class_docstr: Multi-scale Diarization Decoder (MSDD) for overlap-aware diarization and improved diarization accuracy from clustering diarizer.
  Based on the paper: Taejin Park et. al, "Multi-scale Speaker Diarization with Dynamic Scale Weighting", Interspeech 2022.
  Arxiv version: https://arxiv.org/pdf/2203.15974.pdf
  
  Args:
      num_spks (int):
          Max number of speakers that are processed by the model. In `MSDD_module`, `num_spks=2` for pairwise inference.
      hidden_size (int):
          Number of hidden units in sequence models and intermediate layers.
      num_lstm_layers (int):
          Number of the stacked LSTM layers.
      dropout_rate (float):
          Dropout rate for linear layers, CNN and LSTM.
      cnn_output_ch (int):
          Number of channels per each CNN layer.
      emb_dim (int):
          Dimension of the embedding vectors.
      scale_n (int):
          Number of scales in multi-scale system.
      clamp_max (float):
          Maximum value for limiting the scale weight values.
      conv_repeat (int):
          Number of CNN layers after the first CNN layer.
      weighting_scheme (str):
          Name of the methods for estimating the scale weights.
      context_vector_type (str):
          If 'cos_sim', cosine similarity values are used for the input of the sequence models.
          If 'elem_prod', element-wise product values are used for the input of the sequence models.
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docstr: Return  definitions of module input ports.
docstr_tok: ["Return","definitions","of","module","input","ports","."]
code: def input_types(self):
      """
      Return  definitions of module input ports.
      """
      return 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()),
          }
      )
  
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.exportable import Exportable
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types import EncodedRepresentation, LengthsType, NeuralType, SpectrogramType
  from nemo.core.neural_types.elements import ProbsType
label_desc: Use image node_id 2 for calling the MSDD_module obj's underlying member method code with example usage: obj.input_types() and returns: OrderedDict