* Add the matmul, elementwise_euqal, elementwise_sum ops to API2.0
* Fix the import meesage in common_ops_import
* Update the test cast for mm

* Add the sum op to API 2.0, test=develop
* Fix the import meesage in common_ops_import

*  add new op paddle.tensor.mul(x, y, x_num_col_dims=1, y_num_col_dims=1, out=None, name=None)
* add new op paddle.tensor.pow(input, exponent, out=None, name=None)
* add the aboved two new ops unittest (test api param out and name)

add tril op and  triu op

* add new op paddle.tensor.div(x, y, out=None, name=None) 
* add gpu and dygraph unittests.
* Performance optimization: scale op is not called when alpha=1. 
* op error message optimization.

* Add the argmax op to API 2.0, test=develop

* Fix the compiler problem in arg_max op, test=develop

* Fix the import meesage in common_ops_import, test=develop

* Fix the default dtype of arg_min_max, test=develop

* add a new op paddle.tensor.sort(), test=develop
* add a new op paddle.sort(), test=develop

* Add allclose Op, and its function is analogous to numpy.allclose. It returns True if two tensors are elementwise equal within a tolerance.

* sin sqrt tanh atan add out, test=develop

* optimize doc, test=develop

* add dygraph test, test=develop

* add flip op

* update linspace, equal operators to API 2.0, test=develop

* equal support higher performance CUDA kernel, test=develop

* update comment of equal&linspace operator, test=develop

* update comment of equal&linspace operator, test=develop

* define alias in paddle dir and paddle.nn dir test=develop

* define alias in paddle.nn.functional dir test=develop

* define alias in paddle.tensor.__init__.py test=develop

* fix high level API(Inference) bug

* patch the unit tests

* Use for_test=True in the Fluid Trainer to clone the test program

* fix typo

* Should do the samething to the inferencer

* add Inference.infer

* optimize code

* update no_test_word2vec_new_api.py

* update trainer

* split check_and_get_place

* use inference_program to save inference model in Trainer

* update demo

* update save_inference_model

* clean code

* Load/save the params from the params_path

* Switch to use load_persistables and save_persistables

* Instaed of setup the executor to run program and scope. Pass the program to the load_persistables

- The trainer and inferencer will load params from disk if param_path
  argument is not None in their constructor.

- Remove params.py, we will expose core.Scope to the user if needed
  (e.g., for GAN). Currently we will not expose it, unless we clearly
  know doing so can support GAN.

- Add `save_params` to Trainer (a TODO item).

- rename "network" to "program"

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

* add copyright hook

* add copyright hook

* refine copyright hook

* "test copyright hook"

* fix check style

* fix ci

* Fix lots of trainer_config_helpers bug, and complete unittest for `layers.py`