graph neural network for imperative mode

test=develop

test=develop

improve layers.fc api doc

add yolo_box for detection box calc in YOLOv3

test=develop

test=develop

Skip compile infer shape in box_coder_op

* Add moving average absmax op in quantilize-aware training.

Yolov3 loss: add mixup score and label smooth

* Support Sync Batch Norm.
* Note, do not enable it in one device.

Usage:

build_strategy = fluid.BuildStrategy()
build_strategy.sync_batch_norm = True
binary = fluid.compiler.CompiledProgram(tp).with_data_parallel(
        loss_name=loss_mean.name,
        build_strategy=build_strategy)

* Impl fp16 compute kernel for slice_op

test=develop

* Use data() to replace mutable_data()

test=develop

Fix cross_entropy2_op numeric error

test=develop

test=develop

Revert "PaddingRNN model memory optimize"

test=develop

test=develop

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2. remove lod check in reshape op
test=develop

* Add cpu_quantize_squash_pass for C-API quantization

test=develop

* add cpu_quantize_squash_pass teste

* fix lint: add include memory unorderd_map and unordered_set

test=develop

* lint fix 2

* fixes

test=develop

* refactored

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* fix windows ci

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PaddingRNN model memory optimize

* make timeline.py compatible
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* Follow comment
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add all_kernels_must_compute_runtime_shape example for speedup infershape

Fix api.spec

test=develop

* resolve #15618
Backgroud: the PR #15398 raised the box_coder op performance regression, we optimized the code via the more efficency leveraging opemmp.

test=develop

Add the inverse trigonometric function

* fix broadcast with mp mode

* polish code test=develop

* fix bcast strategy test=develop

* fic cpplint test=develop

* fix py3 failed test=develop

* fix comment test=develop

* update comment test=develop

* remove concast_cast and refactor code test=develop

* reduce flag use test=develop