Fix the elementwise_ops axis bug, make elementwise_ops has the same the op function for override ops

* update layers, test=develop

* fix input numpy, test=develop

* fix bugs, test=develop

* follow commments, test=develop

* update getitem, test=develop

* add unittests, test=develop

* set dtype of compare op to bool, test=develop

adds unary operator __neg__ for VarBase in dygraph mode, and for Variable in static graph mode.

* patch math method for varbase using auto-generated op functions, test=develop

* clean code that handles batch_size, test=develop

* follow comments, test=develop

* follow comments, test=develop

* code clean, test=develop

* test=develop, fix docker with paddle nccl problem

* don't expose numerous Tensor.set(), test=develop

* fix condition, test=develop

* fix float16 bug, test=develop

* feed should be Tensor or np.array, not Variable or number, test=develop

* use forcecast to copy numpy slice to new array, test=develop

* remove float16-uint16 hacking, test=develop

* add variable method to varbase and refactor to_variable to support return varbase

* support kwargs in varbase constructor

* add VarBase constructor to support default python args

* refine varbase initial method

* reset branch

* fix ut for change VarBase error info to PaddleEnforce

* cherry is parameter change before

* overload isinstance to replace too many change of is_variable

* rm useless files

* rm useless code merged by git

* test=develop, fix some ut failed error

* test=develop, fix test_graph_wrapper

* add some tests, test=develop

* refine __getitem__, test=develop

* add tests, test=develop

* fix err_msg, test=develop

* test=develop, fix docker with paddle nccl problem

* test=develop, refine en_doc for Variable and Program

* test=document_fix, fix English doc for Variable and Program

* test=document_fix, refine astype code block style

* test=document_fix, add example code for Variable properties

When the second argument contains batch dimension, the axis should be 0.

Also makes elementwise ops more tolerant at handling tensors with trailing
singular dimensions.

* Add reset and guard to unique_name

* dist train support lr decay

* update by comment

* revert elementwise method creator

* delete comment

* add basic interface for learning rate decay
* add exponential_decay
* add natural_exp_decay
* add inverse_time_decay

Users can use `a+b`, `a*10`.