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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_broadcast_to(node, **kwargs):
 """Map MXNet's broadcast_to operator attributes to onnx's Expand operator and return the created node. """

    name, input_nodes, attrs = get_inputs(node, kwargs)

    shape_list = convert_string_to_list(attrs["shape"])

    initializer = kwargs["initializer"]
    output_shape_np = np.array(shape_list, dtype='int64')
    data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
    dims = np.shape(output_shape_np)

    output_shape_name = "expand_attr_tensor" + str(kwargs["idx"])
    tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)

    initializer.append(
        onnx.helper.make_tensor(
            name=output_shape_name,
            data_type=data_type,
            dims=dims,
            vals=shape_list,
            raw=False,
        )
    )

    input_nodes.append(output_shape_name)
    expand_node = onnx.helper.make_node(
        "Expand",
        input_nodes,
        [name],
        name=name
    )

    return [tensor_node, expand_node]