Fix several api docs (#20282)

* Fix several api docs

test=develop, test=document_fix

paddle/fluid/API.spec

@@ -130,7 +130,7 @@ paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', '
 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', '83617c165827e030636c80486d5de6f3'))
 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', '33974b9bfa69f2f1eb85e6f956dff04e'))
 paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b28bdb43160e9667be2a3457d19d9f5b'))
-paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', '933b7e268c4ffa3d5c3ef953a5ee9f0b'))
+paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', '708ce0348b74d3e0c7885c2c524b7fa7'))
 paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', '48ec1ba2d75c4e2faf8d9a47350462ae'))
 paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', 'd1985a930a59c3bd41a7c1d72594f5b9'))
 paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ae57e6e5136dade436f0df1f11770afa'))
@@ -178,7 +178,7 @@ paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_t
 paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'eeb1591cfc854c6ffdac77b376313c44'))
 paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e72db173d4c082e27cb11f31d8c9bfa'))
 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', 'fe352915a543cec434f74e9b32ac49da'))
-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', '83d4ca6dfb957912807f535756e76992'))
+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', '38297567127888e01542857839058d52'))
 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', 'd4435a63d34203339831ee6a86ef9242'))
 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', 'b83e7dfa81059b39bb137922dc914f50'))
 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', '1270395ce97a4e1b556104abbb14f096'))
@@ -190,7 +190,7 @@ paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters',
 paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '54e1675aa0364f4a78fa72804ec0f413'))
 paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'cbe8940643ac80ef75e1abdfbdb09e88'))
 paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'cdf5dc2078f1e20dc61dd0bec7e28a29'))
-paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '98e7927f09ee2270535b29f048e481ec'))
+paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', 'd016c137beb9a4528b7378b437d00151'))
 paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'd7a6d59e464a7ef1184eb6caefeb49f1'))
 paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbac07662a6e22e8e299ced880c7775'))
 paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b9bd3129d36a70e7c4385df51ff71c62'))
@@ -208,7 +208,7 @@ paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len',
 paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCHW')), ('document', '44da7890c8a362a83a1c0902a1dc1e4d'))
 paddle.fluid.layers.resize_trilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCDHW')), ('document', '5b4d0f823f94c260fe5e6f7eec60a797'))
 paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 'NCHW')), ('document', '0107a5cbae1aef3f381d3d769a6068eb'))
-paddle.fluid.layers.gather (ArgSpec(args=['input', 'index', 'overwrite'], varargs=None, keywords=None, defaults=(True,)), ('document', 'f985c9b66e3aec96fa753a8eb44c991c'))
+paddle.fluid.layers.gather (ArgSpec(args=['input', 'index', 'overwrite'], varargs=None, keywords=None, defaults=(True,)), ('document', '5c52e8512f97a84608bc3b8b3250fc70'))
 paddle.fluid.layers.gather_nd (ArgSpec(args=['input', 'index', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a7d625028525167b138106f574dffdf9'))
 paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name', 'overwrite'], varargs=None, keywords=None, defaults=(None, True)), ('document', '3f94c3348dc79b7b40a839d31a3eaa84'))
 paddle.fluid.layers.scatter_nd_add (ArgSpec(args=['ref', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2607b5c9369fbc52f208de066a80fc25'))
@@ -222,7 +222,7 @@ paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None
 paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '32196a194f757b4da114a595a5bc6414'))
 paddle.fluid.layers.crop_tensor (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'd460aaf35afbbeb9beea4789aa6e4343'))
 paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8eb36596bb43d7a907d3397c7aedbdb3'))
-paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '6fc86ed23b420c8a0f6c043563cf3937'))
+paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '1a177f30e5013fae7ee6c45860cf4946'))
 paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '9af1926c06711eacef9e82d7a9e4d308'))
 paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '538fc860b2a1734e118b94e4a1a3ee67'))
 paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '00d437d1e0d9450ea75a0495b93b54a7'))
@@ -256,7 +256,7 @@ paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act',
 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', 'cfa120e583cd4a5bfa120c8a26f98a28'))
 paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
 paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'c39b647b6cf08e058d96ee503d5284fe'))
-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', 'b24d0b21361c4bb8ef2cec8c26fb12b2'))
+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', '2aed0f546f220364fb1da724a3176f74'))
 paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'f4b60847cb0f1ae00823ba6fb1b11310'))
 paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34'))
 paddle.fluid.layers.strided_slice (ArgSpec(args=['input', 'axes', 'starts', 'ends', 'strides'], varargs=None, keywords=None, defaults=None), ('document', '340d8d656272ea396b441aab848429a2'))
@@ -277,12 +277,12 @@ paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], var
 paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '315b50c1cbd9569375b098c56f1e91c9'))
 paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5b32ed21ab89140a8e758002923a0da3'))
 paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ecc4b1323028bde0518d666882d03515'))
-paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '18ec2e3afeb90e70c8b73d2b71c40fdb'))
+paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '57256fcb1119dc35ae031889fa601d61'))
 paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'a0b73c21be618cec0281e7903039e5e3'))
 paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '90c74742f48c70b103f1fbb9eb129066'))
-paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', 'e3993a477c94729526040ff65d95728e'))
+paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', 'ef1701e11d60508fe8f02dd2a8c60bdf'))
 paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e399f9436fed5f7ff480d8532e42c937'))
-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', '45fc3652a8e1aeffbe4eba371c54f756'))
+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', '6755168c4b2308e1e4f54cb56fa7dcb2'))
 paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2b0e5d5c155ce24bafc38b78cd0b164'))
 paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c568321feb4d16c41a83df43f95089d'))
 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', '5193cf1113f9d8d8f682ee5a5fc8b391'))
@@ -921,7 +921,7 @@ paddle.fluid.transpiler.DistributeTranspilerConfig ('paddle.fluid.transpiler.dis
 paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 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', '5e89c978199c4ecce2b26d5fed1ec52b'))
 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', 'b2d435f782ac8ea3ca480b8d24e7f5b4'))
-paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', 'b87bacfc70dd3477ed25ef14aa01389a'))
+paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3efe197c8e3e75f84a4c464d8b74e943'))
 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', 'b1a07a0000eb9103e3a143ca8c13de5b'))
 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', '6033b78da39b8b0ed302fbb0f67da502'))
 paddle.fluid.optimizer.SGDOptimizer ('paddle.fluid.optimizer.SGDOptimizer', ('document', 'c3c8dd3193d991adf8bda505560371d6'))
@@ -1043,7 +1043,7 @@ paddle.fluid.optimizer.DGCMomentumOptimizer.get_opti_var_name_list (ArgSpec(args
 paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
 paddle.fluid.optimizer.DGCMomentumOptimizer.set_dict (ArgSpec(args=['self', 'state_dict'], varargs=None, keywords=None, defaults=None), ('document', '36aa497a2d29abaa4147987d71721d17'))
 paddle.fluid.optimizer.DGCMomentumOptimizer.state_dict (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'deca1537945d33940b350923fb16ddf8'))
-paddle.fluid.optimizer.LambOptimizer ('paddle.fluid.optimizer.LambOptimizer', ('document', '7dd8b270156a52f1f6b4663336960893'))
+paddle.fluid.optimizer.LambOptimizer ('paddle.fluid.optimizer.LambOptimizer', ('document', '56b5b21dc8fb01174c3bdd0b24f8be4b'))
 paddle.fluid.optimizer.LambOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'lamb_weight_decay', 'beta1', 'beta2', 'epsilon', 'regularization', 'exclude_from_weight_decay_fn', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.01, 0.9, 0.999, 1e-06, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.optimizer.LambOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
 paddle.fluid.optimizer.LambOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
@@ -1052,9 +1052,9 @@ paddle.fluid.optimizer.LambOptimizer.get_opti_var_name_list (ArgSpec(args=['self
 paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
 paddle.fluid.optimizer.LambOptimizer.set_dict (ArgSpec(args=['self', 'state_dict'], varargs=None, keywords=None, defaults=None), ('document', '36aa497a2d29abaa4147987d71721d17'))
 paddle.fluid.optimizer.LambOptimizer.state_dict (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'deca1537945d33940b350923fb16ddf8'))
-paddle.fluid.optimizer.ExponentialMovingAverage ('paddle.fluid.optimizer.ExponentialMovingAverage', ('document', 'a38b7d5b9f17a295ed15d4c1b9ab4cd0'))
+paddle.fluid.optimizer.ExponentialMovingAverage ('paddle.fluid.optimizer.ExponentialMovingAverage', ('document', 'c93a5e29890877d94c3180b3d5f2d464'))
 paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '30f494752ac8921dc5835a63637f453a'))
+paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '178fa374c299906eb88c0df8ed21a337'))
 paddle.fluid.optimizer.ExponentialMovingAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '8c8a1791608b02a1ede53d6dd3a4fcec'))
 paddle.fluid.optimizer.ExponentialMovingAverage.update (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'ea10f08af6d7aac3b7974aa976e4085f'))
 paddle.fluid.optimizer.PipelineOptimizer ('paddle.fluid.optimizer.PipelineOptimizer', ('document', '6f85382abedb922387b08d98e8d0b69c'))

