code: def backward(ctx, grad_output):
      grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.grads)
      return (
          ctx.grads.mul(grad_output),
          None,
          None,
          None,
          None,
          None,
          None,
          None,
          None,
      )
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 2 for calling the _NumpyTransducer obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: ctx,  None,  None,  None,  None,  None,  None,  None,  None