code: def test_variable_input_size_backbone(
      checkpoint_name, provided_size, expected_size
  ):
      download_dir = "./ag_automm_tutorial_imgcls"
      train_df, test_df = shopee_dataset(download_dir)
  
      train_transforms = [
          "resize_shorter_side",
          "center_crop",
          "trivial_augment",
      ]
      val_transforms = ["resize_shorter_side", "center_crop"]
  
      timm_model = TimmAutoModelForImagePrediction(
          prefix="timm_image",
          checkpoint_name=checkpoint_name,
          pretrained=False,
      )
  
      image_processor = ImageProcessor(
          model=timm_model,
          train_transforms=train_transforms,
          val_transforms=val_transforms,
          size=provided_size,
      )
      ret = image_processor.process_one_sample(
          {"image": train_df["image"].tolist()},
          feature_modalities={"image": "image"},
          is_training=True,
      )
      assert ret["timm_image_image"].shape[-1] == expected_size
      out = timm_model(
          th.unsqueeze(ret[timm_model.image_key], dim=0),
          th.unsqueeze(
              th.Tensor(ret[timm_model.image_valid_num_key]), dim=0
          ),
      )
  
file_path: autogluon/multimodal/tests/unittests/others/test_process_image.py
imports: import PIL
  import pytest
  import torch
  from autogluon.multimodal.data.process_image import ImageProcessor
  from autogluon.multimodal.models.timm_image import TimmAutoModelForImagePrediction
  from autogluon.multimodal.utils.misc import shopee_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_variable_input_size_backbone(checkpoint_name, provided_size, expected_size) without return types