code: def get_online_subsegments_from_buffer(
      buffer_start: float,
      buffer_end: float,
      sample_rate: int,
      speech_labels_for_update: torch.Tensor,
      audio_buffer: torch.Tensor,
      segment_indexes: List[int],
      window: float,
      shift: float,
  ) -> Tuple[List[torch.Tensor], List[List[float]], List[int]]:
      """
      Generate subsegments for online processing from the given segment information.
      This function extracts subsegments (embedding vector level) time-series from the
      raw time-series buffer based on the segment interval (start and end timestamps) information.
  
      Args:
          buffer_start (float):
              Start point of the time-series signal buffer
          buffer_end (float):
              End point of the time-series signal buffer
          sample_rate (int):
              Sampling rate of the audio input
          speech_labels_for_update (Tensor):
              Tensor containing intervals (start and end timestamps) of the speech segments
          audio_buffer (Tensor):
              Tensor containing the raw time-series signal
          segment_indexes (list):
              List containing the unique indices of segments
          window (float):
              Window length in second
          shift (float):
              Shift length in second
  
      Returns:
          sigs_list (list):
              List containing the tensors of the old and the newly added time-series signals
          sig_rangel_list (list):
              List containing the old and the newly added intervals (timestamps) of the speech segments
          sig_indexes (list):
              List containing the old and the newly added unique indices of segments
      """
      sigs_list: List[torch.Tensor] = []
      sig_rangel_list: List[List[float]] = []
      sig_indexes: List[int] = []
      if len(segment_indexes) > 0:
          ind_offset = segment_indexes[-1]
      else:
          ind_offset = -1
  
      for idx, range_spl in enumerate(speech_labels_for_update):
          range_offs = [
              float(range_spl[0].item() - buffer_start),
              float(range_spl[1].item() - buffer_start),
          ]
          range_t = [max(0, range_offs[0]), range_offs[1]]
  
          subsegments = get_subsegments(
              offset=range_t[0],
              window=window,
              shift=shift,
              duration=(range_t[1] - range_t[0]),
          )
          (
              ind_offset,
              sigs,
              ranges,
              inds,
          ) = get_online_segments_from_slices(
              sig=audio_buffer,
              buffer_start=buffer_start,
              buffer_end=buffer_end,
              subsegments=subsegments,
              window=window,
              ind_offset=ind_offset,
              sample_rate=sample_rate,
          )
  
          sigs_list.extend(sigs)
          sig_rangel_list.extend(ranges)
          sig_indexes.extend(inds)
  
      assert len(sigs_list) == len(sig_rangel_list) == len(sig_indexes)
      return sigs_list, sig_rangel_list, sig_indexes
  
imports: import gc
  import json
  import math
  import os
  import shutil
  from copy import deepcopy
  from typing import Dict, List, Tuple, Union
  import numpy
  import omegaconf
  import soundfile
  import torch
  from pyannote.core import Annotation, Segment
  from tqdm import tqdm
  from nemo.collections.asr.data.audio_to_label import repeat_signal
  from nemo.collections.asr.parts.utils.longform_clustering import LongFormSpeakerClustering
  from nemo.collections.asr.parts.utils.offline_clustering import SpeakerClustering, get_argmin_mat, split_input_data
  from nemo.utils import logging
label_desc: Use image node_id 39 for calling a global function with example usage: get_online_subsegments_from_buffer(buffer_start, buffer_end, sample_rate, speech_labels_for_update, audio_buffer, segment_indexes, window, shift) and returns: sigs_list,  sig_rangel_list,  sig_indexes