Merge remote-tracking branch 'ups/develop' into op/embgrad

paddle/fluid/API.spec

-paddle.fluid.Program.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.block ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.clone ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.Program.current_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.program_guard ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.name_scope ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
-paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.scope_guard ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,))
-paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174'))
-paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False))
-paddle.fluid.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.Program.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.Program.block (ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None), ('document', 'af5346376065ff4cf6832a8ac0ae0945'))
+paddle.fluid.Program.clone (ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ebb7765b2962bd2be041d19720e49d0f'))
+paddle.fluid.Program.current_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5e162d3bf8dd625703463d9e4be36adb'))
+paddle.fluid.Program.global_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'cfb7e05a002b2e64650778cabde7301c'))
+paddle.fluid.Program.list_vars (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '1c8647b14fe57c7824b1c9562394dd3c'))
+paddle.fluid.Program.parse_from_string (ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None), ('document', 'b6a7ffb239a30bf2ce58cfaca8d8b8d5'))
+paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', 'faec17e5a04af28e3776160e34504d15'))
+paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '99e5d53d92d82797093332719c9e3ccd'))
+paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '5430f54ab4895f9f47db6bebbaf71659'))
+paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b54f403e57825a1592aece03afe3afb6'))
+paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ef753f5cec69fef9ae6ad8b867b33a2'))
+paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '78e512cabeda9c7f42cb7c7e88967ae7'))
+paddle.fluid.Executor.run (ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False)), ('document', 'aba8093edebf2d5c869b735b92811e45'))
+paddle.fluid.global_scope (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'e148d3ab1ed8edf3e928212a375959c0'))
+paddle.fluid.scope_guard (ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None), ('document', 'b94d1f6bcc29c4fb58fc0058561250c2'))
+paddle.fluid.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '292ab72977afbe58e6a3bde175452680'))
+paddle.fluid.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8'))
+paddle.fluid.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c'))
+paddle.fluid.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff'))
+paddle.fluid.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4'))
+paddle.fluid.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', 'eda17d0f1639bc6ca215cecf87f588a4'))
+paddle.fluid.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ac4114d3df16264f1946deb3a8434a6f'))
 paddle.fluid.DistributeTranspilerConfig.__init__ 
-paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None))
-paddle.fluid.ParallelExecutor.run ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True))
-paddle.fluid.create_lod_tensor ArgSpec(args=['data', 'recursive_seq_lens', 'place'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.create_random_int_lodtensor ArgSpec(args=['recursive_seq_lens', 'base_shape', 'place', 'low', 'high'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DataFeedDesc.__init__ ArgSpec(args=['self', 'proto_file'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DataFeedDesc.desc ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DataFeedDesc.set_batch_size ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DataFeedDesc.set_dense_slots ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DataFeedDesc.set_use_slots ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.__init__ ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, ''))
-paddle.fluid.AsyncExecutor.config_distributed_nodes ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.download_data ArgSpec(args=['self', 'afs_path', 'local_path', 'fs_default_name', 'ugi', 'file_cnt', 'hadoop_home', 'process_num'], varargs=None, keywords=None, defaults=('$HADOOP_HOME', 12))
-paddle.fluid.AsyncExecutor.get_instance ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.init_model ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.init_server ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.init_worker ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False))
-paddle.fluid.AsyncExecutor.save_model ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.CompiledProgram.__init__ ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.CompiledProgram.with_data_parallel ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.CompiledProgram.with_inference_optimize ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.ParallelExecutor.__init__ (ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.ParallelExecutor.run (ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '2cb4bd74481861345c70228a0f57620c'))
+paddle.fluid.create_lod_tensor (ArgSpec(args=['data', 'recursive_seq_lens', 'place'], varargs=None, keywords=None, defaults=None), ('document', '8e7bb21e83ff4604f5b379672e285b94'))
+paddle.fluid.create_random_int_lodtensor (ArgSpec(args=['recursive_seq_lens', 'base_shape', 'place', 'low', 'high'], varargs=None, keywords=None, defaults=None), ('document', '368f638b99f1dfe59e9b02aa6f077752'))
+paddle.fluid.DataFeedDesc.__init__ (ArgSpec(args=['self', 'proto_file'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.DataFeedDesc.desc (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '4294493e31c4bc9fc4bd48753044235f'))
+paddle.fluid.DataFeedDesc.set_batch_size (ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '8d9f44601e0a99dd431f14fd9250cd21'))
+paddle.fluid.DataFeedDesc.set_dense_slots (ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None), ('document', 'eb894b464bbcd1b4bc8038398954f766'))
+paddle.fluid.DataFeedDesc.set_use_slots (ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None), ('document', '415c56600ce4e198c071cad01409a690'))
+paddle.fluid.AsyncExecutor.__init__ (ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, '')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.AsyncExecutor.config_distributed_nodes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '4810dbe1870452f16b3c60b6c5fd1459'))
+paddle.fluid.AsyncExecutor.download_data (ArgSpec(args=['self', 'afs_path', 'local_path', 'fs_default_name', 'ugi', 'file_cnt', 'hadoop_home', 'process_num'], varargs=None, keywords=None, defaults=('$HADOOP_HOME', 12)), ('document', '799a2066cc26819f1ed31f47c15ad083'))
+paddle.fluid.AsyncExecutor.get_instance (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f8688f76a2db1243c7097a60c507b182'))
+paddle.fluid.AsyncExecutor.init_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '504f39be2007404a17e5cabea1256c7d'))
+paddle.fluid.AsyncExecutor.init_server (ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None), ('document', 'c403ab46c5d3ef25c0f7e94ae75dcb68'))
+paddle.fluid.AsyncExecutor.init_worker (ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None), ('document', 'dcf08f4bf2f3282acf11391f5d39c536'))
+paddle.fluid.AsyncExecutor.run (ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False)), ('document', '848fc53484e8326f6325feea87fe955c'))
+paddle.fluid.AsyncExecutor.save_model (ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None), ('document', 'c8ac0dfcb3b187aba25d03af7fea56b2'))
+paddle.fluid.AsyncExecutor.stop (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5f23d043607bb5d55e466ec3f578e093'))
+paddle.fluid.CompiledProgram.__init__ (ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.CompiledProgram.with_data_parallel (ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'e1af7fd53cf868554f312779fc803864'))
+paddle.fluid.CompiledProgram.with_inference_optimize (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None), ('document', '9e5b009d850191a010e859189c127fd8'))
 paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.ExecutionStrategy) -> None
 paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.GradientScaleStrategy, arg0: int) -> None
 paddle.fluid.BuildStrategy.ReduceStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.ReduceStrategy, arg0: int) -> None
 paddle.fluid.BuildStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy) -> None
-paddle.fluid.io.save_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.io.save_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.io.load_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
-paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
-paddle.fluid.initializer.UniformInitializer.__init__ ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0))
-paddle.fluid.initializer.NormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0))
-paddle.fluid.initializer.TruncatedNormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0))
-paddle.fluid.initializer.XavierInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'fan_out', 'seed'], varargs=None, keywords=None, defaults=(True, None, None, 0))
-paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0))
-paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
-paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
-paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
-paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None))
-paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False))
-paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False))
-paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.crf_decoding ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
-paddle.fluid.layers.bpr_loss ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None))
-paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
-paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
-paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None))
-paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None))
-paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
-paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
-paddle.fluid.layers.adaptive_pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
-paddle.fluid.layers.adaptive_pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
-paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False))
-paddle.fluid.layers.data_norm ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False))
-paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
-paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
-paddle.fluid.layers.sequence_expand ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None))
-paddle.fluid.layers.sequence_expand_as ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.sequence_unpad ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.lstm_unit ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None))
-paddle.fluid.layers.reduce_sum ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
-paddle.fluid.layers.reduce_mean ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
-paddle.fluid.layers.reduce_max ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
-paddle.fluid.layers.reduce_min ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
-paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
-paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer'))
-paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None))
-paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.edit_distance ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None))
-paddle.fluid.layers.l2_normalize ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None))
-paddle.fluid.layers.matmul ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None))
-paddle.fluid.layers.topk ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.warpctc ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False))
-paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
-paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
-paddle.fluid.layers.sampled_softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0))
-paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False))
-paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False))
-paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
-paddle.fluid.layers.group_norm ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None))
-paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, True, False))
-paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
-paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None))
-paddle.fluid.layers.squeeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.unsqueeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.lod_reset ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.lrn ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None))
-paddle.fluid.layers.pad ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None))
-paddle.fluid.layers.pad_constant_like ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None))
-paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None))
-paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0))
-paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None))
-paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,))
-paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1))
-paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
-paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1))
-paddle.fluid.layers.resize_nearest ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True))
-paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.sequence_scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.random_crop ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.mean_iou ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.selu ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.margin_rank_loss ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None))
-paddle.fluid.layers.elu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
-paddle.fluid.layers.relu6 ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None))
-paddle.fluid.layers.pow ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
-paddle.fluid.layers.stanh ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None))
-paddle.fluid.layers.hard_sigmoid ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None))
-paddle.fluid.layers.swish ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
-paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.brelu ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None))
-paddle.fluid.layers.leaky_relu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None))
-paddle.fluid.layers.soft_relu ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None))
-paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
-paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
-paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,))
-paddle.fluid.layers.pad2d ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None))
-paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None))
-paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
-paddle.fluid.layers.expand ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None))
-paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
-paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
-paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
-paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
-paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
-paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
-paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
-paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
-paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
-paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
-paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
-paddle.fluid.layers.sum ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.logical_or ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
-paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False))
-paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
-paddle.fluid.layers.similarity_focus ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
-paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
-paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
-paddle.fluid.layers.shuffle_channel ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.py_func ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.teacher_student_sigmoid_loss ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0))
-paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.tree_conv ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None))
-paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
-paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
-paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.shuffle ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.batch ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.double_buffer ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,))
-paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True))
-paddle.fluid.layers.create_py_reader_by_data ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True))
-paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.Preprocessor.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.Preprocessor.outputs ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
-paddle.fluid.layers.load ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.create_tensor ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False))
-paddle.fluid.layers.create_parameter ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None))
-paddle.fluid.layers.create_global_var ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None))
-paddle.fluid.layers.cast ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.tensor_array_to_tensor ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
-paddle.fluid.layers.concat ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None))
-paddle.fluid.layers.sums ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.assign ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.fill_constant_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0))
-paddle.fluid.layers.fill_constant ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None))
-paddle.fluid.layers.argmin ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,))
-paddle.fluid.layers.argmax ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,))
-paddle.fluid.layers.argsort ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(-1, None))
-paddle.fluid.layers.ones ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.has_inf ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.has_nan ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.isfinite ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None))
-paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.Switch.case ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.Switch.default ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.increment ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True))
-paddle.fluid.layers.array_write ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.create_array ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.less_than ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords='ignored', defaults=(None, None))
-paddle.fluid.layers.equal ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.array_read ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.array_length ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.IfElse.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.IfElse.false_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.IfElse.input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.IfElse.output ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None)
-paddle.fluid.layers.IfElse.true_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.DynamicRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.DynamicRNN.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.DynamicRNN.memory ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32'))
