code: def test_constructor(self):
      preprocessor = {
          "_target_": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
      }
      encoder = {
          "_target_": "nemo.collections.asr.modules.ConvASREncoder",
          "feat_in": 64,
          "activation": "relu",
          "conv_mask": True,
          "jasper": [
              {
                  "filters": 512,
                  "repeat": 1,
                  "kernel": [1],
                  "stride": [1],
                  "dilation": [1],
                  "dropout": 0.0,
                  "residual": False,
                  "separable": False,
              }
          ],
      }
  
      decoder = {
          "_target_": "nemo.collections.asr.modules.SpeakerDecoder",
          "feat_in": 512,
          "num_classes": 2,
          "pool_mode": "xvector",
          "emb_sizes": [1024],
      }
  
      modelConfig = DictConfig(
          {
              "preprocessor": DictConfig(preprocessor),
              "encoder": DictConfig(encoder),
              "decoder": DictConfig(decoder),
          },
      )
      speaker_model = EncDecSpeakerLabelModel(cfg=modelConfig)
      speaker_model.train()
      # TODO: make proper config and assert correct number of weights
  
      # Check to/from config_dict:
      confdict = speaker_model.to_config_dict()
      instance2 = EncDecSpeakerLabelModel.from_config_dict(confdict)
      self.assertTrue(isinstance(instance2, EncDecSpeakerLabelModel))
  
file_path: NeMo/tests/collections/asr/test_speaker_label_models.py
imports: import json
  import os
  import tempfile
  from unittest import TestCase
  import pytest
  import torch
  from omegaconf import DictConfig
  from nemo.collections.asr.models import EncDecSpeakerLabelModel
label_desc: Use image node_id 1 for calling the EncDecSpeechLabelModelTest obj's underlying member method code with example usage: obj.test_constructor() without return types