/usr/local/lib64/python3.6/site-packages/torch/include/ATen/core
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/core/Variadic.h (2257B)
#pragma once
#include
#include
#include
#include
#include
#include
namespace at {
// This class allows you to write variadic functions which
// call a (possibly overloaded) function on each argument,
// in order. This is most commonly used in autogenerated code,
// where it is convenient to have a function that can uniformly
// take arguments of different types. If your arguments
// are homogenous consider using a std::initializer_list instead.
//
// For examples of this in use, see torch/csrc/utils/variadic.h
template
struct IterArgs {
template
inline F& apply() {
return self();
}
// NB: Use perfect forwarding here, otherwise we'll make value
// copies of all arguments!
template
inline F& apply(T&& arg, Args&&... args) {
self()(std::forward(arg));
if (self().short_circuit()) {
return self();
} else {
return apply(std::forward(args)...);
}
}
// Here are some handy overloads which provide sensible
// defaults for container-like structures that one might
// be interested in recursing into. You can enable them
// by adding:
//
// using IterArgs::operator()
//
// to your struct. These are not enabled by default because
// you may be able to process these structures more efficiently
// than handling them one-by-one.
template
void operator()(at::ArrayRef args) {
for (const auto& arg : args) {
self()(arg);
if (self().short_circuit())
return;
}
}
template
void operator()(const torch::List& args) {
for (const auto& arg : args) {
self()(arg);
if (self().short_circuit())
return;
}
}
// NB: we need to specify std::vector manually as C++ won't
// do an implicit conversion to make a template deduction go through.
template
void operator()(const std::vector& args) {
self()(at::ArrayRef{args});
}
constexpr bool short_circuit() const {
return false;
}
private:
inline F& self() {
return *static_cast(this);
}
};
} // namespace torch