'Returns predictions for the given test data.
# Arguments
x: array-like, shape `(n_samples, n_features)`
Test samples where n_samples in the number of samples
and n_features is the number of features.
**kwargs: dictionary arguments
Legal arguments are the arguments of `Sequential.predict`.
# Returns
preds: array-like, shape `(n_samples,)`
Predictions.'

def predict(self, x, **kwargs):

    kwargs = self.filter_sk_params(Sequential.predict, kwargs)
    return np.squeeze(self.model.predict(x, **kwargs))
