class_docstr: Resize an image to the given size.
  
  Parameters
  ----------
  size : int or tuple of (W, H)
      Size of output image.
  keep_ratio : bool
      Whether to resize the short edge or both edges to `size`,
      if size is give as an integer.
  interpolation : int
      Interpolation method for resizing. By default uses bilinear
      interpolation. See OpenCV's resize function for available choices.
  
  
  Inputs:
      - **data**: input tensor with (Hi x Wi x C) shape.
  
  Outputs:
      - **out**: output tensor with (H x W x C) shape.
  
  Examples
  --------
  >>> transformer = vision.transforms.Resize(size=(1000, 500))
  >>> image = mx.nd.random.uniform(0, 255, (224, 224, 3)).astype(dtype=np.uint8)
  >>> transformer(image)
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code: def __init__(self, size, keep_ratio=False, interpolation=1):
      super(Resize, self).__init__()
      self._keep = keep_ratio
      self._size = size
      self._interpolation = interpolation
  
imports: from __future__ import print_function
  import argparse
  from mxnet.image import image
  from mxnet.gluon.data.vision import transforms, CIFAR10, ImageFolderDataset
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import Trainer, nn
  from mxnet.base import numeric_types
  from mxnet.initializer import *
  from mxnet import autograd
  import mxnet
  import mxnet.gluon
  import numpy
  import math
  import os
  from mxboard import SummaryWriter
  from lsun import LSUN
label_desc: Use image node_id 1 to create a new Resize object from inherited base classes: nn with example: obj = Resize(size, keep_ratio, interpolation)