code: def forward_gpu(self, gy):
      xp = cuda.cupy
      gcol = conv.im2col_gpu(
          gy[0],
          self.kh,
          self.kw,
          self.sy,
          self.sx,
          self.ph,
          self.pw,
          cover_all=self.cover_all,
      )
  
      gcol = gcol.transpose(0, 1, 4, 5, 2, 3)
      n, c, oy, ox, ky, kx = gcol.shape
      gcol = gcol.reshape((n, c, oy, ox, ky * kx))
      indexes = xp.asarray(self.indexes, dtype=numpy.int32)
      gx = xp.empty((n, c, oy, ox), dtype=self._in_dtype)
      cuda.elementwise(
          "int32 indexes, raw T gcol, int32 n, int32 c, int32 oy,"
          "int32 ox, int32 ky, int32 kx",
          "raw T gx",
          """
              int ind_n = i / c / oy / ox;
              int ind_c = (i / oy / ox) % c;
              int ind_oy = (i / ox) % oy;
              int ind_ox = i % ox;
              int gcol_ky = indexes / kx;
              int gcol_kx = indexes % kx;
              float top_gx = gcol[ind_n * c * oy * ox * ky * kx +
                                  ind_c * oy * ox * ky * kx +
                                  ind_oy * ox * ky * kx +
                                  ind_ox * ky * kx +
                                  gcol_ky * kx +
                                  gcol_kx];
              gx[ind_n * c * oy * ox +
                 ind_c * oy * ox +
                 ind_oy * ox +
                 ind_ox] = top_gx;
              """,
          "upsampling_2d_bwd",
      )(indexes, gcol, n, c, oy, ox, ky, kx, gx)
  
      return (gx,)
  
imports: import numpy
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.functions.pooling import pooling_2d
  from chainer.utils import conv
  from chainer.utils import type_check
label_desc: Use image node_id 3 for calling the Upsampling2DGrad obj's underlying member method code with example usage: obj.forward_gpu(gy) and returns: gx