code: def test_model_dispatch():
      dense_model_name = "hubert-base-ls960"
      quantizer_name = "kmeans"
      vocab_size = 100
  
      # getting dense model
      dense_model = dispatch_dense_model(dense_model_name)
      assert isinstance(dense_model, torch.nn.Module)
  
      # getting a quantizer for it
      assert (
          dispatch_quantizer(
              dense_model_name, quantizer_name, vocab_size=vocab_size
          )
          is not None
      )
  
      with pytest.raises(KeyError):
          assert dispatch_quantizer(
              dense_model_name, quantizer_name, vocab_size=101
          )
  
      # getting a vocoder for it
      assert (
          TacotronVocoder.by_name(
              dense_model_name=dense_model_name,
              quantizer_model_name=quantizer_name,
              vocab_size=vocab_size,
          )
          is not None
      )
  
file_path: textlesslib/tests/test_model_handling.py
imports: import pytest
  from textless import dispatch_dense_model, dispatch_quantizer
  from textless.data.speech_encoder import SpeechEncoder
  import torch
  from itertools import product
  from textless.vocoders.tacotron2.vocoder import TacotronVocoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_model_dispatch() without return types