code: def transform_eval(
      imgs,
      resize_short=256,
      crop_size=224,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
  ):
      """A util function to transform all images to tensors as network input by applying
      normalizations. This function support 1 NDArray or iterable of NDArrays.
  
      Parameters
      ----------
      imgs : NDArray or iterable of NDArray
          Image(s) to be transformed.
      resize_short : int, default=256
          Resize image short side to this value and keep aspect ratio.
      crop_size : int, default=224
          After resize, crop the center square of size `crop_size`
      mean : iterable of float
          Mean pixel values.
      std : iterable of float
          Standard deviations of pixel values.
  
      Returns
      -------
      mxnet.NDArray or list of such tuple
          A (1, 3, H, W) mxnet NDArray as input to network
          If multiple image names are supplied, return a list.
      """
      if isinstance(imgs, mx.nd.NDArray):
          imgs = [imgs]
      for im in imgs:
          assert isinstance(
              im, mx.nd.NDArray
          ), "Expect NDArray, got {}".format(type(im))
  
      transform_fn = transforms.Compose(
          [
              transforms.Resize(resize_short, keep_ratio=True),
              transforms.CenterCrop(crop_size),
              transforms.ToTensor(),
              transforms.Normalize(mean, std),
          ]
      )
  
      res = [transform_fn(img).expand_dims(0) for img in imgs]
  
      if len(res) == 1:
          return res[0]
      return res
  
label_desc: Use image node_id 1 for calling a global function with example usage: transform_eval(imgs, resize_short, crop_size, mean, std) and returns: res, res