code: def _forward_for_backward_gradients(self):
      func = self.func
      xs = self.xs
      params = self.params
  
      xs = [
          None
          if x is None
          else variable.Variable(x, requires_grad=x.dtype.kind == "f")
          for x in xs
      ]
  
      if self.is_immutable_params:
          params = tuple([chainer.Parameter(p) for p in params])
          ys = func(xs, params)
      else:
          ys = func(*xs)
  
      ys = _as_tuple(ys)
  
      # Clear gradients which may exist if func calls backward inside of
      # itself.
      self._clear_grads(xs)
      self._clear_grads(params)
  
      return xs, ys, params
  
imports: from __future__ import absolute_import
  import warnings
  import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import _cpu
  from chainer.backends import cuda
  from chainer import configuration
  from chainer import testing
  from chainer import utils
  from chainer import variable
  import chainerx
label_desc: Use image node_id 9 for calling the _CheckBackward obj's underlying member method code with example usage: obj._forward_for_backward_gradients() and returns: xs,  ys,  params