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: 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.
      logprobs: (bool) pass True to get log probabilities instead of transcripts.
      return_hypotheses: (bool) Either return hypotheses or text
          With hypotheses can do some postprocessing like getting timestamp or rescoring
      online_normalization: (bool) Perform normalization on the run per chunk.
  Returns:
      A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files
docstr_tok: ["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",".","logprobs",":","(","bool",")","pass","True","to","get","log","probabilities","instead","of","transcripts",".","return_hypotheses",":","(","bool",")","Either","return","hypotheses","or","text","With","hypotheses","can","do","some","postprocessing","like","getting","timestamp","or","rescoring","online_normalization",":","(","bool",")","Perform","normalization","on","the","run","per","chunk",".","Returns",":","A","list","of","transcriptions","(","or","raw","log","probabilities","if","logprobs","is","True",")","in","the","same","order","as","paths2audio_files"]
code: def transcribe_simulate_cache_aware_streaming(
      self,
      paths2audio_files: List[str],
      batch_size: int = 4,
      logprobs: bool = False,
      return_hypotheses: bool = False,
      online_normalization: bool = False,
  ):
      """
      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.
          logprobs: (bool) pass True to get log probabilities instead of transcripts.
          return_hypotheses: (bool) Either return hypotheses or text
              With hypotheses can do some postprocessing like getting timestamp or rescoring
          online_normalization: (bool) Perform normalization on the run per chunk.
      Returns:
          A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files
      """
      if paths2audio_files is None or len(paths2audio_files) == 0:
          return {}
  
      if return_hypotheses and logprobs:
          raise ValueError(
              "Either `return_hypotheses` or `logprobs` can be True at any given time."
              "Returned hypotheses will contain the logprobs."
          )
  
      if not isinstance(self, asr_models.EncDecCTCModel):
          raise NotImplementedError(
              f"simulate streaming does not support {type(self)}!"
          )
  
      if not isinstance(self.encoder, StreamingEncoder):
          raise NotImplementedError(
              f"Encoder of this model does not support streaming!"
          )
  
      data_loader = self._setup_streaming_transcribe_dataloader(
          paths2audio_files, batch_size, online_normalization
      )
  
      total_log_probs = []
      total_texts = []
  
      for streaming_buffer in data_loader:
          streaming_buffer_iter = iter(streaming_buffer)
          batch_size = len(streaming_buffer.streams_length)
          (
              cache_last_channel,
              cache_last_time,
              cache_last_channel_len,
          ) = self.encoder.get_initial_cache_state(
              batch_size=batch_size
          )
          previous_hypotheses = None
          pred_out_stream = None
          encoded_len = None
          transcribed_texts = None
          batch_log_probs = []
  
          for step_num, (chunk_audio, chunk_lengths) in enumerate(
              streaming_buffer_iter
          ):
              drop_extra_pre_encoded = (
                  self.encoder.streaming_cfg.drop_extra_pre_encoded
                  if step_num != 0
                  else 0
              )
              with torch.inference_mode():
                  result = self.conformer_stream_step(
                      processed_signal=chunk_audio,
                      processed_signal_length=chunk_lengths,
                      cache_last_channel=cache_last_channel,
                      cache_last_time=cache_last_time,
                      cache_last_channel_len=cache_last_channel_len,
                      keep_all_outputs=streaming_buffer.is_buffer_empty(),
                      previous_hypotheses=previous_hypotheses,
                      previous_pred_out=pred_out_stream,
                      drop_extra_pre_encoded=drop_extra_pre_encoded,
                      return_transcription=True,
                      return_log_probs=logprobs or return_hypotheses,
                  )
                  if logprobs or return_hypotheses:
                      (
                          pred_out_stream,
                          transcribed_texts,
                          cache_last_channel,
                          cache_last_time,
                          cache_last_channel_len,
                          previous_hypotheses,
                          cur_chunk_log_probs,
                          encoded_len,
                      ) = result
                      batch_log_probs.append(cur_chunk_log_probs.cpu())
                  else:
                      (
                          pred_out_stream,
                          transcribed_texts,
                          cache_last_channel,
                          cache_last_time,
                          cache_last_channel_len,
                          previous_hypotheses,
                      ) = result
  
          if logprobs or return_hypotheses:
              # concatenate chunk log probs on T dim
              batch_log_probs = torch.cat(batch_log_probs, axis=1)
              for log_probs, log_prob_len in zip(
                  batch_log_probs, encoded_len
              ):
                  total_log_probs.append(log_probs[0:log_prob_len])
  
          if transcribed_texts is None:
              total_texts += [""] * batch_size
          else:
              total_texts += transcribed_texts
  
      if logprobs:
          return total_log_probs
  
      if not return_hypotheses:
          return total_texts
  
      hyps = []
      for log_probs, text in zip(total_log_probs, total_texts):
          hyps.append(
              Hypothesis(
                  y_sequence=log_probs,
                  text=text,
                  score=0.0,
                  dec_state=None,
              )
          )
      return hyps
  
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 5 for calling the ASRModuleMixin obj's underlying member method code with example usage: obj.transcribe_simulate_cache_aware_streaming(paths2audio_files, batch_size, logprobs, return_hypotheses, online_normalization) and returns: hyps, dict, total_log_probs, total_texts