code: def speech_classification_model():
      preprocessor = {
          "cls": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
          "params": dict({}),
      }
      encoder = {
          "cls": "nemo.collections.asr.modules.ConvASREncoder",
          "params": {
              "feat_in": 64,
              "activation": "relu",
              "conv_mask": True,
              "jasper": [
                  {
                      "filters": 32,
                      "repeat": 1,
                      "kernel": [1],
                      "stride": [1],
                      "dilation": [1],
                      "dropout": 0.0,
                      "residual": False,
                      "separable": True,
                      "se": True,
                      "se_context_size": -1,
                  }
              ],
          },
      }
  
      decoder = {
          "cls": "nemo.collections.asr.modules.ConvASRDecoderClassification",
          "params": {
              "feat_in": 32,
              "num_classes": 30,
          },
      }
  
      modelConfig = DictConfig(
          {
              "preprocessor": DictConfig(preprocessor),
              "encoder": DictConfig(encoder),
              "decoder": DictConfig(decoder),
              "labels": ListConfig(
                  ["dummy_cls_{}".format(i + 1) for i in range(30)]
              ),
          }
      )
      model = EncDecClassificationModel(cfg=modelConfig)
      return 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 1 for calling a global function with example usage: speech_classification_model() and returns: model