The first argument to map is a function. So, with your code, Julia is trying to make a function call:
round(Int16, floor(rand(TruncatedNormal(150,20,50,250))))()
But the output of round(Int16, ...) isn't a function, it's a number, so you cannot call it. That's why the error says "objects of type Int16 are not callable." You could fix this by using an anonymous function:
map(() -> round(Int16, floor(rand(TruncatedNormal(150,20,50,250)))), 1:2)
But the "Julian" way to do this is to use a comprehension:
[round(Int16, floor(rand(TruncatedNormal(150,20,50,250)))) for _ in 1:2]
EDIT:
If you are going to sum the results, then you can use something that looks like a comprehension but is called a generator expression. This is basically everything above with the [ ] around the expression. A generator expression can be used directly in functions like sum or mean, etc.
sum(round(Int16, floor(rand(TruncatedNormal(150,20,50,250)))) for _ in 1:2)
The advantage to generator expressions is that they don't allocate the memory for the full array. So, if you did this 100 times and used the sum approach above, you wouldn't need to allocate space for 100 numbers.
This goes beyond the original question, but OP wanted to use the sum expression where the 2 in 1:2 is a 1-element vector. Of course, if the input is always a 1-element vector, then I recommend first(x) like the comments. But this is a nice opportunity to show the importance of breaking things down into functions frequently in Julia. For example, you could take the entire sum expression and define a function
generatenumbers(n::Integer) = sum(... for _ in 1:n)
where n is a scalar. Then if you have some odd array expression for n (1-element vector, many such ns in a multi-dim array, etc.), you can just do:
generatenumbers.(ns)
# will apply to each element and return same shape as ns
If the de-sugaring logic is more complex than applying element-wise, you can even define:
generatenumbers(ns::AbstractArray) = # ... something more complex
The point is to define an "atomic" function that expresses the statement or task you want clearly, then use dispatch to apply it to more complicated data-structures that appear in practical code. This is a common design pattern in Julia (not the only option, but an effective one).