-paddle.fluid.layers.DynamicRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
-paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.DynamicRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.DynamicRNN.update_memory ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.StaticRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.StaticRNN.memory ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1))
-paddle.fluid.layers.StaticRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
-paddle.fluid.layers.StaticRNN.step ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.StaticRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.StaticRNN.step_output ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.StaticRNN.update_memory ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.reorder_lod_tensor_by_rank ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.Print ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both'))
-paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords='ignored', defaults=(None,))
-paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.exp ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.tanh ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.tanh_shrink ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.softshrink ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.sqrt ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.abs ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.ceil ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.floor ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.cos ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.sin ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.round ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.reciprocal ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.square ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.softplus ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.softsign ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0))
-paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.prior_box ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False))
-paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None))
-paddle.fluid.layers.multi_box_head ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False))
-paddle.fluid.layers.bipartite_match ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.layers.detection_output ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0))
-paddle.fluid.layers.ssd_loss ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None))
-paddle.fluid.layers.detection_map ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral'))
-paddle.fluid.layers.rpn_target_assign ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True))
-paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None))
-paddle.fluid.layers.roi_perspective_transform ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,))
-paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True))
-paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
-paddle.fluid.layers.generate_mask_labels ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0))
-paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.box_clip ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None))
-paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
-paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
-paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.layers.polynomial_decay ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False))
-paddle.fluid.layers.piecewise_decay ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.noam_decay ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.append_LARS ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.cosine_decay ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.InitState.__init__ ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32'))
-paddle.fluid.contrib.StateCell.__init__ ArgSpec(args=['self', 'inputs', 'states', 'out_state', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.StateCell.compute_state ArgSpec(args=['self', 'inputs'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.StateCell.get_input ArgSpec(args=['self', 'input_name'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.StateCell.get_state ArgSpec(args=['self', 'state_name'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.StateCell.out_state ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.StateCell.set_state ArgSpec(args=['self', 'state_name', 'state_value'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.StateCell.state_updater ArgSpec(args=['self', 'updater'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.StateCell.update_states ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.TrainingDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.TrainingDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.TrainingDecoder.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
-paddle.fluid.contrib.TrainingDecoder.static_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.TrainingDecoder.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.BeamSearchDecoder.__init__ ArgSpec(args=['self', 'state_cell', 'init_ids', 'init_scores', 'target_dict_dim', 'word_dim', 'input_var_dict', 'topk_size', 'sparse_emb', 'max_len', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=({}, 50, True, 100, 1, 1, None))
-paddle.fluid.contrib.BeamSearchDecoder.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
-paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.op_freq_statistic ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000))
-paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
-paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.contrib.Calibrator.__init__ ArgSpec(args=['self'], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.contrib.Calibrator.sample_data ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.Calibrator.save_int8_model ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.reader.ctr_reader.ctr_reader ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.build_compressor ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
-paddle.fluid.contrib.CompressPass.__init__ ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
-paddle.fluid.contrib.CompressPass.add_strategy ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.CompressPass.apply ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.ImitationGraph.__init__ ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.ImitationGraph.all_parameters ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.SensitivePruneStrategy.__init__ ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None))
-paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.MagnitudePruner.__init__ ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.MagnitudePruner.prune ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.RatioPruner.__init__ ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.RatioPruner.prune ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.HDFSClient.__init__ ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.HDFSClient.delete ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.HDFSClient.download ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False))
-paddle.fluid.contrib.HDFSClient.is_dir ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.HDFSClient.is_exist ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.contrib.HDFSClient.ls ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.HDFSClient.lsr ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True))
-paddle.fluid.contrib.HDFSClient.make_local_dirs ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.HDFSClient.makedirs ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.contrib.HDFSClient.rename ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.contrib.HDFSClient.upload ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5))
-paddle.fluid.contrib.multi_download ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,))
-paddle.fluid.contrib.multi_upload ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True))
-paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,))
-paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174'))
-paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False))
-paddle.fluid.transpiler.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.transpiler.HashName.__init__ ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.HashName.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.HashName.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.RoundRobin.__init__ ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.RoundRobin.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.RoundRobin.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.io.save_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b55d6193a1d4198d45b013fc5779e1f2'))
+paddle.fluid.io.save_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '3a7a99abac3e1bf898871fe609354218'))
+paddle.fluid.io.save_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '9141bb5f32caf7975eb3fd88c8a1b2da'))
+paddle.fluid.io.load_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '0a5308f496632ab1ec3ba1f1377e6f95'))
+paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '41779819cef32f2246e83aebc5a002e2'))
+paddle.fluid.io.load_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '28df5bfe26ca7a077f91156abb0fe6d2'))
+paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True)), ('document', '582d87b8df75a5a639a107db8ff86f9c'))
+paddle.fluid.io.load_inference_model (ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '7a5255386075dac3c75b7058254fcdcb'))
+paddle.fluid.initializer.ConstantInitializer.__init__ (ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.initializer.UniformInitializer.__init__ (ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.initializer.NormalInitializer.__init__ (ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.initializer.TruncatedNormalInitializer.__init__ (ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.initializer.XavierInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'fan_out', 'seed'], varargs=None, keywords=None, defaults=(True, None, None, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.initializer.BilinearInitializer.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd389912dc079cbef432335a00017cec0'))
+paddle.fluid.initializer.MSRAInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0)), ('document', '53c757bed9345f2ad3361902531e7cf5'))
+paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6d0f3e22c90d9d500d36ff57daf056ee'))
+paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'a6d7011ca3d8c0d454dac3a56eae0c29'))
+paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '1929058262994f212620599c63aea6bd'))
+paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '89c2c55a0b0656b106064048e068e77a'))
+paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', 'dfbb624f85015df29e994ca6999e8ff6'))
+paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', 'b4b608b986eb9617aa0525e1be21d32d'))
+paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '4ec4845fd7d991bcac822f8b0dfc101f'))
+paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', 'e0e2439f7af069b57badca18a6ba60b8'))
+paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '7c49ef4bbf0adfd4b9a1d98e2e5f3fea'))
+paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,)), ('document', '7642373ab65d3fc3b96d16d10fef1538'))
+paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', 'd740824aa7316b807c4b4a3c6c8c0bbe'))
+paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', '025b364dafb4b7975c801eb33e7831a1'))
+paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '30add751a0f99347a6257634c03ff254'))
+paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '44b6eef4a0f2bc15f7d9745782406736'))
+paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ee152a7ba3036e7b9ede9184545179b4'))
+paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', 'b6543768e1afaa2ecb869709d6e9c7e2'))
+paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '8ca6121acd6d23cd8806a93f493c2e17'))
+paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '37042620f9bd3a2da6e5d3138b2f724b'))
+paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'a194fb80614023f543df3949fbd0d0b8'))
+paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '19ef6f9cdd27feac8a1ae060f19c10b4'))
+paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'f19dd380864e61134ce3814e4be0de4b'))
+paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', 'bbd84e855e660cd1084bb71a2fd0cdaa'))
+paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True)), ('document', '043de7333b79ee0ac55053c14ed81625'))
+paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '859b887174d06f361658f69cb7c06d95'))
+paddle.fluid.layers.adaptive_pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '120f4323a3d7ed9c0916f15a59f0e497'))
+paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', 'c527b71b8a4c60dca8df8a745c2b598d'))
+paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', 'e45e09e65a2658e07cad987222f0d9ab'))
+paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b0b8d53821716cd50c42e09b593f3feb'))
+paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', '03993955ab1e6d3044c44e6f17fc85e9'))
+paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', 'ec113c6a3686ac94f8fccd1a7953d445'))
+paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '79c375214fa427faac504043d162dae9'))
+paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9d2611f84ab364c5da545e6a82f1770a'))
+paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6a1adf3067b20f6e4bcb354d71c19184'))
+paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd12803c903c99aa36ec03aaac5f0cc5b'))
+paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', '027723966f3ef0d7bc598f22287a96cc'))
+paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'b69998ce3ff4980fb21da0df05565f1b'))
+paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd4d80dd98a1a5839f41eeb3a0f85f370'))
+paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '66a622db727551761ce4eb73eaa7f6a4'))
+paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd50ac552b5d131468ed466d08bb2d38c'))
+paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'fcd8301a0ce15f219c7a4bcd0c1e8eca'))
+paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '2b290d3d77882bfe9bb8d331cac8cdd3'))
+paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'c16a892f44f7fe71bfa5afc32d3f34ce'))
+paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fdcea0e8b5bc7d8d4b1b072c521014e6'))
+paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', 'dc7042734c6d8b8ce97321f017f01d6f'))
+paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '652625345c2acb900029c78cc75f8aa6'))
+paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbf2adbd79683dc93db03454dfa18c2'))
+paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', '97f0262f97602644c83142789d784571'))
+paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', '6e428384ce6a77207fa2c70d9f011990'))
+paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', 'b4cbe1ac451005df6dad12e9ffdccca9'))
+paddle.fluid.layers.topk (ArgSpec(args=['input', 'k', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd3570c02f71bcd78e60b3f31dc8f5b32'))
+paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_times', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, False, False)), ('document', 'aaba49c038ba927f0a8e45c0c9a686ab'))
+paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'a10ab9bf88d4a7e328882d411abb6fd1'))
+paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1feac48b843d679db82312dc85885f4'))
+paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '3ce01160ede80b1c26f776f8fef9340f'))
+paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', 'fddad4896dee5193e1cdf70882c2a347'))
+paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', '5db30b8a74e8c93687943a3e8d221da0'))
+paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', '80641ee6810b1cdc3fd6e14fc89ecc9d'))
+paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', 'b350b9a30a18e7efd7e1bb740eef6996'))
+paddle.fluid.layers.row_conv (ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None)), ('document', '17485788fffe4e2d36dc58c2ac8d174e'))
+paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None), ('document', '013795af319e2e86d3506741941078ee'))
+paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None)), ('document', 'de6a906950bae9f3c245cb744d22b94e'))
+paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '419c3a24a83cc89219a029cf4092788b'))
+paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, True, False)), ('document', 'bce1b75e3d95b75cacd1099655cbb3c3'))
+paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c6b175d253c55baf4b9c0eca9b1dda88'))
+paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None), ('document', '6148b6a555cbfb62fdcd030d8982c18c'))
+paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '3f6c828594720c9b2da89c464be94478'))
+paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', '323c019f257e55ddea4a824a362de62f'))
+paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3229d06517f794e86ca3da14c38b1465'))
+paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbd62da391b1df984a1909d069a759b2'))
+paddle.fluid.layers.lod_reset (ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f122194c562bd674f6ecdccf33785f99'))
+paddle.fluid.layers.lrn (ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)), ('document', '0795e9940e42dcd62953514ff7e09f77'))
+paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '2f28153bdd2d5ea6f7bad5867bd03eeb'))
+paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', 'd2e1f45fef51b2c214e3f2aa8976c46c'))
+paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '70c113658102a11cc5d8e3d45145737a'))
+paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', 'c317aa595deb31649083c8faa91cdb97'))
+paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '12c5bbb8b38c42e623fbc47611d766e1'))
+paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '1ba0508d573f65feecf3564dce22aa1d'))
+paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', 'b3ecb819454832885c1f0f3ab9a5b938'))
+paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', '06211aefc50c5a3e940d7204d859cdf7'))
+paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', 'e4fb4ed511b2293b8f04f7e872afbfd7'))
+paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', '735fa9758a6d7ff3b47d7b827f961c1d'))
+paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None), ('document', '98f1c86716b9b7f4dda83f20e2adeee2'))
+paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65f8e9d8ddfd0b412f940579c4faa342'))
+paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '15b522457dfef103f0c20ca9d397678b'))
+paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c9ab9e460ef0a1823249935a30e82c66'))
+paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', '35cbbdfa585d027bb490707c95a176b9'))
+paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '866ffa1cc93f29e23662b526a7596537'))
+paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '9044c7fe667b76cb2d9264f2db11f417'))
+paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '98247c59d1c9b40af6730001b2aea73d'))
+paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '883104791204d3127e24234bb630b2e7'))
+paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c542e39ac6add24a6bef6e79bf5617e2'))
+paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '6d19dcc19917080b7ff3e03bde451bc8'))
+paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '463258ee9f8b60760eb1e26357cc9bfa'))
+paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '6f367339caf6c7124bc262fe1475df70'))
+paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'a5117c1eb84aca2ac0b0abab337a4799'))
+paddle.fluid.layers.stanh (ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None)), ('document', '959936a477efc6c1447a9c8bf8ce94bb'))
+paddle.fluid.layers.hard_sigmoid (ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None)), ('document', 'c82059b6fea1aa730f9aac911807b756'))
+paddle.fluid.layers.swish (ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'ef745e55a48763ee7b46b21a81dc7e84'))
+paddle.fluid.layers.prelu (ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f6acef7ff7d887e49ff499fbb1dad4a9'))
