def METHOD_NAME(self, X, y, X_val=None, y_val=None, **kwargs):
    X, y, X_val, y_val = super().METHOD_NAME(X=X, y=y, X_val=X_val, y_val=y_val, **kwargs)
    X_features = list(X.columns)
    if len(X_features) != 1:
        raise AssertionError(f"ImagePredictorModel only supports one image feature, but {len(X_features)} were given: {X_features}")
    self._image_col_name = X_features[0]
    null_indices = X[self._image_col_name] == ""
    # TODO: Consider some kind of weighting of the two options so there isn't a harsh cutoff at 50
    # FIXME: What if all rows in a class are null? Will probably crash.
    if null_indices.sum() > 50:
        self._dummy_pred_proba = self._compute_dummy_pred_proba(y[null_indices])  # FIXME: Do this one for better results
    else:
        # Not enough null to get a confident estimate of null label average, instead use all data average
        self._dummy_pred_proba = self._compute_dummy_pred_proba(y)
    if null_indices.sum() > 0:
        X = X[~null_indices]
        y = y[~null_indices]
    if X_val is not None:
        null_indices_val = X_val[self._image_col_name] == ""
        if null_indices_val.sum() > 0:
            X_val = X_val[~null_indices_val]
            y_val = y_val[~null_indices_val]
    return X, y, X_val, y_val