official/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/common/__init__.py,sha256=AbpHGcgLb-kRsJGnwFEktk7uzpZOCcBY74-YBdrKVGs,1
official/common/distribute_utils.py,sha256=6Elsresxf9gCJ7Q0C4fo1LzLmwow79Lmb_Z9cwkmOA0,7834
official/common/distribute_utils_test.py,sha256=Wo3lWsw_YwH7j0c2uXnjz2s9i9y01QnQI6mLnCX9e1g,1784
official/common/flags.py,sha256=n33CD71KTeBMXZ3ueNxD2hiOWBhdO66-nzyhpDdh4ZU,3283
official/common/registry_imports.py,sha256=ziXf1OozvOHpIPgiOnpbdVLVhVvH0HJJTuw28DXwB1E,928
official/core/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/core/base_task.py,sha256=4l23IKSlQxuzlE9BGots-woZ9-mNhaZlU24xXqLRBGk,9179
official/core/base_trainer.py,sha256=Yiv_0hjRZuXAuVXa3quK1PXWwiU-4eRd32wZhrDS2f8,12571
official/core/base_trainer_test.py,sha256=yZNO78TQVNIPYEOomNhEbXH2pkQuyp3GM5pBGfMoMrI,7322
official/core/config_definitions.py,sha256=dqT4mSjR-hQ8zNUlfl0Rw2z1Uq2HUIoxYAv4wvtMo-c,10567
official/core/exp_factory.py,sha256=HigV_35mTVFu7vMlUdIVb6W7tPUrphpujwHnWx2vuzY,1283
official/core/input_reader.py,sha256=QV7WzpCEgvyg7lsJJRK5wUXOPWwp7TqKMsONXmJ1omk,11759
official/core/registry.py,sha256=RiJS3zFuq0oL2wKz4xwJpcy7UWbPGUNZYnTkUTWP4is,3819
official/core/registry_test.py,sha256=LXLpKKkC8R2DW1LcPWYf_m04f2SXTIcF41SkkZ3u8-4,2559
official/core/task_factory.py,sha256=2fljWqpPCVW6SMsHtWSL8vEC4nqfkZLI1Xwc8XPvXWM,2450
official/core/train_lib.py,sha256=gcBboBI8n83gEw8rd0OlK4npBYrLaUCpBIE_chz_6fg,5674
official/core/train_lib_test.py,sha256=a3FHJnGZTesgqAo-g0YZSKSrynNFQuWKf6LkOT5VX38,4458
official/core/train_utils.py,sha256=gzwLS2zLKBPFQz8yFL9b3PlRLruMGrTeaXVhbNYr1fE,10581
official/modeling/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/modeling/performance.py,sha256=3WdFmNRRUvIZkRglrraBobisPDZbHXO76x4bAJ4uaaI,3447
official/modeling/tf_utils.py,sha256=Xva8Xmzlg_kjIgI7jIXaNVD0hnxEl9v6fgbYb3q_-zk,5755
official/modeling/activations/__init__.py,sha256=g-G-yQ41SdAkpYDahBrryWAzZyOuBXd2GEJBk9sQEcQ,1072
official/modeling/activations/gelu.py,sha256=YG7k3k-DreDNfcyB2H6s9kCxIJRxqcLhn7irgT_-IJI,1117
official/modeling/activations/gelu_test.py,sha256=b5Hr5SbSRtTYCn2er_YkdDWzjXjtQxcHWPFpHG8kyxY,1292
official/modeling/activations/relu.py,sha256=S_C-NSEN9sfCeFWqRCx1__81mb3vBoKW-LKi1OYduio,1064
official/modeling/activations/relu_test.py,sha256=SEYgESBi7mEOvpjFEWeOQdAR9hCvhSUJzqMCX6XszNE,1280
official/modeling/activations/sigmoid.py,sha256=d1f4c5uOniDZ915FooLRKqpuItSdcwkrxxzb8NozAlI,1109
official/modeling/activations/sigmoid_test.py,sha256=1T1qBfb3axyA8Nt3sdoI-ZhUeBgUxGZIS0hDx_OWENo,1444
official/modeling/activations/swish.py,sha256=IQq6S5HaHQUxVFIDTUAcz1nvyonwMG0J2wQAkFQEvoU,2341
official/modeling/activations/swish_test.py,sha256=pxbesU0lLSYgmaNV_flCY8fugzkLPpHVyHqeP9y63gE,1646
official/modeling/hyperparams/__init__.py,sha256=h26WynrQpWm2pxxk_vuLdQL9yQYFQjEckB5b_mspkdk,945
official/modeling/hyperparams/base_config.py,sha256=p7vy9wuAYQ0_hczD6c8oNmu3y-IW8plti0bsxM4SNDk,9763
official/modeling/hyperparams/base_config_test.py,sha256=VID8c1HwOEiBwlm6-Vt2wPo9yLpqmVHoNtYFTDWClkE,10673
official/modeling/hyperparams/config_definitions.py,sha256=RbHWlFliy_XHT21Q0zfdQLQtkRKmXm3jM9Ph_jBiPYo,2191
official/modeling/hyperparams/oneof.py,sha256=asIb_pVi6S5_hHY934XXtECHXVSg-Pu5bzX5b2Sby4I,1969
official/modeling/hyperparams/oneof_test.py,sha256=KbASndVGHXFTCqPXM7n1tT12Hgwz9BPjgz00BD7MY4w,1982
official/modeling/hyperparams/params_dict.py,sha256=4YbpAn-OFYDtBEmMDUvGgpzTA_d1yZaeMsfxU9eQYS0,15824
official/modeling/hyperparams/params_dict_test.py,sha256=TzrxQVjFH9OQcEGr3C2xk0L0JVMpV9EZUOF7srUpAUI,13366
official/modeling/optimization/__init__.py,sha256=KeKVW5ir0swHYPij7AoW4v0HlqXubTPoy379vt3wQHE,451
official/modeling/optimization/ema_optimizer.py,sha256=p8S0Bi4xrISJfU1UF0IZf-rSeL2UIRGrmKaK-m7HIdc,8638
official/modeling/optimization/lr_schedule.py,sha256=tK-TjllDrc36erxVYHP7gKqTVIIxy1JZd2J81Mkrcb0,7371
official/modeling/optimization/optimizer_factory.py,sha256=ZqCwvkK3shgi1BifcaY1k3vy8q5C-OnktD4WO8VD_6I,5883
official/modeling/optimization/optimizer_factory_test.py,sha256=k6GhavAcm0LSzSC_SfeLLPPKn6zV66RhZI-3uhhCaNs,10915
official/modeling/optimization/configs/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/modeling/optimization/configs/learning_rate_config.py,sha256=N7ctDBkz070-Z7TMDZjhRi3OSeqa6oU5ubioHUcQ1Gs,6968
official/modeling/optimization/configs/optimization_config.py,sha256=fqpH4-s-Ovq7N0HQs2GfaZp78BvclYZfwk01J7chOVs,3799
official/modeling/optimization/configs/optimization_config_test.py,sha256=Z7TWw41-bpMXGH6xkyF3ReaLJrU-M3qihljst0fZj6o,2108
official/modeling/optimization/configs/optimizer_config.py,sha256=k6wHvy46AaAS_F3KtosVCxvDRHMCTvy0cram8CZ_HYQ,5838
official/nlp/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/optimization.py,sha256=NRIcrdbq4n_W1tGRTE9vHkis6Fu2X9tVw5odV_PBd-M,8884
official/nlp/train.py,sha256=UJHpZbT5uvzaDKKHIVDzutj8TlMIoo2PoEbemfdKaVY,2669
official/nlp/train_ctl_continuous_finetune.py,sha256=DqiFaqutkl9irPHs1Bo5tu-R_m98GOKG-CcoaXswGZg,6964
official/nlp/train_ctl_continuous_finetune_test.py,sha256=8MNZ1zadDb0X-L4RPqndCI0GCQvk27Nw1ztJ-DTBPFA,3104
official/nlp/albert/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/albert/configs.py,sha256=kEmLx4Rk4CC8V7raQbOBDKAs6vQJ_raU1xNt8CHB12g,2089
official/nlp/albert/export_albert_tfhub.py,sha256=ad_Y_jjbJ0mn-TjroiEj2IzbC5gCUPDEY2eGb8g00fA,3322
official/nlp/albert/export_albert_tfhub_test.py,sha256=UwdamINT2gPM4Wp435CSN8axW-JdnzPlSIHET0wiPjw,3419
official/nlp/albert/run_classifier.py,sha256=mWpZfhn_HyX-OmEz4Ma6C5KhgyBoaYVwy9V1zklzOoc,4145
official/nlp/albert/run_squad.py,sha256=g9orySq6JgUSGHRxKmSM2FMksYCOAkrq7Xg3tXpvK8E,4769
official/nlp/albert/tf2_albert_encoder_checkpoint_converter.py,sha256=ZRInCKyHLEIcDcoQ2K5spLZgE8OYqU2dlDVp_Fttbus,6674
official/nlp/bert/__init__.py,sha256=AbpHGcgLb-kRsJGnwFEktk7uzpZOCcBY74-YBdrKVGs,1
official/nlp/bert/bert_models.py,sha256=TCaamNcxEP1hy2VQRKbu_pvnG9vgF9WCWoJRGr_p26w,15006
official/nlp/bert/bert_models_test.py,sha256=20axtS-7vidXQ3YISqWdywfXX__bVNjGvlDr0tZCo10,3958
official/nlp/bert/common_flags.py,sha256=Xswx120w3y9pXfYWo1NG479AQ53a3stZ2w-rt0nUGrg,5030
official/nlp/bert/configs.py,sha256=wQXscmZamcUsgvp19DJZwyC1zhGH6jHMiwlmuCVAgoo,4247
official/nlp/bert/export_tfhub.py,sha256=ykW1VMzliwjBQAV0Og-EsKRKp5MEuFWJqbxyBSYOZiI,5505
