code: def msdd_model():
      preprocessor = {
          "cls": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
          "params": {
              "features": 80,
              "window_size": 0.025,
              "window_stride": 0.01,
              "sample_rate": 16000,
          },
      }
  
      speaker_model_encoder = {
          "cls": "nemo.collections.asr.modules.ConvASREncoder",
          "params": {
              "feat_in": 80,
              "activation": "relu",
              "conv_mask": True,
              "jasper": [
                  {
                      "filters": 512,
                      "repeat": 1,
                      "kernel": [1],
                      "stride": [1],
                      "dilation": [1],
                      "dropout": 0.0,
                      "residual": False,
                      "separable": False,
                  }
              ],
          },
      }
  
      speaker_model_decoder = {
          "cls": "nemo.collections.asr.modules.SpeakerDecoder",
          "params": {
              "feat_in": 512,
              "num_classes": 2,
              "pool_mode": "xvector",
              "emb_sizes": [1024],
          },
      }
  
      speaker_model_cfg = DictConfig(
          {
              "preprocessor": DictConfig(preprocessor),
              "encoder": DictConfig(speaker_model_encoder),
              "decoder": DictConfig(speaker_model_decoder),
          }
      )
  
      msdd_module = {
          "cls": "nemo.collections.asr.modules.MSDD_module",
          "params": {
              "num_spks": 2,
              "hidden_size": 256,
              "num_lstm_layers": 3,
              "dropout_rate": 0.5,
              "cnn_output_ch": 32,
              "conv_repeat": 2,
              "emb_dim": 192,
              "scale_n": 5,
              "weighting_scheme": "conv_scale_weight",
              "context_vector_type": "cos_sim",
          },
      }
  
      loss = {
          "cls": "nemo.collections.asr.losses.bce_loss.BCELoss",
          "params": {"weight": None},
      }
  
      diarizer = {
          "out_dir": None,
          "oracle_vad": True,
          "speaker_embeddings": {
              "model_path": None,
              "parameters": {
                  "window_length_in_sec": [1.5, 1.25, 1.0, 0.75, 0.5],
                  "shift_length_in_sec": [
                      0.75,
                      0.625,
                      0.5,
                      0.375,
                      0.25,
                  ],
                  "multiscale_weights": [1, 1, 1, 1, 1],
                  "save_embeddings": True,
              },
          },
      }
  
      modelConfig = DictConfig(
          {
              "msdd_module": DictConfig(msdd_module),
              "preprocessor": DictConfig(preprocessor),
              "diarizer": DictConfig(diarizer),
              "loss": DictConfig(loss),
              "max_num_of_spks": 2,
              "num_workers": 5,
              "emb_batch_size": 0,
              "soft_label_thres": 0.5,
              "scale_n": 5,
              "speaker_model_cfg": speaker_model_cfg,
          }
      )
      model = EncDecDiarLabelModel(cfg=modelConfig)
      return model
  
imports: import pytest
  import torch
  from omegaconf import DictConfig
  from nemo.collections.asr.models import EncDecDiarLabelModel
label_desc: Use image node_id 1 for calling a global function with example usage: msdd_model() and returns: model