class_docstr: Random crop and resize.
  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.
  
  Parameters
  ----------
  size : tuple of int
      Expected output size of each edge
  scale : tuple of float
      Range of size of the origin size cropped
  ratio : tuple of float
      Range of aspect ratio of the origin aspect ratio cropped
  
  Inputs:
      - **data**: a list of frames with shape [H x W x C]
  
  Outputs:
      - **out**: a list of cropped and resized frames with shape [size x size x C]
class_docstr_tok: ["Random","crop","and","resize",".","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",".","Parameters","--","--","--","--","--","size",":","tuple","of","int","Expected","output","size","of","each","edge","scale",":","tuple","of","float","Range","of","size","of","the","origin","size","cropped","ratio",":","tuple","of","float","Range","of","aspect","ratio","of","the","origin","aspect","ratio","cropped","Inputs",":","-","*","*","data","*","*",":","a","list","of","frames","with","shape","[","H","x","W","x","C","]","Outputs",":","-","*","*","out","*","*",":","a","list","of","cropped","and","resized","frames","with","shape","[","size","x","size","x","C","]"]
code: def __init__(
      self, size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)
  ):
      super(RandomResizedCrop, self).__init__()
  
      from ...utils.filesystem import try_import_cv2
  
      self.cv2 = try_import_cv2()
      self.height = size[0]
      self.width = size[1]
      self.scale = scale
      self.ratio = ratio
  
imports: from __future__ import division
  import random
  import math
  import numbers
  import numpy
  from mxnet.gluon import Block
label_desc: Use image node_id 1 to create a new RandomResizedCrop object from inherited base classes: Block with example: obj = RandomResizedCrop(size, scale, ratio)