def METHOD_NAME(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