code: def forward(ctx, acts, clamp):
      if clamp < 0:
          raise ValueError("`clamp` must be 0.0 or positive float.")
  
      res = acts.new(acts)
      ctx.clamp = clamp
      return res
  
imports: import numpy
  import torch
  from torch.autograd import Function, Variable
  from torch.nn import Module
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