class_docstr: A class that enables online (streaming) clustering based diarization.
  
  - The instance created from `OnlineClusteringDiarizer` sets aside a certain amount of memory
    to provide the upcoming inference with history information
  
  - There are two major modules involved: `OnlineSegmentor` and `OnlineSpeakerClustering`.
      OnlineSegmentor: Take the VAD-timestamps and generate segments for each scale
      OnlineSpeakerClustering: Update the entire speaker labels of the given online session
                               while updating the speaker labels of the streaming inputs.
  
  - The overall diarization process is done by calling `diarize_step` function.
    `diarize_step` function goes through the following steps:
      (1) Segmentation (`OnlineSegmentor` class)
      (2) Embedding extraction (`_extract_online_embeddings` function call)
      (3) Online speaker counting and speaker clustering (`OnlineClusteringDiarizer` class)
      (4) Label generation (`generate_cluster_labels` function call)
docstr: Initialize segment variables for each scale.
  Note that we have `uniq_id` variable in case where multiple sessions are handled.
docstr_tok: ["Initialize","segment","variables","for","each","scale",".","Note","that","we","have","`","uniq_id","`","variable","in","case","where","multiple","sessions","are","handled","."]
code: def _init_segment_variables(self):
      """
      Initialize segment variables for each scale.
      Note that we have `uniq_id` variable in case where multiple sessions are handled.
      """
      self.emb_vectors = {}
      self.time_stamps = {}
      self.segment_range_ts = {}
      self.segment_raw_audio = {}
      self.segment_indexes = {}
  
      for scale_idx in self.multiscale_args_dict["scale_dict"].keys():
          self.multiscale_embeddings_and_timestamps[scale_idx] = [
              None,
              None,
          ]
          self.emb_vectors[scale_idx] = torch.tensor([])
          self.time_stamps[scale_idx] = []
          self.segment_range_ts[scale_idx] = []
          self.segment_raw_audio[scale_idx] = []
          self.segment_indexes[scale_idx] = []
  
imports: import os
  import time
  from copy import deepcopy
  from typing import Dict
  import torch
  from omegaconf import DictConfig
  from nemo.collections.asr.models import ClusteringDiarizer
  from nemo.collections.asr.parts.utils.offline_clustering import get_scale_interpolated_embs, split_input_data
  from nemo.collections.asr.parts.utils.online_clustering import OnlineSpeakerClustering
  from nemo.collections.asr.parts.utils.speaker_utils import OnlineSegmentor, audio_rttm_map, generate_cluster_labels, get_embs_and_timestamps
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 6 for calling the OnlineClusteringDiarizer obj's underlying member method code with example usage: obj._init_segment_variables() without return types