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):
      resized = F.resize_clip(
          clip, self.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 Resize obj's underlying member method code with example usage: obj.__call__(clip) and returns: resized