official/nlp/bert/export_tfhub_test.py,sha256=r81yQmXk4-1uhPIDz5hUgUA80YeuFQceHtWZrqPnX0Q,4762
official/nlp/bert/input_pipeline.py,sha256=JTjfD7xNRmYpTtzHiKLeqkNRI57ap_qjQ7LgybSmaEY,11804
official/nlp/bert/model_saving_utils.py,sha256=gNObEiSTh39vpWmncJfi0Qjps40mWUyFVxiy1TFNEGY,2961
official/nlp/bert/model_training_utils.py,sha256=A9GDf94ahR_tMTe6c_FHbiKN2agkKgDYMoY5_0q0tVU,25394
official/nlp/bert/model_training_utils_test.py,sha256=JFwQC8euKfSWwB0mFqkXzWJSGnEvLHRVdqTi7kcGTos,11856
official/nlp/bert/run_classifier.py,sha256=nL72k8jIdaP7_K8cxp37qgTCDb2NzgZ-27UmqBT0qQ0,19034
official/nlp/bert/run_pretraining.py,sha256=E1Z1_d7h4Bo_w45v-RrP-WFnR7ta-_GYZ5DPBccvGP4,8640
official/nlp/bert/run_squad.py,sha256=jf9vno3OwVZCg_bKwWVzfsYSUPA0J3hXlLiU-tR1znY,5439
official/nlp/bert/run_squad_helper.py,sha256=bH_zCgsHdsxkF7b9hxS2VhW42JhsPGCb_yvJvEZqkw8,19173
official/nlp/bert/serving.py,sha256=jPPoJnaBrghKOJADFaI2uEcHUVABULJH8dVnYABFjb4,5147
official/nlp/bert/squad_evaluate_v1_1.py,sha256=RLqM0aGrMv0veAFgbiwrSOtSe5vFQ7KDVbsNTgSzlVA,3802
official/nlp/bert/squad_evaluate_v2_0.py,sha256=F0QVkl9cRo_2jvpbTl_yoqsIrhJ98NHo8mptzHnOuSE,8703
official/nlp/bert/tf1_checkpoint_converter_lib.py,sha256=_Y7XOmbCbWzYxkTkYMxSxliSiQ9bqXQrepTbsdTHUIU,7787
official/nlp/bert/tf2_encoder_checkpoint_converter.py,sha256=EpJUPLKdkQHm_zzcJv3gIx6nyT8onbOeRNf4TcDITVc,5961
official/nlp/bert/tokenization.py,sha256=8TAG0MDa8jbnUr_mULSb2Hlf3RWaWGefSgotRiuS9b8,16671
official/nlp/bert/tokenization_test.py,sha256=xp4enOe1CKqfWrgam8hZJ6HEGQd_IWBBCRPh1mU06lE,5297
official/nlp/configs/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/configs/bert.py,sha256=CLqF8WYts-SPqRlqfnSzxZiJqlMce--zkg1EX7lGuX4,1522
official/nlp/configs/electra.py,sha256=8TpMHmFtQ0vUwrBDmhdORo4oWV2QnVBvTmClyT1xTnk,1480
official/nlp/configs/encoders.py,sha256=dIfLMVY1EcTW0xeKEhV9---z_S3pOJQ05Kn4T3SvD8I,13585
official/nlp/configs/experiment_configs.py,sha256=9gYR-OnOrmc46j2nrw3I8fJU8I9ePC-sFBCmMaHbnvs,826
official/nlp/configs/finetuning_experiments.py,sha256=m26O22wyj8rOAVN05_pLV8Gxb8PqNIK8FiKuK0U-CUk,3862
official/nlp/data/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/data/classifier_data_lib.py,sha256=tS1Xx07N8Q112DOlbKO2SNtY0r2Q4My2KBIhOIzV7nc,50368
official/nlp/data/create_finetuning_data.py,sha256=VcWuHQlAFmAXVY7ppkQeRUEID-9DsoplSOpP2EJqbxQ,16137
official/nlp/data/create_pretraining_data.py,sha256=bagFQg1VSp_YDyMXCpclLqXBoez7J8_DPWIhd0ha86w,23380
official/nlp/data/create_pretraining_data_test.py,sha256=hPoKvOhXEKmAECp84qleLLqXjXqYvKMfIk2qFuCf2Ug,4956
official/nlp/data/create_xlnet_pretraining_data.py,sha256=51MlwNP3XpZ28nQZWiJ5QPvrjhWyeUaoBb3vUKVMxyA,24271
official/nlp/data/create_xlnet_pretraining_data_test.py,sha256=v3f8pMuna88z3XPy6q5bZchS-rw5muSNk7YlrLIIvsk,11029
official/nlp/data/data_loader.py,sha256=35Ejwue5zSBwTEf0QiUyuaMN_OoKggi319xY5nIhwAU,1768
official/nlp/data/data_loader_factory.py,sha256=cfWxqLQwzjjIke6V-f9lVZ08WdI0LfeVdNPPjQ09OQA,1887
official/nlp/data/data_loader_factory_test.py,sha256=K8UDLkczdEYep7_Yjad4G6dPlG0NJUX0BAPOxaScBnU,1424
official/nlp/data/pretrain_dataloader.py,sha256=QJgd5ethC_E8zr2-LmXaC8x3qatRSRXMsMTkINOh-Iw,24626
official/nlp/data/pretrain_dataloader_test.py,sha256=RZRuWHMw_KVPVoxzI4YT66vrIvMj8dlGi4gyIs82qFk,9240
official/nlp/data/question_answering_dataloader.py,sha256=6JTbwCRYDrb0Ky5ZlGTIEpRLl3Rgsdg7o2MuW3DihLQ,4249
official/nlp/data/question_answering_dataloader_test.py,sha256=VzOS1NAgJfQjRThjk3-yPNhSPHMywVwxHdZUpGEy7cM,2929
official/nlp/data/sentence_prediction_dataloader.py,sha256=nkK_5GwmbzneKGNg8HY4SZhZqc0YCv87QCjdRLx5KNg,3429
official/nlp/data/sentence_prediction_dataloader_test.py,sha256=DTVkAPm6sraHFtb2RtHPhhDoGfChf44wNIlYwAoJ01s,3275
official/nlp/data/sentence_retrieval_lib.py,sha256=mDP4fedR_yMRP-6Zku-WFLNKSAnl8TQppPWxHxI10-s,6492
official/nlp/data/squad_lib.py,sha256=WYjxidsWC0ReEIFRK3KxcqSdTfq7SEZ4qwQ_tW-7Ae0,36441
official/nlp/data/squad_lib_sp.py,sha256=V861ihaOxdnowY5B2NG5FkAXEZgrr1zr_Pd4bxwumog,34828
official/nlp/data/tagging_data_lib.py,sha256=dwZ_TciK-MmcW0amQaJmISxvFwxz0sS3GvwlIaId7dw,15582
official/nlp/data/tagging_data_lib_test.py,sha256=y8uvowBnRNerYqg9laUGVNxfPs1Dibkc8zJ2V_k-WCY,3835
official/nlp/data/tagging_dataloader.py,sha256=dAr7wwXWIV93GXnf0THmqH2GDHmBYuYu1YHeGurSQOI,3280
official/nlp/data/tagging_dataloader_test.py,sha256=ul2mndo4fHwuE5r04_J5NnPUcRO-p_vB1bXfEZJwUCo,3295
official/nlp/data/wmt_dataloader.py,sha256=Ig9cWZwZ5f2WEqIGl7X3-C2czRtoj-Nco3R4pb6ibz0,11265
official/nlp/data/wmt_dataloader_test.py,sha256=QEug4lWiags1zE-75sSyo3LaGVXgkoiSrq3XsZReHQI,4991
official/nlp/keras_nlp/__init__.py,sha256=Sqe3JpQWrOf_x-s0Iu3GAAMyZLPlCGBdm-SoXX2Kuw8,845
official/nlp/keras_nlp/setup.py,sha256=dc8skHkn6U9C6O7pvKHNIEAuIk6SO625g1o_vqZJenM,2282
official/nlp/keras_nlp/encoders/__init__.py,sha256=qERuH7E43I-71xwsdURvhEVBN0DRLm6R2j3NUouKDYM,801
official/nlp/keras_nlp/encoders/bert_encoder.py,sha256=AsdM4Zr9lsVZXls0qU_tFLQiMSGyMBtqFsGTEJXxzkk,10869
official/nlp/keras_nlp/encoders/bert_encoder_test.py,sha256=woPwt1DzrG3u2sZcltccYWm1sMgZtgjmVlGbiD0Rub4,9778
official/nlp/keras_nlp/layers/__init__.py,sha256=B5uF7NKzdaw1y9aCDvRuGrcmuFQtIXW64PQRa5KuTUo,1124
official/nlp/keras_nlp/layers/masked_lm.py,sha256=X-khds55L0Keo7NzzDhMwV1bhhyYi3Z2SEeIpBmy1J4,4898
official/nlp/keras_nlp/layers/on_device_embedding.py,sha256=K3gYuggm-29XvrvUv0LMwz1anqF8JH5JvCx_psjgBO0,4050
official/nlp/keras_nlp/layers/on_device_embedding_test.py,sha256=V00WJP5KLaCa6BFos253nkxCKW_sTXGQL7hxWj4fJZY,9209
official/nlp/keras_nlp/layers/position_embedding.py,sha256=ib-djtkNWAuHfVdWrn6nOGifAS0JSFdAcHfCBF3OvDU,3105
official/nlp/keras_nlp/layers/position_embedding_test.py,sha256=4G3rgW784fTEYpI449TeJp0w8Nh9IFUOhfq63TCgGdM,4467
official/nlp/keras_nlp/layers/self_attention_mask.py,sha256=6cA_7ROPqXhV0tHu1q979Rco0bf05yDz6KtY6lEgiQQ,2006
official/nlp/keras_nlp/layers/transformer_encoder_block.py,sha256=vKYsqtYR8-VOnvJFWEj9lrMugVyLDM96uiwcIClNLcU,12298
official/nlp/keras_nlp/layers/transformer_encoder_block_test.py,sha256=5JKUGuehuRUqnxfXIwjvwGhX4i0mM3dt6--We8ku0FA,12350
official/nlp/modeling/__init__.py,sha256=AbpHGcgLb-kRsJGnwFEktk7uzpZOCcBY74-YBdrKVGs,1
official/nlp/modeling/layers/__init__.py,sha256=Kx38QNNCMwAtNZU81NE7y_RPz50Yj2AVIhh5s5AlxFc,2405
official/nlp/modeling/layers/attention.py,sha256=rwxNxSqfgYHsnC4j15PizcdG035n6zEvmq-UDvubtTg,3992