+paddle.fluid.layers.brelu (ArgSpec(args=['x', 't_min', 't_max', 'name'], varargs=None, keywords=None, defaults=(0.0, 24.0, None)), ('document', '3db337c195e156e6ef2b8b4a57113600'))
+paddle.fluid.layers.leaky_relu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(0.02, None)), ('document', 'f878486c82b576938151daad0de995a0'))
+paddle.fluid.layers.soft_relu (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(40.0, None)), ('document', '869adce548c342d6cc1bd88a948d83c9'))
+paddle.fluid.layers.flatten (ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'cb295c13cb957db85cd9609269d7784d'))
+paddle.fluid.layers.sequence_mask (ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None)), ('document', 'f0dd6eddd3bff015a3c05269d82fcbd8'))
+paddle.fluid.layers.stack (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '367cfbb642839beacb5d117e2d2b4041'))
+paddle.fluid.layers.pad2d (ArgSpec(args=['input', 'paddings', 'mode', 'pad_value', 'data_format', 'name'], varargs=None, keywords=None, defaults=([0, 0, 0, 0], 'constant', 0.0, 'NCHW', None)), ('document', '7f4d46320cc077ca2e8db600c35f4030'))
+paddle.fluid.layers.unstack (ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None)), ('document', '98eb9d633116efcfc6f90c114bd44fd6'))
+paddle.fluid.layers.sequence_enumerate (ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'f6028537085dc296103bbbd85fa7763d'))
+paddle.fluid.layers.expand (ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '117d3607d1ffa0571835bbaebc7857ff'))
+paddle.fluid.layers.sequence_concat (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3a1d155dd1bf6e72a0a3e3e1519591d1'))
+paddle.fluid.layers.scale (ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None)), ('document', '30190413b2fa442e7466d6cf2ce5ea07'))
+paddle.fluid.layers.elementwise_add (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '6bfbe72cbadc95ac7ab88c05ed5bf9f0'))
+paddle.fluid.layers.elementwise_div (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'cc6e6cc1cb942a152dde3ef08d5f165c'))
+paddle.fluid.layers.elementwise_sub (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'a12abdab09c3e57af5a6e1e9f138684a'))
+paddle.fluid.layers.elementwise_mul (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '422c77dbfcff355a57b5fdd4ec876daa'))
+paddle.fluid.layers.elementwise_max (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', 'f0bb0b2c454541cfafa761021a5cc776'))
+paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '8a9cdefefbccbf9f6b0991c0946a21e9'))
+paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '1aea4e197c552a284f83888a3c67a32e'))
+paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', '129e0a3257f1d532a948eedf9d5bf671'))
+paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '389dafe36e099841b6a7fb18d11f1b4c'))
+paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '840fdac643d1341c1cae218d4511dbb9'))
+paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '840026b4766613c5705e06563cd103b6'))
+paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'a418e3ccb5e2ac21bd60f5cc221d5860'))
+paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '01dbb91e7c74cb11336cd531013de51a'))
+paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '17db0f814eb7bb5a3fac1ca6e60e16d8'))
+paddle.fluid.layers.logical_and (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cdcf20c494c92060d10feb9374532f42'))
+paddle.fluid.layers.logical_or (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0eae3f726a4afe590757552fa3ced012'))
+paddle.fluid.layers.logical_xor (ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b0daaa3fa4a0aa62f9b58c43d959eb25'))
+paddle.fluid.layers.logical_not (ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cd1c8cf31e040427d4e05711044caeb6'))
+paddle.fluid.layers.clip (ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b020b7aab59719be98a4ae229a76deba'))
+paddle.fluid.layers.clip_by_norm (ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1ea0bc5a926f427458c4254ca022749'))
+paddle.fluid.layers.mean (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd638d915195ce86a8d7963b81110d4c8'))
+paddle.fluid.layers.mul (ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None)), ('document', 'ccd37fa6b53f074adbfb732d738c4c2d'))
+paddle.fluid.layers.sigmoid_cross_entropy_with_logits (ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False)), ('document', '180c284317ea45ef89a460d8d79c0b72'))
+paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '891870d069a6aea746d34cc53b61690c'))
+paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5f207ae10589ebe38a63575ef6ff8e1e'))
+paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '51def402b8910e163cbace9d0c0526ed'))
+paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '77a6d80aa5551ca70324fc975c44507f'))
+paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None)), ('document', '2f46f1ff39a13ab00857e7b9f44b2fa7'))
+paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '70e3b5182a18b40b47ecabd7c8490a35'))
+paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '9bb77f8dc002dd2ce75d4769eaaf5007'))
+paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd256cba1c41a5ed92ce3f31e24a2ca6d'))
+paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', '4b5a2341023afe63157a066c14254f98'))
+paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '4b9c2e8af5817937d831820874b5aa77'))
+paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'aa7540a0fa73ff69a02e11b4091aab75'))
+paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'dc63315b84f591ac79ecca0c3632027a'))
+paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7ffc849e71f31dfe29030ff94e662de6'))
+paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', 'd5e6c494ac35100e2ed4d4bd9a1ed932'))
+paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2fa6782d43d02ae64482d21235a82949'))
+paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '8404e472ac12b4a30a505d3d3a3e5fdb'))
+paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1546136806fef5c08f6918544bd9151d'))
+paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '2f6ff96864054a31aa4bb659c6722c99'))
+paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '431a4301c35032166ec029f7432c80a7'))
+paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '34ea12ac9f10a65dccbc50100d12e607'))
+paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '33bbd42027d872b3818b3d64ec52e139'))
+paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'b1ae2e1cc0750e58726374061ea90ecc'))
+paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'b0a1c2fc51c27a106da28f3308c41f5e'))
+paddle.fluid.layers.shuffle (ArgSpec(args=['reader', 'buffer_size'], varargs=None, keywords=None, defaults=None), ('document', 'f967a73426db26f970bc70bfb03cffca'))
+paddle.fluid.layers.batch (ArgSpec(args=['reader', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', 'f563d376d35e1a4c4db100fd11b381a0'))
+paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '07e5b796674796eb1ef3fee9c10d24e3'))
+paddle.fluid.layers.random_data_generator (ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,)), ('document', '9b7f0f86ec24bbc97643cadcb6499cff'))
+paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', '13dabc57863f62ab3141586784ee356b'))
+paddle.fluid.layers.create_py_reader_by_data (ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True)), ('document', '350f74d93fab9adb2ac4950f1c26416b'))
+paddle.fluid.layers.Preprocessor.__init__ (ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.Preprocessor.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.Preprocessor.inputs (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.Preprocessor.outputs (ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.load (ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,)), ('document', '9d1a4bc97bbce9fa1d4f7a4200a771ff'))
+paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False)), ('document', 'c0c3d0194f83fff8ea99ce0820657dae'))
+paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'd62b866c899bc1fedb5385f95b88e1f8'))
+paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', 'ab914fac893607e29ac6e52bbdbea1a4'))
+paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '60cb8f843d625abf33f8bf12455b8f99'))
+paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'b12717d3d4567e6119589f7f655b0cbb'))
+paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'b19b79be4f05e85d1d6cec642c9fb535'))
+paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '42912092418620b4be07f36af31e7816'))
+paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b690184f3537df5501e4d9d8f31152a5'))
+paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', 'd4059a2f5763036b07018d76429f9acb'))
+paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '1d8b14729639fa38509c79b9784740fa'))
+paddle.fluid.layers.argmin (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '2778a1d34be49263a51211885599ea37'))
+paddle.fluid.layers.argmax (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '04114996cfb98994ba222804a1a6109f'))
+paddle.fluid.layers.argsort (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '68ec45c6fb6b93e47de9c9a0945fb98e'))
+paddle.fluid.layers.ones (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', 'b402489c62e668df42e7daceb63c142b'))
+paddle.fluid.layers.zeros (ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,)), ('document', 'c155e2efc56ffa5ed4658cca0272e491'))
+paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None), ('document', '8ee7cb6ca639e7460e825f953b65d94d'))
+paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '8f8c0306117ea441f20dcbbdba1f0ecc'))
+paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '2e53e83127dbfd86e7098bdfe9a549e8'))
+paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292'))
+paddle.fluid.layers.While.__init__ (ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.While.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.Switch.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.Switch.case (ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None), ('document', 'f7c7160014c1b46cfeda9dd5808d1789'))
+paddle.fluid.layers.Switch.default (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '50853ae884df03d9c36703bb46d9ef07'))
+paddle.fluid.layers.increment (ArgSpec(args=['x', 'value', 'in_place'], varargs=None, keywords=None, defaults=(1.0, True)), ('document', '73bb96ec4783ec1a11e760e8851b0e77'))
+paddle.fluid.layers.array_write (ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)), ('document', '40b6d15f4c86b2b09df340d7778ad713'))
+paddle.fluid.layers.create_array (ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None), ('document', '2d4f20087080ba5105b55205ad5c5b6a'))
+paddle.fluid.layers.less_than (ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords='ignored', defaults=(None, None)), ('document', '067bbc799c66289ca8b8924c26b6673f'))
+paddle.fluid.layers.equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '80c29b1dc64718f0116de90d1ac88a77'))
+paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', '0275133f1dde2aed528b4d3230edf823'))
+paddle.fluid.layers.array_length (ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None), ('document', 'ffb8b9578ec66db565b223d313aa82a2'))
+paddle.fluid.layers.IfElse.__init__ (ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.IfElse.false_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.IfElse.input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.IfElse.output (ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.IfElse.true_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.DynamicRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.DynamicRNN.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7'))
+paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', 'b9174d4e91505b0c8ecc193eb51e248d'))
+paddle.fluid.layers.DynamicRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'b439a176a3328de8a75bdc5c08eece4a'))
+paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', 'f29ad2478b6b2ad4f413d2936a331ea0'))
+paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '169d694d2224f62b4f3afdc3dbc19e95'))
+paddle.fluid.layers.DynamicRNN.update_memory (ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None), ('document', '5d83987da13b98363d6a807a52d8024f'))
+paddle.fluid.layers.StaticRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', 'c24e368e23afac1ed91a78a639d7a9c7'))
+paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '3545f529ef04e8f6ecb76b47fa3df01a'))
+paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', '5fef91b0e21c93610785f2b1f7161732'))
+paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords='ignored', defaults=(None,)), ('document', 'bbe578dbb49ad13e15b014e98c22b519'))
+paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '29a25ba78de79152076cacfc5443137d'))
+paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '81ccb7acafd06c7728e11581f5d342e3'))
+paddle.fluid.layers.exp (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e6b3e769413d96aab4176f96db25984b'))
+paddle.fluid.layers.tanh (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e9d586a0b5bd05f67ee78048f9d503b6'))
+paddle.fluid.layers.tanh_shrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1e521554b9fdda9061ec6d306f0709b7'))
+paddle.fluid.layers.softshrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9eef31597bbafa2bd49691e072296e13'))
+paddle.fluid.layers.sqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '072a8541e0f632366bba10f67cb0db27'))
+paddle.fluid.layers.abs (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '64650ac42cf82e9920cb0b172b1d29fd'))
+paddle.fluid.layers.ceil (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c75d67dc5fe28f68e4cfffead4f698ad'))
+paddle.fluid.layers.floor (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '647b16c5da5ef909649ae02abb434973'))
+paddle.fluid.layers.cos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '485f2686bcc2fe37a4bd893769c8a3e2'))
+paddle.fluid.layers.sin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '01f1766aa76eff1df30147505b59f7c4'))
+paddle.fluid.layers.round (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b47f5da13913d3e56bdb1e612a73f3f2'))
+paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'cc6ac2f14f03c52aaa83a59bf83b8d26'))
+paddle.fluid.layers.square (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '48dfb45d773dbc30126c3a7f777de5ee'))
+paddle.fluid.layers.softplus (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '459c5781e9d1dd88283b7c5769d7872a'))
+paddle.fluid.layers.softsign (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '80846bcd4bd457207457a6d5411f4148'))
+paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '308b619af849caa82bbc31e897f5e641'))
+paddle.fluid.layers.hard_shrink (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c142f5884f3255e0d6075c286bbd531e'))
+paddle.fluid.layers.cumsum (ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '944d7c03057f5fc88bc78acd4d82f926'))
+paddle.fluid.layers.thresholded_relu (ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '90566ea449ea4c681435546e2f70610a'))
+paddle.fluid.layers.prior_box (ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False)), ('document', '14cac0ee643fa6e026ad82aeeee75bd8'))
+paddle.fluid.layers.density_prior_box (ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None)), ('document', 'a0d762bb08de9ce93bc780aa57cd5cd9'))
+paddle.fluid.layers.multi_box_head (ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False)), ('document', 'a6ab47a2fe681e52fabb7057ddf0efdd'))
+paddle.fluid.layers.bipartite_match (ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '3ddb9b966f193900193a95a3df77c3c1'))
+paddle.fluid.layers.target_assign (ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c0b334f917828f95056f6ebe10907b1c'))
+paddle.fluid.layers.detection_output (ArgSpec(args=['loc', 'scores', 'prior_box', 'prior_box_var', 'background_label', 'nms_threshold', 'nms_top_k', 'keep_top_k', 'score_threshold', 'nms_eta'], varargs=None, keywords=None, defaults=(0, 0.3, 400, 200, 0.01, 1.0)), ('document', 'c33093a82a46e3091e789e5572588db1'))
+paddle.fluid.layers.ssd_loss (ArgSpec(args=['location', 'confidence', 'gt_box', 'gt_label', 'prior_box', 'prior_box_var', 'background_label', 'overlap_threshold', 'neg_pos_ratio', 'neg_overlap', 'loc_loss_weight', 'conf_loss_weight', 'match_type', 'mining_type', 'normalize', 'sample_size'], varargs=None, keywords=None, defaults=(None, 0, 0.5, 3.0, 0.5, 1.0, 1.0, 'per_prediction', 'max_negative', True, None)), ('document', '6d5028fd09d01ab82d296adc0ea95aee'))
+paddle.fluid.layers.detection_map (ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral')), ('document', '1467d91b50c22cd52103b4aa1ee9d0a1'))
+paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', '1dddef3eb4b3cbd4df8e03ac480dbf97'))
+paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', '23337cc57bbf5be73884b6bd0f849603'))
+paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', '5761f9ed83654314416e24372b33bb84'))
+paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)), ('document', '87863717edeb7fe87a1268976cbc015d'))
+paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', '57ab49f3f324f310b7eed322e7c1057a'))
+paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'f73706a65468e9ca3e0bee4a31521b0a'))
+paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '587845f60c5d97ffdf2dfd21da52eca1'))
+paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '032d0f4b7d8f6235ee5d91e473344f0e'))
+paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b'))
+paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '991e934c3e09abf0edec7c9c978b4691'))
+paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e'))
+paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0'))
+paddle.fluid.layers.accuracy (ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None)), ('document', '9808534c12c5e739a10f73ebb0b4eafd'))
+paddle.fluid.layers.auc (ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1)), ('document', 'e0e95334fce92d16c2d9db6e7caffc47'))
+paddle.fluid.layers.exponential_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '98a5050bee8522fcea81aa795adaba51'))
+paddle.fluid.layers.natural_exp_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', '676a7bc2a218691db50bca233903d21e'))
+paddle.fluid.layers.inverse_time_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,)), ('document', 'd07e767d59c4a5e6c930f3e6756d3f82'))
+paddle.fluid.layers.polynomial_decay (ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False)), ('document', '882634f420f626642f0874481263da40'))
+paddle.fluid.layers.piecewise_decay (ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None), ('document', 'c717d9d1d78a53c809d01b8bc56f3cae'))
+paddle.fluid.layers.noam_decay (ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None), ('document', 'd9a95746353fd574be36dc28d8726c28'))
+paddle.fluid.layers.append_LARS (ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None), ('document', 'd24fa1e7d62ac8a534fc6a86002f84f8'))
+paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', '9588c64c26ffaef3c466e404a6af9d9b'))
+paddle.fluid.contrib.InitState.__init__ (ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.StateCell.__init__ (ArgSpec(args=['self', 'inputs', 'states', 'out_state', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.StateCell.compute_state (ArgSpec(args=['self', 'inputs'], varargs=None, keywords=None, defaults=None), ('document', '92973b3f222081a1d17069c683cf4a99'))
+paddle.fluid.contrib.StateCell.get_input (ArgSpec(args=['self', 'input_name'], varargs=None, keywords=None, defaults=None), ('document', '6f24a007cfa184e32f01a960703bfd70'))
+paddle.fluid.contrib.StateCell.get_state (ArgSpec(args=['self', 'state_name'], varargs=None, keywords=None, defaults=None), ('document', '630a4945cfe659ea4f307598fbbce5d2'))
+paddle.fluid.contrib.StateCell.out_state (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '7ad681dff0393ddf13a724194e720f28'))
+paddle.fluid.contrib.StateCell.set_state (ArgSpec(args=['self', 'state_name', 'state_value'], varargs=None, keywords=None, defaults=None), ('document', 'd4e0e08cd5d9d9a571cbc52d114f5ae9'))
+paddle.fluid.contrib.StateCell.state_updater (ArgSpec(args=['self', 'updater'], varargs=None, keywords=None, defaults=None), ('document', 'd5afe1b7665d94fb023b15cf913ca510'))
+paddle.fluid.contrib.StateCell.update_states (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'fe0b0f1338723516a35a30247899c81b'))
