code: def decode(self, emissions):
      B, T, N = emissions.size()
      hypos = []
      for b in range(B):
          emissions_ptr = (
              emissions.data_ptr() + 4 * b * emissions.stride(0)
          )
          results = self.decoder.decode(emissions_ptr, T, N)
  
          nbest_results = results[: self.nbest]
          hypos.append(
              [
                  {
                      "tokens": self.get_tokens(result.tokens),
                      "score": result.score,
                      "timesteps": self.get_timesteps(result.tokens),
                      "words": [
                          self.word_dict.get_entry(x)
                          for x in result.words
                          if x >= 0
                      ],
                  }
                  for result in nbest_results
              ]
          )
      return hypos
  
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 3 for calling the W2lKenLMDecoder obj's underlying member method code with example usage: obj.decode(emissions) and returns: hypos