official/nlp/modeling/layers/attention_test.py,sha256=rJ7kVFI2snZ_i8dxC-n1fHXIb7coCbqc_Or6Uizrn5A,3761
official/nlp/modeling/layers/cls_head.py,sha256=4bQF9mX9sybBgcSIX7aTCTm1R14M0Nzdha04wN6uAeU,5532
official/nlp/modeling/layers/cls_head_test.py,sha256=DB5T0WQqPwoTfYkTQjH-8qPtQf-zZ6AYdJb9p3l2HNU,2497
official/nlp/modeling/layers/dense_einsum.py,sha256=uwMhMvDB2mUaWNDofwwg4hAvapaBfQ8apzuAFf7uX5c,7194
official/nlp/modeling/layers/dense_einsum_test.py,sha256=fHw9WIgYDutBSEkbWASiGARo3vDSZx1jbg5CzpOGlTc,5389
official/nlp/modeling/layers/gated_feedforward.py,sha256=9a7dl093bxzQNaPfWMctRuelLRvzZUYUJrUeBnU824U,9156
official/nlp/modeling/layers/gated_feedforward_test.py,sha256=FaCwtZaTrlAqtp0krotrIcm_T7OC8-YPUnG8kRMimJg,4904
official/nlp/modeling/layers/masked_lm.py,sha256=sfGdJTSzQPQMnnoGdYhMHbuqtqKBmK0mroZRuVxF9qw,844
official/nlp/modeling/layers/masked_lm_test.py,sha256=NbzbTHXrH6rh4QbwqTvRAvYZZWtJ7A5w81Dcbs955Lo,5933
official/nlp/modeling/layers/masked_softmax.py,sha256=WXqoafc4HJ5DUqsb-V1A0xOvbGZjnVPCU_BhCzrZmnA,3110
official/nlp/modeling/layers/masked_softmax_test.py,sha256=nkp5zV1vkSxFObFn0IB0jQtgfgTIt7SPfkPqRfOM2IQ,4774
official/nlp/modeling/layers/mat_mul_with_margin.py,sha256=IXOQlPJRXW2fvZfjsEX4uePjhbBGeuRt6yG8oAXE-ZM,2379
official/nlp/modeling/layers/mat_mul_with_margin_test.py,sha256=elqbbt4KnAF3FSRY2zrO6g7X9l_S2SiQ0YQ1OP0Iv5s,2216
official/nlp/modeling/layers/mobile_bert_layers.py,sha256=Wh50_fJNWo-MJrAvgg4-GYmrrFq0DgljsF2LSyS2P1M,17243
official/nlp/modeling/layers/mobile_bert_layers_test.py,sha256=6UWjaxyq8xAp00FLQVaFyu2jITQJgtH4RzlRuWvdVXU,5178
official/nlp/modeling/layers/multi_channel_attention.py,sha256=7LEjsI_W3R342LCZkRImdEJFKAfF6DBoHwPTuxy0PXM,7246
official/nlp/modeling/layers/multi_channel_attention_test.py,sha256=Jjt66iMKc3PYKSP4n2XGgODm5E2lLt7w9Pi7JlPS7pI,2006
official/nlp/modeling/layers/on_device_embedding.py,sha256=Tutxuhs04cMfQUaEofjsf4g5V9WypzkE6Ao1PeGHzBU,869
official/nlp/modeling/layers/position_embedding.py,sha256=tmowTuE0j0ydc3ZOo-vnmX5A19XAT_nx_aCZQojwgNE,4082
official/nlp/modeling/layers/position_embedding_test.py,sha256=yC2yk8MQzL6_y_Q6S8DzoKQdRUI8zIrks-mS66UaVXA,2436
official/nlp/modeling/layers/relative_attention.py,sha256=A2ydMwh1e1aqQQA52C9zCBZptT1eqHP0g1RDPFIwugQ,20555
official/nlp/modeling/layers/relative_attention_test.py,sha256=FIw8hqv8mlH86HLgMP6iQkBaTg8QhrjI0x2OHC3ZLw0,6886
official/nlp/modeling/layers/rezero_transformer.py,sha256=Uud0GztHMgJK1CHOJboUTN7o_mo79bTze6MjXheN-U0,10279
official/nlp/modeling/layers/rezero_transformer_test.py,sha256=Tkd8M1-un7RAdKnl8kJcle0ckRu5fuQYsVQs2QpGNCM,5172
official/nlp/modeling/layers/self_attention_mask.py,sha256=T5qlqkkjE35m5rn42TrV46P8rcvnvAS7IwsU1IGDMJs,2167
official/nlp/modeling/layers/talking_heads_attention.py,sha256=AQfMQ105C_b4xEFSBpDHcv7FT2LfwjONn6KTwvJTzLA,6876
official/nlp/modeling/layers/talking_heads_attention_test.py,sha256=CzxgjLBzf86uPYTKxLRAJmZ4YTzkwGboeVh-z-We8oE,7369
official/nlp/modeling/layers/tn_expand_condense.py,sha256=NQ_-LrsVrpVOMcyrEuAXWmDo42zcRFf1Mpf3rUhKsDk,6698
official/nlp/modeling/layers/tn_expand_condense_test.py,sha256=Vf3YksCGmRl1FLiXiAERam7-0N9RZckEwmFp1yAO4Lw,6568
official/nlp/modeling/layers/tn_transformer_expand_condense.py,sha256=W-C8Tkyxp0Yjhsfc0yyqz1mqkuOS7sVKBsUR9EQUrl0,11100
official/nlp/modeling/layers/tn_transformer_test.py,sha256=GNx2Tc4JUoICvKtdQoORhxRo2xx-7Sv1puJLGzav3ME,9252
official/nlp/modeling/layers/transformer.py,sha256=U02HhloETf3KH2I5-ETDnODhWhpvY6-50sTJ7pWjPLc,16855
official/nlp/modeling/layers/transformer_scaffold.py,sha256=xKAsFEci2HCE95G2ggScTCnE4R34JbfUeQ8KeQjkP3g,12844
official/nlp/modeling/layers/transformer_scaffold_test.py,sha256=ylNfwRNAmT0cPVg0WTQDo0CG12FvUANtcR7RYPhe240,20792
official/nlp/modeling/layers/transformer_test.py,sha256=-JtgjsZklgrhRw5-c3NM1gE5-nS8VMDfHqUSNznY6os,3798
official/nlp/modeling/layers/transformer_xl.py,sha256=N3SG2YBkapUwIU8tlq5IiZKZiBUZlSgKBYaCKnx-DCY,22223
official/nlp/modeling/layers/transformer_xl_test.py,sha256=M6qMDCye2zLyw5Og6juwN-1hhJEN3SUfdjF97SJoFXQ,9594
official/nlp/modeling/layers/util.py,sha256=cPXT8qSuLUnHOCDR3Md9IUGIfoLvWdWrdUh-OR9D1rw,1677
official/nlp/modeling/losses/__init__.py,sha256=kjGonLtS1PPUN8xib1_xOtav2qY7KCi_vt2jE9Iis58,882
official/nlp/modeling/losses/weighted_sparse_categorical_crossentropy.py,sha256=EXGF4SVjDfwVbBbFgip7toTiKzhROB9YwVTEDotKpTw,2939
official/nlp/modeling/losses/weighted_sparse_categorical_crossentropy_test.py,sha256=ZHOi7lQ-voORJhZZLn6yTVRGbFI0hIF3uDkvKpPe9b8,8699
official/nlp/modeling/models/__init__.py,sha256=2Wnfktt7mj9TXU9rGQf1tqcGmh5m1wiNlHct5cJGlwo,1408
official/nlp/modeling/models/bert_classifier.py,sha256=pji2YOfpoml4-Lo7M-bMaUpIablAqkNfwQZuT4nbK2o,5484
official/nlp/modeling/models/bert_classifier_test.py,sha256=FCGNY3Zaueos_dMdjSa9XFMjfZU2Np64PeT4ct-FWlU,4708
official/nlp/modeling/models/bert_pretrainer.py,sha256=kCjxkJbJ338uuM-aIllycxi1CtH9RwgzM1g_ZqGuTRA,11030
official/nlp/modeling/models/bert_pretrainer_test.py,sha256=phfwguCi_OWZBj76ztb0uRrg92NlAn2nUwuZ9Y1rN1A,10407
official/nlp/modeling/models/bert_span_labeler.py,sha256=6OKewM9NW12Th1TaGuppl5ArZVOTvySggLE0ZR8gS1I,5082
official/nlp/modeling/models/bert_span_labeler_test.py,sha256=RInxtQNO_2jx4JgsIAZGp9DtBigmlaO5AeG9zKfbLuY,5223
official/nlp/modeling/models/bert_token_classifier.py,sha256=-eEC-0d8JyzEAP45vLeyCRFR0dMRxsL6ZKnglFGZAPA,4883
official/nlp/modeling/models/bert_token_classifier_test.py,sha256=IxE3B1wFyU50sSnGwTJcW6VKGlRwpTCvvN42Lb8aWbc,4789
official/nlp/modeling/models/dual_encoder.py,sha256=e9SUKym_bZ0oDTk82uSd0g1TBFeMqrV1q54Wj06tKwo,6668
official/nlp/modeling/models/dual_encoder_test.py,sha256=fUv_VuRaJ8CFz80y4uTLRTK--5HV_VGT_jiAG9OR1jE,5709
official/nlp/modeling/models/electra_pretrainer.py,sha256=Cnon45K-0XMB2F1xAQ4evfUCsxtqKkwFdYfOigRyCMM,12877
official/nlp/modeling/models/electra_pretrainer_test.py,sha256=eayhP2YBvIGq1rsjZjquKZJFn7SV9Sah2Lj6D5gKzWo,6938
official/nlp/modeling/models/seq2seq_transformer.py,sha256=qjBBCk1Xz4243922MgxDF_JeFhYKxCTuF4J0-REP1qk,24510
official/nlp/modeling/models/seq2seq_transformer_test.py,sha256=dR_5YakN8aMPCJ34LgebSpyErmKCB6rnIp1EV8ICev4,4403
official/nlp/modeling/models/xlnet.py,sha256=azGRZA60NX-AUKGqmG8-lqtQdsRc4vUe6pvF_g1cZc0,11622
official/nlp/modeling/models/xlnet_test.py,sha256=8K52VkVvZw_rfwId8dc9dPSBh-WHgrr-dcOBHBt--Ek,13124
