code: def backward(ctx, grad_output):
      if grad_output is not None and ctx.grads is not None:
          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,
          )
  
imports: import torch
  from torch.autograd import Function
  from torch.nn import Module
  from nemo.collections.asr.parts.numba.rnnt_loss import rnnt
  from nemo.collections.asr.parts.numba.rnnt_loss.utils.cpu_utils import cpu_rnnt
label_desc: Use image node_id 2 for calling the _RNNTNumba obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: ctx,  None,  None,  None,  None,  None,  None,  None