code: def get_online_segments_from_slices(
      sig: torch.Tensor,
      buffer_start: float,
      buffer_end: float,
      subsegments: List[List[float]],
      ind_offset: int,
      window: float,
      sample_rate: int,
  ) -> Tuple[int, List[torch.Tensor], List[List[float]], List[int]]:
      """
      Create short speech segments from slices for online processing purpose.
  
      Args:
          sig (Tensor):
              Tensor containing the raw time-series signal
          buffer_start (float):
              Start point of the time-series signal buffer
          buffer_end (float):
              End point of the time-series signal buffer
          subsegments (list):
              List containing the interval information (start and duration) of each segment
          ind_offset (int):
              Offset for index that compensates the point of the current position in the streaming session
          window (float):
              Window length in second
          shift (float):
              Shift length in second
  
      Returns:
          sigs_list  (list):
              list of sliced input signal
          audio_lengths (list):
              list of audio sample lengths
      """
      sig_rangel_list: List[List[float]] = []
      sig_indexes: List[int] = []
      sigs_list: List[torch.Tensor] = []
      slice_length: int = int(window * sample_rate)
      end_sec: float = 0.0
      for subseg in subsegments:
          start_sec, dur = subseg[0], subseg[1]
  
          if start_sec > buffer_end:
              continue
          ind_offset += 1
  
          buffer_len = buffer_end - buffer_start
          end_sec = float(start_sec + dur)
  
          if end_sec > buffer_len:
              end_sec = float(min(end_sec, buffer_len))
  
          signal = get_target_sig(
              sig, start_sec, end_sec, slice_length, sample_rate
          )
  
          if len(signal) == 0:
              raise ValueError(
                  "len(signal) is zero. Signal length should not be zero."
              )
          if len(signal) < slice_length:
              signal = repeat_signal(signal, len(signal), slice_length)
  
          start_abs_sec = buffer_start + start_sec
          end_abs_sec = buffer_start + end_sec
  
          sigs_list.append(signal)
          sig_rangel_list.append([start_abs_sec, end_abs_sec])
          sig_indexes.append(ind_offset)
  
      if not len(sigs_list) == len(sig_rangel_list) == len(sig_indexes):
          raise ValueError("Signal information lists have a mismatch.")
  
      return ind_offset, 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 38 for calling a global function with example usage: get_online_segments_from_slices(sig, buffer_start, buffer_end, subsegments, ind_offset, window, sample_rate) and returns: ind_offset,  sigs_list,  sig_rangel_list,  sig_indexes