code: def prepare(backend, args):
      idim = 10
      odim = 3
      batchsize = 2
      ilens = [30, 20]
      olens = [5, 4]
      n_token = odim - 1
      if backend == "pytorch":
          model = th.E2E(idim, odim, args)
          x = torch.randn(batchsize, max(ilens), idim)
          y = (
              torch.rand(batchsize, max(olens)) * n_token % n_token
          ).long()
      else:
          model = ch.E2E(idim, odim, args)
          x = numpy.random.randn(batchsize, max(ilens), idim).astype(
              numpy.float32
          )
          y = (
              numpy.random.rand(batchsize, max(olens))
              * n_token
              % n_token
          )
          y = y.astype(numpy.int32)
      for i in range(batchsize):
          x[i, ilens[i] :] = -1
          y[i, olens[i] :] = model.ignore_id
  
      data = {}
      uttid_list = []
      for i in range(batchsize):
          data["utt%d" % i] = {
              "input": [{"shape": [ilens[i], idim]}],
              "output": [{"shape": [olens[i]]}],
          }
          uttid_list.append("utt%d" % i)
  
      if backend == "pytorch":
          return model, x, torch.tensor(ilens), y, data, uttid_list
      else:
          return model, x, ilens, y, data, uttid_list
  
file_path: espnet/test/test_e2e_asr_transformer.py
imports: import argparse
  import chainer
  import numpy
  import pytest
  import torch
  import espnet.nets.chainer_backend.e2e_asr_transformer
  import espnet.nets.pytorch_backend.e2e_asr_transformer
  from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
  from espnet.nets.pytorch_backend.transformer import plot
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
label_desc: Use image node_id 5 for calling a global function with example usage: prepare(backend, args) and returns: model,  x,  torch,  y,  data,  uttid_list, model,  x,  ilens,  y,  data,  uttid_list