code: def test_elastic_transform_consistency(
      device, interpolation, dt, fill
  ):
      script_elastic_transform = torch.jit.script(F.elastic_transform)
      img_tensor, _ = _create_data(32, 34, device=device)
      # As there is no PIL implementation for elastic_transform,
      # thus we do not run tests tensor vs pillow
  
      if dt is not None:
          img_tensor = img_tensor.to(dt)
  
      displacement = T.ElasticTransform.get_params(
          [1.5, 1.5], [2.0, 2.0], [32, 34]
      )
      kwargs = dict(
          displacement=displacement,
          interpolation=interpolation,
          fill=fill,
      )
  
      out_tensor1 = F.elastic_transform(img_tensor, **kwargs)
      out_tensor2 = script_elastic_transform(img_tensor, **kwargs)
      assert_equal(out_tensor1, out_tensor2)
  
      batch_tensors = _create_data_batch(
          16, 18, num_samples=4, device=device
      )
      displacement = T.ElasticTransform.get_params(
          [1.5, 1.5], [2.0, 2.0], [16, 18]
      )
      kwargs["displacement"] = displacement
      if dt is not None:
          batch_tensors = batch_tensors.to(dt)
      _test_fn_on_batch(batch_tensors, F.elastic_transform, **kwargs)
  
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 40 for calling a global function with example usage: test_elastic_transform_consistency(device, interpolation, dt, fill) without return types