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* Update dataset_loader.cpp

* Update config.cpp

* Update application.cpp

* Update linkers_socket.cpp

This allows for network retries, to scale well with the
number of machines, and still retains the existing functionality
for cases with smaller num_machines ( 500 )

Fixes #3301
Co-authored-by: Aakarsh Gopi <aakarsh@vaticlabs.com>

This reverts commit 656d2676.

* change network retry delay strategy

* refine

* added all necessary includes - fixed build/include_what_you_use error

* fixed the order of includes (build/include_order)

* [WIP] refine config

* [wip] ready for the auto code generate

* auto generate config codes

* use with to open file

* fix bug

* fix pylint

* fix bug

* fix pylint

* fix bugs.

* tmp for failed test.

* fix tests.

* added nthreads alias

* added new aliases from new config.h

* fixed duplicated alias

* refactored parameter_generator.py

* added new aliases from config.h and removed remaining old names

* fix bugs & some miss alias

* added aliases

* add more descriptions.

* add comment.

* decode error description

* added break line char in log massages

* unfinished

* some refines.

* fix a bug

* some refines

* remove useless files

* some small fixes

* clean code

* clean code

* fix connect

* fix a broken link

* fix a broken link

* update network.h

Improve training speed in parallel learning where workers is not powers of 2

* update linders_socket.cpp

Improve training speed in parallel learning where workers is not powers of 2

* update linder_topo.cpp

Improve training speed in parallel learning where workers is not powers of 2

* update network.cpp

Improve training speed in parallel learning where workers is not powers of 2

* update linder_topo.cpp

fix a bug

* add network apis.

* support parallel loading dataset in c api.

* fix bug

* fix bug

1. merge python-package
2. add dump model to json
3. fix bugs
4. clean code with pylint
5. update python examples

* RAII for utils, application and c_api(partical)

* raii for class in include folder

* raii for application and boosting

* raii for dataset and dataset loader

* raii for dense bin and parser

* RAII refactor for almost all classes

* RAII for c_api

* clean code

* refine repeated code

* Decouple the "sigmoid" between objective and boosting.

* change std::vector<bool> back to std::vector<char> due to concurrence problem

* slight reduce some memory cost

Packages that parse LightGBM’s logs will require minor changes to
parsing logic to work correctly.