official/nlp/modeling/networks/__init__.py,sha256=t1slgn7bLCWRibEbngfftvH1Qv6BFI5g-ORglEX5yLs,1483
official/nlp/modeling/networks/albert_encoder.py,sha256=2-0XAEIXkQEim2xPOgrAz5DtRBV7YHBF6PZK5z55J0I,8843
official/nlp/modeling/networks/albert_encoder_test.py,sha256=6-DsWllTZZIyPf49rSYEQMierhx_pWv_XMs63ljtV9k,7592
official/nlp/modeling/networks/bert_encoder.py,sha256=Dp_9B_H-pvM0iWt9T3zZmK98MDYs3X4ayTQU5VCD9e4,7147
official/nlp/modeling/networks/bert_encoder_test.py,sha256=5Ob3H04k_ebzhvn-NpK9dOmsj2PNxXLfvyfrQHqD71Y,10538
official/nlp/modeling/networks/classification.py,sha256=e_igmDruqgvyOCVVskHWvQZkAKmYdT8HYjUcj8uzS5c,4136
official/nlp/modeling/networks/classification_test.py,sha256=oeDGacOQkCCXJCBOgZzIBac_iPmR4s4QcjAvqIrHTDE,7528
official/nlp/modeling/networks/encoder_scaffold.py,sha256=1VOe9nzJ_h4RBs_H9kOr4YIIbc9UKSsixMdlBLlcQls,13527
official/nlp/modeling/networks/encoder_scaffold_test.py,sha256=II5eoDfKnUxKmXQy05_vNYL_Z63rPq73iOXG6GS3_2o,23899
official/nlp/modeling/networks/mobile_bert_encoder.py,sha256=f6VCaKgWS2f8Wa3cFSH4Hh5_QQD25kpTfn5lusHJQXI,7148
official/nlp/modeling/networks/mobile_bert_encoder_test.py,sha256=DRxdQTEFaPsG95xG_3SMRPS7a7goeFg_UuaM9sku-FA,6986
official/nlp/modeling/networks/packed_sequence_embedding.py,sha256=5ciW_5PgTiN-Kfrqt_cvP7SYYQHr3gUDTTkbKk6vrsY,12537
official/nlp/modeling/networks/packed_sequence_embedding_test.py,sha256=9k7--vwqzbKv00blNC5w7rayMUhVL-ARnPGQfFEa-hQ,5089
official/nlp/modeling/networks/span_labeling.py,sha256=QBf5kQrVdq3oC_cPqkMO7rQV9UQzxmAgq7YV17goFXM,13115
official/nlp/modeling/networks/span_labeling_test.py,sha256=jSDSCbIGMYZNZpM90qYhquZ95tzy_46V55y-UXO9hT0,12410
official/nlp/modeling/networks/xlnet_base.py,sha256=LuPP2Z35gTZvY7EbnGNv1kMNtMYUTQx6Q1d-NzjfII0,25946
official/nlp/modeling/networks/xlnet_base_test.py,sha256=UIOTZ7F1DxyrPsx1nr09IvYNFjAO1XFc1nTt78bw4XY,15018
official/nlp/modeling/ops/__init__.py,sha256=AbpHGcgLb-kRsJGnwFEktk7uzpZOCcBY74-YBdrKVGs,1
official/nlp/modeling/ops/beam_search.py,sha256=463nj01tO4pwCGram22kytwDhQ1xPGKUEa9R6GLloWo,28740
official/nlp/modeling/ops/beam_search_test.py,sha256=oz1gxy5VNbTXe5iIncZGW_SK_vb4zqz9RtSMK7pZFfk,3860
official/nlp/modeling/ops/decoding_module.py,sha256=s40Mk5AaatSbb_tnIykjmMbgSMVRqSvoJblWQywpxUM,10391
official/nlp/modeling/ops/decoding_module_test.py,sha256=53PV1Cfpt03OWyfXDhMoDjG_z0f1H6he94SdrUZscOI,2653
official/nlp/modeling/ops/sampling_module.py,sha256=w8R7GQbcsE-RdBRQ23pA-rhr8MfW_gQQm6H38-RDB48,17696
official/nlp/modeling/ops/segment_extractor.py,sha256=TuJAGQPMhUKjbSwomc5EPCPH8AggcX5BTqdAFVdStGQ,9696
official/nlp/modeling/ops/segment_extractor_test.py,sha256=KK7kMzpF-7hRXia3IlK1TVUI5RO6UtwtUIAMM2ASRTA,5498
official/nlp/nhnet/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/nhnet/configs.py,sha256=BKuAsrYJ7NlJ15si5HsdZF5hSlHjhQcO8ATLhFZB73Y,3302
official/nlp/nhnet/configs_test.py,sha256=D4R2pFSabTWxWEUFTxsuz7Dw27D6tvfmUx_XYDpkXwg,3191
official/nlp/nhnet/decoder.py,sha256=HnjHlKCDfK3wuneFiGAVNZqceFXkzVq0ZhR54yL-Y0A,15338
official/nlp/nhnet/decoder_test.py,sha256=2RrrlW-waI5ip7f2aoRZ8ustZNsY7ez6CUoyMpiZvLc,6084
official/nlp/nhnet/evaluation.py,sha256=bLywpO65QOZ1xEUS_xFAF4shlkLCS_16SW2_30UywUw,6172
official/nlp/nhnet/input_pipeline.py,sha256=VRivQI6IqMgmNW1GdincwI_iuYleNhqSgoLSVyFVJcE,9133
official/nlp/nhnet/models.py,sha256=CH4dAmThSuDXX7ha1kLCdS6ru2pjEvG4-daOvwYrKCE,23148
official/nlp/nhnet/models_test.py,sha256=pvq8Ib2bYgliDbY6rEwb87Y9tmgF_uHyGBWq89E6_c0,11869
official/nlp/nhnet/optimizer.py,sha256=zJFc3i-tWfZBP1-ZBdY0N58z6dJlV9VEy50XyQS3nxU,2889
official/nlp/nhnet/raw_data_process.py,sha256=ib-ZYt0wV1SrM6WNe7HSXBl7AkJMgafoT07bNY5YR2c,3829
official/nlp/nhnet/raw_data_processor.py,sha256=nwpDJ2AMI48Y2RQDpos5cbwOpxdGd39pSMmTHYPFG60,9336
official/nlp/nhnet/trainer.py,sha256=GDVIUv0ajUvtl3X0Z9CHfrbG2nYdh7Xyrx0BUTVGxnE,8797
official/nlp/nhnet/trainer_test.py,sha256=7WnHxhXulPXxNrRISt1K3V1TrVnSSAmukkyWcUlzTGI,3396
official/nlp/nhnet/utils.py,sha256=X5CkS_7PQMD2qXFDbftvol6xFA3GE2YpfVdZ-bhWZuE,3364
official/nlp/projects/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/projects/bigbird/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/projects/bigbird/attention.py,sha256=f99juHrgIxDexyD-rO_qa2RK088hQkuSR4KcALGWmsA,21067
official/nlp/projects/bigbird/attention_test.py,sha256=rnjEPk4NFO5RtvvKvrR_q9NwNQ2V6CLsGfQYH17l2WU,2249
official/nlp/projects/bigbird/encoder.py,sha256=V-aj84vk51-lZbZGVgSfBSTQIWdl4aQEGZqHKJT451I,7913
official/nlp/projects/bigbird/encoder_test.py,sha256=T_bsF2Eb2xBF5OU-6ygY7BGZT5Ly1AOkPt2zGJTyWkg,2416
official/nlp/projects/triviaqa/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/projects/triviaqa/dataset.py,sha256=cGFm55NJypU7nkPDQTwG3Z3OnsrmEMhoMoKuYQAIhzc,16324
official/nlp/projects/triviaqa/download_and_prepare.py,sha256=-70gCSVptRLH5Le7wlmEs1ST31irov8xSKa45904IpM,2501
official/nlp/projects/triviaqa/evaluate.py,sha256=I07271K2ltPBF3fe7bzoViD3UqIIG2rtAHEa1eoW8bM,1554
official/nlp/projects/triviaqa/evaluation.py,sha256=bI18984pQq0ycP7c7L0uvQF4NRK8aFu3ayVU_Hw0PWE,5380
official/nlp/projects/triviaqa/inputs.py,sha256=qtJzy3xxSazG4bXpmfPofZKMduvInZroB8Mut6oxZN4,21507
official/nlp/projects/triviaqa/modeling.py,sha256=CbUcxrQoHnd6RdkuvdHwvo5nBzLKnx0-WXpUvd82aX0,4629
official/nlp/projects/triviaqa/predict.py,sha256=83Ob-Bezbbwo5GZC4mYoR3gf4fbUtzC6y_jbGsOhfjk,7099
official/nlp/projects/triviaqa/prediction.py,sha256=kyfgHzZGQAMfBjNUgjeRUxfA1kVZnFPuUCsJzIyNqjQ,2735
official/nlp/projects/triviaqa/preprocess.py,sha256=5aOsMcUw6HYv3kHNTDLEPIqNyMf9L9yo35c7ixaA13E,18805
official/nlp/projects/triviaqa/sentencepiece_pb2.py,sha256=RbIQ2LoyvUz587gfaGGGHQSB_Eo-j5Nl2HXrdcJ-kBU,10818
official/nlp/projects/triviaqa/train.py,sha256=zuxudRXRc6x7xRXjOKkKY4q6QCa1dLU15bxsEkTl7ts,14304
official/nlp/tasks/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/tasks/electra_task.py,sha256=Oep4zR66cYcXvYqtud4nMeVP1UOgDwgZ7G4dyrrYVZo,9736
official/nlp/tasks/electra_task_test.py,sha256=_zPyqU2K9IG5skUFavMQuKQjcLZnlxPLF4vtd9RecFE,2381
official/nlp/tasks/masked_lm.py,sha256=njSHVZ298Ik_STzH7pca3nbuB71dTUDVdA7hr-Vy5QY,7716
official/nlp/tasks/masked_lm_test.py,sha256=y--yQDAsy0jFGFkdwW7KlrvVFsWfpjAygB04WTX4Wgs,2259
official/nlp/tasks/question_answering.py,sha256=6HZs32iWWQeAEFG48Cd2MYIsHMQBq1Aj8vRF7Jao5vk,19890
