code: def random_resized_crop(
      images,
      target_height,
      target_width,
      scale=(0.8, 1.0),
      ratio=(3.0 / 4.0, 4.0 / 3.0),
  ):
      """
      Crop the given images to random size and aspect ratio. A crop of random
      size (default: of 0.08 to 1.0) of the original size and a random aspect
      ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This
      crop is finally resized to given size. This is popularly used to train the
      Inception networks.
  
      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.
      """
  
      height = images.shape[2]
      width = images.shape[3]
  
      i, j, h, w = _get_param_spatial_crop(scale, ratio, height, width)
      cropped = images[:, :, i : i + h, j : j + w]
      return torch.nn.functional.interpolate(
          cropped,
          size=(target_height, target_width),
          mode="bilinear",
          align_corners=False,
      )
  
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 17 for calling a global function with example usage: random_resized_crop(images, target_height, target_width, scale, ratio) and returns: torch