code: def backward(ctx, *grad_outputs):
      (y,) = grad_outputs
  
      x, bound, gradient = ctx.saved_tensors
  
      if int(gradient) == _LowerBoundGradient.disconnected:
          return x, None, None
  
      if int(gradient) == _LowerBoundGradient.identity:
          return y, None, None
  
      return (((x >= bound) | (y < 0)) * y), None, None
  
file_path: NeuralCompression/neuralcompression/functional/_lower_bound.py
imports: import enum
  from enum import IntEnum
  from typing import Union
  import torch
  import torch.nn
  from torch import Tensor
  from torch.autograd import Function
label_desc: Use image node_id 1 for calling the _LowerBound obj's underlying member method code with example usage: obj.backward(ctx) and returns: unknown,  None,  None, x,  None,  None, y,  None,  None