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* Introduce a short sleep before ds.prefetch in tf.data.

* Further limit dataset threads to reduce CPU contention

* Tuned dataset sleep time

* Rename dataset sleep flag; enable it only for Keras Graph mode

* before moving test cases to the base class

* Added tests for keras cifar and keras imagenet

* fix cifar10_test

* add blank lines

* fix lint errors

* fix lint

* Resolve comments

* Modified two resnet keras tests

* Tests passed

* Remove keras_test_base

* Remove gpu from the no-dist tests

* Revert "Set input layer `batch_size` in multi-replica mode (#6578)"

This reverts commit f1a59682.

* Rename variables.

* Revert "Revert " Ensure static shapes when enabling XLA in Resnet Keras model (#6508)" (#6517)"

This reverts commit cc9eef76.

* Set `batch_size` to keras.Input in non-eager mode.

Eager mode currently has OOM problem.

* Add comments for enable_eager flag.

* Always set drop_remainder=True.

* Only set drop_remainder=True for XLA.

* Refactored ResNet code and added additional architectures.

* Added numerical layer names instead of alphabetical.

* Change dash to underscore.

* Corrected return statement.

* Use conv_strides argument.

* Set classes=10

* Use partial to reduce code duplication.

* add ds support for ncf

* remove comments for in_top_k

* avoid expanding the input layers

* resolve comments and fix lint

* Added some comments in code and fix lint

* fix lint

* add some documentation

* add tensorflow imports

* Added main function to the two keras resent benchmarks

* remove asserts

Forgot to add the `dtype='fp16'` flag in previous CL...

* Add profiler callback for Keras models

* Update build stats to identify time callback by type

* Add warning message when both TensorBoard and profiler callbacks are used

* Add benchmarks for dynamic loss scaling.

I add one convergence benchmark and five performance benchmarks.

* Fix lint error

This should give better performance in tweaked benchmarks.

Reason: break 1-gpu nightly test.

This reverts commit 371645fc.

Don't pass `batch_size` to keras.layers.Input in DS multi-replica case. There is currently a bug in Keras side which will cause a batch size incompatible error.

* Update resnet_model.py

* Ensure static shapes when enabling XLA.

* Define `drop_remainder` as a variable.

* Handles per_replica_batch_size in non-XLA mode

* Remove trailing whitespace.

* Add tweaked test for fp16 nonXLA

* Add dummy test for dataset warmup

* Add tests for Resnet data input pipeline

* Added trivial model class to isolate data input pipeline tests

* initial commit

* bug fix

* Move build_stats from common to keras main, because it is only applicable in keras

* remove tailing blank line

* add test for synth data

* add kwargs to init

* add kwargs to function invokation

* correctly pass kwargs

* debug

* debug

* debug

* fix super init

* bug fix

* fix local_flags

* fix import

* bug fix

* fix log_steps flag

* bug fix

* bug fix: add missing return value

* resolve double-defined flags

* lint fix

* move log_steps flag to benchmarK flag

* fix lint

* lint fix

* lint fix

* try flag core default values

* bug fix

* bug fix

* bug fix

* debug

* debug

* remove debug prints

* rename benchmark methods

* flag bug fix for synth benchmark

* Initial commit

* Fininshed

* bug fix

* bug fix

* bug fix

* Resolve review comments

* Typo fix

* resolve comments

* fix number error

* Resolve comments

required by multi-node collective ops in eager mode.

* xla to bs=128 for num_gpu=8

* remove todo

* Mixed precision support

* Add TODOs

* fix resnet breakage and add keras end-to-end tests

* delint

* address PR comments