class_docstr: Inference model Class for offline speaker diarization. 
  This class handles required functionality for diarization : Speech Activity Detection, Segmentation, 
  Extract Embeddings, Clustering, Resegmentation and Scoring. 
  All the parameters are passed through config file 
code: def verbose(self) -> bool:
      return self._cfg.verbose
  
imports: import json
  import os
  import pickle
  import shutil
  import tarfile
  import tempfile
  from copy import deepcopy
  from typing import Any, List, Optional, Union
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning.utilities import rank_zero_only
  from tqdm import tqdm
  from nemo.collections.asr.metrics.der import score_labels
  from nemo.collections.asr.models.classification_models import EncDecClassificationModel
  from nemo.collections.asr.models.label_models import EncDecSpeakerLabelModel
  from nemo.collections.asr.parts.mixins.mixins import DiarizationMixin
  from nemo.collections.asr.parts.utils.speaker_utils import audio_rttm_map, get_embs_and_timestamps, get_uniqname_from_filepath, parse_scale_configs, perform_clustering, segments_manifest_to_subsegments_manifest, validate_vad_manifest, write_rttm2manifest
  from nemo.collections.asr.parts.utils.vad_utils import generate_overlap_vad_seq, generate_vad_segment_table, get_vad_stream_status, prepare_manifest
  from nemo.core.classes import Model
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 17 for calling the ClusteringDiarizer obj's underlying member method code with example usage: obj.verbose() and returns: self