code: def unpooling_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      kernel,
      channel_last=False,
  ):
      """
      Args:
        grad_inputs (list of :obj:`nnabla.Variable`): Propagated grads to this backward function.
        inputs (list of :obj:`nnabla.Variable` and None): Input Variables of the forward function
            if this backward function depends on it. Otherwise, None is set instead.
        input_shapes (list of tuple of :obj:`int`): Input shapes of the forward function.
            The shapes of the inputs in which None is set can be passed.
        outputs (list of :obj:`nnabla.Variable` and None): Output Variables of the forward function
            if this backward function depends on it. Otherwise, None is set instead.
        output_shapes (list of tuple of :obj:`int`): Output shapes of the forward function.
            The shapes of the outputs in which None is set can be passed.
        kwargs (dict of arguments): Dictionary of the corresponding function arguments.
  
      Return:
        list of Variable: Return the gradients wrt inputs of the corresponding function.
      """
      dy = grad_inputs[0]
      x0_shape = input_shapes[0]
      ctx = nn.get_current_context()
      df = UnpoolingDataGrad(ctx, kernel, channel_last)
      df.xshape = x0_shape
      dx0 = df(dy)
      return dx0
  
file_path: nnabla/python/src/nnabla/backward_function/unpooling.py
imports: import nnabla
  import nnabla.function
  import nnabla.functions
  from .backward_function import UnaryDataGrad
label_desc: Use image node_id 1 for calling a global function with example usage: unpooling_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, kernel, channel_last) and returns: dx0