code: def backward(ctx, grad_output):  # pragma: no cover
      grad_input = None
      _, alpha_ = ctx.saved_tensors
      if ctx.needs_input_grad[0]:
          grad_input = -grad_output * alpha_
      return grad_input, None
  
imports: from __future__ import division
  from __future__ import print_function
  import copy
  import math
  from typing import Text, Union
  import numpy
  import pandas
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from qlib.contrib.model.pytorch_gru import GRUModel
  from qlib.contrib.model.pytorch_lstm import LSTMModel
  from qlib.contrib.model.pytorch_utils import count_parameters
  from qlib.data.dataset import DatasetH
  from qlib.data.dataset.handler import DataHandlerLP
  from qlib.log import get_module_logger
  from qlib.model.base import Model
  from qlib.utils import get_or_create_path
  from torch.autograd import Function
label_desc: Use image node_id 2 for calling the RevGradFunc obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_input,  None