data:image/png;base64,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

Encode is the procdure name, and it has two parameters text and shift #.

for= a loop

110i

encode is the procedure with two parameters, char and shift.

4.2.2 ITIT 5

As the lab says, it's okay to use ">" and "<" instead of "≥" and "≤."

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==Encipher or Decipher?1DoutputText

[}x)Sup!8