code: def test_translations(self, device, height, width, dt, t, fn):
      # 3) Test translation
      tensor, pil_img = _create_data(height, width, device=device)
  
      if dt == torch.float16 and device == "cpu":
          # skip float16 on CPU case
          return
  
      if dt is not None:
          tensor = tensor.to(dtype=dt)
  
      out_pil_img = F.affine(
          pil_img,
          angle=0,
          translate=t,
          scale=1.0,
          shear=[0.0, 0.0],
          interpolation=NEAREST,
      )
  
      out_tensor = fn(
          tensor,
          angle=0,
          translate=t,
          scale=1.0,
          shear=[0.0, 0.0],
          interpolation=NEAREST,
      )
  
      if out_tensor.dtype != torch.uint8:
          out_tensor = out_tensor.to(torch.uint8)
  
      _assert_equal_tensor_to_pil(out_tensor, out_pil_img)
  
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 4 for calling the TestAffine obj's underlying member method code with example usage: obj.test_translations(device, height, width, dt, t, fn) without return types