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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 get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios, strides, pads, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
 """Build network for testing SSD Parameters network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list ratios : list or list of list normalizations : int or list of int -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """

    body = import_module(network).get_symbol(num_classes, **kwargs)
    layers = multi_layer_feature(body, from_layers, num_filters, strides, pads,
        min_filter=min_filter)

    loc_preds, cls_preds, anchor_boxes = multibox_layer(layers, \
        num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \
        num_channels=num_filters, clip=False, interm_layer=0, steps=steps)

    cls_prob = mx.symbol.softmax(data=cls_preds, axis=1, name='cls_prob')
    out = mx.symbol.contrib.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \
        name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress,
        variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk)
    return out