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

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ cont

de:wirf _ ca:llança _ es:lanzar _ fr:lance _ pt:lança _ catchtag

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ cont

de:wirf _ _ ca:llança _ _ es:lanzar _ _ fr:lance _ _ pt:lança _ _ catchtag

de:falls _ dann _ und pause $pause-1-255-220-0 ca:si _ fes _ i atura-ho tot $pause-1-255-220-0 es:si _ haz _ y páralo todo $pause-1-255-220-0 fr:si _ faire _ et mettre tout en pause $pause-1-255-220-0 pt:se _ , então _ e faz pausa em tudo $pause-1-255-220-0

de:ignoriere _ ca:ignora _ es:ignorar _ fr:ignore _ pt:ignora _

Call f(f(f(...(f(x))))) n times where the three input slots are n, f, and x from left to right. The # variable can be used inside f to represent how many times f has been called.

pt:o resultado de _ invocações em cascata de _ com argumento inicial _ _

Call f(f(f(...(f(x))))) until condition is true, where the three input slots are condition, f, and x from left to right. The # variable can be used inside f or condition to indicate how many times f has been called.

pt:o resultado da invocação em cascata até que _ de _ com argumento inicial _ _

Returns the function f(g(x)) where f and g are the two inputs.

pt:a composição de _ com _

Like the built-in REPEAT UNTIL block, except that the ending condition is not tested until the script has been run the first time. So the script is run at least once.

pt:repete _ até que _ $loop-0.7

Run the script repeatedly, as long as the given condition is true. Runs the script at least once before testing the condition.

pt:repete _ enquanto _ $loop-0.7

Runs the script repeatedly, as long as the condition is true. Tests the condition before the first time the script is run. Like the built in REPEAT UNTIL except that in this block the condition must be true, not false.

pt:enquanto _ , repete _ $loop-0.7

Runs the script the specified number of times, like the built-in REPEAT block, but this one provides the # variable that can be used inside the script. Try REPEAT (200) MOVE (#) STEPS RIGHT 92 with the pen down.

pt:repete _ vezes _ _ $loop-0.7

pt:para _ de _ com passo _ a _ _ $loop-0.7 1110

Provides LOOP as a function of one input that runs the body of the LET with A set to the function input, so the body can run itself recursively. See COPY block in Variables for an example of use.

pt:tendo _ o valor inicial _ , executa _ definido como _ new value