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 __repr__(self):
      if isinstance(self.interpolation, (tuple, list)):
          interpolate_str = " ".join(
              [_pil_interpolation_to_str[x] for x in self.interpolation]
          )
      else:
          interpolate_str = _pil_interpolation_to_str[
              self.interpolation
          ]
      format_string = self.__class__.__name__ + "(size={0}".format(
          self.size
      )
      format_string += ", scale={0}".format(
          tuple(round(s, 4) for s in self.scale)
      )
      format_string += ", ratio={0}".format(
          tuple(round(r, 4) for r in self.ratio)
      )
      format_string += ", interpolation={0})".format(interpolate_str)
      return format_string
  
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 4 for calling the RandomResizedCropAndInterpolation obj's underlying member method code with example usage: obj.__repr__() and returns: format_string