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* forbid resnet v1 fp16

* address PR comments

* rename --version flag and fix tests to correctly specify version rather than verbosity

* rename version to resnet_version throughout

* fix bugs

* delint

* missed layer_test

* fix indent

* Add dataset info and hyper parameter logging for benchmark.

* Address review comments.

* Address the view comment for data schema name.

* Fix test cases.

* Lint fix.

* Revert 823da318. This restores distribution strategies for resnet.

This commit is not a direct revert due to significant merge conflict
resolution.

* fix flags test

* npc is no longer used in resnet

* squash of modular absl usage commits

* delint

* address PR comments

* change hooks to comma separated list, as absl behavior for space separated lists is not as expected

1. trainning hooks and train/eval function does not need to be
declared several times.
2. change to use tf.logging.info instead of print
3. Show the current index and total count of training cycle, which
give user some hint about where they are in the whole process.

* Update the benchmark logger to have default logging.

1. Create global instance of benchmark logger, which default log to
tf.logging.info
2. Allow user to config the logging location.
3. Fix nits in code and comment.

* Fix lint and test error.

* Address review comments.

* Remove the duplicated print statement.

This reverts commit 32aa6563.

* begin transfer from contrib fork

more changes to resnet_run_loop

use AUTOTUNE in prefetch

first pass at resnet with functional distribution strategies

fix syntax error

delint

aesthetic tweaks

delint and fix typos

rip multi_gpu flag out of resnet entirely. Subject to saved model load verification

update cifar10 and imagenet tests to reflect that the model function no longer need to know about multi_gpu

fix imagenet test

start addressing PR comments

more PR response work

* misc tweaks

* add a comment

* final pr tweaks

* fix parsers

* Adding tests

* Adding tests

* Repackaging

* Adding logging

* Linting

* Add fp16 support to resnet.

* address PR comments

* add dtype checking to model definition

* delint

* more PR comments

* few more tweaks

* update resnet checkpoints

* Updating name of logging package to avoid overwriting Python builtin logging.

* Updating name of logging package to avoid overwriting Python builtin logging.

* Adding export_dir and model saving for Resnet

* Moving to utils for tests

* Adding batch_size

* Adding multi-gpu export warning

* Responding to CR

* Py3 compliance

* Add benchmark upload util to bigquery.

Also update the benchmark logger and bigquery schema for the
errors found during the integration test.

* Fix lint error.

* Update test to clear all the env vars during test.

This was causing error since the Kokoro test has TF_PKG=tf-nightly
injected during test.

* Update lintrc to ignore google related package.

* Another attempt to fix lint import error.

* Address the review comment.

* Fix lint error.

* Another fix for lint.

* Update test comment for env var clean up.

* Update reset model for benchmark logging.

To enable benchmark logging, just add "--hooks LoggingMetricHook"

* Benchmark logger fix for resnet.

1. Update default at_end to False for metric logger to avoid
checkpoint error.
2. Update resnet run to log final evaluation result.

* Update log output for final eval_result.

* Typo fix.

* Unset the default value for benchmark_log_dir.

Usually the benchmark should be logged to different directly for
each run. Having a default value will hide the choice from user.

* Bug fix for benchmark logger initialization.

* Fix lint error.

* Address the review comment.

1. Update the logger to cover evaluation result.
2. Move the flag to performance parser.

* Undo the change for arg_parser.

* Glint everything

* Adding rcfile and pylinting

* Extra newline

* Few last lints

Use util functions hooks_helper and parser in mnist and wide_deep, and rename epochs_between_eval (from epochs_per_eval) (#3650)

This commit adds a basic end to end test for resnet cifar10 and imagenet models to check for syntax errors outside of the core neural net code.