official/nlp/tasks/question_answering_test.py,sha256=777yVdy8TN2xhkNVLWVDO1zkQ-6ytQuw--PHdSwsegI,10472
official/nlp/tasks/sentence_prediction.py,sha256=xjQyyeB6NALnyvbfFbOasWQNAt_lKeu7iepS-1wo8YI,11432
official/nlp/tasks/sentence_prediction_test.py,sha256=abmPYvi_TGm0gtx2FN0Dq65YIY2r3ogEQA3b3Ld9Bpg,10903
official/nlp/tasks/tagging.py,sha256=u1Iiw9QhjDkKPkOFiu4Ra4t4Of1QMAhbrcSqFN1Kzn8,10136
official/nlp/tasks/tagging_test.py,sha256=weTWJtK2RAhJlxNJJQm1_xKh7P3uohE8ubyX8-xLYfg,6978
official/nlp/tasks/utils.py,sha256=DHluvKUkoUwr8MDHd1MLEMIk8v5_HCbPCQUThSZajCs,3468
official/nlp/transformer/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/transformer/attention_layer.py,sha256=vZ2c7HMJoD61uROBjyIQNA9ohNATKOOEMqSh9Lrfi8k,6987
official/nlp/transformer/beam_search_v1.py,sha256=nWHwPRn55dm0VYRC1JRF1QfBalDUka5KWvDCUeKmoEU,3759
official/nlp/transformer/compute_bleu.py,sha256=BN9tMBs-cEuR21RUjhfYs4bhQnkquhXjrcCVF2Ckjdo,5242
official/nlp/transformer/compute_bleu_test.py,sha256=s0Pgjg6ZctOof2a72yVLe23vxrshdCqkzoqxkiytzBc,2662
official/nlp/transformer/data_download.py,sha256=f2tamNyjB4uPtsIlhgji9RJ3HMQx4UIHl_z9KG0zSnA,15377
official/nlp/transformer/data_pipeline.py,sha256=mjxMJPXuDJ5ukOpxz5wcdMVnRkcnMofYAS2azDk-Yz4,13287
official/nlp/transformer/embedding_layer.py,sha256=JfOIk4mxDEu8QJsLUC1UZPl-Nyc0r0VXZEszfQU7ElI,3760
official/nlp/transformer/ffn_layer.py,sha256=1vrGNomVMDvGvpMk6apgIyOhaAZxio9h4zBs3GxrfJk,2460
official/nlp/transformer/metrics.py,sha256=mmtcxNImbNBNIlnEewoRdPmgL-GTPBUAQ-CaHgN1IT0,7087
official/nlp/transformer/misc.py,sha256=slBtS6MHk-6_whA4YTVgiXtUvAER404WXgYwKG4XzYM,10423
official/nlp/transformer/model_params.py,sha256=njFMLgmNuvgBtN9DID7CoaxLbYiS4KVI_9CQp9bQicg,3021
official/nlp/transformer/model_utils.py,sha256=L8dyCXdqFQuuz_Q96QCPzPoA9T5y5kIxkKU0uz2-sKE,4507
official/nlp/transformer/model_utils_test.py,sha256=FDx804e63QhsVjnuqH36lJWQSylB4kzlgmjcnJNsTAs,1968
official/nlp/transformer/optimizer.py,sha256=NCwBJHDn8B8CI-korhaPEFIo0Dmnl9fw54RtZIutheY,2514
official/nlp/transformer/transformer.py,sha256=jC6CMYEBCwzlFqb5wMEhXckJWpONfMXHRV0Fu7Tq7lo,21830
official/nlp/transformer/transformer_forward_test.py,sha256=WPvOVfaEDT61h4EBJxAMeX-OQRqhY1oiOSEpzZtckwE,6132
official/nlp/transformer/transformer_layers_test.py,sha256=165NnNqAAg9a6wNbyr75xV6kQ82nHeoIorZhid7oMpI,3634
official/nlp/transformer/transformer_main.py,sha256=adhVwU6zJSpj8HfQgkXHz6s39d3S1qvtEWtuJDfNWho,18378
official/nlp/transformer/transformer_main_test.py,sha256=_jI0N_m1pJ41VgDKsegCln4_PkWwT4ti6e_frtIYLGE,6688
official/nlp/transformer/transformer_test.py,sha256=XCRqHfO5068ei6n3IIUpdnNiq6ch6HPUsf_iFLgrP4I,3702
official/nlp/transformer/translate.py,sha256=ZPc4CFCd2r0T_P-REK-yH3L-Rn_AC5r9ISaNZBblAmg,7311
official/nlp/transformer/utils/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/nlp/transformer/utils/metrics.py,sha256=iB46aF7qX-02F62FWJv_g1iX2-LJv5uVr8XdZGm6MIM,16726
official/nlp/transformer/utils/tokenizer.py,sha256=FL7LaBflMbepQF2UpL3TFQfOd-ex9bhv2cbOfnFIBEk,24597
official/nlp/transformer/utils/tokenizer_test.py,sha256=gH2wQXzT1PX6wMKY5P_DTPC1g4L5Uq6EK1Bu_HcPWl0,6735
official/nlp/xlnet/__init__.py,sha256=AbpHGcgLb-kRsJGnwFEktk7uzpZOCcBY74-YBdrKVGs,1
official/nlp/xlnet/classifier_utils.py,sha256=vDXK4apZUlgWotDwVbHes5DFnkZ9CiojGTBaBh49UK0,5498
official/nlp/xlnet/common_flags.py,sha256=CPlcKAzLdxldtfVpHS02EnFOqt-BDg0x58-lvUFE4Ao,5447
official/nlp/xlnet/data_utils.py,sha256=dZRK-IhGkU8p8y1h4n3jPifu-FVcemBwmTVYc2NMZA8,30175
official/nlp/xlnet/optimization.py,sha256=TXKmyj_8SjM4Kd2qCjUJUARk60S5r1u7G-_vZhg0AI8,3842
official/nlp/xlnet/preprocess_classification_data.py,sha256=HtlNXcidjpwH8vqVK6QnMTXb0M46tuyddnNQXpfSbqE,15418
official/nlp/xlnet/preprocess_pretrain_data.py,sha256=hN3gV1cS9E39PRUFujHEEym4WHD7NwArAEInUxKZ8Ek,32402
official/nlp/xlnet/preprocess_squad_data.py,sha256=1EVUh5NbaR9CmIjp7ENbwJWBIjfqvSsKKWlSpAXEMzU,4123
official/nlp/xlnet/preprocess_utils.py,sha256=Y9gWVxPg4wdQN_NZ29CkvwFHpdBleYBYVURVAQ5_HSY,3697
official/nlp/xlnet/run_classifier.py,sha256=_2dOpbPQgfqZjMF2YnsNV5teroYPco-A4CKD37oa5Oc,7038
official/nlp/xlnet/run_pretrain.py,sha256=f5_U7sj6pgrcdLvIsspMn5FgwVtGKkvDAKOAfVnwMDQ,5772
official/nlp/xlnet/run_squad.py,sha256=dDZg1A2VpWSjXtSB9oCwlwkaN-G69s4CPcRjSxwiXtY,11656
official/nlp/xlnet/squad_utils.py,sha256=tOafmFmTZg-5UctZhnFbA9x1u9bhCRtejXbjhwEyLzA,32311
official/nlp/xlnet/training_utils.py,sha256=DeqXDS7BoH9Alo-HqROV_SsIzfJmpyeVvCpyjAZGjrY,11796
official/nlp/xlnet/xlnet_config.py,sha256=K76hSkTOqzv2i9tYQ_auZiPzzvzW4RjrC6Ai-d8wlW0,5974
official/nlp/xlnet/xlnet_modeling.py,sha256=00XICvcQwg0E4eYlM7VkAbxwuVXpH1Oa3BgR7OXn8Vw,47678
official/recommendation/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/recommendation/constants.py,sha256=tVWsNaX8WEphAc7bformCUjjfRBKiegko1pYpBRu_H8,2957
official/recommendation/create_ncf_data.py,sha256=YLZnlEIw04UTOeNncJLddvCtzGUETqP1pAWAY87q1fs,4208
official/recommendation/data_pipeline.py,sha256=sEXf498zrPmLyhBvvlfhm-Pz1U9EgyqEr8_xeKgiYCY,37275
official/recommendation/data_preprocessing.py,sha256=xjQ-BoEMgSwNX-si02U86sld2_NLlon5vCXVMz5Zj5Q,10532
official/recommendation/data_test.py,sha256=Y9LtA731xqvUHRwbQgKQFH07v4PYEYaTiqJgBV-1CDs,12901
official/recommendation/movielens.py,sha256=Fs1fHlrbxiTKc5cr-rDfBq0Hjqeu6dKoZxAwGjmB1vs,9813
official/recommendation/ncf_common.py,sha256=dSioJ9TTnLRQOpeL8nOG1rcnBbwh7TrIa-E_vbKvoMQ,12433
official/recommendation/ncf_input_pipeline.py,sha256=x_btVYjL0XrwNmBcv6kl-6TBE-YibvlzfhZiaAA_-7s,7165
official/recommendation/ncf_keras_main.py,sha256=CaC9K0VcNt0Z7NCOSWn_FnNlMx1qbNQJwVM49gaMkWc,20019
official/recommendation/ncf_test.py,sha256=QBgb2ynihiVr8FL2fAYX3BIyeLk6bqDwolhuIOrRU40,4171
official/recommendation/neumf_model.py,sha256=EF-1-AYcRfnzFWKyuBv0IqgOyvXc0ahRE6nx6k-WBxE,17020
official/recommendation/popen_helper.py,sha256=moFOPlnWDGsOtZ3gOBQNB2eMvIYuq8GTXaDJgqY6U-E,1997
official/recommendation/stat_utils.py,sha256=6ASIgkIuiGg3P30AW_WANA92FBcRGX4WuKEOXJWCQEg,3163
official/staging/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/staging/training/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/staging/training/grad_utils.py,sha256=sYhKS49W4XEz2yCFd1M-aIX_6YyW0l2UR7bLl7ok9DA,6937
official/utils/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/utils/hyperparams_flags.py,sha256=Hjuc5iT4VAXWEvphT6Dlt3KzP3AvPVreOu0Rdgxb0jo,4846
