code: def test_batches(self, device, dt):
      if dt == torch.float16 and device == "cpu":
          # skip float16 on CPU case
          return
  
      batch_tensors = _create_data_batch(
          26, 36, num_samples=4, device=device
      )
      if dt is not None:
          batch_tensors = batch_tensors.to(dtype=dt)
  
      _test_fn_on_batch(
          batch_tensors,
          F.affine,
          angle=-43,
          translate=[-3, 4],
          scale=1.2,
          shear=[4.0, 5.0],
      )
  
imports: import colorsys
  import itertools
  import math
  import os
  from functools import partial
  from typing import Sequence
  import numpy
  import PIL.Image
  import pytest
  import torch
  import torchvision.transforms
  import torchvision.transforms._functional_pil
  import torchvision.transforms._functional_tensor
  import torchvision.transforms.functional
  from common_utils import _assert_approx_equal_tensor_to_pil, _assert_equal_tensor_to_pil, _create_data, _create_data_batch, _test_fn_on_batch, assert_equal, cpu_and_cuda, needs_cuda
  from torchvision.transforms import InterpolationMode
label_desc: Use image node_id 6 for calling the TestAffine obj's underlying member method code with example usage: obj.test_batches(device, dt) without return types