def METHOD_NAME(cell_type, num_layers, batch_size):
    hidden_size = 256
    vocab_size = 50
    max_sequence_length = 10
    # make repeatable
    set_random_seed(RANDOM_SEED)
    combiner_outputs = {HIDDEN: torch.rand([batch_size, hidden_size])}
    sequence_rnn_decoder = SequenceGeneratorDecoder(
        input_size=hidden_size,
        vocab_size=vocab_size,
        max_sequence_length=max_sequence_length,
        cell_type=cell_type,
        num_layers=num_layers,
    )
    output = sequence_rnn_decoder(combiner_outputs, target=None)
    assert list(output[LOGITS].size()) == [batch_size, max_sequence_length, vocab_size]
    # check for parameter updating
    target = torch.randn(output[LOGITS].shape)
    fpc, tpc, upc, not_updated = check_module_parameters_updated(sequence_rnn_decoder, (combiner_outputs, None), target)
    assert upc == tpc, f"Failed to update parameters. Parameters not updated: {not_updated}"