code: def min_backward(
      grad_inputs,
      inputs,
      input_shapes,
      outputs,
      output_shapes,
      axes=None,
      keep_dims=False,
      with_index=False,
      only_index=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.
      """
      # In auto-forward mode, the dynamic clear of inputs[0] and outputs[0] are
      # blocked by Min::auto_grad_depends_input/output_data.
      dy = grad_inputs[0]
      x0 = inputs[0]
      y0 = outputs[0]
      if keep_dims:
          y0 = F.broadcast(y0, x0.shape)
          dy = F.broadcast(dy, x0.shape)
      else:
          axes = (
              [i for i in range(x0.ndim)]
              if axes is None
              else force_list(axes)
          )
          shape = [
              1 if i in axes else s for i, s in enumerate(x0.shape)
          ]
          y0 = F.broadcast(
              F.reshape(y0, shape, inplace=False), x0.shape
          )
          dy = F.broadcast(
              F.reshape(dy, shape, inplace=False), x0.shape
          )
      m0 = F.equal(x0, y0)
      m0 = no_grad(m0)
      dx0 = dy * m0
      if not with_index and not only_index:
          return dx0
      elif with_index:
          return dx0, None
      elif only_index:
          return None
  
label_desc: Use image node_id 1 for calling a global function with example usage: min_backward(grad_inputs, inputs, input_shapes, outputs, output_shapes, axes, keep_dims, with_index, only_index) and returns: dx0, dx0,  None, None