def insert_get_tuple(gddict):
    """
    TensorFlow uses input "nodename:i" to denote "get tuple i" from "nodename".
    Here we split it so that:

    node1:i -> node2

    gets transformed into

    node1 -> get_tuple(i) --> node2

    Takes a graph in "dict{str, ParsedTFNode}" form, and returns a new graph.

    We do not do this for control flow nodes(Switch, Enter, Exit, Merge
    LoopCond, NextIteration).  For these nodes, we just convert

    node1:i -> node2

    to

    node1 -> node2
    """
    retdict = {}
    get_tuple_op_var_index = 1

    inserted_ops = {}

    def make_op(input_node, index, new_node_name, gto_make_op_cache):
        cache_key = (
            input_node,
            index,
        )
        if cache_key in gto_make_op_cache:
            return gto_make_op_cache[cache_key]

        inserted_op_name = new_node_name
        inserted_op = ParsedTFNode()
        inserted_op.name = inserted_op_name
        inserted_op.op = "get_tuple"
        inserted_op.inputs = [input_node]
        inserted_op.attr["index"] = index
        inserted_ops[inserted_op_name] = inserted_op
        gto_make_op_cache[cache_key] = inserted_op
        return inserted_op

    exclusions = [
        "Switch",
        "Enter",
        "Exit",
        "Merge",
        "LoopCond",
        "NextIteration",
        "TensorArrayV3",
        "Const",
    ]
    inclusions = ["IdentityN", "Split", "SplitV", "LSTMBlockCell", "TopK", "TopKV2", "Unpack", "BlockLSTM", "BlockLSTMV2", "NonMaxSuppressionV5"]
    gto_make_op_cache = {}
    for name in list(gddict.keys()):
        new_node = ParsedTFNode()
        new_node = copy.deepcopy(gddict[name])
        new_inputs = []
        for idx in range(len(new_node.inputs)):
            if ":" in new_node.inputs[idx]:
                input_node, input_index = new_node.inputs[idx].split(":")
            else:
                input_node = new_node.inputs[idx]
                input_index = 0

            if (
                "_output_shapes" in gddict[input_node].attr
                and len(gddict[input_node].attr["_output_shapes"]) > 1
                and gddict[input_node].op not in exclusions
            ) or (gddict[input_node].op in inclusions):
                get_tuple_node_name = "gto_%s" % (get_tuple_op_var_index)
                new_inputs.append(
                    make_op(
                        input_node,
                        int(input_index),
                        get_tuple_node_name,
                        gto_make_op_cache,
                    ).name
                )
                get_tuple_op_var_index += 1
            else:
                new_inputs.append(new_node.inputs[idx])
        new_node.inputs = new_inputs

        retdict[name] = new_node

    for k, v in inserted_ops.items():
        retdict[k] = v

    # Force fix up the remaining node names by dropping the :
    #
    for k, v in retdict.items():
        for idx in range(len(v.inputs)):
            if ":" in v.inputs[idx]:
                nodename, nodeindex = v.inputs[idx].split(":")
                v.inputs[idx] = nodename

    return retdict

    TensorFlow uses input "nodename:i" to denote "get tuple i" from "nodename".
    Here we split it so that:

    node1:i -> node2

    gets transformed into

    node1 -> get_tuple(i) --> node2

    Takes a graph in "dict{str, ParsedTFNode}" form, and returns a new graph.

    We do not do this for control flow nodes(Switch, Enter, Exit, Merge
    LoopCond, NextIteration).  For these nodes, we just convert

    node1:i -> node2

    to

    node1 -> node2
    
coremltools/converters/mil/frontend/tensorflow/tf_graph_pass/insert_get_tuple.py