code: def prepare():
      batch_size = 2
      vocab_size = 4
      n_token = vocab_size - 1
  
      label_len = [13, 9]
      labels = (
          torch.rand(batch_size, max(label_len)) * n_token % n_token
      ).long()
      for i in range(2):
          labels[i, label_len[i] :] = 0
  
      return vocab_size, labels
  
file_path: espnet/test/espnet2/asr_transducer/test_decoder.py
imports: import pytest
  import torch
  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
label_desc: Use image node_id 1 for calling a global function with example usage: prepare() and returns: vocab_size,  labels