official/utils/flags/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/utils/flags/_base.py,sha256=gD53Krf3rZOa4v9JBFxXPLU_KuGBr0mFDjF7bmNqvjw,6585
official/utils/flags/_benchmark.py,sha256=1mFXwxb7Pcsi_TIP08tPOCk7Q8KqvGRQ9REbXaSKi6o,4272
official/utils/flags/_conventions.py,sha256=OGhCZBNYvrZ0F-y13eAmGPykg-Ibv9T94I0GXMSF7T4,1808
official/utils/flags/_device.py,sha256=idcHZmODf6Ihfkoo4-yljIqpoEocZ0niF47siIFmeE0,3016
official/utils/flags/_distribution.py,sha256=8bjQropCStm5yrc4-jhgC2Q2Z4dH2L3Z40nhJA6RUXo,1884
official/utils/flags/_misc.py,sha256=Xoc0bIPSj1IyF82TBPnTW2rcyGzPr6GNelMnD1_Zc2I,1731
official/utils/flags/_performance.py,sha256=1r4ZSaM5cXzv79ImRkAWMeeCechK8cTHFKYVxzGoKps,12410
official/utils/flags/core.py,sha256=umYVHbpfzKMe4lDLEfdoSKdrWgYqXf3Wq9PqmcKsNoI,4617
official/utils/flags/flags_test.py,sha256=f8AYIn0LtPZrW7UwRJ3ROrjX5blSYt6aAB0KKz1avxk,5414
official/utils/misc/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/utils/misc/callstack_sampler.py,sha256=WdFYJ14HIV2JdALQXVIIn8Ks8c2Vw9jmK1e1Go5LkT8,1640
official/utils/misc/distribution_utils.py,sha256=bNXsSZ09atCZW4liCnylz31MrviIS49HYYThfbrT4W4,838
official/utils/misc/keras_utils.py,sha256=ardLnzGCqXkDbu2CwyTa2ADHRB2pn2Hm_TVJ7IlQb2w,7863
official/utils/misc/model_helpers.py,sha256=W-ZzR4qKNNJ9r8PNq1TpvPiLLR45yu5OOI83S9f7sts,3550
official/utils/misc/model_helpers_test.py,sha256=Q1Y7b1-BEMtAg3lNFdR1V2bkN5NJZSVLggx4HnEBEsY,4739
official/utils/testing/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/utils/testing/integration.py,sha256=9iAszB8HCG5oU-Ku9jExIjxwEHjuJAQv2E0eH57Apxc,2410
official/utils/testing/mock_task.py,sha256=icIzDH83-ZQXy0DoBGHaMF-93I-OTnaOdK-NNcxMn-Q,3289
official/vision/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/vision/beta/__init__.py,sha256=17xSd3uvrXgYD9zqjfuMPVmqciQ9SaCVujqktD9e1jU,853
official/vision/beta/train.py,sha256=dmb4iAbEBPgruXhEas_0qUt-LQx5cZ-X52o3bqApJ80,2645
official/vision/beta/train_spatial_partitioning.py,sha256=6wt57mJV3AlQKUa-N5--qgYOCE0FaDiNu0ysPnMktK8,5426
official/vision/beta/configs/__init__.py,sha256=rVlMAuLW0ePDJMb65qXxcm6_YCXcosTvRFErgvooor0,1031
official/vision/beta/configs/backbones.py,sha256=OV-pw_pHoIBGlM386N8ciQ-L91HbwcL687MufZBEjEw,2706
official/vision/beta/configs/backbones_3d.py,sha256=t13abXwgHu_fk9KlsN1nUmTSsxT79ib9Gv_ZrblnNcE,2464
official/vision/beta/configs/common.py,sha256=y4xaZmio1hbOUeZzGAq1JR2-BRf07NoOo5g9Jl7pce8,1000
official/vision/beta/configs/decoders.py,sha256=vhHy6o0hPgA8pXK8eOTBDA2HXDWeEWq9IjSiHyRAIOQ,1883
official/vision/beta/configs/image_classification.py,sha256=-R7mEUCfoEtJ-RjqZICIsXFdIjTfexKnDluDpoEoZHQ,11199
official/vision/beta/configs/image_classification_test.py,sha256=Z9KBPQpLB5TEgMYkvNmuOJC6d3eMQzNLpFEp8nynVFY,1843
official/vision/beta/configs/maskrcnn.py,sha256=JlpmVdrBr_AXhcgPRlqb21_KeZHrIWgJl780aJDgESw,14081
official/vision/beta/configs/retinanet.py,sha256=quCOpF38RWv7b0bxrkm97g3QKMd7SKBHO5ltcPw2d-Y,10453
official/vision/beta/configs/semantic_segmentation.py,sha256=EYxCNHMHn2AQ5oS31dIDMtRuOtnTqD9hUtbEBcAToWU,18059
official/vision/beta/configs/semantic_segmentation_test.py,sha256=ovjIgPpjQ35p4Am8lZgP8AiOKziLZW4E_g8Ve56st2w,1824
official/vision/beta/configs/video_classification.py,sha256=qYp1DFHGtDt32T_cJsd2PXBcHiTrUZQZiXqgNT6iTVE,8336
official/vision/beta/configs/video_classification_test.py,sha256=pZjNBfZtjLasqhok6JBuUvTl_YdiIzSn1jzdCMg6uxg,1787
official/vision/beta/tasks/__init__.py,sha256=T-4FUy95_aIODlmnzD94x9r3srE6ITxVMBUPBLfsuss,1019
official/vision/beta/tasks/image_classification.py,sha256=24ZkG5n_LN3x5TlUcfzd9-T-luaLHzTtiGV2_wyAf1Q,8439
official/vision/beta/tasks/maskrcnn.py,sha256=i3hgXFGjcaSbl_vmOMjC_Nnr5L85j0CdcIMK86nDzK8,12774
official/vision/beta/tasks/retinanet.py,sha256=6RAGZgHEoPExI8sg2amVWfFrYZuBaRVVIrQcECR0JAE,11476
official/vision/beta/tasks/semantic_segmentation.py,sha256=OS5pXsyXgclPM1gICH6cOxyFw6yUIpfkFAcDjpvPniw,9364
official/vision/beta/tasks/video_classification.py,sha256=gRDyMn26XKRBGdv2HAcy55kvhWlvA4XeX3lPMnYkrk4,10417
official/vision/detection/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/vision/detection/main.py,sha256=SKzFNiM4YSdWgEtKBOjyKgGIiKE81zdj6Zu6h9Pp_sY,9157
official/vision/detection/configs/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/configs/base_config.py,sha256=e8XSEYlIGZfu-fdczRqcNKlBVVJm84OqiyzdE5kFCvA,4548
official/vision/detection/configs/factory.py,sha256=Qk3drSQtY-K3ifyvCXvfOnnj04rd8EHMLPw84CU70Ns,1772
official/vision/detection/configs/maskrcnn_config.py,sha256=giz6hsz1pRnqcjbVMGW10KFHB463Jq0Z2oC5NWh8ys0,3408
official/vision/detection/configs/olnmask_config.py,sha256=NNv2v6YE_i4SeNK-ZIXqI4c3yrmOD9lIZM-uoZcymOY,4351
official/vision/detection/configs/retinanet_config.py,sha256=rMm_BZu8CE5Mls4aWDedaOaZPRVSpGTABnVSRO_Aiw4,1856
official/vision/detection/configs/shapemask_config.py,sha256=cOmlSU6y8nRrI0fT_yteiI3FFCUf2WIepgdB2jpZyuU,3260
official/vision/detection/dataloader/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/dataloader/anchor.py,sha256=11tPnFCxkHkdCN92hLTRArv8S2WCiYKbzcH4bU-n4yQ,20805
official/vision/detection/dataloader/factory.py,sha256=wxN7II0zamTuZrK-ke3sSXS_TCDnILFcrlE9xHiirOU,6643
official/vision/detection/dataloader/input_reader.py,sha256=QymU_edhwR6-d8GT7-0JryaF08rVqUJVw5zEgZwd8KI,3784
official/vision/detection/dataloader/maskrcnn_parser.py,sha256=ciLfgHbpSOSfGef3t1RUhSZkffHdiF_IZjtBPpB9_Cg,15827
official/vision/detection/dataloader/mode_keys.py,sha256=Lu-U0NEEBBWISDmdD_vA0urJGLgpbuIb9C3xx9hK504,1141
official/vision/detection/dataloader/olnmask_parser.py,sha256=v_g4UKhvTKhnw1bHO6HNbLIQQwMtUPEzEwmSzwPqN60,14298
official/vision/detection/dataloader/retinanet_parser.py,sha256=IjgBRmtotOiS95Yj1D47iMfmdJw9i46QB8xDc3xK_Wo,18363
official/vision/detection/dataloader/shapemask_parser.py,sha256=6mE4rLrFZd9w6R-DsbknhGk4cCvbGS2lfmdO6_5uwc0,22411
official/vision/detection/dataloader/tf_example_decoder.py,sha256=RNoCeGKin-lVW9_gdjIWSjpcVzU7EJwayK4BhdtweqE,6299
official/vision/detection/evaluation/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/evaluation/coco_evaluator.py,sha256=uwd-EkFpBF03DgtaA_fBl71CdSivdOZ6e-Fng-Nn-60,24838
official/vision/detection/evaluation/coco_utils.py,sha256=hkEihcEG4BfYNsRRPTtyWZ8xkzqUbtoRfbeZK9llx4s,15249
