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 __init__(
      self,
      size,
      scale=(0.08, 1.0),
      ratio=(3.0 / 4.0, 4.0 / 3.0),
      interpolation="bilinear",
  ):
      if isinstance(size, (list, tuple)):
          self.size = tuple(size)
      else:
          self.size = (size, size)
      if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
          warnings.warn("range should be of kind (min, max)")
  
      if interpolation == "random":
          self.interpolation = _RANDOM_INTERPOLATION
      else:
          self.interpolation = str_to_interp_mode(interpolation)
      self.scale = scale
      self.ratio = ratio
  
imports: import math
  import numbers
  import random
  import warnings
  from typing import List, Sequence, Tuple, Union
  import torch
  import torchvision.transforms.functional
  from PIL import Image
  import numpy
label_desc: Use image node_id 1 to create a new RandomResizedCropAndInterpolation object  with example: obj = RandomResizedCropAndInterpolation(size, scale, ratio, interpolation)