code: def speaker_label_model():
      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": "attention",
          "emb_sizes": [1024],
      }
  
      modelConfig = DictConfig(
          {
              "preprocessor": DictConfig(preprocessor),
              "encoder": DictConfig(encoder),
              "decoder": DictConfig(decoder),
          }
      )
      speaker_model = EncDecSpeakerLabelModel(cfg=modelConfig)
      return speaker_model
  
imports: import os
  import tempfile
  import onnx
  import pytest
  import torch.cuda
  from omegaconf import DictConfig, ListConfig, OmegaConf
  from nemo.collections.asr.models import EncDecClassificationModel, EncDecCTCModel, EncDecRNNTModel, EncDecSpeakerLabelModel
  from nemo.collections.asr.parts.utils import asr_module_utils
  from nemo.collections.common.parts.adapter_modules import LinearAdapterConfig
  from nemo.core.utils import numba_utils
  from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
label_desc: Use image node_id 2 for calling a global function with example usage: speaker_label_model() and returns: speaker_model