code: def forward_cpu(self, inputs):
      x = inputs[0]
      b, c, h, w = x.shape
      py = self.kh // 2 * abs(self.dy)
      px = self.kw // 2 * abs(self.dx)
      x = numpy.pad(x, ((0, 0), (0, 0), (py, py), (px, px)), "constant")
      n_groups = self.kh * self.kw
      group_size = c // n_groups
  
      ret = []
      for i, group_idx in enumerate(range(n_groups)):
          # Make sure that center group is last
          if group_idx == (n_groups - 1) // 2:
              group_idx = n_groups - 1
          elif group_idx == (n_groups - 1):
              group_idx = (n_groups - 1) // 2
  
          ky = (group_idx // self.kw) - py // abs(self.dy)
          kx = (group_idx % self.kw) - px // abs(self.dx)
  
          hs = py + -ky * self.dy
          ws = px + -kx * self.dx
          he = hs + h
          we = ws + w
          cs = i * group_size
          ce = (i + 1) * group_size if i < n_groups - 1 else None
          ret.append(x[:, cs:ce, hs:he, ws:we])
  
      return (numpy.concatenate(ret, axis=1),)
  
file_path: chainer/chainer/functions/connection/shift.py
imports: import numpy
  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 Shift obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: numpy