class_docstr: Resizes a list of (H x W x C) numpy.ndarray to the final size
  
  The larger the original image is, the more times it takes to
  interpolate
  
  Args:
  interpolation (str): Can be one of 'nearest', 'bilinear'
  defaults to nearest
  size (tuple): (widht, height)
class_docstr_tok: ["Resizes","a","list","of","(","H","x","W","x","C",")","numpy.ndarray","to","the","final","size","The","larger","the","original","image","is",",","the","more","times","it","takes","to","interpolate","Args",":","interpolation","(","str",")",":","Can","be","one","of","'nearest","'",",","'bilinear'","defaults","to","nearest","size","(","tuple",")",":","(","widht",",","height",")"]
code: def __init__(
      self, ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation="nearest"
  ):
      self.ratio = ratio
      self.interpolation = interpolation
  
imports: import numbers
  import random
  import numpy
  import PIL
  import torch
  import torchvision
  from .None import functional
label_desc: Use image node_id 1 to create a new RandomResize object from inherited base classes: object with example: obj = RandomResize(ratio, interpolation)