code: def random_resized_crop_with_shift(
      images,
      target_height,
      target_width,
      scale=(0.8, 1.0),
      ratio=(3.0 / 4.0, 4.0 / 3.0),
  ):
      """
      This is similar to random_resized_crop. However, it samples two different
      boxes (for cropping) for the first and last frame. It then linearly
      interpolates the two boxes for other frames.
  
      Args:
          images: Images to perform resizing and cropping.
          target_height: Desired height after cropping.
          target_width: Desired width after cropping.
          scale: Scale range of Inception-style area based random resizing.
          ratio: Aspect ratio range of Inception-style area based random resizing.
      """
      t = images.shape[1]
      height = images.shape[2]
      width = images.shape[3]
  
      i, j, h, w = _get_param_spatial_crop(scale, ratio, height, width)
      i_, j_, h_, w_ = _get_param_spatial_crop(
          scale, ratio, height, width
      )
      i_s = [int(i) for i in torch.linspace(i, i_, steps=t).tolist()]
      j_s = [int(i) for i in torch.linspace(j, j_, steps=t).tolist()]
      h_s = [int(i) for i in torch.linspace(h, h_, steps=t).tolist()]
      w_s = [int(i) for i in torch.linspace(w, w_, steps=t).tolist()]
      out = torch.zeros((3, t, target_height, target_width))
      for ind in range(t):
          out[:, ind : ind + 1, :, :] = torch.nn.functional.interpolate(
              images[
                  :,
                  ind : ind + 1,
                  i_s[ind] : i_s[ind] + h_s[ind],
                  j_s[ind] : j_s[ind] + w_s[ind],
              ],
              size=(target_height, target_width),
              mode="bilinear",
              align_corners=False,
          )
      return out
  
imports: import logging
  import math
  import numpy
  import random
  import torch
  import torchvision
  import torchvision.transforms.functional
  from PIL import Image, ImageFilter
  from scipy.ndimage import gaussian_filter
  from torchvision import transforms
  from .rand_augment import rand_augment_transform
  from .random_erasing import RandomErasing
label_desc: Use image node_id 18 for calling a global function with example usage: random_resized_crop_with_shift(images, target_height, target_width, scale, ratio) and returns: out