SYMBOL INDEX (8209 symbols across 1230 files) FILE: benchmark/citation/appnp.py class Net (line 31) | class Net(torch.nn.Module): method __init__ (line 32) | def __init__(self, dataset): method reset_parameters (line 38) | def reset_parameters(self): method forward (line 42) | def forward(self, data): FILE: benchmark/citation/arma.py class Net (line 32) | class Net(torch.nn.Module): method __init__ (line 33) | def __init__(self, dataset): method reset_parameters (line 42) | def reset_parameters(self): method forward (line 46) | def forward(self, data): FILE: benchmark/citation/cheb.py class Net (line 29) | class Net(torch.nn.Module): method __init__ (line 30) | def __init__(self, dataset): method reset_parameters (line 35) | def reset_parameters(self): method forward (line 39) | def forward(self, data): FILE: benchmark/citation/datasets.py function get_planetoid_dataset (line 7) | def get_planetoid_dataset(name, normalize_features=False, transform=None): FILE: benchmark/citation/gat.py class Net (line 30) | class Net(torch.nn.Module): method __init__ (line 31) | def __init__(self, dataset): method reset_parameters (line 39) | def reset_parameters(self): method forward (line 43) | def forward(self, data): FILE: benchmark/citation/gcn.py class Net (line 28) | class Net(torch.nn.Module): method __init__ (line 29) | def __init__(self, dataset): method reset_parameters (line 34) | def reset_parameters(self): method forward (line 38) | def forward(self, data): FILE: benchmark/citation/sgc.py class Net (line 27) | class Net(torch.nn.Module): method __init__ (line 28) | def __init__(self, dataset): method reset_parameters (line 33) | def reset_parameters(self): method forward (line 36) | def forward(self, data): FILE: benchmark/citation/statistics.py function print_dataset (line 4) | def print_dataset(dataset): FILE: benchmark/citation/train_eval.py function random_planetoid_splits (line 19) | def random_planetoid_splits(data, num_classes): function run_train (line 43) | def run_train(dataset, model, runs, epochs, lr, weight_decay, early_stop... function run_inference (line 121) | def run_inference(dataset, model, epochs, profiling, bf16, use_compile, function run (line 152) | def run(dataset, model, runs, epochs, lr, weight_decay, early_stopping, function train (line 164) | def train(model, optimizer, data): function evaluate (line 174) | def evaluate(model, data): function inference (line 193) | def inference(model, data): FILE: benchmark/inference/inference_benchmark.py function full_batch_inference (line 35) | def full_batch_inference(model, data): function run (line 44) | def run(args: argparse.ArgumentParser): FILE: benchmark/kernel/asap.py class ASAP (line 13) | class ASAP(torch.nn.Module): method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden, ratio=0.8, dropout=0): method reset_parameters (line 31) | def reset_parameters(self): method forward (line 40) | def forward(self, data): method __repr__ (line 60) | def __repr__(self): FILE: benchmark/kernel/datasets.py class NormalizedDegree (line 10) | class NormalizedDegree: method __init__ (line 11) | def __init__(self, mean, std): method __call__ (line 15) | def __call__(self, data): function get_dataset (line 22) | def get_dataset(name, sparse=True, cleaned=False): FILE: benchmark/kernel/diff_pool.py class Block (line 10) | class Block(torch.nn.Module): method __init__ (line 11) | def __init__(self, in_channels, hidden_channels, out_channels, mode='c... method reset_parameters (line 22) | def reset_parameters(self): method forward (line 27) | def forward(self, x, adj, mask=None): class DiffPool (line 33) | class DiffPool(torch.nn.Module): method __init__ (line 34) | def __init__(self, dataset, num_layers, hidden, ratio=0.25): method reset_parameters (line 52) | def reset_parameters(self): method forward (line 63) | def forward(self, data): method __repr__ (line 85) | def __repr__(self): FILE: benchmark/kernel/edge_pool.py class EdgePool (line 13) | class