paddle/fluid/operators/crf_decoding_op.cc

@@ -21,45 +21,47 @@ class CRFDecodingOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput(
         "Emission",
-        "(Tensor<float>/LoDTensor<float>). For a LoDTensor input, its "
-        "shape is [N x D] where N is the total sequence length of the "
-        "mini-batch and D is the total tag number. While for a tensor "
-        "input, its shape is [B X S X D] with B the batch size and S the "
-        "sequence length of each sample after padding. This input is the "
-        "unscaled emission weight matrix of the linear_chain_crf operator.");
+        "(Tensor/LoDTensor). For a LoDTensor input, its shape is [N x D] "
+        "where N is the total sequence length of the mini-batch and D is "
+        "the total tag number. While for a tensor input, its shape is "
+        "[B X S X D] with B the batch size and S the sequence length of each "
+        "sample after padding. This input is the unscaled emission weight "
+        "matrix of the linear_chain_crf operator. The data type is float32 "
+        "or float64.");
     AddInput(
         "Transition",
-        "(Tensor<float>). A Tensor with shape [(D + 2) x D]. "
+        "(Tensor). A Tensor with shape [(D + 2) x D]. "
         "This input is the transition weights learned by the linear_chain_crf "
         "operator, denoted as w. The 1st row of w are transition weights for "
         "the start mask. The 2nd row of w are transition weights for the end "
         "mask. Transition weights between other tags begin from the 3rd row of "
-        "w. See more details in comments of the linear_chain_crf operator.");
+        "w. See more details in comments of the linear_chain_crf operator. "
+        "The data type is the same as Input(Emission).");
     AddInput(
         "Label",
-        "(Tensor<int64_t>/LoDTensor<int64_t>). The ground truth with shape "
+        "(Tensor/LoDTensor). The ground truth with shape "
         "[N x 1] (for LoDTensor) or [B x S] (for Tensor). This input is "
-        "optional. See more details in the operator's comments.")
+        "optional. See more details in the operator's comments. The data type "
+        "is int64.")
         .AsDispensable();
     AddOutput(
         "ViterbiPath",
-        "(Tensor<int64_t>/LoDTensor<int64_t>). The decoding results. What to "
+        "(Tensor/LoDTensor). The decoding results. What to "
         "return changes depending on whether the Input(Label) (the ground "
-        "truth) is given. See more details in the operator's comment.");
+        "truth) is given. See more details in the operator's comment. "
+        "The data type is int64.");
     AddInput("Length",
-             "(Tensor<int64_t>). The actual length of each sample before "
+             "(Tensor). The actual length of each sample before "
              "padding with shape [B x 1]. It means the Input(Emission), "
-             "Input(Label) "
-             "and Output(ViterbiPath) are common tensors with padding when "
-             "this input "
-             "is given.")
+             "Input(Label) and Output(ViterbiPath) are common tensors with "
+             "padding when this input is given. The data type is int64.")
         .AsDispensable();
     AddComment(R"DOC(
 The crf_decoding operator reads the emission feature weights and the transition
-feature weights learned by the linear_chain_crf operator. It implements the
-Viterbi algorithm which is a dynamic programming algorithm for finding the most
-likely sequence of hidden states, called the Viterbi path, that results in a
-sequence of observed tags.
+feature weights learned by the linear_chain_crf operator and performs decoding. 
+It implements the Viterbi algorithm which is a dynamic programming algorithm 
+for finding the most likely sequence of hidden states, called the Viterbi path, 
+that results in a sequence of observed tags.
 
