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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def load_model(self, tid, custom_objects=None):
 """Load saved keras model of the trial. If tid = None, get the best model Not applicable for trials ran in cross validion (i.e. not applicable for `CompileFN.cv_n_folds is None` """

        if tid is None:
            tid = self.best_trial_tid()

        model_path = self.get_trial(tid)["result"]["path"]["model"]
        return load_model(model_path, custom_objects=custom_objects)