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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 evaluate(self, dataset, metric='auto', output_type='dict', iou_threshold=None, confidence_threshold=None, verbose=True):
 """ Evaluate the model by making predictions and comparing these to ground truth bounding box annotations. Parameters dataset : SFrame Dataset of new observations. Must include columns with the same names as the annotations and feature used for model training. Additional columns are ignored. metric : str or list, optional Name of the evaluation metric or list of several names. The primary metric is average precision, which is the area under the precision/recall curve and reported as a value between 0 and 1 (1 being perfect). Possible values are: - 'auto' : Returns all primary metrics. - 'all' : Returns all available metrics. - 'average_precision_50' : Average precision per class with intersection-over-union threshold at 50% (PASCAL VOC metric). - 'average_precision' : Average precision per class calculated over multiple intersection-over-union thresholds - 'mean_average_precision_50' : Mean over all classes (for ``'average_precision_50'``). This is the primary single-value metric. - 'mean_average_precision' : Mean over all classes (for ``'average_precision'``) output_type : str Type of output: - 'dict' : You are given a dictionary where each key is a metric name and the value is another dictionary containing class-to-metric entries. - 'sframe' : All metrics are returned as a single `SFrame`, where each row is a class and each column is a metric. Metrics that are averaged over class cannot be returned and are ignored under this format. However, these are easily computed from the `SFrame` (e.g. ``results['average_precision'].mean()``). iou_threshold : float Threshold value for non-maximum suppression. Non-maximum suppression prevents multiple bounding boxes appearing over a single object. This threshold, set between 0 and 1, controls how aggressive this suppression is. A value of 1 means no maximum suppression will occur, while a value of 0 will maximally suppress neighboring boxes around a prediction. confidence_threshold : float Only return predictions above this level of confidence. The threshold can range from 0 to 1. verbose : bool If True, prints evaluation progress. Returns ------- out : dict / SFrame Output type depends on the option `output_type`. See Also -------- create, predict Examples -------- mAP: 43.2% """

        if iou_threshold is None: iou_threshold = self.non_maximum_suppression_threshold
        if confidence_threshold is None: confidence_threshold = 0.001

        AP = 'average_precision'
        MAP = 'mean_average_precision'
        AP50 = 'average_precision_50'
        MAP50 = 'mean_average_precision_50'
        ALL_METRICS = {AP, MAP, AP50, MAP50}
        if isinstance(metric, (list, tuple, set)):
            metrics = metric
        elif metric == 'all':
            metrics = ALL_METRICS
        elif metric == 'auto':
            metrics = {AP50, MAP50}
        elif metric in ALL_METRICS:
            metrics = {metric}
        else:
            raise _ToolkitError("Metric '{}' not supported".format(metric))

        pred, gt = self._predict_with_options(dataset, with_ground_truth=True,
                                              confidence_threshold=confidence_threshold,
                                              iou_threshold=iou_threshold,
                                              verbose=verbose)

        pred_df = pred.to_dataframe()
        gt_df = gt.to_dataframe()

        thresholds = _np.arange(0.5, 1.0, 0.05)
        all_th_aps = _average_precision(pred_df, gt_df,
                                        class_to_index=self._class_to_index,
                                        iou_thresholds=thresholds)

        def class_dict(aps):
            return {classname: aps[index]
                    for classname, index in self._class_to_index.items()}

        if output_type == 'dict':
            ret = {}
            if AP50 in metrics:
                ret[AP50] = class_dict(all_th_aps[0])
            if AP in metrics:
                ret[AP] = class_dict(all_th_aps.mean(0))
            if MAP50 in metrics:
                ret[MAP50] = all_th_aps[0].mean()
            if MAP in metrics:
                ret[MAP] = all_th_aps.mean()
        elif output_type == 'sframe':
            ret = _tc.SFrame({'label': self.classes})
            if AP50 in metrics:
                ret[AP50] = all_th_aps[0]
            if AP in metrics:
                ret[AP] = all_th_aps.mean(0)
        else:
            raise _ToolkitError("Output type '{}' not supported".format(output_type))

        return ret