code: def forward(self, inputs, device):
      x, gamma, beta, gy = inputs
      running_mean = device.send(self.running_mean.copy())
      running_var = device.send(self.running_var.copy())
      y = batch_renormalization.batch_renormalization(
          x,
          gamma,
          beta,
          self.rmax,
          self.dmax,
          eps=self.eps,
          running_mean=running_mean,
          running_var=running_var,
          update_statistics=self.update_statistics,
      )
  
      # backward gradients
      gx, ggamma, gbeta = self._compute_backward(x, gamma, beta, y, gy)
  
      return (
          y,
          chainer.Variable(running_mean),
          chainer.Variable(running_var),
          chainer.Variable(gx),
          chainer.Variable(ggamma),
          chainer.Variable(gbeta),
      )
  
imports: import numpy
  import six
  import chainer
  from chainer.functions.normalization import batch_renormalization
  from chainer import testing
  import chainerx
label_desc: Use image node_id 4 for calling the TestBatchRenormalizationForward obj's underlying member method code with example usage: obj.forward(inputs, device) and returns: y,  chainer,  chainer,  chainer,  chainer,  chainer