namespace tf {

/** @page ModuleAlgorithm Module Algorithm

%Taskflow provides template methods that let users create reusable building blocks
called @em modules.
Users can connect modules together to build more complex parallel algorithms.

@tableofcontents

@section ModuleAlgorithmInclude Include the Header

You need to include the header file, <tt>taskflow/algorithm/module.hpp</tt>,
for creating a module task over a schedulable graph target.

@code{.cpp}
#include <taskflow/algorithm/module.hpp>
@endcode

@section WhatIsAModuleTask What is a Module Task

Similar to @ref ComposableTasking, but in a more generic setting,
the template function, tf::make_module_task,
allows you to create a task over a taskflow graph that can be executed by an executor.
This provides a flexible way to encapsulate and reuse complex task logic within your 
%Taskflow applications.
The example below demonstrates how to create and launch multiple taskflows in parallel 
using asynchronous tasking:

@code{.cpp}
tf::Executor executor;

tf::Taskflow A;
tf::Taskflow B;
tf::Taskflow C;
tf::Taskflow D;

A.emplace([](){ printf("Taskflow A\n"); }); 
B.emplace([](){ printf("Taskflow B\n"); }); 
C.emplace([](){ printf("Taskflow C\n"); }); 
D.emplace([](){ printf("Taskflow D\n"); }); 

// launch the four taskflows using asynchronous tasking
executor.async(tf::make_module_task(A));
executor.async(tf::make_module_task(B));
executor.async(tf::make_module_task(C));
executor.async(tf::make_module_task(D));
executor.wait_for_all();  
@endcode

@dotfile images/module_task_1.dot

Since the four taskflows are launched asynchronously without any dependencies between them,
we can observe any order of the output message:

@code{.shell-session}
# one possible output
Taskflow B
Taskflow C
Taskflow A
Taskflow D

# another possible output
Taskflow D
Taskflow A
Taskflow B
Taskflow C
@endcode

If you need to enforce dependencies among these four taskflows,
you can use dependent-async tasks.
The example below launches the four taskflows one by one in sequential:

@code{.cpp}
tf::Executor executor;

tf::Taskflow A;
tf::Taskflow B;
tf::Taskflow C;
tf::Taskflow D;

A.emplace([](){ printf("Taskflow A\n"); }); 
B.emplace([](){ printf("Taskflow B\n"); }); 
C.emplace([](){ printf("Taskflow C\n"); }); 
D.emplace([](){ printf("Taskflow D\n"); }); 

auto TA = executor.silent_dependent_async(tf::make_module_task(A));
auto TB = executor.silent_dependent_async(tf::make_module_task(B), TA);
auto TC = executor.silent_dependent_async(tf::make_module_task(C), TB);
auto [TD, FD] = executor.dependent_async(tf::make_module_task(D), TC);
FD.get();
@endcode

@dotfile images/module_task_2.dot

@code{.shell-session}
# dependent-async tasks enforce a sequential execution of the four taskflows
Taskflow A
Taskflow B
Taskflow C
Taskflow D
@endcode

The module task maker, tf::make_module_task, functions basically similar to tf::Taskflow::composed_of 
but provides a more generic interface that can be used beyond %Taskflow.
Specifically, the following two approaches achieve the same functionality.

@code{.cpp}
// approach 1: composition using composed_of
tf::Task m1 = taskflow1.composed_of(taskflow2);

// approach 2: composition using make_module_task
tf::Task m1 = taskflow1.emplace(tf::make_module_task(taskflow2));
@endcode

@attention
Similar to tf::Taskflow::composed_of, tf::make_module_task does not assume ownership of 
the provided taskflow but a soft reference.
You are responsible for ensuring that the encapsulated taskflow remains valid
throughout its execution.


@section CreateAModuleTaskOverACustomGraph Create a Module Task over a Custom Graph

In addition to encapsulate taskflow graphs, you can create a module task to schedule 
a custom graph target.
A schedulable target (of type `T`) must define the method `T::graph()` that returns a reference 
to the tf::Graph object managed by `T`.
The following example defines a custom graph that can be scheduled through making module tasks:

@code{.cpp}
struct CustomGraph {
  tf::Graph graph;
  CustomGraph() {
    // use flow builder to inherit all task creation methods in tf::Taskflow
    tf::FlowBuilder builder(graph);
    tf::Task task = builder.emplace([](){
      std::cout << "a task\n";  // static task
    });
  }
  // returns a reference to the graph for taskflow composition
  Graph& graph() { return graph; }
};

CustomGraph target;
executor.async(tf::make_module_task(target));
@endcode

@attention
Users are responsible for ensuring the given custom graph remains valid throughout its execution.
The executor does not assume ownership of the custom graph.


*/

}