 The output of this operator changes according to whether Input(Label) is given:
 

paddle/fluid/operators/gaussian_random_batch_size_like_op.cc

@@ -46,7 +46,7 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
     AddAttr<int>("seed",
                  "(int, default 0) "
                  "Random seed of generator."
-                 "0 means use system wide seed."
+                 "0 means don't specify random seed."
                  "Note that if seed is not 0, this operator will always "
                  "generate the same random numbers every time.")
         .SetDefault(0);
@@ -58,9 +58,9 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
     AddComment(R"DOC(
 
 Used to initialize tensors with gaussian random generator.
-The default mean of the distribution is 0. and default standard
-deviation (std) of the distribution is 1.. Uers can set mean and std
-by input arguments.
+The default mean of the distribution is 0, and default standard
+deviation (std) of the distribution is 1.0. Uers can set mean and std
+via input arguments.
 )DOC");
   }
 };

python/paddle/fluid/nets.py

@@ -318,8 +318,9 @@ def sequence_conv_pool(input,
 
 def glu(input, dim=-1):
     """
-    The Gated Linear Units(GLU) composed by split, sigmoid activation and element-wise
-    multiplication. Specifically, Split the input into two equal sized parts,
+    The Gated Linear Units(GLU) composed by :ref:`api_fluid_layers_split` , 
+    :ref:`api_fluid_layers_sigmoid`  and :ref:`api_fluid_layers_elementwise_mul` . 
+    Specifically, GLU will plit the input into two equal-sized parts,
     :math:`a` and :math:`b`, along the given dimension and then compute as
     following:
 
@@ -332,17 +333,19 @@ def glu(input, dim=-1):
 
     Args:
         input (Variable): The input variable which is a Tensor or LoDTensor. 
-        dim (int): The dimension along which to split. If :math:`dim < 0`, the
+                          The supported data types include float32, float64 
+                          and float16 (only for GPU).
+        dim (int, optional): The dimension along which to split. If :math:`dim < 0`, the
             dimension to split along is :math:`rank(input) + dim`. Default -1.
 
     Returns:
-        Variable: Variable with half the size of input.
+        Variable: Variable with half the size and same data type of input.
 
     Examples:
         .. code-block:: python
 
             import paddle.fluid as fluid
-            data = fluid.layers.data(
+            data = fluid.data(
                 name="words", shape=[-1, 6, 3, 9], dtype="float32")
             # shape of output: [-1, 3, 3, 9]
             output = fluid.nets.glu(input=data, dim=1)

python/paddle/fluid/optimizer.py

@@ -2297,11 +2297,11 @@ class LambOptimizer(AdamOptimizer):
 
     ..  math::
 
-        m_t &= \\beta_1 m_{t - 1}+ (1 - \\beta_1)g_t \\
+        m_t &= \\beta_1 m_{t - 1}+ (1 - \\beta_1)g_t 
 
