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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_fully_connected(node, **kwargs):
 """Map MXNet's FullyConnected operator attributes to onnx's Gemm operator and return the created node. """

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

    initializer = kwargs["initializer"]

    no_bias = get_boolean_attribute_value(attrs, "no_bias")

    fcnode = []

    op_name = "flatten_" + str(kwargs["idx"])
    flatten_node = onnx.helper.make_node(
        'Flatten',
        inputs=[input_nodes[0]],
        outputs=[op_name],
        name=op_name
    )

    input_nodes[0] = op_name
    fcnode.append(flatten_node)

    if no_bias:
        data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
        bias_name = "bias" + str(kwargs["idx"])
        tensor_node = onnx.helper.make_tensor_value_info(bias_name, data_type, (1,))
        initializer.append(
            onnx.helper.make_tensor(
                name=bias_name,
                data_type=data_type,
                dims=(1,),
                vals=[0],
                raw=False,
            )
        )
        input_nodes.append(bias_name)
        fcnode.append(tensor_node)

    node = onnx.helper.make_node(
        "Gemm",
        input_nodes,  # input (A, B, C) - C can be in place
        [name],  # output
        alpha=1.0,
        beta=1.0,
        transA=False,
        transB=True,
        name=name
    )

    fcnode.append(node)

    return fcnode