code: def _ctc_decode(emission, decoder: CTCDecoder) -> List[str]:
      """Run CTC decoding with a KenLM language model.
  
      Args:
          emission (torch.Tensor): Output of CTC layer. Tensor with dimensions `(..., time, num_tokens)`.
          decoder (CTCDecoder): The initialized CTCDecoder.
  
      Returns:
          (List of str): The decoding result. List of string in lower case.
      """
      hypothesis = decoder(emission)
      hypothesis = hypothesis[0][0].words
      hypothesis = [word for word in hypothesis if word != " "]
      return hypothesis
  
file_path: audio/examples/hubert/evaluate.py
imports: import argparse
  import logging
  from typing import Dict, List
  import torch
  import torch.nn.functional
  import torchaudio
  from torchaudio.models.decoder import ctc_decoder, CTCDecoder, download_pretrained_files
  from utils import _get_id2label
label_desc: Use image node_id 3 for calling a global function with example usage: _ctc_decode(emission, decoder) and returns: hypothesis