+paddle.fluid.contrib.TrainingDecoder.__init__ (ArgSpec(args=['self', 'state_cell', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.TrainingDecoder.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '98d88fa1c989748410a12517c6a585bf'))
+paddle.fluid.contrib.TrainingDecoder.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'f0a457dee586559036202087ce2eff69'))
+paddle.fluid.contrib.TrainingDecoder.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', 'a024c72664fe815068423ba630b7658a'))
+paddle.fluid.contrib.TrainingDecoder.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '4659db7a888a2495e71c1838a0483909'))
+paddle.fluid.contrib.BeamSearchDecoder.__init__ (ArgSpec(args=['self', 'state_cell', 'init_ids', 'init_scores', 'target_dict_dim', 'word_dim', 'input_var_dict', 'topk_size', 'sparse_emb', 'max_len', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=({}, 50, True, 100, 1, 1, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.BeamSearchDecoder.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '98d88fa1c989748410a12517c6a585bf'))
+paddle.fluid.contrib.BeamSearchDecoder.decode (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '1e47c60f080c1343ebb6ceaef89656b2'))
+paddle.fluid.contrib.BeamSearchDecoder.early_stop (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3a84a7454ed6707f79b9e954d92a7575'))
+paddle.fluid.contrib.BeamSearchDecoder.read_array (ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False)), ('document', 'aa89eb8fd5e4cabaf5cc1bcae14665a4'))
+paddle.fluid.contrib.BeamSearchDecoder.update_array (ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None), ('document', '5754e9b3212b7c09497151516a0de5a7'))
+paddle.fluid.contrib.memory_usage (ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '8fcb2f93bb743693baa8d4860a5ccc47'))
+paddle.fluid.contrib.op_freq_statistic (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '4d43687113c4bf5b29d15aee2f4e4afa'))
+paddle.fluid.contrib.QuantizeTranspiler.__init__ (ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000)), ('document', '14b39f1fcd5667ff556b1aad94357d1d'))
+paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 (ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.QuantizeTranspiler.freeze_program (ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None)), ('document', '909675a1ab055c69b436a7893fcae4fd'))
+paddle.fluid.contrib.QuantizeTranspiler.training_transpile (ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6dd9909f10b283ba2892a99058a72884'))
+paddle.fluid.contrib.Calibrator.__init__ (ArgSpec(args=['self'], varargs='args', keywords='kwargs', defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.Calibrator.sample_data (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3b8c85ca1e2cf753cc8c90a6c6992958'))
+paddle.fluid.contrib.Calibrator.save_int8_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.reader.ctr_reader.ctr_reader (ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2ebf3de2a6ef1af2c3b88d2db7591ab'))
+paddle.fluid.contrib.build_compressor (ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.CompressPass.__init__ (ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.CompressPass.add_strategy (ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None), ('document', '3bf6010b6f47d3c86df0ec8957be95e0'))
+paddle.fluid.contrib.CompressPass.apply (ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None), ('document', 'a92bf85d4b59bd4f2ac1706d7c4899a6'))
+paddle.fluid.contrib.ImitationGraph.__init__ (ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.ImitationGraph.all_parameters (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.SensitivePruneStrategy.__init__ (ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.MagnitudePruner.__init__ (ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.MagnitudePruner.prune (ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.RatioPruner.__init__ (ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e7a81a325b296a9ca502ee5adb4fc85d'))
+paddle.fluid.contrib.RatioPruner.prune (ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,)), ('document', '358cbf2978c91028fb96a195a9884645'))
+paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '11fbf7e8dd2289805de291b453a33ee7'))
+paddle.fluid.contrib.load_persistables_for_inference (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None), ('document', '5b5577bb3d24070da819674255d16196'))
+paddle.fluid.contrib.convert_dist_to_sparse_program (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '4efbd93876832d4d35497cdbc7a1e6d8'))
+paddle.fluid.contrib.HDFSClient.__init__ (ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.contrib.HDFSClient.delete (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None), ('document', 'c3721aa2d4d9ef5a857dd47b2681c03e'))
+paddle.fluid.contrib.HDFSClient.download (ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False)), ('document', 'ca55bde92184d3fd0f9f5c963b25e634'))
+paddle.fluid.contrib.HDFSClient.is_dir (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,)), ('document', '45bde1bae02605a205c8245b58b9156d'))
+paddle.fluid.contrib.HDFSClient.is_exist (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,)), ('document', 'be9c94bccff7ba0c1d95883ac62b5864'))
+paddle.fluid.contrib.HDFSClient.ls (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None), ('document', '808acac504870c7e46594b95674f8a86'))
+paddle.fluid.contrib.HDFSClient.lsr (ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True)), ('document', 'fae835aa3354eb6a0434c0f9ba3c2747'))
+paddle.fluid.contrib.HDFSClient.make_local_dirs (ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None), ('document', 'e76b89c8e7f019b5da576c0026fcf689'))
+paddle.fluid.contrib.HDFSClient.makedirs (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None), ('document', '44d9972aae390aedf40aaea731a37e4b'))
+paddle.fluid.contrib.HDFSClient.rename (ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,)), ('document', '0eb133644d9a9f4da45bb39261ff0955'))
+paddle.fluid.contrib.HDFSClient.upload (ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5)), ('document', '7d053b4bfd6dcfdd2c9dda0e0dbd9665'))
+paddle.fluid.contrib.multi_download (ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,)), ('document', '100927be598ed8f9eaa1f3ef1b23568a'))
+paddle.fluid.contrib.multi_upload (ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True)), ('document', '183f34c83d30dbe16e09e8716c41958a'))
+paddle.fluid.transpiler.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '292ab72977afbe58e6a3bde175452680'))
+paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8'))
+paddle.fluid.transpiler.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c'))
+paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff'))
+paddle.fluid.transpiler.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4'))
+paddle.fluid.transpiler.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', 'eda17d0f1639bc6ca215cecf87f588a4'))
+paddle.fluid.transpiler.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ac4114d3df16264f1946deb3a8434a6f'))
+paddle.fluid.transpiler.HashName.__init__ (ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.transpiler.HashName.dispatch (ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.transpiler.HashName.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.transpiler.RoundRobin.__init__ (ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.transpiler.RoundRobin.dispatch (ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.transpiler.RoundRobin.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ 
-paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True))
-paddle.fluid.nets.sequence_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None))
-paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,))
-paddle.fluid.nets.scaled_dot_product_attention ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0))
-paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True))
-paddle.fluid.optimizer.SGDOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.optimizer.SGDOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.SGDOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.SGDOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
-paddle.fluid.optimizer.MomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.MomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0))
-paddle.fluid.optimizer.AdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.AdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
-paddle.fluid.optimizer.AdamOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.AdamOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
-paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.AdamaxOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None))
-paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.DecayedAdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.FtrlOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None))
-paddle.fluid.optimizer.FtrlOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.FtrlOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.FtrlOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None))
-paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.RMSPropOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.RMSPropOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.AdadeltaOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None))
-paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.AdadeltaOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None))
-paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,))
-paddle.fluid.optimizer.ModelAverage.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.ModelAverage.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.ModelAverage.restore ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None))
-paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.optimizer.LarsMomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
-paddle.fluid.optimizer.LarsMomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.backward.append_backward ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.regularizer.L1DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))
-paddle.fluid.regularizer.L2DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))
+paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', 'e0f67f35abf27f666f81003113b90244'))
+paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', '48c434dd7bb827f69d90e5135d77470f'))
+paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '08c1c57e1db6b20bf87b264cb7cf3ca8'))
+paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', '921714c9bfb351b41403418265393203'))
+paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '3802be78fbfb206dae64a2d9f8480970'))
+paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.SGDOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.MomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.MomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.MomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.AdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.AdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.AdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.AdamOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.AdamOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.AdamOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.AdamaxOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.AdamaxOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.DecayedAdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.FtrlOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.FtrlOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.FtrlOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.RMSPropOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.RMSPropOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.AdadeltaOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '46234a5470590feb336346f70a3db715'))
+paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '18db9c70be9c4dd466f9844457b21bfe'))
+paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
+paddle.fluid.optimizer.LarsMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
+paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
+paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '1a79bd7d10ae54ca763ec81bca36ba24'))
+paddle.fluid.regularizer.L1DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.regularizer.L2DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None  2. __init__(self: paddle.fluid.core.LoDTensor) -> None
 paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> bool
 paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
@@ -483,38 +483,38 @@ paddle.fluid.LoDTensorArray.append append(self: paddle.fluid.core.LoDTensorArray
 paddle.fluid.CPUPlace.__init__ __init__(self: paddle.fluid.core.CPUPlace) -> None
 paddle.fluid.CUDAPlace.__init__ __init__(self: paddle.fluid.core.CUDAPlace, arg0: int) -> None
 paddle.fluid.CUDAPinnedPlace.__init__ __init__(self: paddle.fluid.core.CUDAPinnedPlace) -> None
-paddle.fluid.ParamAttr.__init__ ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, False))
-paddle.fluid.WeightNormParamAttr.__init__ ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False))
-paddle.fluid.DataFeeder.__init__ ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.DataFeeder.decorate_reader ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True))
-paddle.fluid.DataFeeder.feed ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DataFeeder.feed_parallel ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.clip.ErrorClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.clip.GradientClipByValue.__init__ ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.clip.GradientClipByNorm.__init__ ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.clip.GradientClipByGlobalNorm.__init__ ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',))
-paddle.fluid.profiler.cuda_profiler ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.profiler.reset_profiler ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
-paddle.fluid.profiler.profiler ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
-paddle.fluid.profiler.start_profiler ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.profiler.stop_profiler ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile'))
-paddle.fluid.unique_name.generate ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.unique_name.switch ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.unique_name.guard ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.recordio_writer.convert_reader_to_recordio_file ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
-paddle.fluid.recordio_writer.convert_reader_to_recordio_files ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
+paddle.fluid.ParamAttr.__init__ (ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.WeightNormParamAttr.__init__ (ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.DataFeeder.__init__ (ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', '0eed2f198dc73c08a41b61edbc755753'))
+paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', '459e316301279dfd82001b46f0b8ffca'))
+paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', '543863d1f9d4853758adb613b8659e85'))
+paddle.fluid.clip.ErrorClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.clip.GradientClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.clip.GradientClipByNorm.__init__ (ArgSpec(args=['self', 'clip_norm'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.clip.GradientClipByGlobalNorm.__init__ (ArgSpec(args=['self', 'clip_norm', 'group_name'], varargs=None, keywords=None, defaults=('default_group',)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.profiler.cuda_profiler (ArgSpec(args=['output_file', 'output_mode', 'config'], varargs=None, keywords=None, defaults=(None, None)), ('document', '2e2fb1cfc469a67f19fb578a2ed6be79'))
+paddle.fluid.profiler.reset_profiler (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '397ce757fabbe5c622e0c3458c41fcd0'))
+paddle.fluid.profiler.profiler (ArgSpec(args=['state', 'sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'bd3a07eeb68e384f4d2d416cb2e28d86'))
+paddle.fluid.profiler.start_profiler (ArgSpec(args=['state'], varargs=None, keywords=None, defaults=None), ('document', '88da8fb6dbebaee2f7520188a09574f9'))
+paddle.fluid.profiler.stop_profiler (ArgSpec(args=['sorted_key', 'profile_path'], varargs=None, keywords=None, defaults=(None, '/tmp/profile')), ('document', 'a7500e39dd033f1e64f562e909333a8a'))
+paddle.fluid.unique_name.generate (ArgSpec(args=['key'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.unique_name.switch (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.unique_name.guard (ArgSpec(args=['new_generator'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.fluid.recordio_writer.convert_reader_to_recordio_file (ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', '65c7523e86f0c50bb729b01667f36310'))
+paddle.fluid.recordio_writer.convert_reader_to_recordio_files (ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', 'bc643f0f5f1b9db57ff0d8a57d379bd7'))
 paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope
-paddle.reader.map_readers ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None)
-paddle.reader.buffered ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None)
-paddle.reader.compose ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None)
-paddle.reader.chain ArgSpec(args=[], varargs='readers', keywords=None, defaults=None)
-paddle.reader.shuffle ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None)
-paddle.reader.firstn ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None)
-paddle.reader.xmap_readers ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,))
-paddle.reader.PipeReader.__init__ ArgSpec(args=['self', 'command', 'bufsize', 'file_type'], varargs=None, keywords=None, defaults=(8192, 'plain'))
-paddle.reader.PipeReader.get_line ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n'))
-paddle.reader.multiprocess_reader ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000))
-paddle.reader.Fake.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.reader.creator.np_array ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
-paddle.reader.creator.text_file ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None)
-paddle.reader.creator.recordio ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,))
+paddle.reader.map_readers (ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None), ('document', '77cbadb09df588e21e5cc0819b69c87d'))
+paddle.reader.buffered (ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None), ('document', '0d6186f109feceb99f60ec50a0a624cb'))
+paddle.reader.compose (ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None), ('document', '884291104e1c3f37f33aae44b7deeb0d'))
+paddle.reader.chain (ArgSpec(args=[], varargs='readers', keywords=None, defaults=None), ('document', 'd22c34e379a53901ae67a6bca7f4def4'))
+paddle.reader.shuffle (ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None), ('document', 'e42ea6fee23ce26b23cb142cd1d6522d'))
+paddle.reader.firstn (ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None), ('document', 'c5bb8f7dd4f917f1569a368aab5b8aad'))
+paddle.reader.xmap_readers (ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,)), ('document', '283bc0b8a0e26ae186b8b9bee4aec560'))
+paddle.reader.PipeReader.__init__ (ArgSpec(args=['self', 'command', 'bufsize', 'file_type'], varargs=None, keywords=None, defaults=(8192, 'plain')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.reader.PipeReader.get_line (ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n')), ('document', '5f80a7ed70052f01665e4c74acccfa69'))
+paddle.reader.multiprocess_reader (ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000)), ('document', '7d8b3a96e592107c893d5d51ce968ba0'))
+paddle.reader.Fake.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+paddle.reader.creator.np_array (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '28d457fbc9a71efa4ac91a3be179cada'))
+paddle.reader.creator.text_file (ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None), ('document', '44fe286ab6175a5464d3a961a68c266a'))
+paddle.reader.creator.recordio (ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,)), ('document', '11b3704ea42cfd537953387a7e58dae8'))

