[ruff] Doc-string should start at the first line [4/n] (pyg-team#8333)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

examples/contrib/pgm_explainer_graph_classification.py

@@ -1,5 +1,4 @@
-"""
-This is an example of using the PGM explainer algorithm
+"""This is an example of using the PGM explainer algorithm
 on a graph classification task
 """
 import os.path as osp

examples/contrib/pgm_explainer_node_classification.py

@@ -1,5 +1,4 @@
-"""
-This is an example of using the PGM explainer algorithm
+"""This is an example of using the PGM explainer algorithm
 on a node classification task
 """
 import os.path as osp

examples/equilibrium_median.py

@@ -1,5 +1,4 @@
-r"""
-Replicates the experiment from `"Deep Graph Infomax"
+r"""Replicates the experiment from `"Deep Graph Infomax"
 <https://arxiv.org/abs/1809.10341>`_ to try and teach
 `EquilibriumAggregation` to learn to take the median of
 a set of numbers

examples/multi_gpu/distributed_sampling_xpu.py

@@ -1,5 +1,4 @@
-"""
-Distributed GAT training, targeting XPU devices.
+"""Distributed GAT training, targeting XPU devices.
 PVC has 2 tiles, each reports itself as a separate
 device. DDP approach allows us to employ both tiles.
 

examples/multi_gpu/multinode_multigpu_papers100m_gcn.py

@@ -1,5 +1,4 @@
-"""
-To run:
+"""To run:
 srun -l -N<num_nodes> --ntasks-per-node=<ngpu_per_node> \
 --container-name=cont --container-image=<image_url> \
 --container-mounts=/ogb-papers100m/:/workspace/dataset

examples/randlanet_classification.py

@@ -1,5 +1,4 @@
-"""
-An adaptation of RandLA-Net to the classification task, which was not
+"""An adaptation of RandLA-Net to the classification task, which was not
 addressed in the paper:
 RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
 Reference: https://arxiv.org/abs/1911.11236

examples/randlanet_segmentation.py

@@ -1,5 +1,4 @@
-"""
-An implementation of RandLA-Net based on the paper:
+"""An implementation of RandLA-Net based on the paper:
 RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
 Reference: https://arxiv.org/abs/1911.11236
 """

graphgym/custom_graphgym/encoder/example.py

@@ -9,8 +9,7 @@
 
 @register_node_encoder('example')
 class ExampleNodeEncoder(torch.nn.Module):
-    """
-    Provides an encoder for integer node features.
+    """Provides an encoder for integer node features.
 
     Args:
         num_classes (int): The number of classes for the embedding mapping to

pyproject.toml

@@ -131,7 +131,6 @@ ignore = [
     "D107",  # TODO: Don't ignore "Missing docstring in __init__"
     "D200",  # Ignore "One-line docstring should fit on one line"
     "D205",  # Ignore "1 blank line required between summary line and description"
-    "D212",  # Ignore "Multi-line docstring summary should start at the first line"
     "D415",  # Ignore "First line should end with a period, question mark, or exclamation point"
 
 ]

torch_geometric/data/feature_store.py

@@ -1,7 +1,7 @@
-r"""
-This class defines the abstraction for a backend-agnostic feature store. The
-goal of the feature store is to abstract away all node and edge feature memory
-management so that varying implementations can allow for independent scale-out.
+r"""This class defines the abstraction for a backend-agnostic feature store.
+The goal of the feature store is to abstract away all node and edge feature
+memory management so that varying implementations can allow for independent
+scale-out.
 
 This particular feature store abstraction makes a few key assumptions:
 * The features we care about storing are node and edge features of a graph.

torch_geometric/data/graph_store.py

@@ -1,5 +1,4 @@
-r"""
-This class defines the abstraction for a backend-agnostic graph store. The
+r"""This class defines the abstraction for a backend-agnostic graph store. The
 goal of the graph store is to abstract away all graph edge index memory
 management so that varying implementations can allow for independent scale-out.
 

torch_geometric/graphgym/config.py

@@ -21,8 +21,7 @@
 
 
 def set_cfg(cfg):
-    r"""
-    This function sets the default config value.
+    r"""This function sets the default config value.
     1) Note that for an experiment, only part of the arguments will be used
     The remaining unused arguments won't affect anything.
     So feel free to register any argument in graphgym.contrib.config
@@ -476,8 +475,7 @@ def assert_cfg(cfg):
 
 
 def dump_cfg(cfg):
-    r"""
-    Dumps the config to the output directory specified in
+    r"""Dumps the config to the output directory specified in
     :obj:`cfg.out_dir`
 
