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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 convert_dense(net, node, module, builder):
 """Convert a dense layer from mxnet to coreml. Parameters net: network A mxnet network object. node: layer Node to convert. module: module An module for MXNet builder: NeuralNetworkBuilder A neural network builder object. """

    input_name, output_name = _get_input_output_name(net, node)
    has_bias = True
    name = node['name']

    inputs = node['inputs']
    args, _ = module.get_params()
    W = args[_get_node_name(net, inputs[1][0])].asnumpy()
    if has_bias:
        Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
    else:
        Wb = None
    nC, nB = W.shape

    builder.add_inner_product(
        name=name,
        W=W,
        b=Wb,
        input_channels=nB,
        output_channels=nC,
        has_bias=has_bias,
        input_name=input_name,
        output_name=output_name
    )