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -12,7 +12,9 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/details/fetch_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
@@ -55,7 +57,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
   std::vector<FetchOpHandle *> fetch_ops;
 
   for (auto &fetch_var_name : fetch_tensors) {
-    for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
+    for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
       auto it = var_map.find(fetch_var_name);
       if (it != var_map.end()) {
         fetched_vars[fetch_var_name].push_back(*it->second.rbegin());

paddle/fluid/framework/tensor_util.cc

@@ -14,6 +14,8 @@
 #include "paddle/fluid/framework/tensor_util.h"
 #include <algorithm>
 #include <limits>
+#include <memory>
+#include <utility>
 #include <vector>
 #include "paddle/fluid/framework/data_type.h"
 

paddle/fluid/operators/interpolate_op.cc

@@ -84,13 +84,13 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault("bilinear");
     AddAttr<bool>(
         "align_corners",
-        "an optinal bool. Defaults to True. "
+        "an optional bool. Defaults to True. "
         "If True, the centers of 4 corner pixels of the input and output "
         "tensors are aligned, preserving the values at the corner pixels, "
-        "if Flase, are not aligned")
+        "If False, are not aligned")
         .SetDefault(true);
     AddAttr<int>("align_mode",
-                 "(int, default \'1\'), optional for bilinear interpolation"
+                 "(int, default \'1\'), optional for bilinear interpolation, "
                  "can be \'0\' for src_idx = scale*(dst_indx+0.5)-0.5 , "
                  "can be \'1\' for src_idx = scale*dst_index .")
         .SetDefault(1);

paddle/fluid/operators/ngraph/ngraph_bridge.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include <algorithm>
 #include <functional>
+#include <memory>
 #include <vector>
 
 #include "ngraph/ngraph.hpp"

paddle/fluid/operators/ngraph/ngraph_bridge.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <algorithm>
 #include <map>
+#include <memory>
 #include <string>
 #include <unordered_map>
 

paddle/fluid/operators/ngraph/ops/accuracy_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"

paddle/fluid/operators/ngraph/ops/activation_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"

paddle/fluid/operators/ngraph/ops/batch_norm_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include "ngraph/ngraph.hpp"

paddle/fluid/operators/ngraph/ops/binary_unary_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
 #include "paddle/fluid/platform/ngraph_helper.h"

paddle/fluid/operators/ngraph/ops/conv2d_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"

paddle/fluid/operators/ngraph/ops/cross_entropy_op.h

@@ -15,7 +15,9 @@ limitations under the License. */
 #pragma once
 
 #include <functional>
+#include <memory>
 #include <string>
+#include <unordered_map>
 
