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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 predict(self, dataset, confidence_threshold=0.25, iou_threshold=None, verbose=True):
 """ Predict object instances in an sframe of images. Parameters dataset : SFrame | SArray | turicreate.Image The images on which to perform object detection. If dataset is an SFrame, it must have a column with the same name as the feature column during training. Additional columns are ignored. confidence_threshold : float Only return predictions above this level of confidence. The threshold can range from 0 to 1. 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. verbose : bool If True, prints prediction progress. Returns ------- out : SArray An SArray with model predictions. Each element corresponds to an image and contains a list of dictionaries. Each dictionary describes an object instances that was found in the image. If `dataset` is a single image, the return value will be a single prediction. See Also -------- evaluate Examples -------- .. sourcecode:: python # Make predictions # Stack predictions, for a better overview Data: | row_id | confidence | label | x | y | width | height | | 0 | 0.98 | dog | 123.0 | 128.0 | 80.0 | 182.0 | | 0 | 0.67 | cat | 150.0 | 183.0 | 129.0 | 101.0 | | 1 | 0.8 | dog | 50.0 | 432.0 | 65.0 | 98.0 | [3 rows x 7 columns] # Visualize predictions by generating a new column of marked up images """

        _numeric_param_check_range('confidence_threshold', confidence_threshold, 0.0, 1.0)
        dataset, unpack = self._canonize_input(dataset)
        stacked_pred = self._predict_with_options(dataset, with_ground_truth=False,
                                                  confidence_threshold=confidence_threshold,
                                                  iou_threshold=iou_threshold,
                                                  verbose=verbose)

        from . import util
        return unpack(util.unstack_annotations(stacked_pred, num_rows=len(dataset)))