code: def backward(ctx, grad_output):
      # Clamp the gradients of loss(logsoftmax(...))
      # CPU computes logsoftmax explicitly, so we need to override t
      grad_output = torch.clamp(grad_output, -ctx.clamp, ctx.clamp)
      return (
          grad_output,
          None,
      )
  
imports: import math
  import multiprocessing
  from typing import Optional
  import numba
  import torch
  from torch.autograd import Function
  from espnet2.asr.transducer.rnnt_multi_blank.utils import global_constants
label_desc: Use image node_id 2 for calling the LogSoftmaxGradModification obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_output,  None