1. Add a new attribute named 'startuo_seed' to RandomCropOp. If the input
'Seed' is not initialized, the 'startup_seed' will be used to replace
it.

2. Refine CustomReader. Add a member variable 'scope_' to it. The
'scope_' will act as the global scope of preprocesing, making it
possiable to save something cross batches.

Give assign layer's second parameter 'output' a default value: None. If
it is None, the output variable will be created inside the layer.

* Add sub_blocks of lr_decay_op to pserver_prog after distribute_transpiler

* Remove unused logs and logics

* 1. Add ops to new block (considering the nested block condition)
2. Follow the original hierarchy of blocks
3. Change the function's name and remove debug lines

* Modify lod_tensor.md and nn.py

* Modify control_flow.py doc

* undo change in lod_tensor.md

* add lod_tensor util and modify pybind

* refind pybind LoDTensor API and modify LoDTensor and DataFeeder test

* fix test error

* fix detection map op test

* fix reorder_lod_tensor test

* fix seq_concat_op

* fix chunk evel op test

* fix target assign op

* fix warp ctc op

* address comments step 1: reverse reset_lod op

* step 2: modify op test

* add warning message

* remove has_valid_lod

* add back has_valid_lod

* address comments

* add exception catching trial

* Implement a bilinear initializer for transposed convolution.

* Update some error message.

* Update some doc about layers' API.

* Fix format.

* Fix example bug in random_data_generator.

* Fix example bug in dropout.

* Follow comments and some small fix for some examples.