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
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docstr: Args:
      img (PIL Image): Image to be cropped and resized.
  Returns:
      PIL Image: Randomly cropped and resized image.
docstr_tok: ["Args",":","img","(","PIL","Image",")",":","Image","to","be","cropped","and","resized",".","Returns",":","PIL","Image",":","Randomly","cropped","and","resized","image","."]
code: def __call__(self, img):
      """
      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
      return F.resized_crop(img, i, j, h, w, self.size, interpolation)
  
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 3 for calling the RandomResizedCropAndInterpolation obj's underlying member method code with example usage: obj.__call__(img) and returns: F