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"

paddle/fluid/operators/ngraph/ops/elementwise_add_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include "ngraph/ngraph.hpp"

paddle/fluid/operators/ngraph/ops/fill_constant_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"

paddle/fluid/operators/ngraph/ops/mean_op.h

@@ -15,7 +15,9 @@ limitations under the License. */
 #pragma once
 
 #include <functional>
+#include <memory>
 #include <string>
+#include <unordered_map>
 
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"

paddle/fluid/operators/ngraph/ops/momentum_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"

paddle/fluid/operators/ngraph/ops/mul_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
 #include "paddle/fluid/platform/ngraph_helper.h"

paddle/fluid/operators/ngraph/ops/pool2d_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include "ngraph/ngraph.hpp"

paddle/fluid/operators/ngraph/ops/scale_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"

paddle/fluid/operators/ngraph/ops/softmax_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include <vector>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"

paddle/fluid/operators/ngraph/ops/top_k_op.h

@@ -14,7 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
+#include <unordered_map>
 #include "ngraph/ngraph.hpp"
 #include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
 #include "paddle/fluid/platform/ngraph_helper.h"

paddle/fluid/operators/reader/buffered_reader.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/reader/buffered_reader.h"
+#include <memory>
 #include <vector>
 #include "paddle/fluid/framework/data_type.h"
 

paddle/scripts/paddle_build.sh

@@ -415,10 +415,11 @@ function assert_api_not_changed() {
     source .env/bin/activate
     pip install ${PADDLE_ROOT}/build/python/dist/*whl
     python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid,paddle.reader > new.spec
+
     if [ "$1" == "cp35-cp35m" ] || [ "$1" == "cp36-cp36m" ] || [ "$1" == "cp37-cp37m" ]; then
         # Use sed to make python2 and python3 sepc keeps the same
         sed -i 's/arg0: str/arg0: unicode/g' new.spec
-        sed -i "s/\(.*Transpiler.*\).__init__ ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec
+        sed -i "s/\(.*Transpiler.*\).__init__ (ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec
     fi
     # ComposeNotAligned has significant difference between py2 and py3
     sed -i '/.*ComposeNotAligned.*/d' new.spec
@@ -452,12 +453,21 @@ function assert_api_spec_approvals() {
       echo "checking ${API_FILE} change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}"
       if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
           # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
-          APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-          python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
+          if [ "$API_FILE" == "paddle/fluid/API.spec" ];then
+            APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
+            python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 2887803 35982308`
+          else
+            APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
+            python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
+          fi
           echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
           if [ "${APPROVALS}" == "FALSE" ]; then
+            if [ "$API_FILE" == "paddle/fluid/API.spec" ];then
+              echo "You must have panyx0718 and shanyi15 approval for the api change! ${API_FILE}"
+            else
               echo "You must have panyx0718 approval for the api change! ${API_FILE}"
-              exit 1
+            fi
+            exit 1
           fi
       fi
     done
@@ -472,19 +482,6 @@ function assert_api_spec_approvals() {
             exit 1
         fi
     fi
-
-    pip install ${PADDLE_ROOT}/build/opt/paddle/share/wheels/*.whl
-    CHECK_DOCK_MD5=`python ${PADDLE_ROOT}/tools/check_doc_approval.py`
-    if [ "True" != ${CHECK_DOCK_MD5} ]; then
-        APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-        python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 35982308`
-        echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
-        if [ "${APPROVALS}" == "FALSE" ]; then
-            echo "You must have shanyi15 approval for the api doc change! "
-            exit 1
-        fi
-        echo ${CHECK_DOCK_MD5} >/root/.cache/doc_md5.txt
-    fi
 }
 
 

python/paddle/fluid/compiler.py

@@ -17,7 +17,6 @@ import os
 import six
 import sys
 from .. import compat as cpt
-from . import framework
 
 from . import core
 from . import framework
@@ -36,6 +35,30 @@ def _place_obj(place):
     return p
 
 
+def _is_pserver_mode(main_program):
+    main = main_program if main_program \
+        else default_main_program()
+    for op in main.global_block().ops:
+        if op.type in ["send", "recv"]:
+            return True
+    return False
+
+
+def get_available_places(use_cuda):
+    if use_cuda:
+        gpus_env = os.getenv("FLAGS_selected_gpus")
+        if gpus_env:
+            gpus = [int(s) for s in gpus_env.split(",")]
+        else:
+            gpus = [i for i in six.moves.range(core.get_cuda_device_count())]
+        places = [core.CUDAPlace(i) for i in gpus]
+    else:
+        cpu_num = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+        places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
+    assert places, "no place for execution"
+    return places
+
+
 class CompiledProgram(object):
     """
     Compiles to Graph for execution.
@@ -127,8 +150,7 @@ class CompiledProgram(object):
             self._exec_strategy = ExecutionStrategy()
         if self._build_strategy is None:
             self._build_strategy = BuildStrategy()
-        self._build_strategy.is_distribution = framework.is_pserver_mode(
-            self._program)
+        self._build_strategy.is_distribution = _is_pserver_mode(self._program)
         return self
 
     def with_inference_optimize(self, config):
@@ -153,9 +175,9 @@ class CompiledProgram(object):
     def _with_distributed(self):
         raise NotImplementedError()
 
-    def _compile_data_parallel(self):
+    def _compile_data_parallel(self, use_cuda=False, scope=None):
         if self._share_vars_from:
-            if self._scope:
+            if scope:
                 sys.stderr.write("share_vars_from is set, scope is ignored.\n")
             if not self._share_vars_from._is_data_parallel:
                 raise ValueError("share_vars_from is not data parallel. Cannot "
@@ -166,23 +188,11 @@ class CompiledProgram(object):
                     "var to share.")
             self._local_scopes = self._share_vars_from._executor.local_scopes()
         else:
+            assert scope is not None, ""
             self._local_scopes = []
 
-        self._exec_strategy.use_cuda = isinstance(self._place, core.CUDAPlace)
-        if self._exec_strategy.use_cuda:
-            gpus_env = os.getenv("FLAGS_selected_gpus")
-            if gpus_env:
-                gpus = [int(s) for s in gpus_env.split(",")]
-            else:
-                gpus = [
-                    i for i in six.moves.range(core.get_cuda_device_count())
-                ]
-            self._places = [core.CUDAPlace(i) for i in gpus]
-        else:
-            cpu_num = int(
-                os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-            self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
-        assert self._places, "no place for execution"
+        self._exec_strategy.use_cuda = use_cuda
+        self._places = get_available_places(self._exec_strategy.use_cuda)
 
         if self._exec_strategy.num_threads == 0:
             if self._exec_strategy.use_cuda:
@@ -197,9 +207,11 @@ class CompiledProgram(object):
         # FIXME(dzhwinter): enable_inplace should be after memory_optimize
         # if turn on python memory optimize, turn off the inplace_pass.
         if self._build_strategy.memory_optimize is None:
-            self._build_strategy.memory_optimize = False if self._program and self._program._is_mem_optimized else True
+            self._build_strategy.memory_optimize = False \
+                if self._program and self._program._is_mem_optimized else True
         if self._build_strategy.enable_inplace is None:
-            self._build_strategy.enable_inplace = False if self._program and self._program._is_mem_optimized else True
+            self._build_strategy.enable_inplace = False \
+                if self._program and self._program._is_mem_optimized else True
 
         # TODO(wuyi): trainer endpoings should be passed in through
         # build_strategy, not program.xxx.
@@ -221,12 +233,12 @@ class CompiledProgram(object):
 
         places = list(map(_place_obj, self._places))
 
-        return core.ParallelExecutor(
-            places,
-            set(self._persistable_vars),
-            cpt.to_text(self._loss_name)
-            if self._loss_name else six.u(''), self._scope, self._local_scopes,
-            self._exec_strategy, self._build_strategy, self._graph)
+        return core.ParallelExecutor(places,
+                                     set(self._persistable_vars),
+                                     cpt.to_text(self._loss_name)
+                                     if self._loss_name else six.u(''), scope,
+                                     self._local_scopes, self._exec_strategy,
+                                     self._build_strategy, self._graph)
 
     def _compile_inference(self):
         return core.create_paddle_predictor(self._infer_config)
@@ -253,7 +265,9 @@ class CompiledProgram(object):
         self._scope = scope
         self._place = place
         if self._is_data_parallel:
-            self._executor = self._compile_data_parallel()
+            self._executor = self._compile_data_parallel(
+                use_cuda=isinstance(self._place, core.CUDAPlace),
+                scope=self._scope)
         elif self._is_inference:
             self._executor = self._compile_inference()
         else:

python/paddle/fluid/executor.py

@@ -261,45 +261,42 @@ def _as_lodtensor(data, place):
 
 class Executor(object):
     """
-    An Executor in Python, only support the single-GPU running. For multi-cards, please refer to
-    ParallelExecutor.
-    Python executor takes a program, add feed operators and fetch operators to this program according
+    An Executor in Python, supports single/multiple-GPU running, and single/multiple-CPU running.
+    Python executor takes a program, adds feed operators and fetch operators to this program according
     to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides
-    the variables(or names) that user want to get after program run. Note: the executor will run all
+    the variables(or names) that user wants to get after program runs. Note: the executor will run all
     operators in the program but not only the operators dependent by the fetch_list.
-    It store the global variables into the global scope, and create a local scope for the temporary
-    variables. The local scope contents will be discarded after every minibatch forward/backward finished.
-    But the global scope variables will be persistent through different runs.
-    All of ops in program will be running in sequence.
+    It stores the global variables into the global scope, and creates a local scope for the temporary
+    variables. The contents in local scope may be discarded after every minibatch forward/backward
+    finished. But the global scope variables will be persistent through different runs.
 
 
     Example:
-    .. code-block:: python
-        # First create the Executor.
-        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-        exe = fluid.Executor(place)
-
-        # Run the startup program once and only once.
-        # Not need to optimize/compile the startup program.
-        exe.run(fluid.default_startup_program())
-
-        # Run the main program directly without compile.
-        loss, = exe.run(fluid.default_main_program(),
-                        feed=feed_dict,
-                        fetch_list=[loss.name])
-        # Or, compiled the program and run. See `CompiledProgram` for more detail.
-        compiled_prog = compiler.CompiledProgram(
-            fluid.default_main_program()).with_data_parallel(
-            loss_name=loss.name)
-        loss, = exe.run(compiled_prog,
-                        feed=feed_dict,
-                        fetch_list=[loss.name])
+
+        .. code-block:: python
+
+            # First create the Executor.
+            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+
+            # Run the startup program once and only once.
+            # Not need to optimize/compile the startup program.
+            exe.run(fluid.default_startup_program())
+
+            # Run the main program directly without compile.
+            loss, = exe.run(fluid.default_main_program(),
+                            feed=feed_dict,
+                            fetch_list=[loss.name])
+            # Or, compiled the program and run. See `CompiledProgram` for more detail.
+            compiled_prog = compiler.CompiledProgram(
+                fluid.default_main_program()).with_data_parallel(
+                loss_name=loss.name)
+            loss, = exe.run(compiled_prog,
+                            feed=feed_dict,
+                            fetch_list=[loss.name])
 
     Args:
         place(core.CPUPlace|core.CUDAPlace(n)): indicate the executor run on which device
-
-    Note: For debugging complicated network in parallel-GPUs, you can test it on the executor.
-    They has the exactly same arguments, and expected the same results.
     """
 
     def __init__(self, place):
@@ -382,6 +379,12 @@ class Executor(object):
         ]
         return outs
 
+    '''
+    TODO(typhoonzero): Define "no longer use" meaning? Can user create
+    a new Executor for the same program and run?
+    TODO(panyx0718): Why ParallelExecutor doesn't have close?
+    '''
+
     def close(self):
         """
         Close this executor.
@@ -389,9 +392,6 @@ class Executor(object):
         You can no longer use this executor after calling this method.
         For the distributed training, this method would free the resource on PServers related to
         the current Trainer.
-        TODO(typhoonzero): Define "no longer use" meaning? Can user create
-        a new Executor for the same program and run?
-        TODO(panyx0718): Why ParallelExecutor doesn't have close?
 