official/vision/detection/evaluation/factory.py,sha256=AOJNd6A28RGxWeo5yDFqkNLzVgLGjob3HtTkJEiteLc,2249
official/vision/detection/executor/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/executor/detection_executor.py,sha256=2982Lex8oxENerFyxd6iv-SrLzz6otR_QhppdKTHBh4,6366
official/vision/detection/executor/distributed_executor.py,sha256=Spt1ne6p7_RaZCFWTRoucwx2uAC9ObEa1n9jbrrZKw4,30236
official/vision/detection/modeling/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/modeling/base_model.py,sha256=XjYoSCVhsZDe5jEQrppxnasRW8dzDglMcWVvcs-foOs,4631
official/vision/detection/modeling/checkpoint_utils.py,sha256=4ryYTyoaN3kNUrzMR5haaY2wIBWMHMjqawqtkUJKypI,4647
official/vision/detection/modeling/factory.py,sha256=i861qT7pW_tAuzNhurlxqvy1ibVf6jz-adhLDdm7NDE,1482
official/vision/detection/modeling/learning_rates.py,sha256=Ha7v8-DYLAlo5ug0jk47qqldjWs70ajy5kcZY0eAfbQ,3899
official/vision/detection/modeling/losses.py,sha256=ViZkQwnlhavOkxztwP8UuQqSwx2TMKOd0ebIKqn542s,30283
official/vision/detection/modeling/maskrcnn_model.py,sha256=UtwVCBnSuQ6s3mcxoclFQR6QRdb16PlbrgKuQ1I0MVw,13674
official/vision/detection/modeling/olnmask_model.py,sha256=32KX-K0xmLHslM5tXJYT2lqHb4NbQMjGBsSy1ByFk24,16990
official/vision/detection/modeling/optimizers.py,sha256=DeKD33Xk8Yfkt9TI_VGSm4LorrF8LwRUBG8gu5FWDV4,1815
official/vision/detection/modeling/retinanet_model.py,sha256=2Tuwzmn0siY680ffITPLjdLBwk_2odHwfihUvkuYgeY,6871
official/vision/detection/modeling/shapemask_model.py,sha256=ze7rWsZMDdt2GgFkqBU4JjKqcyMl4M4eoW9RhxEBeqg,12326
official/vision/detection/modeling/architecture/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/modeling/architecture/factory.py,sha256=DojLuH7bihbj-nH6ZdVaVrkOBDUkh39i332N4XzRY_8,8074
official/vision/detection/modeling/architecture/fpn.py,sha256=jxrwNRm23FG76hqZGu-yIoOZdAACXOdXCnklbUYYLmI,6126
official/vision/detection/modeling/architecture/heads.py,sha256=cleY_M-kCMwCq7gDlHVrRkDmxHTFTkoyYqa30cFgE_o,49854
official/vision/detection/modeling/architecture/identity.py,sha256=8tuWcNWuTxXJG5Cw1TKjlBfSAdicvwN5WZE4JwwKt58,1054
official/vision/detection/modeling/architecture/keras_utils.py,sha256=1hM7v_NGBIFgNjYaR0BeTBlEUiwjSbP1V_hQ6GK01so,1400
official/vision/detection/modeling/architecture/nn_blocks.py,sha256=4q0EYcI0A4o6h240vnIhAf1V1UoQ1UzXospbcPRcQUs,11423
official/vision/detection/modeling/architecture/nn_ops.py,sha256=mXCOp3FTIpT21qQcBydfMaL5xV1MpmNFnVa-U2bjxzA,3932
official/vision/detection/modeling/architecture/resnet.py,sha256=mLIVdvNaETqBZ8V2y1VmB_oN4SeNTOhFPtlaz00YYzM,13347
official/vision/detection/modeling/architecture/spinenet.py,sha256=FsfkNxJsUivFBY0weEoBwYjveF5SR1M6U1ERRcZadGw,17507
official/vision/detection/ops/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/ops/nms.py,sha256=00iBq4xn3rqSNSVuJh2mRtzg8NknegJzVGwvAB031wY,8205
official/vision/detection/ops/postprocess_ops.py,sha256=WCQ9ZWe0Gh6OKZD0tBfr14vzYQVtnS9WP8suJp-7Cws,21861
official/vision/detection/ops/roi_ops.py,sha256=-2GioJieYPnK7Vxq7g2N1Jn1g-G3p8sOJp1-8RuLf-M,23000
official/vision/detection/ops/spatial_transform_ops.py,sha256=bAF434YkI7OlCZSvm_bzTKVQRbpSf7ZLDP2EsSNQwQ8,26078
official/vision/detection/ops/target_ops.py,sha256=Nx5Z2TJWGv1AZ_USuMohDkQ7YfbkFbrlNH6Zx9V5mIY,28225
official/vision/detection/utils/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/detection/utils/box_utils.py,sha256=C7Ox4uG4SS4NcUJn2M6qlBRPrNw7DQLm_dTN3jxaET0,26179
official/vision/detection/utils/class_utils.py,sha256=IuPkYzvlO9cMCQzfOO6WpKiKChwpEcLZPKEk-tswEYA,1545
official/vision/detection/utils/dataloader_utils.py,sha256=Z23gGQzFbb8aNzHjD5RGmUsU6H6OXp3lDWFPxLrqe6M,1655
official/vision/detection/utils/input_utils.py,sha256=vngH2aE_UC1GnrTRhEl24ljEjAN7IjLnG8qEMujXfhs,14387
official/vision/detection/utils/mask_utils.py,sha256=16yLG05p-4NNcxLLeEVc9h71_HXu2xic3FY9kcFk5Is,6727
official/vision/detection/utils/object_detection/__init__.py,sha256=TQ3vWdbSZYHcAHvtYAluI94_iUOxBqEgnCDJptqCNvI,689
official/vision/detection/utils/object_detection/argmax_matcher.py,sha256=pr_pZ-N8oSQ_b71P8nzZiqo3Yp7XKq4OAElEWszXC9w,9111
official/vision/detection/utils/object_detection/balanced_positive_negative_sampler.py,sha256=xYblAP_JhW1-FPJKAVm0Usf6jQIRSnK7do9SkOWuX5Y,12081
official/vision/detection/utils/object_detection/box_coder.py,sha256=Xg1Vxg_7hevrkgg0QHNUgfCnl8bnWRgIU98ZGai98wg,4856
official/vision/detection/utils/object_detection/box_list.py,sha256=Xiu_n3mlaD6BaDZ8GDew-h5O_TCjwmqnu2aWmE-hDec,6819
official/vision/detection/utils/object_detection/box_list_ops.py,sha256=2uj-FLvJdHtpH22SAp1vjsnTz8tfvtBleZzw_7YXLmA,42025
official/vision/detection/utils/object_detection/faster_rcnn_box_coder.py,sha256=lAdiaYFwRgAgVSxy_3j685Vv4AI-2j-fwlDcuFK9_Uo,3906
official/vision/detection/utils/object_detection/matcher.py,sha256=S2nXt9sJW3TNnFpi_BQJQwjqIL71yyiUg0RxKBXcGKE,8831
official/vision/detection/utils/object_detection/minibatch_sampler.py,sha256=9RuI2JaTzWIK0cRZCE7M8evow8i6YovQX-T4kJu7Lfo,3164
official/vision/detection/utils/object_detection/ops.py,sha256=Ss7mcrCTj_JrMf3Hjr1TJqyQsw67Zyd3Ya4X0c34JQg,3302
official/vision/detection/utils/object_detection/preprocessor.py,sha256=97XwDwkcNPhxU_V-CFtbV6WMHIYwvr5VynlJYA0yqZ4,20448
official/vision/detection/utils/object_detection/region_similarity_calculator.py,sha256=6AeV1QmDGuBoXMsWjA3VupFOtQtNKXPkrdywNqFhawc,4624
official/vision/detection/utils/object_detection/shape_utils.py,sha256=ASX-IBjfKtBbYu1GAz2sH6gmlrV8VSFn58zYOWpK9dc,3688
official/vision/detection/utils/object_detection/target_assigner.py,sha256=yOOfvXH7O-2KeOBI1mKfW5DZCjh7Wrpayq7k2kXtWHI,24298
official/vision/detection/utils/object_detection/visualization_utils.py,sha256=0VDVHKEmEJ88tGvWqEGJj0p1X3FOkH5ieMq8qoE8LQY,29074
official/vision/image_classification/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/vision/image_classification/augment.py,sha256=bRqSmGGYy3sfNGi37fm8pMen_IdMaORVE3rAd8qcJTw,34462
official/vision/image_classification/augment_test.py,sha256=4E0x--Z369iERk9s68VJp1h-iIg6r4VJCyW6ojin6TU,4442
official/vision/image_classification/callbacks.py,sha256=AzVYfU5jcHR9Wk-VjQjmA_r6OL6xJvyqeYB3_rT9QLE,9294
official/vision/image_classification/classifier_trainer.py,sha256=9ko4ei7Pns8jVB-Zs9-sW0Kud1bPJ6xGafRLISbInIc,16270
official/vision/image_classification/classifier_trainer_test.py,sha256=EtS7HbCMUbgR4e87IrmGO43oHAtrE2a8AXQDMnvJZic,7973
