Let's consider the following situation:
I have generic function which can accepts function arguments defined as
any. Now if want to know exactly the type of function arguments I have to
use the reflect package. The problem is that although on function
invocation we can explicitly define the type constraints, these are not
taken into consideration when we are matching the concrete types using the
reflect type switch.
So let's say, that I have the following function:
// Flatten flattens the slice all the way to the deepest nesting level.
func Flatten[T any](slice any) ([]T, error) {
return baseFlatten([]T{}, slice)
}
func baseFlatten[T any](acc []T, slice any) ([]T, error) {
var err error
switch v := any(slice).(type) {
case T:
acc = append(acc, v)
case []T:
acc = append(acc, v...)
case []any:
for _, sv := range v {
acc, err = baseFlatten(acc, sv)
if err != nil {
return nil, errors.New("flattening error")
}
}
default:
return nil, errors.New("flattening error")
}
return acc, nil
}
I would expect that calling the Flatten method in the following way, the
types to be inferred correctly and the input values to be recognized as
float32 and NOT float64. Because otherwise I don't see the reason to use an
explicit type constraint on function invocation.
input := []any{[]float32{1.0, 2.0}, 1.1}
result, err := Flatten[float32](input)
The generic type switch cases as defined in the proposal (
https://go.googlesource.com/proposal/+/HEAD/design/43651-type-parameters.md#generic-types-as-type-switch-cases)
is not intuitive, at least for me.
Now the question is how could I infer correctly the parameter type without
explicitly defining the value 1.1 as float32 like this:
input := []any{[]float32{1.0, 2.0}, float32(1.1)}
result, err := Flatten[float32](input)
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