         Example:
             >>> cpu = core.CPUPlace()

python/paddle/fluid/framework.py

@@ -87,15 +87,6 @@ def _current_expected_place():
     return _imperative_current_expected_place_
 
 
-def is_pserver_mode(main_program):
-    main = main_program if main_program \
-        else default_main_program()
-    for op in main.global_block().ops:
-        if op.type in ["send", "recv"]:
-            return True
-    return False
-
-
 class NameScope(object):
     def __init__(self, name="", parent=None):
         self._children = dict()

python/paddle/fluid/io.py

@@ -468,9 +468,10 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
 
             exe = fluid.Executor(fluid.CPUPlace())
             param_path = "./my_paddle_model"
+            # `prog` can be a program defined by the user
             prog = fluid.default_main_program()
             fluid.io.save_persistables(executor=exe, dirname=param_path,
-                                       main_program=None)
+                                       main_program=prog)
     """
 
     if main_program and main_program._is_distributed:

python/paddle/fluid/layers/nn.py

@@ -6844,56 +6844,58 @@ def image_resize(input,
 
     Example:
 
-      For scale:
-      
-        if align_corners = True && out_size > 1 :
+    .. code-block:: text
 
-          scale_factor = (in_size-1.0)/(out_size-1.0)
-        
-        else:
+        For scale:
           
-          scale_factor = float(in_size/out_size)
-        
-      
-      Nearest neighbor interpolation:
-      
-      if:
-          align_corners = False
+            if align_corners = True && out_size > 1 :
 
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
+              scale_factor = (in_size-1.0)/(out_size-1.0)
+            
+            else:
+              
+              scale_factor = float(in_size/out_size)
+            
+          
+        Nearest neighbor interpolation:
+          
+          if:
+              align_corners = False
 
-          H_out = \left \lfloor {H_{in} * scale_{}factor}} \right \rfloor
-          W_out = \left \lfloor {W_{in} * scale_{}factor}} \right \rfloor
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
 
-      else:
-          align_corners = True
+              H_out = floor (H_{in} * scale_{factor})
+              W_out = floor (W_{in} * scale_{factor})
 
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
+          else:
+              align_corners = True
 
-          H_out = round(H_{in} * scale_{factor})
-          W_out = round(W_{in} * scale_{factor})
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
 
-      Bilinear interpolation:
+              H_out = round(H_{in} * scale_{factor})
+              W_out = round(W_{in} * scale_{factor})
 
-      if:
-          align_corners = False , align_mode = 0
-          
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
-          
-          H_out = (H_{in}+0.5) * scale_{factor} - 0.5
-          W_out = (W_{in}+0.5) * scale_{factor} - 0.5
+        Bilinear interpolation:
+
+          if:
+              align_corners = False , align_mode = 0
+              
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
+              
+              H_out = (H_{in}+0.5) * scale_{factor} - 0.5
+              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
 
 
-      else:
-       
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
+          else:
+           
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
 
-          H_out = H_{in} * scale_{factor}
-          W_out = W_{in} * scale_{factor}
+              H_out = H_{in} * scale_{factor}
+              W_out = W_{in} * scale_{factor}
 
     For details of nearest neighbor interpolation, please refer to Wikipedia: 
     https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
@@ -7048,41 +7050,39 @@ def resize_bilinear(input,
     Align_corners and align_mode are optinal parameters,the calculation 
     method of interpolation can be selected by them.
 
-
-    Align_corners and align_mode are optinal parameters,the calculation method 
-    of interpolation can be selected by them.
-
     Example:
 
-      For scale:
-      
-        if align_corners = True && out_size > 1 :
+    .. code-block:: text
 
-          scale_factor = (in_size-1.0)/(out_size-1.0)
-        
-        else:
+        For scale:
           
-          scale_factor = float(in_size/out_size)     
+            if align_corners = True && out_size > 1 :
 
-    Bilinear interpolation:
+              scale_factor = (in_size-1.0)/(out_size-1.0)
+            
+            else:
+              
+              scale_factor = float(in_size/out_size)     
 
-      if:
-          align_corners = False , align_mode = 0
-          
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
-          
-          H_out = (H_{in}+0.5) * scale_{factor} - 0.5
-          W_out = (W_{in}+0.5) * scale_{factor} - 0.5
+        Bilinear interpolation:
+
+          if:
+              align_corners = False , align_mode = 0
+              
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
+              
+              H_out = (H_{in}+0.5) * scale_{factor} - 0.5
+              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
 
 
-      else:
+          else:
 
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
 
-          H_out = H_{in} * scale_{factor}
-          W_out = W_{in} * scale_{factor}
+              H_out = H_{in} * scale_{factor}
+              W_out = W_{in} * scale_{factor}
 
 
 
@@ -7134,42 +7134,44 @@ def resize_nearest(input,
                    align_corners=True):
     """
     Resize input by performing nearest neighbor interpolation in both the
-    3rd dimention(in height direction) and the 4th dimention(in width
-    direction) based on given output shape which specified by actual_shape,
+    3rd dimension(in height direction) and the 4th dimension(in width
+    direction) based on given output shape which is specified by actual_shape,
     out_shape and scale in priority order.
 
     Example:
 
-      For scale:
-      
-        if align_corners = True && out_size > 1 :
+    .. code-block:: text
+
+        For scale:
+          
+            if align_corners = True && out_size > 1 :
 
-          scale_factor = (in_size-1.0)/(out_size-1.0)
-        
-        else:
+              scale_factor = (in_size-1.0)/(out_size-1.0)
+            
+            else:
+              
+              scale_factor = float(in_size/out_size)
+            
+          
+        Nearest neighbor interpolation:
           
-          scale_factor = float(in_size/out_size)
-        
-      
-      Nearest neighbor interpolation:
-      
-      if:
-          align_corners = False
+          if:
+              align_corners = False
 
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
 
-          H_out = \left \lfloor {H_{in} * scale_{}factor}} \right \rfloor
-          W_out = \left \lfloor {W_{in} * scale_{}factor}} \right \rfloor
+              H_out = floor(H_{in} * scale_{factor})
+              W_out = floor(W_{in} * scale_{factor})
 
-      else:
-          align_corners = True
+          else:
+              align_corners = True
 
-          input : (N,C,H_in,W_in)
-          output: (N,C,H_out,W_out) where:
+              input : (N,C,H_in,W_in)
+              output: (N,C,H_out,W_out) where:
 
-          H_out = round(H_{in} * scale_{factor})
-          W_out = round(W_{in} * scale_{factor})
+              H_out = round(H_{in} * scale_{factor})
+              W_out = round(W_{in} * scale_{factor})
 
 
     For details of nearest neighbor interpolation, please refer to Wikipedia:

python/paddle/fluid/parallel_executor.py

@@ -13,15 +13,11 @@
 # limitations under the License.
 
 from __future__ import print_function
-import multiprocessing
 from . import core
 from . import framework
 from . import executor
-from .. import compat as cpt
-import warnings
+from . import compiler
 import sys
-import six
-import os
 
 __all__ = ['ParallelExecutor']
 
@@ -97,99 +93,27 @@ class ParallelExecutor(object):
             'Please use CompiledProgram and Executor. CompiledProgram '
             'is a central place for optimization and Executor is the '
             'unified executor. Example can be found in compiler.py.\n')
-        # step1: get places, the places are used in run too.
-        self._places = []
-        if use_cuda:
-            gpus_env = os.getenv("FLAGS_selected_gpus")
-            if gpus_env:
-                gpus = [int(s) for s in gpus_env.split(",")]
-            else:
-                gpus = [
-                    i for i in six.moves.range(core.get_cuda_device_count())
-                ]
-            self._places = [core.CUDAPlace(i) for i in gpus]
-        else:
-            cpu_num = int(
-                os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-            self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
-        assert self._places, "no place for execution"
 
-        # step2: init exec_strategy
-        if exec_strategy is None:
-            exec_strategy = ExecutionStrategy()
-        exec_strategy.use_cuda = use_cuda
-        if exec_strategy.num_threads == 0:
-            if use_cuda:
-                # Experiments on se-resnext shows that too many threads hurt
-                # performance. Worth tunning for other models in the future.
-                exec_strategy.num_threads = len(self._places) * 4
-            else:
-                cpu_num = int(
-                    os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
-                exec_strategy.num_threads = cpu_num * 2
-
-        # step3: init build_strategy
         if build_strategy is None:
             build_strategy = BuildStrategy()
         build_strategy.num_trainers = num_trainers
         build_strategy.trainer_id = trainer_id
-        # FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
-        # num_trainers is 1, so the current fields of build_strategy doesn't tell if
-        # it's distributed model.
-        build_strategy.is_distribution = framework.is_pserver_mode(
-            main_program) or num_trainers > 1
-
-        # step4: get main_program, scope, local_scopes
-        main = main_program if main_program \
-            else framework.default_main_program()
-        # FIXME(dzhwinter): enable_inplace should be after memory_optimize
-        # if turn on python memory optimize, turn off the inplace_pass.
-        if build_strategy.memory_optimize is None:
-            build_strategy.memory_optimize = False if main._is_mem_optimized else True
-        if build_strategy.enable_inplace is None:
-            build_strategy.enable_inplace = False if main._is_mem_optimized else True
-        scope = scope if scope is not None else executor.global_scope()
-
-        if share_vars_from and not isinstance(share_vars_from,
-                                              ParallelExecutor):
-            raise TypeError("share_vars_from must be ParallelExecutor.")
-
-        local_scopes = share_vars_from.executor.local_scopes()\
-            if share_vars_from else []
-
-        # step5: check trainers_endpoints, it is used for distribution.
-        trainers_endpoints = main._trainers_endpoints
-        if num_trainers > 1 and trainers_endpoints:
-            assert num_trainers == len(
-                trainers_endpoints), "num_trainers == len(endpoints)"
-            build_strategy.trainers_endpoints = trainers_endpoints
-
-        # step6: get persistable_vars, places. persistable_vars
-        # need be broadcast to other local_scope.
-        persistable_vars = set([
-            cpt.to_text(v.name) for v in [
-                var for var in main.list_vars()
-                if var.persistable and var.type != core.VarDesc.VarType.RAW
-            ]
-        ])
-
-        def place_obj(place):
-            p = core.Place()
-            p.set_place(place)
-            return p
-
-        places = list(map(place_obj, self._places))
 
-        # step7: init ParallelExecutor
-        # ParallelExecutor API will be deprecated, don't support parallel graph.
-        self._graph = core.Graph(main.desc)
+        self._places = compiler.get_available_places(use_cuda)
+        self._scope = scope if scope is not None else executor.global_scope()
 
-        self.executor = core.ParallelExecutor(
-            places, persistable_vars,
-            cpt.to_text(loss_name) if loss_name else six.u(''), scope,
-            local_scopes, exec_strategy, build_strategy, self._graph)
+        main_program = main_program if main_program is not None \
+            else framework.default_main_program()
 
