code: def elastic_transform(
      img: Tensor,
      displacement: Tensor,
      interpolation: InterpolationMode = InterpolationMode.BILINEAR,
      fill: Optional[List[float]] = None,
  ) -> Tensor:
      """Transform a tensor image with elastic transformations.
      Given alpha and sigma, it will generate displacement
      vectors for all pixels based on random offsets. Alpha controls the strength
      and sigma controls the smoothness of the displacements.
      The displacements are added to an identity grid and the resulting grid is
      used to grid_sample from the image.
  
      Applications:
          Randomly transforms the morphology of objects in images and produces a
          see-through-water-like effect.
  
      Args:
          img (PIL Image or Tensor): Image on which elastic_transform is applied.
              If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
              where ... means it can have an arbitrary number of leading dimensions.
              If img is PIL Image, it is expected to be in mode "P", "L" or "RGB".
          displacement (Tensor): The displacement field. Expected shape is [1, H, W, 2].
          interpolation (InterpolationMode): Desired interpolation enum defined by
              :class:`torchvision.transforms.InterpolationMode`.
              Default is ``InterpolationMode.BILINEAR``.
              The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
          fill (number or str or tuple): Pixel fill value for constant fill. Default is 0.
              If a tuple of length 3, it is used to fill R, G, B channels respectively.
              This value is only used when the padding_mode is constant.
      """
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(elastic_transform)
      # Backward compatibility with integer value
      if isinstance(interpolation, int):
          warnings.warn(
              "Argument interpolation should be of type InterpolationMode instead of int. "
              "Please, use InterpolationMode enum."
          )
          interpolation = _interpolation_modes_from_int(interpolation)
  
      if not isinstance(displacement, torch.Tensor):
          raise TypeError("Argument displacement should be a Tensor")
  
      t_img = img
      if not isinstance(img, torch.Tensor):
          if not F_pil._is_pil_image(img):
              raise TypeError(
                  f"img should be PIL Image or Tensor. Got {type(img)}"
              )
          t_img = pil_to_tensor(img)
  
      shape = t_img.shape
      shape = (1,) + shape[-2:] + (2,)
      if shape != displacement.shape:
          raise ValueError(
              f"Argument displacement shape should be {shape}, but given {displacement.shape}"
          )
  
      # TODO: if image shape is [N1, N2, ..., C, H, W] and
      # displacement is [1, H, W, 2] we need to reshape input image
      # such grid_sampler takes internal code for 4D input
  
      output = F_t.elastic_transform(
          t_img,
          displacement,
          interpolation=interpolation.value,
          fill=fill,
      )
  
      if not isinstance(img, torch.Tensor):
          output = to_pil_image(output, mode=img.mode)
      return output
  
imports: import math
  import numbers
  import sys
  import warnings
  from enum import Enum
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  import torch
  from PIL import Image
  from torch import Tensor
  from ..utils import _log_api_usage_once
  from .None import _functional_pil, _functional_tensor
label_desc: Use image node_id 42 for calling a global function with example usage: elastic_transform(img, displacement, interpolation, fill) and returns: output