code: def monai_warp(img, ddf):
      """
      warp with MONAI
      Args:
          img: numpy array of shape (D, H, W)
          ddf: numpy array of shape (3, D, H, W)
  
      Returns:
          warped_img: numpy arrap of shape (D, H, W)
      """
      warp_layer = Warp(padding_mode="zeros")
      # turn to tensor and add channel dim
      monai_img = torch.tensor(img).unsqueeze(0)
      ddf = torch.tensor(ddf)
      # img -> batch -> img
      warped_img = warp_layer(
          monai_img.unsqueeze(0), ddf.unsqueeze(0)
      ).squeeze(0)
      # remove channel dim
      warped_img = np.asarray(warped_img.squeeze(0))
  
      return warped_img
  
imports: from __future__ import annotations
  import os
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from torch.autograd import gradcheck
  from monai.config.deviceconfig import USE_COMPILED
  from monai.networks.blocks.warp import Warp
  from monai.transforms import LoadImaged
  from monai.utils import GridSampleMode, GridSamplePadMode
  from tests.utils import SkipIfBeforePyTorchVersion, SkipIfNoModule, download_url_or_skip_test, skip_if_quick, testing_data_config
label_desc: Use image node_id 3 for calling a global function with example usage: monai_warp(img, ddf) and returns: warped_img