-        v_t &= \\beta_2 v_{t - 1}  + (1 - \\beta_2)g_t^2 \\
+        v_t &= \\beta_2 v_{t - 1}  + (1 - \\beta_2)g_t^2
 
-        r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon} \\
+        r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon}
 
         w_t &= w_{t-1} -\\eta_t \\frac{\\left \| w_{t-1}\\right \|}{\\left \| r_t + \\lambda w_{t-1}\\right \|} (r_t + \\lambda w_{t-1})
 
@@ -2310,25 +2310,28 @@ class LambOptimizer(AdamOptimizer):
     learning rate, :math:`\\lambda` the LAMB weight decay rate.
 
     Args:
-        learning_rate (float|Variable): the learning rate used to update parameters. \
-                                        Can be a float value or a Variable with one \
-                                        float value as data element.
-        lamb_weight_decay (float): The LAMB weight decay rate.
-        beta1 (float): The exponential decay rate for the 1st moment estimates.
-        beta2 (float): The exponential decay rate for the 2nd moment estimates.
-        epsilon (float): A small float value for numerical stability.
-        regularization (Regularizer): A Regularizer, such as
-                        fluid.regularizer.L1DecayRegularizer.
-        exclude_from_weight_decay_fn (function): Exclude a parameter from weight 
+        learning_rate (float|Variable, optional): the learning rate used to update parameters. \
+            Can be a float value or a Variable with data type float32. Default 0.001.
+        lamb_weight_decay (float, optional): The LAMB weight decay rate. Default 0.01.
+        beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
+            Default 0.9.
+        beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
+            Default 0.999.
+        epsilon (float, optional): A small float value for numerical stability. Default 1e-6.
+        regularization (Regularizer|None): A Regularizer, such as
+           fluid.regularizer.L1DecayRegularizer. Default None.
+        exclude_from_weight_decay_fn (function|None): Exclude a parameter from weight 
             decay when **exclude_from_weight_decay_fn(parameter)** returns true. 
-        name (str|None): An optional name prefix.
+            Default None.
+        name(str|None): For detailed information, please refer to 
+            :ref:`api_guide_Name` . Usually name is no need to set and None by default.
 
     Examples:
         .. code-block:: python
             
             import paddle.fluid as fluid 
 
-            data = fluid.layers.data(name='x', shape=[5], dtype='float32')
+            data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
             hidden = fluid.layers.fc(input=data, size=10)
             cost = fluid.layers.mean(hidden)
 
@@ -2781,10 +2784,13 @@ class ExponentialMovingAverage(object):
 
 
     Args:
-	decay (float): The exponential decay rate, usually close to 1, such as 
-                       0.999, 0.9999, ... .
+	decay (float, optional): The exponential decay rate, usually close to 1, such as 
+            0.999, 0.9999, ... . Default 0.999.
         thres_steps (Variable|None): If not `None`, schedule the decay rate. 
-	name (str|None): An optional name prefix.
+            Default None.
+        name (str|None): For detailed information, please refer to 
+            :ref:`api_guide_Name`. Usually name is no need to set and None by 
+            default.
 
 
     Examples:
@@ -2795,7 +2801,7 @@ class ExponentialMovingAverage(object):
 	    import paddle
 	    import paddle.fluid as fluid
 
-	    data = fluid.layers.data(name='x', shape=[5], dtype='float32')
+	    data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
 	    hidden = fluid.layers.fc(input=data, size=10)
 	    cost = fluid.layers.mean(hidden)
 
@@ -2953,7 +2959,8 @@ class ExponentialMovingAverage(object):
         
         Args:
             executor (Executor): The Executor to execute applying.
-            need_restore (bool): Whether to restore parameters after applying.
+            need_restore (bool, optional): Whether to restore parameters after 
+                applying. Default True.
         """
         executor.run(self.apply_program)
         try: