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 __call__(self, clip):
      scaling_factor = random.uniform(self.ratio[0], self.ratio[1])
  
      if isinstance(clip[0], np.ndarray):
          im_h, im_w, im_c = clip[0].shape
      elif isinstance(clip[0], PIL.Image.Image):
          im_w, im_h = clip[0].size
  
      new_w = int(im_w * scaling_factor)
      new_h = int(im_h * scaling_factor)
      new_size = (new_w, new_h)
      resized = F.resize_clip(
          clip, new_size, interpolation=self.interpolation
      )
      return resized
  
imports: import numbers
  import random
  import numpy
  import PIL
  import torch
  import torchvision
  from .None import functional
label_desc: Use image node_id 2 for calling the RandomResize obj's underlying member method code with example usage: obj.__call__(clip) and returns: resized