* add inputs

* add ut for multiple inputs

* fix AddToLayer

* op_desc -> op_proto

* CreateArgumentOffsetMap -> CreateInOutOffsetMap

* move CreateInOutOffsetMap from OperatorBase to op registry

* arg_idxs_ -> in_out_idxs_

* add NDEBUG switch to PADDLE_ENFORCE

* using etcd as fault tolerant training

* update

* workable version, ft not tested

* small fix

* update

* remove TODO

* OperatorBase should not store OpDesc because not All op contains an
  OpDesc and not all ops create from OpDesc.
  * Networks do not contain OpDesc and are not created by OpDesc
* Do not register Network to OpRegistry.
  * The network is directly created by the user in Python. Not from
    registry.
* Correctly handle the `inputs` and `outputs` of a Network.
  * Add CompleteAddOp() methods
* Remove `AddOp(OpDesc&)` in net-op. All op are added by OperatorPtr.
* Rewrite unit test for truly tested what networks do.
* optimise operator_test

1. Refactor DDim's product() to make it more efficiently.

2. Add slice_ddim().

* add ADD_OP_CPU to enable add op with only cpu kernel

* It is used to create an operator library. It handles to split CPU and
  GPU sources and links operator common libraries.
* It also give a reasonable warning and error when operator developer
  not correctly implement an operator.
  * Warning for lack of GPU kernel.
* Same interface as `cc_library` to make code style consistent.

All OpCreation method are generated by
`create_op_creation_methods::__bootstrap__` method, and stores in
`op_creations` object and its methods.

There are three parts to implement this feature.

1. Get all registered `OpProto` from C++ side. It is implemented in
`get_all_op_protos` method.
1. Create a function to convert `kwargs` to `OpDesc` base on each op's
`OpProto`. The `OpDescCreationMethod` class.
1. Convert `OpProto` to `docstring` by `get_docstring_from_op_proto`
method.

All three methods are unit tested. The `__bootstrap__` just combines
them together and create a method in runtime.

For details, please reference the doc string in
`create_op_creation_methods.py` and the unit test
`test_op_creation_methods.py`.

The newest developing image was push to dockerhub, named xreki/paddle-android:dev.

1. Add template T which indicates data type to `CopyFrom()`, `Slice()`
and `ShareData()` functions. This makes `CopyData()` code much clearer.

2. Add `set_dim()`.

3. `product(DDim)` transforms `DDim` to `vector<int>` first and then calculate
its product. That might be quite slow. For `product(dims_)` is frequently
used in Tensor, we add a mumber variable `numel_` as a cache of the
product result.
TODO: refactor `product()` to make it more efficient.

4. Unable Tensor::operator=

5. Remove the limit of POD type, because `float16` and `int8` are not POD type.

* PyBind and SWIG of paddle cannot be load in a single Python process,
  lazy import all SWIG library of Paddle. Otherwise, the glog, gflags
  are imported twice in a same Python process.
* Note that all PyBind11 return C++ std::string as an unicode. For
  protobuf, it is need be cast to `str` before use them.
* Add unit test for Get `OpProtos`

* use OperatorPtr = std::shared_ptr<OperatorBase>;
* use ScopePtr = std::share_ptr<Scope>;