code: def prepare(model, input_size, vocab_size, batch_size):
      n_token = vocab_size - 1
  
      feat_len = [15, 11]
      label_len = [13, 9]
  
      feats = torch.randn(batch_size, max(feat_len), input_size)
      labels = (
          torch.rand(batch_size, max(label_len)) * n_token % n_token
      ).long()
  
      for i in range(2):
          feats[i, feat_len[i] :] = model.ignore_id
          labels[i, label_len[i] :] = model.ignore_id
      labels[labels == 0] = vocab_size - 2
  
      return (
          feats,
          labels,
          torch.tensor(feat_len),
          torch.tensor(label_len),
      )
  
imports: import pytest
  import torch
  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
label_desc: Use image node_id 1 for calling a global function with example usage: prepare(model, input_size, vocab_size, batch_size) and returns: feats,  labels,  torch,  torch