class_docstr: Dataset class that loads a json file containing paths to audio files,
  RTTM files and number of speakers. This Dataset class is built for diarization inference and
  evaluation. Speaker embedding sequences, segment timestamps, cluster-average speaker embeddings
  are loaded from memory and fed into the dataloader.
  
  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):
          A threshold that determines the label of each segment based on RTTM file information.
      featurizer:
          Featurizer instance for generating features from raw waveform.
      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.
      use_single_scale_clus (bool):
          Use only one scale for clustering instead of using multiple scales of embeddings for clustering.
      pairwise_infer (bool):
          This variable should be True if dataloader is created for an inference task.
docstr: Returns definitions of module output ports.
docstr_tok: ["Returns","definitions","of","module","output","ports","."]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      """Returns definitions of module output ports."""
      output_types = OrderedDict(
          {
              "ms_emb_seq": NeuralType(
                  ("B", "T", "C", "D"), SpectrogramType()
              ),
              "length": NeuralType(tuple("B"), LengthsType()),
              "ms_avg_embs": NeuralType(
                  ("B", "C", "D", "C"), EncodedRepresentation()
              ),
              "targets": NeuralType(("B", "T", "C"), ProbsType()),
          }
      )
      return output_types
  
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 1 for calling the _AudioMSDDInferDataset obj's underlying member method code with example usage: obj.output_types() and returns: output_types