code: def test_model_training(
      enc_params,
      enc_gen_params,
      dec_params,
      joint_net_params,
      main_params,
      stats_file,
  ):
      batch_size = 2
      input_size = 10
  
      token_list = ["<blank>", "a", "b", "c", "<space>"]
      vocab_size = len(token_list)
  
      if (
          dec_params.get("is_rwkv") is not None
          and not torch.cuda.is_available()
      ):
          pytest.skip("A GPU is required for WKV kernel computation")
  
      encoder = Encoder(
          input_size, enc_params, main_conf=enc_gen_params
      )
      decoder = get_decoder(vocab_size, dec_params)
  
      joint_network = JointNetwork(
          vocab_size,
          encoder.output_size,
          decoder.output_size,
          **joint_net_params,
      )
  
      specaug = (
          get_specaug() if main_params.pop("specaug", False) else None
      )
  
      normalize = main_params.pop("normalize", None)
      if normalize is not None:
          if normalize == "utterance":
              normalize = UtteranceMVN(
                  norm_means=True, norm_vars=True, eps=1e-13
              )
          else:
              normalize = GlobalMVN(
                  stats_file, norm_means=True, norm_vars=True
              )
  
      model = ESPnetASRTransducerModel(
          vocab_size,
          token_list,
          frontend=None,
          specaug=specaug,
          normalize=normalize,
          encoder=encoder,
          decoder=decoder,
          joint_network=joint_network,
          **main_params,
      )
  
      feats, labels, feat_len, label_len = prepare(
          model, input_size, vocab_size, batch_size
      )
  
      _ = model(feats, feat_len, labels, label_len)
  
      if main_params.get("report_cer") or main_params.get("report_wer"):
          model.training = False
  
          _ = model(feats, feat_len, labels, label_len)
  
imports: from pathlib import Path
  import numpy
  import pytest
  import torch
  from espnet2.asr.specaug.specaug import SpecAug
  from espnet2.asr_transducer.decoder.mega_decoder import MEGADecoder
  from espnet2.asr_transducer.decoder.rnn_decoder import RNNDecoder
  from espnet2.asr_transducer.decoder.rwkv_decoder import RWKVDecoder
  from espnet2.asr_transducer.decoder.stateless_decoder import StatelessDecoder
  from espnet2.asr_transducer.encoder.encoder import Encoder
  from espnet2.asr_transducer.espnet_transducer_model import ESPnetASRTransducerModel
  from espnet2.asr_transducer.joint_network import JointNetwork
  from espnet2.layers.global_mvn import GlobalMVN
  from espnet2.layers.utterance_mvn import UtteranceMVN
label_desc: Use image node_id 5 for calling a global function with example usage: test_model_training(enc_params, enc_gen_params, dec_params, joint_net_params, main_params, stats_file) without return types