* Also fix unit test

* 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_

* Implement InferShape and register them, give a stub Kernel method
  by LOG(INFO)

* 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>;

* Let OpProto support multiple and temporary

* Each input/output of Paddle's Op could be a list. Add multiple mark to
  OpProto. Also add a `input_format`/`output_format` attribute if that
  Op has multiple input or output. The format of that attribute please
  reference the comments in `op_proto.proto`
* Add temporary mark, because some output of an Op is not used by user
  but used by other op for faster computation. Explicitly mark which
  output is temporary could let future memory/computation optimization.
* Add generated field to AttrProto.

* Add `AddInputs`/`AddOutputs` function

* It is more readable to invoke `AddInputs` not
  `AddInput(multiple=true)`.