code: def forward(self, inputs):
      self.retain_inputs((0, 1, 2))
      x_hat, gamma, gy = inputs
  
      batch_size, channels = gy.shape[:2]
      gy = gy.reshape((batch_size, channels, -1))
  
      reduced_shape = x_hat.shape
      x_hat = x_hat.reshape((batch_size, channels, -1))
  
      gx_hat = gy * gamma[:, None]
      gbeta = gy.sum(axis=(0, 2))
      if backend.get_array_module(x_hat) is cuda.cupy:
          ggamma = cuda.reduce(
              "T gy, T x_hat",
              "T ggamma",
              "gy * x_hat",
              "a + b",
              "ggamma = a",
              "0",
              "groupnorm_ggamma",
          )(gy, x_hat, axis=(0, 2))
      else:
          ggamma = (gy * x_hat).sum(axis=(0, 2))
  
      gx_hat = gx_hat.reshape(reduced_shape)
      return gx_hat, ggamma, gbeta
  
imports: import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import configuration
  from chainer import function_node
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling the _ScaleShiftGrad obj's underlying member method code with example usage: obj.forward(inputs) and returns: gx_hat,  ggamma,  gbeta