code: def prepare(args):
      idim = 10
      odim = 5
      model = E2E(idim, odim, args)
      batchsize = 2
      ilens = [10, 9]
      olens = [3, 4]
      n_token = odim - 1
      x = torch.randn(batchsize, max(ilens), idim)
      y_src = (
          torch.rand(batchsize, max(olens)) * n_token % n_token
      ).long()
      y_tgt = (
          torch.rand(batchsize, max(olens)) * n_token % n_token
      ).long()
      for i in range(batchsize):
          x[i, ilens[i] :] = -1
          y_tgt[i, olens[i] :] = model.ignore_id
          y_src[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)
  
      return (
          model,
          x,
          torch.tensor(ilens),
          y_tgt,
          y_src,
          data,
          uttid_list,
      )
  
file_path: espnet/test/test_e2e_st_transformer.py
imports: import argparse
  import pytest
  import torch
  from espnet.nets.pytorch_backend.e2e_st_transformer import E2E
  from espnet.nets.pytorch_backend.transformer import plot
label_desc: Use image node_id 2 for calling a global function with example usage: prepare(args) and returns: model,  x,  torch,  y_tgt,  y_src,  data,  uttid_list