     Args:
@@ -490,8 +488,7 @@ def dump_cfg(cfg):
 
 
 def load_cfg(cfg, args):
-    r"""
-    Load configurations from file system and command line
+    r"""Load configurations from file system and command line
 
     Args:
         cfg (CfgNode): Configuration node
@@ -509,8 +506,7 @@ def makedirs_rm_exist(dir):
 
 
 def get_fname(fname):
-    r"""
-    Extract filename from file name path
+    r"""Extract filename from file name path
 
     Args:
         fname (str): Filename for the yaml format configuration file
@@ -524,8 +520,7 @@ def get_fname(fname):
 
 
 def set_out_dir(out_dir, fname):
-    r"""
-    Create the directory for full experiment run
+    r"""Create the directory for full experiment run
 
     Args:
         out_dir (str): Directory for output, specified in :obj:`cfg.out_dir`
@@ -541,8 +536,7 @@ def set_out_dir(out_dir, fname):
 
 
 def set_run_dir(out_dir):
-    r"""
-    Create the directory for each random seed experiment run
+    r"""Create the directory for each random seed experiment run
 
     Args:
         out_dir (str): Directory for output, specified in :obj:`cfg.out_dir`

torch_geometric/graphgym/init.py

@@ -2,8 +2,7 @@
 
 
 def init_weights(m):
-    r"""
-    Performs weight initialization
+    r"""Performs weight initialization
 
     Args:
         m (nn.Module): PyTorch module

torch_geometric/graphgym/loader.py

@@ -49,8 +49,7 @@ def planetoid_dataset(name: str) -> Callable:
 
 
 def load_pyg(name, dataset_dir):
-    """
-    Load PyG dataset objects. (More PyG datasets will be supported)
+    """Load PyG dataset objects. (More PyG datasets will be supported)
 
     Args:
         name (str): dataset name
@@ -101,9 +100,7 @@ def set_dataset_attr(dataset, name, value, size):
 
 
 def load_ogb(name, dataset_dir):
-    r"""
-
-    Load OGB dataset objects.
+    r"""Load OGB dataset objects.
 
 
     Args:
@@ -173,9 +170,7 @@ def load_ogb(name, dataset_dir):
 
 
 def load_dataset():
-    r"""
-
-    Load dataset objects.
+    r"""Load dataset objects.
 
     Returns: PyG dataset object
 
@@ -200,8 +195,7 @@ def load_dataset():
 
 
 def set_dataset_info(dataset):
-    r"""
-    Set global dataset information
+    r"""Set global dataset information
 
     Args:
         dataset: PyG dataset object
@@ -233,8 +227,7 @@ def set_dataset_info(dataset):
 
 
 def create_dataset():
-    r"""
-    Create dataset object
+    r"""Create dataset object
 
     Returns: PyG dataset object
 
@@ -311,8 +304,7 @@ def get_loader(dataset, sampler, batch_size, shuffle=True):
 
 
 def create_loader():
-    """
-    Create data loader object
+    """Create data loader object
 
     Returns: List of PyTorch data loaders
 

torch_geometric/graphgym/logger.py

@@ -15,8 +15,7 @@
 
 
 def set_printing():
-    """
-    Set up printing options
+    """Set up printing options
 
     """
     logging.root.handlers = []

torch_geometric/graphgym/loss.py

@@ -6,8 +6,7 @@
 
 
 def compute_loss(pred, true):
-    """
-    Compute loss and prediction score
+    """Compute loss and prediction score
 
     Args:
         pred (torch.tensor): Unnormalized prediction

torch_geometric/graphgym/models/transform.py

@@ -4,8 +4,7 @@
 
 
 def create_link_label(pos_edge_index, neg_edge_index):
-    """
-    Create labels for link prediction, based on positive and negative edges
+    """Create labels for link prediction, based on positive and negative edges
 
     Args:
         pos_edge_index (torch.tensor): Positive edge index [2, num_edges]
@@ -22,8 +21,7 @@ def create_link_label(pos_edge_index, neg_edge_index):
 
 
 def neg_sampling_transform(data):
-    """
-    Do negative sampling for link prediction tasks
+    """Do negative sampling for link prediction tasks
 
     Args:
         data (torch_geometric.data): Input data object

torch_geometric/graphgym/utils/agg_runs.py

@@ -43,8 +43,7 @@ def join_list(l1, l2):
 
 
 def agg_dict_list(dict_list):
-    """
-    Aggregate a list of dictionaries: mean + std
+    """Aggregate a list of dictionaries: mean + std
     Args:
         dict_list: list of dictionaries
 
