docstr: Check that biased NNLM only produces biased words
docstr_tok: ["Check","that","biased","NNLM","only","produces","biased","words"]
code: def test_custom_nnlm_decoder(self, lm_dict, keyword, use_lexicon):
      """Check that biased NNLM only produces biased words"""
      emissions = self._get_emissions()
      custom_nnlm = self._get_biased_nnlm(lm_dict, keyword)
      nnlm_decoder = self._get_decoder(
          lm=custom_nnlm,
          lm_dict=lm_dict,
          use_lexicon=use_lexicon,
          lm_weight=10,
      )
      nnlm_results = nnlm_decoder(emissions)
  
      if use_lexicon:
          output = [result[0].words for result in nnlm_results]
      else:
          tokens = [
              nnlm_decoder.idxs_to_tokens(result[0].tokens)
              for result in nnlm_results
          ]
          output = [
              list(filter(("|").__ne__, t)) for t in tokens
          ]  # filter out silence characters
  
      lens = [len(out) for out in output]
      expected = [
          [keyword] * len for len in lens
      ]  # all of output should match the biased keyword
      assert expected == output
  
imports: import itertools
  import torch
  from parameterized import parameterized
  from torchaudio_unittest.common_utils import get_asset_path, skipIfNoCtcDecoder, TempDirMixin, TorchaudioTestCase
label_desc: Use image node_id 10 for calling the CTCDecoderTest obj's underlying member method code with example usage: obj.test_custom_nnlm_decoder(lm_dict, keyword, use_lexicon) without return types