class_docstr: ASRModuleMixin is a mixin class added to ASR models in order to add methods that are specific
  to a particular instantiation of a module inside of an ASRModel.
  
  Each method should first check that the module is present within the subclass, and support additional
  functionality if the corresponding module is present.
docstr: Setup function for a temporary data loader which wraps the provided audio file.
  
  Args:
      paths2audio_files: (a list) of paths to audio files.
      batch_size: (int) batch size to use during inference.                 Bigger will result in better throughput performance but would use more memory.
      online_normalization: whether to do online normalization
  Returns:
      a new batch streaming buffer
docstr_tok: ["Setup","function","for","a","temporary","data","loader","which","wraps","the","provided","audio","file",".","Args",":","paths2audio_files",":","(","a","list",")","of","paths","to","audio","files",".","batch_size",":","(","int",")","batch","size","to","use","during","inference",".","Bigger","will","result","in","better","throughput","performance","but","would","use","more","memory",".","online_normalization",":","whether","to","do","online","normalization","Returns",":","a","new","batch","streaming","buffer"]
code: def _setup_streaming_transcribe_dataloader(
      self,
      paths2audio_files: List[str],
      batch_size: int,
      online_normalization=False,
  ):
      """
          Setup function for a temporary data loader which wraps the provided audio file.
  
          Args:
              paths2audio_files: (a list) of paths to audio files.
              batch_size: (int) batch size to use during inference. \
                  Bigger will result in better throughput performance but would use more memory.
              online_normalization: whether to do online normalization
          Returns:
              a new batch streaming buffer
          """
      from nemo.collections.asr.parts.utils.streaming_utils import (
          CacheAwareStreamingAudioBuffer,
      )
  
      streaming_buffer = CacheAwareStreamingAudioBuffer(
          model=self, online_normalization=online_normalization
      )
      for sample_idx, sample in enumerate(paths2audio_files):
          (
              processed_signal,
              processed_signal_length,
              stream_id,
          ) = streaming_buffer.append_audio_file(sample, stream_id=-1)
          logging.info(f"Added this sample to the buffer: {sample}")
          if (sample_idx + 1) % batch_size == 0 or sample_idx == len(
              paths2audio_files
          ) - 1:
              logging.info(
                  f"Starting to stream samples {sample_idx - len(streaming_buffer) + 1} to {sample_idx}..."
              )
              yield streaming_buffer
              streaming_buffer.reset_buffer()
  
imports: import os
  from abc import ABC, abstractmethod
  from typing import List
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  import nemo.collections.asr.models
  from nemo.collections.asr.parts.mixins.asr_adapter_mixins import ASRAdapterModelMixin
  from nemo.collections.asr.parts.mixins.streaming import StreamingEncoder
  from nemo.collections.asr.parts.utils import asr_module_utils
  from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
  from nemo.collections.common import tokenizers
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling the ASRModuleMixin obj's underlying member method code with example usage: obj._setup_streaming_transcribe_dataloader(paths2audio_files, batch_size, online_normalization) without return types