code: def backward(self, indexes, grad_outputs):
      x, gy = self.get_retained_inputs()
      (ggW,) = grad_outputs
  
      ret = []
      with chainer.using_config("use_ideep", self._config_use_ideep):
          if 0 in indexes:
              (gx,) = LinearGradData().apply((ggW, gy))
              ret.append(chainer.functions.cast(gx, x.dtype))
          if 1 in indexes:
              ggy = linear(x, ggW)
              ret.append(chainer.functions.cast(ggy, gy.dtype))
      return ret
  
imports: import numpy
  from chainer import backend
  from chainer.backends import intel64
  from chainer import function_node
  import chainer.functions
  from chainer.graph_optimizations import static_code
  from chainer import utils
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 4 for calling the LinearGradWeight obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ret