code: def resize_images(
      x, output_shape, *, mode="bilinear", align_corners=True
  ):
      """Resize images to the given shape.
  
      This function resizes 2D data to :obj:`output_shape`.
  
      Notation: here is a notation for dimensionalities.
  
      - :math:`n` is the batch size.
      - :math:`c_I` is the number of the input channels.
      - :math:`h` and :math:`w` are the height and width of the input image,
        respectively.
      - :math:`h_O` and :math:`w_O` are the height and width of the output
        image.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`):
              Input variable of shape :math:`(n, c_I, h, w)`.
          output_shape (tuple): This is a tuple of length 2 whose values are
              :obj:`(h_O, w_O)`. Note that the order of height and width is
              opposite of the one in OpenCV.
          mode ({\'bilinear\', \'nearest\'}): Defines the sampling rule.
          align_corners (bool): When this value is :obj:`True`,
              the corners of the input are mapped to the corners of
              the output. When :obj:`False`, the behavior is the same as
              OpenCV.
  
      Returns:
          ~chainer.Variable: Resized image whose shape is \
              :math:`(n, c_I, h_O, w_O)`.
  
      """
      return ResizeImages(output_shape, mode, align_corners).apply(
          (x,)
      )[0]
  
imports: from __future__ import division
  import numpy
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.utils import type_check
label_desc: Use image node_id 7 for calling a global function with example usage: resize_images(x, output_shape) and returns: ResizeImages