code: def prepare_inputs(idim, odim, ilens, olens, is_cuda=False):
      np.random.seed(1)
  
      feats = [
          np.random.randn(ilen, idim).astype(np.float32)
          for ilen in ilens
      ]
      labels = [
          np.random.randint(1, odim, olen).astype(np.int32)
          for olen in olens
      ]
      feats_len = np.array([x.shape[0] for x in feats], dtype=np.int32)
  
      feats = pad_list([torch.from_numpy(x).float() for x in feats], 0)
      labels = pad_list(
          [torch.from_numpy(y).long() for y in labels], -1
      )
  
      feats_len = torch.from_numpy(feats_len).long()
  
      if is_cuda:
          feats = feats.cuda()
          labels = labels.cuda()
          feats_len = feats_len.cuda()
  
      return feats, feats_len, labels
  
imports: import argparse
  import json
  import tempfile
  import numpy
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 6 for calling a global function with example usage: prepare_inputs(idim, odim, ilens, olens, is_cuda) and returns: feats,  feats_len,  labels