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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 multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=128):
 """Wrapper function to extract features from base network, attaching extra layers and SSD specific layers Parameters 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 min_filter : int minimum number of filters used in 1x1 convolution Returns ------- list of mx.Symbols """

    # arguments check
    assert len(from_layers) > 0
    assert isinstance(from_layers[0], str) and len(from_layers[0].strip()) > 0
    assert len(from_layers) == len(num_filters) == len(strides) == len(pads)

    internals = body.get_internals()
    layers = []
    for k, params in enumerate(zip(from_layers, num_filters, strides, pads)):
        from_layer, num_filter, s, p = params
        if from_layer.strip():
            # extract from base network
            layer = internals[from_layer.strip() + '_output']
            layers.append(layer)
        else:
            # attach from last feature layer
            assert len(layers) > 0
            assert num_filter > 0
            layer = layers[-1]
            num_1x1 = max(min_filter, num_filter // 2)
            conv_1x1 = conv_act_layer(layer, 'multi_feat_%d_conv_1x1' % (k),
                num_1x1, kernel=(1, 1), pad=(0, 0), stride=(1, 1), act_type='relu')
            conv_3x3 = conv_act_layer(conv_1x1, 'multi_feat_%d_conv_3x3' % (k),
                num_filter, kernel=(3, 3), pad=(p, p), stride=(s, s), act_type='relu')
            layers.append(conv_3x3)
    return layers