code: def forward(ctx, acts, clamp):
      if clamp < 0:
          raise ValueError("`clamp` must be 0.0 or positive float.")
  
      # This is needed for correctness (inplace is problematic),
      # but it wastes a log of memory.
      res = acts.new(acts)
      ctx.clamp = clamp
      return res
  
imports: import math
  import multiprocessing
  from typing import Optional
  import numba
  import torch
  from torch.autograd import Function
  from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants
label_desc: Use image node_id 1 for calling the LogSoftmaxGradModification obj's underlying member method code with example usage: obj.forward(ctx, acts, clamp) and returns: res