code: def forward(self, inputs):
      self.retain_inputs((0, 1, 2))
      x, mean, projection = inputs
      xp = backend.get_array_module(x)
      x_shape = x.shape
      b, c = x_shape[:2]
      g = self.groups
      C = c // g
      spatial_axis, m = _calc_axis_and_m(x_shape, b)
  
      x_hat = x.transpose((1, 0) + spatial_axis).reshape(g, C, m)
      x_hat = x_hat - xp.expand_dims(mean, axis=2)
  
      y_hat = _matmul(projection, x_hat, xp)
  
      y = y_hat.reshape((c, b) + x_shape[2:]).transpose(
          (1, 0) + spatial_axis
      )
  
      return (y,)
  
imports: import numpy
  from chainer import backend
  from chainer import function_node
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 3 for calling the FixedDecorrelatedBatchNormalization obj's underlying member method code with example usage: obj.forward(inputs) and returns: y