def METHOD_NAME(self, X, **kwargs):
    X = self.preprocess(X, **kwargs)
    pred_method = self.model.predict_proba if self._has_predict_proba else self.model.predict
    # TODO: Add option to crash if null is present for faster predict_proba
    null_indices = X[self._image_col_name] == ""
    if null_indices.sum() > 0:
        if self.num_classes is None:
            y_pred_proba = np.zeros(len(X))
        else:
            y_pred_proba = np.zeros((len(X), self.num_classes))
        X = X.reset_index(drop=True)
        null_indices = X[self._image_col_name] == ""
        X = X[~null_indices]
        null_indices_rows = list(null_indices[null_indices].index)
        non_null_indices_rows = list(null_indices[~null_indices].index)
        y_pred_proba[null_indices_rows] = self._dummy_pred_proba
        y_pred_proba[non_null_indices_rows] = pred_method(X, as_pandas=False)
    else:
        y_pred_proba = pred_method(X, as_pandas=False)
    return self._convert_proba_to_unified_form(y_pred_proba)