class_docstr: Dataset class that loads a json file containing paths to audio files,
  rttm files and number of speakers. The created labels are used for diarization inference.
  
  Example:
  {"audio_filepath": "/path/to/audio_0.wav", "num_speakers": 2,
  "rttm_filepath": "/path/to/diar_label_0.rttm}
  ...
  {"audio_filepath": "/path/to/audio_n.wav", "num_speakers": 2,
  "rttm_filepath": "/path/to/diar_label_n.rttm}
  
  Args:
      manifest_filepath (str):
          Path to input manifest json files.
      emb_dict (dict):
          Dictionary containing cluster-average embeddings and speaker mapping information.
      emb_seq (dict):
          Dictionary containing multiscale speaker embedding sequence, scale mapping and corresponding segment timestamps.
      clus_label_dict (dict):
          Subsegment-level (from base-scale) speaker labels from clustering results.
      soft_label_thres (float):
          Threshold that determines speaker labels of segments depending on the overlap with groundtruth speaker timestamps.
      featurizer:
          Featurizer instance for generating features from raw waveform.
      use_single_scale_clus (bool):
          Use only one scale for clustering instead of using multiple scales of embeddings for clustering.
      seq_eval_mode (bool):
          If True, F1 score will be calculated for each speaker pair during inference mode.
      window_stride (float):
          Window stride for acoustic feature. This value is used for calculating the numbers of feature-level frames.
      pairwise_infer (bool):
          If True, this Dataset class operates in inference mode. In inference mode, a set of speakers in the input audio
          is split into multiple pairs of speakers and speaker tuples (e.g. 3 speakers: [(0,1), (1,2), (0,2)]) and then
          fed into the MSDD to merge the individual results.
code: def msdd_infer_collate_fn(self, batch):
      return _msdd_infer_collate_fn(self, batch)
  
imports: import os
  from collections import OrderedDict
  from statistics import mode
  from typing import Dict, Optional
  import torch
  from nemo.collections.asr.parts.utils.offline_clustering import get_argmin_mat
  from nemo.collections.asr.parts.utils.speaker_utils import convert_rttm_line, prepare_split_data
  from nemo.collections.common.parts.preprocessing.collections import DiarizationSpeechLabel
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import AudioSignal, EncodedRepresentation, LengthsType, NeuralType, ProbsType
label_desc: Use image node_id 2 for calling the AudioToSpeechMSDDInferDataset obj's underlying member method code with example usage: obj.msdd_infer_collate_fn(batch) and returns: _msdd_infer_collate_fn