class_docstr: Crop the given PIL Image to random size and aspect ratio with random interpolation.
  
  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:
      size: expected output size of each edge
      scale: range of size of the origin size cropped
      ratio: range of aspect ratio of the origin aspect ratio cropped
      interpolation: Default: PIL.Image.BILINEAR
class_docstr_tok: ["Crop","the","given","PIL","Image","to","random","size","and","aspect","ratio","with","random","interpolation",".","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",":","size",":","expected","output","size","of","each","edge","scale",":","range","of","size","of","the","origin","size","cropped","ratio",":","range","of","aspect","ratio","of","the","origin","aspect","ratio","cropped","interpolation",":","Default",":","PIL.Image.BILINEAR"]
code: def __call__(self, img):
      import torchvision.transforms.functional as F
  
      """
          Args:
              img (PIL Image): Image to be cropped and resized.
  
          Returns:
              PIL Image: Randomly cropped and resized image.
          """
      i, j, h, w = self.get_params(img, self.scale, self.ratio)
      if isinstance(self.interpolation, (tuple, list)):
          interpolation = random.choice(self.interpolation)
      else:
          interpolation = self.interpolation
      if self.second_size is None:
          return F.resized_crop(
              img, i, j, h, w, self.size, interpolation
          )
      else:
          return F.resized_crop(
              img, i, j, h, w, self.size, interpolation
          ), F.resized_crop(
              img,
              i,
              j,
              h,
              w,
              self.second_size,
              self.second_interpolation,
          )
  
imports: from functools import partial
  import logging
  import math
  import random
  import time
  import numpy
  import os
  import torch
  from torchvision import datasets, transforms
  from .path_dataset import PathDataset
  from fairseq.data import FairseqDataset
  from fairseq.data.data_utils import compute_block_mask_1d, compute_block_mask_2d
  from shutil import copyfile
label_desc: Use image node_id 4 for calling the RandomResizedCropAndInterpolationWithTwoPic obj's underlying member method code with example usage: obj.__call__(img) and returns: F, F,  F