EdgePool(torch.nn.Module): method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 29) | def reset_parameters(self): method forward (line 38) | def forward(self, data): method __repr__ (line 54) | def __repr__(self): FILE: benchmark/kernel/gcn.py class GCN (line 8) | class GCN(torch.nn.Module): method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 18) | def reset_parameters(self): method forward (line 25) | def forward(self, data): method __repr__ (line 36) | def __repr__(self): class GCNWithJK (line 40) | class GCNWithJK(torch.nn.Module): method __init__ (line 41) | def __init__(self, dataset, num_layers, hidden, mode='cat'): method reset_parameters (line 54) | def reset_parameters(self): method forward (line 62) | def forward(self, data): method __repr__ (line 76) | def __repr__(self): FILE: benchmark/kernel/gin.py class GIN0 (line 9) | class GIN0(torch.nn.Module): method __init__ (line 10) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 36) | def reset_parameters(self): method forward (line 43) | def forward(self, data): method __repr__ (line 54) | def __repr__(self): class GIN0WithJK (line 58) | class GIN0WithJK(torch.nn.Module): method __init__ (line 59) | def __init__(self, dataset, num_layers, hidden, mode='cat'): method reset_parameters (line 89) | def reset_parameters(self): method forward (line 97) | def forward(self, data): method __repr__ (line 111) | def __repr__(self): class GIN (line 115) | class GIN(torch.nn.Module): method __init__ (line 116) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 142) | def reset_parameters(self): method forward (line 149) | def forward(self, data): method __repr__ (line 160) | def __repr__(self): class GINWithJK (line 164) | class GINWithJK(torch.nn.Module): method __init__ (line 165) | def __init__(self, dataset, num_layers, hidden, mode='cat'): method reset_parameters (line 195) | def reset_parameters(self): method forward (line 203) | def forward(self, data): method __repr__ (line 217) | def __repr__(self): FILE: benchmark/kernel/global_attention.py class GlobalAttentionNet (line 8) | class GlobalAttentionNet(torch.nn.Module): method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 19) | def reset_parameters(self): method forward (line 27) | def forward(self, data): method __repr__ (line 38) | def __repr__(self): FILE: benchmark/kernel/graclus.py class Graclus (line 15) | class Graclus(torch.nn.Module): method __init__ (line 16) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 26) | def reset_parameters(self): method forward (line 34) | def forward(self, data): method __repr__ (line 52) | def __repr__(self): FILE: benchmark/kernel/graph_sage.py class GraphSAGE (line 8) | class GraphSAGE(torch.nn.Module): method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 18) | def reset_parameters(self): method forward (line 25) | def forward(self, data): method __repr__ (line 36) | def __repr__(self): class GraphSAGEWithJK (line 40) | class GraphSAGEWithJK(torch.nn.Module): method __init__ (line 41) | def __init__(self, dataset, num_layers, hidden, mode='cat'): method reset_parameters (line 54) | def reset_parameters(self): method forward (line 62) | def forward(self, data): method __repr__ (line 76) | def __repr__(self): FILE: benchmark/kernel/main.py function logger (line 51) | def logger(info): FILE: benchmark/kernel/main_performance.py function prepare_dataloader (line 57) | def prepare_dataloader(dataset_name): function run_train (line 75) | def run_train(): function run_inference (line 117) | def run_inference(): FILE: benchmark/kernel/sag_pool.py class SAGPool (line 13) | class SAGPool(torch.nn.Module): method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden, ratio=0.8): method reset_parameters (line 29) | def reset_parameters(self): method forward (line 38) | def forward(self, data): method __repr__ (line 55) | def __repr__(self): FILE: benchmark/kernel/set2set.py class Set2SetNet (line 8) | class Set2SetNet(torch.nn.Module): method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 