def METHOD_NAME(cls, parameter_set="default"):
    """Return testing parameter settings for the estimator.
    Parameters
    ----------
    parameter_set : str, default="default"
        Name of the set of test parameters to return, for use in tests. If no
        special parameters are defined for a value, will return `"default"` set.
        For classifiers, a "default" set of parameters should be provided for
        general testing, and a "results_comparison" set for comparing against
        previously recorded results if the general set does not produce suitable
        probabilities to compare against.
    Returns
    -------
    params : dict or list of dict, default={}
        Parameters to create testing instances of the class.
        Each dict are parameters to construct an "interesting" test instance, i.e.,
        `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
        `create_test_instance` uses the first (or only) dictionary in `params`.
    """
    from sktime.utils.validation._dependencies import _check_soft_dependencies
    param1 = {
        "n_epochs": 10,
        "batch_size": 4,
        "use_bias": False,
    }
    param2 = {
        "n_epochs": 12,
        "batch_size": 6,
        "use_bias": True,
    }
    test_params = [param1, param2]
    if _check_soft_dependencies("keras", severity="none"):
        from keras.callbacks import LambdaCallback
        test_params.append(
            {
                "n_epochs": 2,
                "callbacks": [LambdaCallback()],
            }
        )
    return test_params