code: def forward(self, inputs):
      (x,) = inputs
      xp = backend.get_array_module(x)
  
      v, u, vw, uw = compute_indices_and_weights(
          (self.out_H, self.out_W),
          x.shape[2:],
          self.mode,
          self.align_corners,
          xp,
      )
      v = v.astype(numpy.intp)
      u = u.astype(numpy.intp)
      vw = vw.astype(x.dtype)
      uw = uw.astype(x.dtype)
  
      # Meshgrid-like operation. Meshgrid can cause
      # performance loss due to memory consumption.
      # Note that numpy 1.9 doesn't support broadcast_to method.
      v, u, vw, uw = xp.broadcast_arrays(
          v[:, None], u[None, :], vw[:, None], uw[None, :]
      )
  
      if xp is numpy:
          y = interpolate_bilinear_cpu(x, v, u, vw, uw)
      else:
          y = interpolate_bilinear_gpu(x, v, u, vw, uw)
      return (y,)
  
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 3 for calling the ResizeImages obj's underlying member method code with example usage: obj.forward(inputs) and returns: y