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 _pil_interp(self, method):
      from PIL import Image
  
      if method == "bicubic":
          return Image.BICUBIC
      elif method == "lanczos":
          return Image.LANCZOS
      elif method == "hamming":
          return Image.HAMMING
      else:
          # default bilinear, do we want to allow nearest?
          return Image.BILINEAR
  
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 2 for calling the RandomResizedCropAndInterpolationWithTwoPic obj's underlying member method code with example usage: obj._pil_interp(method) and returns: Image, Image, Image, Image