<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class, nms_thresh=0.5, force_nms=True, nms_topk=400):
 """ wrapper for initialize a detector Parameters: net : str test network name prefix : str load model prefix epoch : int load model epoch data_shape : int resize image shape mean_pixels : tuple (float, float, float) mean pixel values (R, G, B) ctx : mx.ctx running context, mx.cpu() or mx.gpu(?) num_class : int number of classes nms_thresh : float non-maximum suppression threshold force_nms : bool force suppress different categories """

    if net is not None:
        if isinstance(data_shape, tuple):
            data_shape = data_shape[0]
        net = get_symbol(net, data_shape, num_classes=num_class, nms_thresh=nms_thresh,
            force_nms=force_nms, nms_topk=nms_topk)
    detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx)
    return detector