* add + - * / @ [] operator to ComplexVariable, also add unittest

* fix circular reference bug

* fit for py2.7

* remove reverse oprators which not supported now

* add sharding

update code examples for paddle.sums 

* modify cond while_loop to paddle.static.nn.cond

* modify crop_tensor to paddle.crop

* modify Variable to paddle.static.Variable

* remove nn.beam_search, nn.beam_search_decode, nn.gather_tree

* remove bpr_loss, center_loss, rank_loss, smooth_l1, teacher_student_sigmoid_loss, edit_distance, sampled_softmax_with_cross_entropy in nn.functional

* remove apis in nn.functional.learn_rate.py

* remove pool2d, pool3d, adaptive_pool2d, adaptive_pool3d in nn.functional

* remove apis in nn.functional.vision

* remove erf, soft_relu in nn.functional.activation

* remove apis in nn.functional.extension

* remove nn.functional.rnn

* remove hash from nn.functional.lod

* remove row_conv from nn.functional.extension

* remove one_hot, pad2d, pad_constant_like from nn.functional.common

* remove nn.gather_tree, nn.BilinearTensorProduct, nn.Pool2D, nn.Pad2D

* remove apis from optimizer.__init

* remove tensor.creation.fill_constant

* remove elementwise_mul in nn.functional.common and  modify to paddle.multiply

* remove  tensor.stat.reduce_mean

* remove reduce_all, reduce_any in tensor.logic

* remove apis in tensor.math

* remove apis in tensor.__init__

* remove has_inf, has_nan in tensor.search

* remove apis in framework.__init__

* remove apis in paddle.__init__

* remove apis in nn.functional.__init__

* modify removed alias apis to raw api in doc and unittests

* fix remove grid_sample bug

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* modify removed alias apis to raw api in doc and unittests

* delete alias api relastions in doc

* reserve paddle.compat, paddle.sysconfig

* remove unittest for paddle.reduce_all, paddle.reduce_any

* modify removed alias apis to raw api in doc and unittests

* recover paddle.save and paddle.load

* resolve conflicts

* fix sample code missing paddle.enable_static() bug

* fix sample code missing paddle.enable_static() bug

* fix to_string sample code error

* fix gpu version paddle Error when have no CUDA device

* optimize format and add new unittest

* fix coverage problem

* fix unittest format

* change static mode to dygraph mode

* use subprocess in unittest

* add sumary feature

* refine printting tensor

* add sci_mode

* add sample code

* fix indent error

* fix _format_item

* polish code

* support item indent

* add ut

* set place for ut

* fix py2 issue

* fix ut

* polish Program api doc & example

* polish CompiledProgram api doc & example

* polish ParallelEnv api doc & examples

* polish details, test=document_fix

* polish program doc details, test=document_fix

* polish details, test=document_fix

* fix note format error, test=document_fix

* add lost example, test=document_fix

* fix lost example, test=document_fix

* modify doc

* update name_scope example code

test=document_fix

* refine name_scope doc

test=document_fix

* modify sample code

* variable -> tensor

* migrate program_guard sample code

* refine error message

* migrate program_guard

* refine comment style

* fix indent

* add static check on data()

* follow comments

* fix ut

* update set_dict method name & add aliases

* fix var name error

* fix alias formats

* use set_state_dict in unittest

* add decorator solve compatible problem

* polish decorator

* replace layer set_state_dict by patched method

* remove import monkey path layer

* fix import function error

* add unittest for coverage

* update the demo code and the doc of varbase.backward.

* update the doc of the fake interface `paddle.fluid.Variable`.

* remove BackwardStrategy.

* Reconstruct all API related to lr scheduler, unify dygraph and static

* Reconstruct all API related to lr scheduler, unify dygraph and static

* fix doc

* fix doc

* fix doc of lr_scheduler

* fix unittest and english doc

* fix english doc

* fix confilt

* fix doc

* support Baidu AI Accelerator
  * test=kunlun

* minor
 * test=kunlun

* support xpu op in separate file
 * test=kunlun

* update XPU error message and remove duplicated code

 * test=kunlun

* minor
 * test=kunlun

* minor
 * test=kunlun

* expose and unify the Tensor concepts to the user

* expose tensor to user

* add copy place for Tensor

* add copy place for Tensor

* add note

* add macro PADDLE_WITH_CUDA

* remove RUN_TYPE=DIST

* fix some error

* Directory migration, test=develop

* Change imperative from paddle init to paddle framework, test=develop

* Fixed jit bug, test=develop

* default static mode, test=develop

* fixed format and create parameter belongs to framework, test=develop

* Fixed import package, test=develop

* fix __init__ format, test=develop

* fixed alias problem

* fixed paddle.enable_imperative problems, test=develop

* Add unittest

* delete install_check comment

* Fixed unittest timeout

* fixed unittest error

* move Program default_xx_program to static package

* optimize unittest method

* fixed framework __init__ format

* fixed jit path

* delete alias

* move jit to paddle

* Fixed unittest format

* fixed paddle.default_main_program

* Fixed save load API in paddle __init__.py

* fixed ci paddle.imperative.to_variable

* refine error stack format

* polish compile traceback format

* polish detail format

Integrated Trainer of Parameter Server (API add `fluid.contrib.layers.sparse_embedding` only) (#22957)

* Integrated Trainer of Parameter Server

* add more settings for distributed strategy
Basically, DistributedStrategy has several parts of configurations:
- BuildStrategy: the same as paddle.fluid.BuildStrategy, but the distributed arguments are moved out of BuildStrategy
- ExecutionStrategy: the same as paddle.fluid.ExecutionStrategy
- collective communication configs: nccl_comm_num, hierarchical allreduce and so on
- distributed algorithms: async_update(mainly used in PS), lars, lamb and so on

* Refine Model

1. Take the network (instance of Layer) as the input of Model.
2. Refine set_dict/load_dict of Layer.
3. Refine Input interface, so update code sample about Input

* clean __str__ of VarBase and ParamBase, test=develop

* clean to_string, test=develop

* update unittest, test=develop

* connect stop_gradient with trainable; test=develop

* add value error message; test=develop

* add user defined func test=develop

* update test=develop

* update test=develop

* fix name conflicts test=develop

* add unittest test=develop

* change 2018 to 2020 test=develop

* add comment test=develop

* add comment for function test=develop

* fix details test=develop

* fix details test=develop

* add the support of device index for device_guard.

* add queue_generator_op, dequeue_op, enqueue_op and ut, test=develop

* Compatible int32 and int64 for attr in op slice/strided_slice. test=develop

* Polish code in nn.py  test=develop

* Fix bug: set the same dtype for the inputs of elementwise_add. test=develop

* Convert int32 to int64 in slice op to avoid data overflow. test=develop

* Convert int32 to int64 in strided_slice_op to avoid data overflow. test=develop

* test=develop, test=document_fix

* test=develop, test=document_fix
Co-authored-by: Nswtkiwi <1208425345@qq.com>