code: def prepare(
      model,
      input_size,
      vocab_size,
      batch_size,
      use_k2_modified_loss=False,
  ):
      n_token = vocab_size - 1
  
      label_len = [13, 9]
  
      # (b-flo): For k2 "modified", we need to ensure that T >= U after subsampling.
      if use_k2_modified_loss:
          feat_len = [i * 5 for i in label_len]
      else:
          feat_len = [15, 11]
  
      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: 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 2 for calling a global function with example usage: prepare(model, input_size, vocab_size, batch_size, use_k2_modified_loss) and returns: feats,  labels,  torch,  torch