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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 assess_trial(self, trial_job_id, trial_history):
 """assess whether a trial should be early stop by curve fitting algorithm Parameters trial_job_id: int trial job id trial_history: list The history performance matrix of each trial Returns ------- bool AssessResult.Good or AssessResult.Bad Raises ------ Exception unrecognize exception in curvefitting_assessor """

        self.trial_job_id = trial_job_id
        self.trial_history = trial_history
        if not self.set_best_performance:
            return AssessResult.Good
        curr_step = len(trial_history)
        if curr_step < self.start_step:
            return AssessResult.Good
        
        if trial_job_id in self.last_judgment_num.keys() and curr_step - self.last_judgment_num[trial_job_id] < self.gap:
            return AssessResult.Good
        self.last_judgment_num[trial_job_id] = curr_step

        try:
            start_time = datetime.datetime.now()
            # Predict the final result
            curvemodel = CurveModel(self.target_pos)
            predict_y = curvemodel.predict(trial_history)
            logger.info('Prediction done. Trial job id = ', trial_job_id, '. Predict value = ', predict_y)
            if predict_y is None:
                logger.info('wait for more information to predict precisely')
                return AssessResult.Good
            standard_performance = self.completed_best_performance * self.threshold

            end_time = datetime.datetime.now()
            if (end_time - start_time).seconds > 60:
                logger.warning('Curve Fitting Assessor Runtime Exceeds 60s, Trial Id = ', self.trial_job_id, 'Trial History = ', self.trial_history)

            if self.higher_better:
                if predict_y > standard_performance:
                    return AssessResult.Good
                return AssessResult.Bad
            else:
                if predict_y < standard_performance:
                    return AssessResult.Good
                return AssessResult.Bad

        except Exception as exception:
            logger.exception('unrecognize exception in curvefitting_assessor', exception)