19) | def reset_parameters(self): method forward (line 27) | def forward(self, data): method __repr__ (line 38) | def __repr__(self): FILE: benchmark/kernel/sort_pool.py class SortPool (line 8) | class SortPool(torch.nn.Module): method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden): method reset_parameters (line 20) | def reset_parameters(self): method forward (line 28) | def forward(self, data): method __repr__ (line 42) | def __repr__(self): FILE: benchmark/kernel/statistics.py function print_dataset (line 4) | def print_dataset(dataset): FILE: benchmark/kernel/top_k.py class TopK (line 13) | class TopK(torch.nn.Module): method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden, ratio=0.8): method reset_parameters (line 29) | def reset_parameters(self): method forward (line 38) | def forward(self, data): method __repr__ (line 55) | def __repr__(self): FILE: benchmark/kernel/train_eval.py function cross_validation_with_val_set (line 20) | def cross_validation_with_val_set(dataset, model, folds, epochs, batch_s... function k_fold (line 98) | def k_fold(dataset, folds): function num_graphs (line 116) | def num_graphs(data): function train (line 123) | def train(model, optimizer, loader): function eval_acc (line 138) | def eval_acc(model, loader): function eval_loss (line 150) | def eval_loss(model, loader): function inference_run (line 163) | def inference_run(model, loader, bf16): FILE: benchmark/loader/neighbor_loader.py function run (line 16) | def run(args: argparse.ArgumentParser): FILE: benchmark/multi_gpu/training/common.py function train_homo (line 28) | def train_homo(model: Any, loader: NeighborLoader, optimizer: torch.opti... function train_hetero (line 44) | def train_hetero(model: Any, loader: NeighborLoader, function maybe_synchronize (line 61) | def maybe_synchronize(device: str): function create_mask_per_rank (line 68) | def create_mask_per_rank( function run (line 86) | def run(rank: int, world_size: int, args: argparse.ArgumentParser, function get_predefined_args (line 256) | def get_predefined_args() -> argparse.ArgumentParser: FILE: benchmark/multi_gpu/training/training_benchmark_cuda.py function run_cuda (line 18) | def run_cuda(rank: int, world_size: int, args: argparse.ArgumentParser, FILE: benchmark/multi_gpu/training/training_benchmark_xpu.py function get_dist_params (line 16) | def get_dist_params() -> Tuple[int, int, str]: function custom_optimizer (line 36) | def custom_optimizer(model: Any, optimizer: Any) -> Tuple[Any, Any]: FILE: benchmark/points/datasets.py function get_dataset (line 7) | def get_dataset(num_points): FILE: benchmark/points/edge_cnn.py class Net (line 28) | class Net(torch.nn.Module): method __init__ (line 29) | def __init__(self, num_classes): method forward (line 45) | def forward(self, pos, batch): FILE: benchmark/points/mpnn.py class Net (line 28) | class Net(torch.nn.Module): method __init__ (line 29) | def __init__(self, num_classes): method forward (line 45) | def forward(self, pos, batch): FILE: benchmark/points/point_cnn.py class Net (line 26) | class Net(torch.nn.Module): method __init__ (line 27) | def __init__(self, num_classes): method forward (line 42) | def forward(self, pos, batch): FILE: benchmark/points/point_net.py class Net (line 28) | class Net(torch.nn.Module): method __init__ (line 29) | def __init__(self, num_classes): method forward (line 45) | def forward(self, pos, batch): FILE: benchmark/points/spline_cnn.py class Net (line 26) | class Net(torch.nn.Module): method __init__ (line 27) | def __init__(self, num_classes): method forward (line 38) | def forward(self, pos, batch): FILE: benchmark/points/statistics.py function print_dataset (line 6) | def print_dataset(train_dataset, test_dataset): FILE: benchmark/points/train_eval.py function run_train (line 18) | def run_train(train_dataset, test_dataset, model, epochs, batch_size, function run_inference (line 58) | def