official/vision/image_classification/classifier_trainer_util_test.py,sha256=ExOzaJz3FYbsE6hmDvXWoQOqvdpj3CSdTAjoCqGdpVQ,6146
official/vision/image_classification/dataset_factory.py,sha256=ueIG80Gysu_dwr_SfU2FqueVmt0gGWY1nGCaV-JwG3w,19599
official/vision/image_classification/learning_rate.py,sha256=G1aea_m_RgZQwrZ7qV5BUwo6UERz5Bv0MPI4tcTVNyM,4372
official/vision/image_classification/learning_rate_test.py,sha256=S3a-dqddVAPk2J0SZKCnlLcxuf8nWaKWpihchW0-dVk,2021
official/vision/image_classification/mnist_main.py,sha256=cNrwebPaXq9_Wy-O-ZXC3KC8RFT6uBthLpbbguWCjSs,5993
official/vision/image_classification/mnist_test.py,sha256=Cwrv46S65wfeHwvos1gzxLDWyorR-JkNwqBrqcEpo5M,2684
official/vision/image_classification/optimizer_factory.py,sha256=OrTIpLli5iC_Fi18jYMEJEt7uJcvrhH1DdSMFtYsoyM,6924
official/vision/image_classification/optimizer_factory_test.py,sha256=s_p5kFi23rHnFEQCUKx9SMuIEuSOEqDsM2na79cy028,4196
official/vision/image_classification/preprocessing.py,sha256=mbvkfuqcOfKskdTAA-sxS7iUS_oYTWa7zV1pUZZ-Pe4,13601
official/vision/image_classification/test_utils.py,sha256=h4nsjQXNwFqrgHtPVJ0OwrHMpGVujlkLDKsaMhGX2Lc,1452
official/vision/image_classification/configs/__init__.py,sha256=nxhM3rvGukp5ZXNRrf2sIpdE6z0_03tR8ds56y_SVgs,689
official/vision/image_classification/configs/base_configs.py,sha256=ZLssTIARh0YTTHJDML0RCEGVUv4EnVD5jxsc92nNSYs,7856
official/vision/image_classification/configs/configs.py,sha256=cMu37ke6J-CdpgeQp48rAQErXGRPcIeWyhtxA-IdXI0,4980
official/vision/image_classification/efficientnet/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/vision/image_classification/efficientnet/common_modules.py,sha256=4GnGl94wxckpD8J3tXXAJGbFWh9dL8frM-rqk8HDL0g,4690
official/vision/image_classification/efficientnet/efficientnet_config.py,sha256=8izxwSWl4oP65BTbSrNdNU1Iv3aXOTjvuCqRwmvV3uQ,3001
official/vision/image_classification/efficientnet/efficientnet_model.py,sha256=gHJhzJ52yy4VPlIK8wBSPa8FCbTi-l8j_jFxLNeDUsE,16458
official/vision/image_classification/efficientnet/tfhub_export.py,sha256=BsEkNx5WUZUXO20CYGAnKAxhRWBTgyfNHAlilw8bRlI,2446
official/vision/image_classification/resnet/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
official/vision/image_classification/resnet/common.py,sha256=ibsGkHnmi-6GmIlLfV2_jyJsSvoXBDYPv35sL1BoXsA,16356
official/vision/image_classification/resnet/imagenet_preprocessing.py,sha256=4dV9tBwmgXQ9EokOUBGyFRF0fJEQYXi3YNEC9JZsEmI,21248
official/vision/image_classification/resnet/resnet_config.py,sha256=b0u30oj2BOcySffzp2Rv-gaA_HGzKMq5Dl1dQV3sOus,2099
official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py,sha256=zBaX_u6QIiiDZGfPhJJxt3t1RVDUs1T8Z-yYpv07Vn4,7076
official/vision/image_classification/resnet/resnet_model.py,sha256=EFwXnOmQbWQmWMiWlrEe1VdvXdaHA_XzGlFfseDWE5k,11118
official/vision/image_classification/resnet/resnet_runnable.py,sha256=c_A3U_ixsUr04eO8PXVKcgDBU2I6oPz7UmkbuO8Hwn0,8396
official/vision/image_classification/resnet/tfhub_export.py,sha256=qsYdSVjRjk8ye_JkvIWHcA1IMa8dbkrAwbFwEEhAGXk,2318
official/vision/keras_cv/__init__.py,sha256=qCsqXQRfwXkE0tdbotnl3Q-HV8bBpY9hbwe_XEO6ZTc,887
official/vision/keras_cv/setup.py,sha256=0WqvNEWbbgct_KxxQZtUxQMRw4e_OF84Krd8f2TpsJo,2315
official/vision/keras_cv/layers/__init__.py,sha256=1KxL8Y8CuZQfxfDzkqdQuUawdLmt0i2U3VqLAfVvyk4,805
official/vision/keras_cv/layers/deeplab.py,sha256=QKPKqA6ikBshtBQHyrNGNhRLJNvY5iLelClGrjSYYg4,7368
official/vision/keras_cv/layers/deeplab_test.py,sha256=RgFBuyOU3pWDPMaYZdtmXGfaa00o3TzetLF56Tfgots,2015
official/vision/keras_cv/losses/__init__.py,sha256=x8S0gE7OyiXcFRVKtq2xpQL4nZTv0EcHbalZ00YYs58,871
official/vision/keras_cv/losses/focal_loss.py,sha256=KSBorENSybT9MMuoC2ocITXkiYguFE4G9c3pNghkhvg,3319
official/vision/keras_cv/losses/loss_utils.py,sha256=leUbD_v3Tqe9z08xYU3Cpoatimd-pTXaUKEwRqs4nIs,1654
official/vision/keras_cv/ops/__init__.py,sha256=84DV-MRoE1CpatvX387xp-5CRNPDGvlKEMuKWkbrGOE,1007
official/vision/keras_cv/ops/anchor_generator.py,sha256=Y7MxJiZ1HGJXwDowtC1_suTH14OOct8EpAq4EjA1LeY,7313
official/vision/keras_cv/ops/anchor_generator_test.py,sha256=kbwewJsrEIUIIlgwhFlKsKDK7SZrXzL3wsu-ekokeCw,5375
official/vision/keras_cv/ops/box_matcher.py,sha256=Gc-hEtLMZInBeJGEMOOkQq35g-pvn4BZ391YDMIroJY,8243
official/vision/keras_cv/ops/box_matcher_test.py,sha256=-D4fpOzp_qwW90tFeQv17Zy-Xdx7I5xXE9d0sSYioi4,2517
official/vision/keras_cv/ops/iou_similarity.py,sha256=dSbtR-Q5yVeL7b9m6hiGq8Ra6YeRhLKurvl7YLloU34,5834
official/vision/keras_cv/ops/iou_similarity_test.py,sha256=kGdgJa0ez5xVECQylc-Td7z6pPGY2jClA8ehYFrNcww,1987
official/vision/keras_cv/ops/target_gather.py,sha256=iZ6zhVj0wM890BOzq7Ba0kLnlkweFq_yhfHH_uINjoE,4035
official/vision/keras_cv/ops/target_gather_test.py,sha256=RkJDoo0m2uNj3KYm0LA5MMo2TKxboMmGNHWjO-NtQuA,2640
orbit/__init__.py,sha256=sk1viymq9F-Kpxpy2KUMMkxS69GhwQX397BTVnSvLaU,1110
orbit/controller.py,sha256=0xbAbWh3MqgjblPRqySlq79vKrReimnPRroV8XtPYjQ,20567
orbit/controller_test.py,sha256=rh0r6vDqgjBqv3k2JvPuxuzS_Kj2he943feajZil66c,27087
orbit/runner.py,sha256=Q71m6Q2Z7GcNu7tAIKAQdg_qe4rpagiOwZcRbvsae7w,3589
orbit/standard_runner.py,sha256=qBzmL7tbwaRPDaPxADtcYkuBnfTPKCnld0L9LXp9RGw,14396
orbit/standard_runner_test.py,sha256=KmYP5WEVbavTzJlwC_y-gmwh5WNB2HznBcVlmvJlX6Y,3005
orbit/utils/__init__.py,sha256=t_00GglW-_Ty1xXVKKMZpzmYZoWJBLlNlb3GDgYNYjk,1171
orbit/utils/common.py,sha256=HhLyL6UMg_WCu-z8Ch4-38F7b8GvIN94yUjo0feSvpU,3720
orbit/utils/common_test.py,sha256=UKBbhf5NbjIAxWPoZpDGSX9H_sybA9EY4Z6Fdhv5pvk,1112
orbit/utils/epoch_helper.py,sha256=7AqjaAi-wP9z8T8TpsTORnnjcZg8pENmi-69YuUw-Lw,2216
orbit/utils/loop_fns.py,sha256=pYZu7HBDsMAFg_cxttjK1qnmZIqPdWqSE8rDOypWDb4,5016
orbit/utils/summary_manager.py,sha256=pH-TY30DgTwPv4e-XCm0EtRH9noZihBw-nhlAzIYU98,4256
orbit/utils/tpu_summaries.py,sha256=PTSEIEkYURYbemP03Miwm-waxu87MdSL_iiFKp7vssY,5710
orbit/utils/tpu_summaries_test.py,sha256=qbbTfV7Ggw-gI7VTyUGbY6-AjbVHjiVLzU6vNn8Fsos,4593
tf_models_nightly-2.3.0.dev20201205.dist-info/METADATA,sha256=E6fNMOx3QJA95D-P_pBIte8AtnYWVtMRYY-rHa8OpEg,1431
tf_models_nightly-2.3.0.dev20201205.dist-info/RECORD,,
tf_models_nightly-2.3.0.dev20201205.dist-info/WHEEL,sha256=gduuPyBvFJQSQ0zdyxF7k0zynDXbIbvg5ZBHoXum5uk,110
tf_models_nightly-2.3.0.dev20201205.dist-info/top_level.txt,sha256=aBNQp3JGIHaB5olG-a69BGJajwdakvxOLkUrp3FERTM,15
