpdir)
  
      (tmpdir / "a").mkdir()
      (tmpdir / "b").mkdir()
      _rand_image().save(tmpdir / "a_1.png")
      _rand_image().save(tmpdir / "b_1.png")
  
      train_images = [
          str(tmpdir / "a_1.png"),
          str(tmpdir / "b_1.png"),
      ]
  
      img_data = ImageClassificationData.from_files(
          train_files=train_images,
          train_targets=[1, 2],
          batch_size=2,
          num_workers=0,
      )
      assert img_data.train_dataloader() is not None
  
      data = next(iter(img_data.train_dataloader()))
      imgs, labels = data["input"], data["target"]
      assert imgs.shape == (2, 3, 196, 196)
      assert labels.shape == (2,)
      assert sorted(labels.numpy()) == [1, 2]
  
imports: import csv
  import os
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Tuple
  import numpy
  import pandas
  import pytest
  import torch
  from flash.core.data.io.input import DataKeys
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _MATPLOTLIB_AVAILABLE, _PIL_AVAILABLE, _TOPIC_IMAGE_AVAILABLE, _TORCHVISION_AVAILABLE
  from flash.image import ImageClassificationData, ImageClassificationInputTransform
label_desc: Use image node_id 2 for calling a global function with example usage: test_from_filepaths_smoke(tmpdir) without return types