`Paddle` manages Scope as programming language's scope.  It just a
thread-local stack of Scope. Top of that stack is current scope, the
bottom of that stack is all scopes' parent.

Invoking `create_var/get_var`  can `create/get` variable in current
scope. Invoking `enter_local_scope/leave_local_scope` can create or
destroy local scope.

A `scoped_function` will take a `function` as input. That function will
be invoked in a new local scope.

* Move static variable defined in .cc

We cannot define static variable in .h, because it will be multi-defined
errors.

Also fix some cpp syntax, like:

* Prefer to use algorithm not manually for-loop, to make code more
  readable.
* Remove unused `()`.
* Enforce take a bool. It is no need `xxx==true`.
* Use range-based for-loop iterator from op_desc.attrs

* Fix a protential static variable init order error

* Expose paddle.framework by pybind11

* Export paddle.framework.{Scope, Variable} to paddle.v2.framework.core.
* See python/paddle/v2/framework/tests/test_scope.py for Python usage
* See paddle/pybind/pybind.cc for C++ bind code.

* add copyright

add TaskFail interface

Fix slow parsing a recursive depends topology

* generic.cmake can propogate dependencies through libraries. It is no
  need to specific all dependencies.

* Fix  #2797
* It because trainer_config_helpers' __dfs_travel__ did not record the
  node which travelled, and if the topology has a recursive dependency,
  there are some nodes will be travelled multiple times.
* Add a `travelled` set to record which node is travelled.
* Also add a unittest for this situation.

update docs url

fix dynload link error: undefined reference dlsym

Network implement

Pserver Save state

update the md5sum of WMT14 trained model.

* add device_context

* add unittest for device_context

* transfer to use function paddle::platform::throw_on_error

* fix cuda build error

* using dynload functions

* follow comments

"lr state serialization"

Fix paddle build when it's a subdirectory of another project

Update comments of maxout_layer