code: def prepare(args):
      idim = 10
      odim = len(args.char_list)
      model = E2E(idim, odim, args)
      batchsize = 2
  
      x = torch.randn(batchsize, 15, idim)
      ilens = [15, 10]
  
      n_token = model.odim - 2  # w/o <eos>/<sos>, <mask>
      y = (torch.rand(batchsize, 10) * n_token % n_token).long()
      olens = [7, 6]
      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)
  
      return model, x, torch.tensor(ilens), y, data, uttid_list
  
file_path: espnet/test/test_e2e_asr_maskctc.py
imports: import argparse
  import pytest
  import torch
  from espnet.nets.pytorch_backend.e2e_asr_maskctc import E2E
  from espnet.nets.pytorch_backend.maskctc.add_mask_token import mask_uniform
  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,  data,  uttid_list