code: def test_elastic_transform(device, channels, fill):
      if (
          isinstance(fill, (list, tuple))
          and len(fill) > 1
          and channels == 1
      ):
          # For this the test would correctly fail, since the number of channels in the image does not match `fill`.
          # Thus, this is not an issue in the transform, but rather a problem of parametrization that just gives the
          # product of `fill` and `channels`.
          return
  
      _test_class_op(
          T.ElasticTransform,
          meth_kwargs=dict(fill=fill),
          channels=channels,
          device=device,
      )
  
imports: import os
  import sys
  import numpy
  import PIL.Image
  import pytest
  import torch
  from common_utils import _assert_approx_equal_tensor_to_pil, _assert_equal_tensor_to_pil, _create_data, _create_data_batch, assert_equal, cpu_and_cuda, float_dtypes, get_tmp_dir, int_dtypes
  from torchvision import transforms
  from torchvision.transforms import functional, InterpolationMode
  from torchvision.transforms.autoaugment import _apply_op
label_desc: Use image node_id 44 for calling a global function with example usage: test_elastic_transform(device, channels, fill) without return types