code: def get_wer_feat_logit(
      audio_file_path,
      asr,
      frame_len,
      tokens_per_chunk,
      delay,
      model_stride_in_secs,
  ):
      """
      Create a preprocessor to convert audio samples into raw features,
      Normalization will be done per buffer in frame_bufferer.
      """
      asr.reset()
      asr.read_audio_file_and_return(
          audio_file_path, delay, model_stride_in_secs
      )
      hyp, tokens, log_prob = asr.transcribe_with_ts(
          tokens_per_chunk, delay
      )
      return hyp, tokens, log_prob
  
imports: import copy
  import math
  from typing import Dict, List, Tuple, Type, Union
  import numpy
  import torch
  from omegaconf import OmegaConf
  import nemo.collections.asr
  from nemo.collections.asr.metrics.wer import WER
  from nemo.collections.asr.models import EncDecCTCModel, EncDecCTCModelBPE
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig, CTCDecoding, CTCDecodingConfig
  from nemo.collections.asr.parts.utils.audio_utils import get_samples
  from nemo.collections.asr.parts.utils.speaker_utils import audio_rttm_map, get_uniqname_from_filepath
  from nemo.collections.asr.parts.utils.streaming_utils import AudioFeatureIterator, FrameBatchASR
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: get_wer_feat_logit(audio_file_path, asr, frame_len, tokens_per_chunk, delay, model_stride_in_secs) and returns: hyp,  tokens,  log_prob