data:image/png;base64,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:image/png;base64,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Take any number of input lists, and create a new list containing the items of the input lists. So APPEND [A B] [C D] where the [,,,] are lists reports the list [A B C D] not [[A B] [C D]].

1111

C,B,D,A,F0000