class_docstr: Numba class for multi-blank transducer loss (https://arxiv.org/pdf/2211.03541.pdf)
class_docstr_tok: ["Numba","class","for","multi-blank","transducer","loss","(","https",":","\/\/arxiv.org\/pdf\/2211.03541.pdf",")"]
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,
              None,
              None,
              None,
          )
  
imports: import torch
  from torch.autograd import Function
  from torch.nn import Module
  from espnet2.asr.transducer.rnnt_multi_blank import rnnt
  from espnet2.asr.transducer.rnnt_multi_blank.utils.cpu_utils import cpu_rnnt
label_desc: Use image node_id 2 for calling the _MultiblankRNNTNumba 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,  None,  None