code: def _inference_with_wer_parse(self, generator, sample, model):
      import editdistance
  
      def decode(toks):
          s = self.target_dictionary.string(
              toks.int().cpu(),
              self.cfg.eval_wer_post_process,
              escape_unk=True,
          )
          if self.tokenizer:
              s = self.tokenizer.decode(s)
          return s
  
      def decode_to_list(toks):
          def token_string(i):
              if i == self.target_dictionary.unk():
                  return self.target_dictionary.unk_string(False)
              else:
                  return self.target_dictionary[i]
  
          return [token_string(i) for i in toks]
  
      def is_ont_token(token):
          return "[" in token or "]" in token
  
      def post_process(l):
          o = []
          for w in l:
              if w == self.target_dictionary.eos_word or w == "|":
                  continue
              if w == "_":
                  o.append(" ")
              else:
                  o.append(w)
                  if is_ont_token(w):
                      o.append(" ")
          return o
  
      num_word_errors, num_char_errors = 0, 0
      num_chars, num_words = 0, 0
      num_em_errors, num_ems = 0, 0
      num_tree_errors, num_trees = 0, 0
      gen_out = self.inference_step(generator, [model], sample, None)
      for i in range(len(gen_out)):
          hyp_tokens = gen_out[i][0]["tokens"]
          # hyp = decode(hyp_tokens)
          ref_tokens = utils.strip_pad(
              sample["target"][i], self.target_dictionary.pad()
          )
          # ref = decode(ref_tokens)
          hyp_list = decode_to_list(hyp_tokens)
          ref_list = decode_to_list(ref_tokens)
  
          hyp_list = post_process(hyp_list)
          ref_list = post_process(ref_list)
  
          hyp = "".join(hyp_list).strip()
          ref = "".join(ref_list).strip()
          num_chars += len(ref)
          num_char_errors += editdistance.eval(hyp, ref)
          hyp_words = hyp.split()
          ref_words = ref.split()
          hyp_tree = [
              word for word in hyp_list if ("[" in word or "]" in word)
          ]
          ref_tree = [
              word for word in ref_list if ("[" in word or "]" in word)
          ]
          # num_word_errors += editdistance.eval(hyp_words, ref_words)
          hyp_before = decode(hyp_tokens).split()
          ref_before = decode(ref_tokens).split()
  
          num_word_errors += editdistance.eval(hyp_before, ref_before)
          num_words += len(ref_before)
          if hyp != ref:
              num_em_errors += 1
          if hyp_tree != ref_tree:
              num_tree_errors += 1
          num_ems += 1
          num_trees += 1
  
      return {
          "num_char_errors": num_char_errors,
          "num_chars": num_chars,
          "num_word_errors": num_word_errors,
          "num_words": num_words,
          "num_ems": num_ems,
          "num_em_errors": num_em_errors,
          "num_trees": num_trees,
          "num_tree_errors": num_tree_errors,
      }
  
imports: import logging
  import os
  import torch
  import json
  from argparse import Namespace
  from dataclasses import dataclass, field
  from typing import Optional, Any
  from fairseq.data import AddTargetDataset, Dictionary, encoders
  from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.dataclass.configs import GenerationConfig
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
  from .None import register_task
  from ..None import utils
  from ..logging import metrics
label_desc: Use image node_id 7 for calling the NLUFinetuningTask obj's underlying member method code with example usage: obj._inference_with_wer_parse(generator, sample, model) and returns: dict, s, unknown, unknown, o, self, self