@@ -80,8 +79,7 @@ def rm_keys(dict, keys):
 
 
 def agg_runs(dir, metric_best='auto'):
-    r"""
-    Aggregate over different random seeds of a single experiment
+    r"""Aggregate over different random seeds of a single experiment
 
     Args:
         dir (str): Directory of the results, containing 1 experiment
@@ -161,8 +159,7 @@ def agg_runs(dir, metric_best='auto'):
 
 
 def agg_batch(dir, metric_best='auto'):
-    r"""
-    Aggregate across results from multiple experiments via grid search
+    r"""Aggregate across results from multiple experiments via grid search
 
     Args:
         dir (str): Directory of the results, containing multiple experiments

torch_geometric/nn/aggr/base.py

@@ -68,7 +68,8 @@ def forward(
         dim: int = -2,
         max_num_elements: Optional[int] = None,
     ) -> Tensor:
-        r"""
+        r"""Forward pass.
+
         Args:
             x (torch.Tensor): The source tensor.
             index (torch.Tensor, optional): The indices of elements for

torch_geometric/nn/aggr/equilibrium.py

@@ -48,8 +48,7 @@ def forward(self, x: Tensor, y: Tensor, index: Optional[Tensor],
 
 
 class MomentumOptimizer(torch.nn.Module):
-    r"""
-    Provides an inner loop optimizer for the implicitly defined output
+    r"""Provides an inner loop optimizer for the implicitly defined output
     layer. It is based on an unrolled Nesterov momentum algorithm.
 
     Args:

torch_geometric/nn/attention/performer.py

@@ -145,7 +145,7 @@ def __init__(
         self.dropout = torch.nn.Dropout(dropout)
 
     def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor:
-        r"""
+        r"""Forward pass.
 
         Args:
             x (torch.Tensor): Node feature tensor

torch_geometric/nn/conv/han_conv.py

@@ -31,8 +31,7 @@ def group(
 
 
 class HANConv(MessagePassing):
-    r"""
-    The Heterogenous Graph Attention Operator from the
+    r"""The Heterogenous Graph Attention Operator from the
     `"Heterogenous Graph Attention Network"
     <https://arxiv.org/pdf/1903.07293.pdf>`_ paper.
 

torch_geometric/nn/dense/dense_gat_conv.py

@@ -55,7 +55,8 @@ def reset_parameters(self):
 
     def forward(self, x: Tensor, adj: Tensor, mask: Optional[Tensor] = None,
                 add_loop: bool = True):
-        r"""
+        r"""Forward pass.
+
         Args:
             x (torch.Tensor): Node feature tensor
                 :math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with

torch_geometric/nn/dense/dense_gcn_conv.py

@@ -39,7 +39,8 @@ def reset_parameters(self):
 
     def forward(self, x: Tensor, adj: Tensor, mask: OptTensor = None,
                 add_loop: bool = True) -> Tensor:
-        r"""
+        r"""Forward pass.
+
         Args:
             x (torch.Tensor): Node feature tensor
                 :math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with

torch_geometric/nn/dense/dense_gin_conv.py

@@ -32,7 +32,8 @@ def reset_parameters(self):
 
     def forward(self, x: Tensor, adj: Tensor, mask: Optional[Tensor] = None,
                 add_loop: bool = True) -> Tensor:
-        r"""
+        r"""Forward pass.
+
         Args:
             x (torch.Tensor): Node feature tensor
                 :math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with

torch_geometric/nn/dense/dense_graph_conv.py

@@ -33,7 +33,8 @@ def reset_parameters(self):
 
     def forward(self, x: Tensor, adj: Tensor,
                 mask: Optional[Tensor] = None) -> Tensor:
-        r"""
+        r"""Forward pass.
+
         Args:
             x (torch.Tensor): Node feature tensor
                 :math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with

torch_geometric/nn/dense/dense_sage_conv.py

@@ -41,7 +41,8 @@ def reset_parameters(self):
 
     def forward(self, x: Tensor, adj: Tensor,
                 mask: OptTensor = None) -> Tensor:
-        r"""
+        r"""Forward pass.
+
         Args:
             x (torch.Tensor): Node feature tensor
                 :math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with

torch_geometric/nn/dense/dmon_pool.py

@@ -81,7 +81,8 @@ def forward(
         adj: Tensor,
         mask: Optional[Tensor] = None,
     ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
-        r"""
+        r"""Forward pass.
+
         Args:
             x (torch.Tensor): Node feature tensor
                 :math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with