-        self.scope = scope
+        self._compiled_program = compiler.CompiledProgram(main_program)
+        self._compiled_program.with_data_parallel(
+            loss_name=loss_name,
+            build_strategy=build_strategy,
+            exec_strategy=exec_strategy,
+            share_vars_from=share_vars_from)
+        self._place = core.CUDAPlace(0) if use_cuda else core.CPUPlace()
+        self._executor = executor.Executor(self._place)
+        self._compiled_program._compile(place=self._place, scope=self._scope)
 
     def run(self, fetch_list, feed=None, feed_dict=None, return_numpy=True):
         """
@@ -256,56 +180,11 @@ class ParallelExecutor(object):
                 loss = pe.run(feed=feeder.feed(cur_batch),
                               fetch_list=[avg_cost.name]))
         """
-        if feed is None and feed_dict is not None:
-            feed = feed_dict
-            print(
-                "`feed_dict` is deprecated. Please use `feed=`",
-                file=sys.stderr)
-
-        if isinstance(feed, dict):
-            feed_tensor_dict = dict()
-            for feed_name in feed:
-                feed_tensor = feed[feed_name]
-                if not isinstance(feed_tensor, core.LoDTensor):
-                    feed_tensor = core.LoDTensor()
-                    # always set to CPU place, since the tensor need to be splitted
-                    # it is fast in CPU
-                    feed_tensor.set(feed[feed_name], core.CPUPlace())
-                feed_tensor_dict[feed_name] = feed_tensor
-
-            self.executor.feed_and_split_tensor_into_local_scopes(
-                feed_tensor_dict)
-        elif isinstance(feed, list) or isinstance(feed, tuple):
-            if len(feed) != len(self._places):
-                raise ValueError(
-                    "Feed a list of tensor, the list should be the same size as places"
-                )
-
-            res = list()
-
-            for i, each in enumerate(feed):
-                if not isinstance(each, dict):
-                    raise TypeError(
-                        "Each element of feed list should be a dict")
-                res_dict = dict()
-                for feed_name in each:
-                    tensor = each[feed_name]
-                    if not isinstance(tensor, core.LoDTensor):
-                        tmp = core.LoDTensor()
-                        tmp.set(tensor, self._places[i])
-                        tensor = tmp
-                    res_dict[feed_name] = tensor
-                res.append(res_dict)
-            self.executor.feed_tensors_into_local_scopes(res)
-
-        fetch_var_name = 'fetch'
-        self.executor.run(fetch_list, fetch_var_name)
-        arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
-
-        if return_numpy:
-            return executor.as_numpy(arr)
-
-        return [arr[i] for i in range(len(arr))]
+        return self._executor.run(program=self._compiled_program,
+                                  scope=self._scope,
+                                  feed=feed,
+                                  fetch_list=fetch_list,
+                                  return_numpy=return_numpy)
 
     @property
     def device_count(self):

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py

@@ -15,44 +15,139 @@
 from __future__ import print_function
 
 import unittest
+import numpy as np
 
-from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1
+import paddle.fluid.core as core
+from paddle.fluid.tests.unittests.op_test import OpTest
+from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp
 
 
-class TestMKLDNN(TestConv2dOp):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-        self.data_format = "NCHW"
+def conv2d_bias_naive(out, bias):
+    _, out_c, _, _ = out.shape
 
+    for l in range(out_c):
+        out[:, l, :, :] = out[:, l, :, :] + bias[l]
+    return out
 
-class TestMKLDNNWithPad(TestWithPad):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-        self.data_format = "NCHW"
 
+def conv2d_residual_naive(out, residual):
+    assert out.shape == residual.shape
+    out = np.add(out, residual)
+    return out
 
-class TestMKLDNNWithStride(TestWithStride):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-        self.data_format = "NCHW"
 
+class TestConv2dMKLDNNOp(TestConv2dOp):
+    def init_group(self):
+        self.groups = 1
 
-class TestMKLDNNWithGroup(TestWithGroup):
     def init_kernel_type(self):
-        self.use_mkldnn = True
         self.data_format = "NCHW"
+        self.use_mkldnn = True
+        self._cpu_only = True
 
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
 
-class TestMKLDNNWith1x1(TestWith1x1):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-        self.data_format = "NCHW"
+    def setUp(self):
+        self.fuse_bias = False
+        self.bias_size = None
+        self.fuse_relu = False
+        self.fuse_residual_connection = False
+        self.input_residual_size = None
+        TestConv2dOp.setUp(self)
 
+        output = self.outputs['Output']
 
-class TestMKLDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-        self.data_format = "NCHW"
+        #mkldnn only support either conv-sum-relu, or conv-relu.
+        if self.fuse_bias and self.bias_size is not None:
+            bias = np.random.random(self.bias_size).astype(self.dtype)
+            output = conv2d_bias_naive(output, bias)
+            output = output.astype(self.dtype)
+            self.attrs['fuse_bias'] = self.fuse_bias
+            self.inputs['Bias'] = OpTest.np_dtype_to_fluid_dtype(bias)
+
+        if self.fuse_residual_connection and self.input_residual_size is not None:
+            input_residual = np.random.random(self.input_residual_size).astype(
+                self.dtype)
+            output = conv2d_residual_naive(output, input_residual)
+
+            self.attrs[
+                'fuse_residual_connection'] = self.fuse_residual_connection
+            self.inputs['ResidualData'] = OpTest.np_dtype_to_fluid_dtype(
+                input_residual)
+
+        if self.fuse_relu:
+            output = np.maximum(output, 0).astype(self.dsttype)
+
+        output = output.astype(self.dtype)
+
+        self.attrs['fuse_bias'] = self.fuse_bias
+        self.attrs['fuse_relu'] = self.fuse_relu
+        self.attrs['fuse_residual_connection'] = self.fuse_residual_connection
+
+        self.outputs['Output'] = output
+
+
+class TestWithFuse(TestConv2dMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dMKLDNNOp.init_test_case(self)
+        self.pad = [1, 1]
+        self.fuse_bias = True
+        self.bias_size = [6]
+        self.fuse_residual_connection = True
+        self.input_residual_size = [2, 6, 5, 5]
+
+    def test_check_grad(self):
+        pass
+
+    def test_check_grad_no_filter(self):
+        pass
+
+    def test_check_grad_no_input(self):
+        pass
+
+
+class TestWithPadWithBias(TestConv2dMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dMKLDNNOp.init_test_case(self)
+        self.pad = [1, 1]
+        self.input_size = [2, 3, 6, 6]
+
+
+class TestWithStride(TestConv2dMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dMKLDNNOp.init_test_case(self)
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 6, 6]
+
+
+class TestWithGroup(TestConv2dMKLDNNOp):
+    def init_group(self):
+        self.groups = 3
+
+
+class TestWith1x1(TestConv2dMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dMKLDNNOp.init_test_case(self)
+        self.filter_size = [6, 3, 1, 1]
+
+
+class TestWithInput1x1Filter1x1(TestConv2dMKLDNNOp):
+    def init_test_case(self):
+        TestConv2dMKLDNNOp.init_test_case(self)
+        self.input_size = [2, 3, 1, 1]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 1, 1]
+
+    def init_group(self):
+        self.groups = 3
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/mkldnn/test_pool2d_mkldnn_op.py

@@ -18,6 +18,24 @@ import unittest
 from paddle.fluid.tests.unittests.test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
 
 
+def create_test_mkldnn_use_ceil_class(parent):
+    class TestMKLDNNPool2DUseCeilCase(parent):
+        def init_kernel_type(self):
+            self.use_mkldnn = True
+
+        def init_ceil_mode(self):
+            self.ceil_mode = True
+
+    cls_name = "{0}_{1}".format(parent.__name__, "MKLDNNCeilModeCast")
+    TestMKLDNNPool2DUseCeilCase.__name__ = cls_name
+    globals()[cls_name] = TestMKLDNNPool2DUseCeilCase
+
+
+create_test_mkldnn_use_ceil_class(TestPool2D_Op)
+create_test_mkldnn_use_ceil_class(TestCase1)
+create_test_mkldnn_use_ceil_class(TestCase2)
+
+
 def create_test_mkldnn_class(parent):
     class TestMKLDNNCase(parent):
         def init_kernel_type(self):

tools/check_doc_approval.py

-# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-import ast
-import hashlib
-import importlib
-import paddle.fluid
-
-files = [
-    "paddle.fluid", "paddle.fluid.average", "paddle.fluid.backward",
-    "paddle.fluid.clip", "paddle.fluid.data_feeder", "paddle.fluid.executor",
-    "paddle.fluid.initializer", "paddle.fluid.io", "paddle.fluid.layers",
-    "paddle.fluid.metrics", "paddle.fluid.nets", "paddle.fluid.optimizer",
-    "paddle.fluid.profiler", "paddle.fluid.recordio_writer",
-    "paddle.fluid.regularizer", "paddle.fluid.transpiler"
-]
-
-
-def md5(doc):
-    hash = hashlib.md5()
-    hash.update(str(doc))
-    return hash.hexdigest()
-
-
-def get_module():
-    for fi in files:
-        fi_lib = importlib.import_module(fi)
-        doc_function = getattr(fi_lib, "__all__")
-        for api in doc_function:
-            api_name = fi + "." + api
-            try:
-                doc_module = getattr(eval(api_name), "__doc__")
-            except:
-                pass
-            doc_md5_code = md5(doc_module)
-            doc_dict[api_name] = doc_md5_code
-
-
-def doc_md5_dict(doc_md5_path):
-    with open(doc_md5_path, "rb") as f:
-        doc_md5 = f.read()
-        doc_md5_dict = ast.literal_eval(doc_md5)
-    return doc_md5_dict
-
-
-def check_doc_md5():
-    for k, v in doc_dict.items():
-        try:
-            if doc_ci_dict[k] != v:
-                return doc_dict
-        except:
-            return doc_dict
-    return True
-
-
-if __name__ == "__main__":
-    doc_dict = {}
-    doc_ci_dict = {}
-    doc_md5_file = "/root/.cache/doc_md5.txt"
-    if not os.path.exists(doc_md5_file):
-        os.mknod(doc_md5_file)
-    else:
-        doc_ci_dict = doc_md5_dict(doc_md5_file)
-    get_module()
-    if not os.path.getsize(doc_md5_file):
-        with open(doc_md5_file, 'w') as f:
-            f.write(str(doc_dict))
-        check_dic = True
-        print(check_dic)
-    else:
-        check_dic = check_doc_md5()
-        print(check_dic)

tools/diff_api.py

@@ -26,4 +26,10 @@ for each_diff in result:
         print(each_diff)
 
 if error:
+    print(
+        '''If you modify/add/delete the API files, including code and comment, please follow these steps in order to pass the CI:
+    1. cd ${paddle_path}, compile paddle;
+    2. pip install build/python/dist/(build whl package);
+    3. run "python tools/print_signatures.py paddle.fluid, paddle.reader > paddle/fluid/API.spec"'''
+    )
     sys.exit(1)

tools/print_signatures.py

@@ -24,12 +24,19 @@ import inspect
 import collections
 import sys
 import pydoc
+import hashlib
 
 member_dict = collections.OrderedDict()
 
 experimental_namespace = {"paddle.fluid.imperative"}
 
 
+def md5(doc):
+    hash = hashlib.md5()
+    hash.update(str(doc).encode('utf-8'))
+    return hash.hexdigest()
+
+
 def visit_member(parent_name, member):
     cur_name = ".".join([parent_name, member.__name__])
     if inspect.isclass(member):
@@ -39,7 +46,10 @@ def visit_member(parent_name, member):
                 visit_member(cur_name, value)
     elif callable(member):
         try:
-            member_dict[cur_name] = inspect.getargspec(member)
+            doc = ('document', md5(member.__doc__))
+            args = inspect.getargspec(member)
+            all = (args, doc)
+            member_dict[cur_name] = all
         except TypeError:  # special for PyBind method
             member_dict[cur_name] = "  ".join([
                 line.strip() for line in pydoc.render_doc(member).split('\n')