def test_labram_n_outputs_0(default_labram_params):
    """
    Testing if the model is returning the correct shapes for the different
    return options.

    Parameters
    ----------
    default_labram_params: dict with default parameters for Labram model

    """
    default_labram_params["n_outputs"] = 0
    labram_base = Labram(n_layers=12, att_num_heads=12,
                         **default_labram_params)
    # Defining a random data
    X = torch.rand(1, default_labram_params["n_chans"],
                   default_labram_params["n_times"])

    with torch.no_grad():
        out = labram_base(X)
        assert out.shape[-1] == default_labram_params["patch_size"]
        assert isinstance(labram_base.final_layer, nn.Identity)

    Testing if the model is returning the correct shapes for the different
    return options.

    Parameters
    ----------
    default_labram_params: dict with default parameters for Labram model

    
test/unit_tests/models/test_models.py