Refactor TensorFlow 2 code to hybrid functions

[khatchad]

Convert several eager execution function to hybrid execution. We have some preliminary evidence that this improves the run-time performance of the models:

Test	Python version	TF version	Before accuracy	After accuracy	Before loss	After loss	Before elapsed time (s)	After elapsed time (s)	Speedup
neural_network	3.10.0	2.9.3	0.9624	0.9635			9.333428266	3.812376191	2.448191836
autoencoder	3.10.0	2.9.3			0.006999	0.007014	110.4210886	34.1057281	3.23761124
logistic_regression	3.10.0	2.9.3	0.8286328125	0.8316015625	0.918056736	0.9068725395	1.415692188	0.7934420485	1.784241446
bidirectional_rnn	3.10.0	2.9.3	0.85625	0.821875	0.5128818989	0.58627882	28.0902812	5.041457747	5.571856913
convolutional_network	3.10.0	2.9.3	0.9867734375	0.9869921875	1.48369342	1.483417908	31.07854785	17.71073562	1.754785827
dcgan	3.10.0	2.9.3			1.208782502	0.04901289759	78.12116778	36.0855548	2.164887535
dynamic_rnn	3.10.0	2.9.3	0.8580357143	0.8657738095	0.3000548454	0.285470572	48.15241052	8.490720483	5.671180745
recurrent_network	3.10.0	2.9.3	0.9375	0.93125	0.1873067699	0.2336050078	42.15870964	7.818362872	5.39226822
build_custom_layers	3.10.0	2.9.3	0.907109375	0.919921875	3.339067001	3.328515396	1.387739662	0.843345543	1.645517277
save_restore_model	3.10.0	2.9.3	0.8957291667	0.8922395833	107.3751221	110.7431885	4.18468971	1.885201852	2.219756842
tensorboard_example	3.10.0	2.9.3	0.872734375	0.8712239583	110.2372933	112.0809294	8.789215875	4.572643171	1.922130275

For dcgan, we believe that the difference in loss is due to a TF bug that is still present in 2.15.0. This test can be reverted if desired.