(1) use channel instead of vector/BlockingQueue in Dataset，to keep same with existing implementation, and make code more readable and flexible (dataset single output channel or multi output channel). one previous memory out of limit problem is cause by not release memory after training.
(2) add Record because MultiSlotType costs too much memory (80B)，fix memory out of limit problem.
(3) add Channel, Archive in paddle/fluid/framework
(4) change dataset from shared_ptr to unique_ptr in pybind
(5) move create/destroy readers from trainer to dataset
(6) move shuffle from datafeed to dataset. dataset holds memory, datafeed is only for load data and feed data to network.
(7) fix thread num bug of Dataset when filelist size < thread num
(8) support set_queue_num in InMemoryDataset

Add Pipeline Concurrency Train Mode:
- Cpp: pipeline_trainer & section_worker
- Python: PipelineOptimizer
- Add a new data_feed type: PrivateInstantDataFeed
- Add a test demo of pipeline trainer and the test model is gnn
- Do not support win32 now

add inductive shape index

* add save/load model, shrink table, cvm, config file & fix pull dense bug
test=develop

* fix global shuffle bug, fix pull dense bug, fix release memeory bug, fix shrink error
add client flush, add get data size
test=develop

* fix global shuffle bug
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* fix global shuffle bug
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* fix code style
test=develop

* fix code style & modify pslib cmake
test=develop

* fix error of _role_maker
test=develop

* fix code style
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* fix code style
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* fix code style
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* fix code style
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* fix code style
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* fix windows compile error of fleet
test=develop

* fix global shuffle bug

* add comment
test=develop

* update pslib.cmake
test=develop

* fix fill sparse bug
test=develop

* fix push sparse bug
test=develop

* fix train_from_dataset and infer_from_dataset example

* add inductive dim for data_reader, example: shape=[-1, 1], then -1 will be inducted through run-time reading of number of elements

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