code: def _create_fwd_inputs_outputs(self, inputs, outputs):
      # inputs_fwd: x, beta, gamma, rmean, rvar(, z)
      # outputs_fwd: y(, bmean, bvar)
      x0 = nn.Variable(inputs[1].shape).apply(need_grad=True)
      b0 = nn.Variable(inputs[2].shape).apply(need_grad=True)
      g0 = nn.Variable(inputs[3].shape).apply(need_grad=True)
      rm = nn.Variable(inputs[4].shape).apply(need_grad=False)
      rv = nn.Variable(inputs[5].shape).apply(need_grad=False)
      z0 = (
          nn.Variable(inputs[7].shape).apply(need_grad=True)
          if self._is_add
          else None
      )
      inputs_fwd = (
          [x0, b0, g0, rm, rv, z0] if z0 else [x0, b0, g0, rm, rv]
      )
      oshape = inputs[0].shape
      outputs_fwd = [nn.Variable(oshape)]
      return inputs_fwd, outputs_fwd
  
imports: import nnabla
  import nnabla.function
  import nnabla.functions
  from nnabla.function import PythonFunction
  from .batch_normalization import double_backward_for_batch
  from .batch_normalization import double_backward_for_global
  from .utils import no_grad
label_desc: Use image node_id 6 for calling the FusedBatchNormalizationBackward obj's underlying member method code with example usage: obj._create_fwd_inputs_outputs(inputs, outputs) and returns: inputs_fwd,  outputs_fwd