code: def forward(ctx, *args):
      x, bound, gradient = args
  
      bound = bound.to(x.dtype)
  
      ctx.save_for_backward(
          x,
          bound,
          torch.tensor(gradient, dtype=torch.uint8),
      )
  
      return torch.max(x, bound)
  
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 2 for calling the _LowerBound obj's underlying member method code with example usage: obj.forward(ctx) and returns: torch