code: def decode(self, emissions):
      B, T, N = emissions.size()
      hypos = []
      if self.asg_transitions is None:
          transitions = torch.FloatTensor(N, N).zero_()
      else:
          transitions = torch.FloatTensor(self.asg_transitions).view(
              N, N
          )
      viterbi_path = torch.IntTensor(B, T)
      workspace = torch.ByteTensor(
          CpuViterbiPath.get_workspace_size(B, T, N)
      )
      CpuViterbiPath.compute(
          B,
          T,
          N,
          get_data_ptr_as_bytes(emissions),
          get_data_ptr_as_bytes(transitions),
          get_data_ptr_as_bytes(viterbi_path),
          get_data_ptr_as_bytes(workspace),
      )
      return [
          [
              {
                  "tokens": self.get_tokens(viterbi_path[b].tolist()),
                  "score": 0,
              }
          ]
          for b in range(B)
      ]
  
imports: import gc
  import itertools
  import os.path
  from typing import List
  import warnings
  from collections import deque, namedtuple
  import numpy
  import torch
  from examples.speech_recognition.data.replabels import unpack_replabels
  from fairseq import tasks
  from fairseq.utils import apply_to_sample
  from omegaconf import open_dict
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
label_desc: Use image node_id 2 for calling the W2lViterbiDecoder obj's underlying member method code with example usage: obj.decode(emissions) and returns: unknown