run_inference(test_dataset, model, epochs, batch_size, profiling, bf16, function run (line 84) | def run(train_dataset, test_dataset, model, epochs, batch_size, lr, function train (line 96) | def train(model, optimizer, train_loader, device): function test (line 109) | def test(model, test_loader, device): function inference (line 123) | def inference(model, test_loader, device, bf16): FILE: benchmark/runtime/dgl/gat.py class GATConv (line 10) | class GATConv(torch.nn.Module): method __init__ (line 11) | def __init__(self, g, in_channels, out_channels, heads=1, method reset_parameters (line 27) | def reset_parameters(self): method gat_msg (line 32) | def gat_msg(self, edge): method gat_reduce (line 38) | def gat_reduce(self, node): method forward (line 44) | def forward(self, x): class GAT (line 54) | class GAT(torch.nn.Module): method __init__ (line 55) | def __init__(self, g, in_channels, out_channels): method forward (line 61) | def forward(self, x): class GATSPMVConv (line 69) | class GATSPMVConv(torch.nn.Module): method __init__ (line 70) | def __init__(self, g, in_channels, out_channels, heads=1, method reset_parameters (line 85) | def reset_parameters(self): method forward (line 91) | def forward(self, x): method edge_attention (line 104) | def edge_attention(self, edge): method edge_softmax (line 108) | def edge_softmax(self): class GATSPMV (line 116) | class GATSPMV(torch.nn.Module): method __init__ (line 117) | def __init__(self, g, in_channels, out_channels): method forward (line 123) | def forward(self, x): FILE: benchmark/runtime/dgl/gcn.py class GCNConv (line 9) | class GCNConv(torch.nn.Module): method __init__ (line 10) | def __init__(self, g, in_channels, out_channels): method reset_parameters (line 17) | def reset_parameters(self): method gcn_msg (line 21) | def gcn_msg(self, edge): method gcn_reduce (line 24) | def gcn_reduce(self, node): method forward (line 27) | def forward(self, x): class GCN (line 35) | class GCN(torch.nn.Module): method __init__ (line 36) | def __init__(self, g, in_channels, out_channels): method forward (line 41) | def forward(self, x): class GCNSPMVConv (line 48) | class GCNSPMVConv(torch.nn.Module): method __init__ (line 49) | def __init__(self, g, in_channels, out_channels): method reset_parameters (line 56) | def reset_parameters(self): method forward (line 60) | def forward(self, x): class GCNSPMV (line 70) | class GCNSPMV(torch.nn.Module): method __init__ (line 71) | def __init__(self, g, in_channels, out_channels): method forward (line 76) | def forward(self, x): FILE: benchmark/runtime/dgl/hidden.py class HiddenPrint (line 8) | class HiddenPrint: method __enter__ (line 9) | def __enter__(self): method __exit__ (line 13) | def __exit__(self, exc_type, exc_val, exc_tb): FILE: benchmark/runtime/dgl/rgcn.py class RGCNConv (line 9) | class RGCNConv(torch.nn.Module): method __init__ (line 10) | def __init__(self, g, in_channels, out_channels, num_relations, num_ba... method reset_parameters (line 26) | def reset_parameters(self): method rgcn_reduce (line 33) | def rgcn_reduce(self, node): method forward (line 36) | def forward(self, x): class RGCN (line 69) | class RGCN(torch.nn.Module): method __init__ (line 70) | def __init__(self, g, in_channels, out_channels, num_relations): method forward (line 75) | def forward(self, x): class RGCNSPMVConv (line 81) | class RGCNSPMVConv(torch.nn.Module): method __init__ (line 82) | def __init__(self, g, in_channels, out_channels, num_relations, num_ba... method reset_parameters (line 98) | def reset_parameters(self): method forward (line 105) | def forward(self, x): class RGCNSPMV (line 138) | class RGCNSPMV(torch.nn.Module): method __init__ (line 139) | def __init__(self, g, in_channels, out_channels, num_relations): method forward (line 146) | def forward(self, x): FILE: benchmark/runtime/dgl/train.py function train_runtime (line 7) | def train_runtime(model, data, epochs, device): FILE: benchmark/runtime/gat.py class GAT (line 7) | class GAT(torch.nn.Module): method __init__ (line 8) | def __init__(self, in_channels, out_channels): method forward (line 13) | def forward(self, data): FILE: benchmark/runtime/gcn.py class GCN (line 7) | class GCN(torch.nn.Module): method __init__ (line 8) | def __init__(self, in_channels, out_channels): method forward (line 13) | def forward(self, data): FILE: benchmark/runtime/rgcn.py class RGCN (line 7) | class RGCN(torch.nn.Module): method __init__ (line 8) | def __init__(self, in_channels, out_channels, num_relations): method forward (line 14) | def forward(self, data): FILE: benchmark/runtime/train.py function train_runtime (line 7) | def train_runtime(model, data, epochs, device): FILE: benchmark/training/training_benchmark.py function train_homo (line 46) | def train_homo(model, loader, optimizer, device, progress_bar=True, desc... function train_hetero (line 74) | def train_hetero(model, loader, optimizer, device, progress_bar=True, de... function run (line 98) | def run(args: argparse.ArgumentParser): FILE: benchmark/utils/hetero_gat.py class HeteroGAT (line 7) | class HeteroGAT(torch.nn.Module): method __init__ (line 8) | def __init__(self, metadata, hidden_channels, num_layers, output_chann... method forward (line 15) | def forward(self, x_dict, edge_index_dict): method inference (line 19) | def inference(self, loader, device, progress_bar=False, **kwargs): method test (line 31) | def test(self, x, loader, device, progress_bar=False): FILE: benchmark/utils/hetero_sage.py class HeteroGraphSAGE (line 7) | class HeteroGraphSAGE(torch.nn.Module): method __init__ (line 8) | def __init__(self, metadata, hidden_channels, num_layers, output_chann... method forward (line 14) | def forward(self, x_dict, edge_index_dict): method inference (line 18) | def inference(self, loader, device, progress_bar=False, **kwargs): method test (line 30) | def test(self, loader, device, progress_bar=False): FILE: benchmark/utils/utils.py function emit_itt (line 24) | def emit_itt(*args, **kwargs): function get_dataset_with_transformation (line 39) | def get_dataset_with_transformation(name, root, use_sparse_tensor=False, function get_dataset (line 83) | def get_dataset(name, root, use_sparse_tensor=False, bf16=False): function get_model (line 89) | def get_model(name, params, metadata=None): function get_split_masks (line 117) | def get_split_masks(data, dataset_name): function save_benchmark_data (line 129) | def save_benchmark_data(csv_data, batch_size, layers, num_neighbors, function write_to_csv (line 144) | def write_to_csv(csv_data, write_csv='bench', training=False): function test (line 163) | def test(model, loader, device, hetero, progress_bar=True, FILE: docs/source/_static/js/version_alert.js function warnOnLatestVersion (line 1) | function warnOnLatestVersion() { FILE: docs/source/conf.py function rst_jinja_render (line 71) | def rst_jinja_render(app, _, source): function setup (line 77) | def setup(app): FILE: examples/agnn.py class Net (line 16) | class Net(torch.nn.Module): method __init__ (line 17) | def __init__(self): method forward (line 24) | def forward(self): function train (line 39) | def train(): function test (line 47) | def test(): FILE: examples/ar_link_pred.py class GCNEncoder (line 13) | class GCNEncoder(torch.nn.Module): method __init__ (line 14) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 19) | def forward(self, x, edge_index): class LinkPredictor (line 24) | class LinkPredictor(torch.nn.Module): method __init__ (line 25) | def __init__(self, in_channels, hidden_channels): method forward (line 30) | def forward(self, z_i, z_j): class ARLinkPredictor (line 37) | class ARLinkPredictor(torch.nn.Module): method __init__ (line 38) | def __init__(self, in_channels): method forward (line 44) | def forward(self, z_i, z_j): function train (line 59) | def train(encoder, predictor, data, optimizer): function test (line 93) | def test(encoder, predictor, data): function compute_auc (line 115) | def compute_auc(pos_out, neg_out): function main (line 129) | def main(): FILE: examples/argva_node_clustering.py class Encoder (line 35) | class Encoder(torch.nn.Module): method __init__ (line 36) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 42) | def forward(self, x, edge_index): class Discriminator (line 47) | class Discriminator(torch.nn.Module): method __init__ (line 48) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 54) | def forward(self, x): function train (line 70) | def train(): function test (line 91) | def test(data): function plot_points (line 121) | def plot_points(data, colors): FILE: examples/arma.py class Net (line 16) | class Net(torch.nn.Module): method __init__ (line 17) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 27) | def forward(self, x, edge_index): function train (line 47) | def train(): function test (line 56) | def test(): FILE: examples/attentive_fp.py class GenFeatures (line 13) | class GenFeatures: method __init__ (line 14) | def __init__(self): method __call__ (line 36) | def __call__(self, data): function train (line 119) | def train(): function test (line 134) | def test(loader): FILE: examples/autoencoder.py class GCNEncoder (line 37) | class GCNEncoder(torch.nn.Module): method __init__ (line 38) | def __init__(self, in_channels, out_channels): method forward (line 43) | def forward(self, x, edge_index): class VariationalGCNEncoder (line 48) | class VariationalGCNEncoder(torch.nn.Module): method __init__ (line 49) | def __init__(self, in_channels, out_channels): method forward (line 55) | def forward(self, x, edge_index): class LinearEncoder (line 60) | class LinearEncoder(torch.nn.Module): method __init__ (line 61) | def __init__(self, in_channels, out_channels): method forward (line 65) | def forward(self, x, edge_index): class VariationalLinearEncoder (line 69) | class VariationalLinearEncoder(torch.nn.Module): method __init__ (line 70) | def __init__(self, in_channels, out_channels): method forward (line 75) | def forward(self, x, edge_index): function train (line 94) | def train(): function test (line 107) | def test(data): FILE: examples/cluster_gcn_ppi.py class Net (line 28) | class Net(torch.nn.Module): method __init__ (line 29) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method forward (line 40) | def forward(self, x, edge_index): function train (line 56) | def train(): function test (line 71) | def test(loader): FILE: examples/cluster_gcn_reddit.py class Net (line 24) | class Net(torch.nn.Module): method __init__ (line 25) | def __init__(self, in_channels, out_channels): method forward (line 31) | def forward(self, x, edge_index): method inference (line 39) | def inference(self, x_all): function train (line 71) | def train(): function test (line 91) | def test(): # Inference should be performed on the full graph. FILE: examples/colors_topk_pool.py class HandleNodeAttention (line 16) | class HandleNodeAttention: method __call__ (line 17) | def __call__(self, data): class Net (line 33) | class Net(torch.nn.Module): method __init__ (line 34) | def __init__(self, in_channels): method forward (line 43) | def forward(self, data): function train (line 71) | def train(epoch): function test (line 87) | def test(loader): FILE: examples/compile/gcn.py class GCN (line 28) | class GCN(torch.nn.Module): method __init__ (line 29) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 35) | def forward(self, x, edge_index, edge_weight): function train (line 58) | def train(): function test (line 69) | def test(): FILE: examples/compile/gin.py class GIN (line 31) | class GIN(torch.nn.Module): method __init__ (line 32) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method forward (line 44) | def forward(self, x, edge_index, batch, batch_size): function train (line 65) | def train(): function test (line 81) | def test(loader): FILE: examples/contrib/pgm_explainer_graph_classification.py function normalized_cut_2d (line 33) | def normalized_cut_2d(edge_index, pos): class Net (line 39) | class Net(torch.nn.Module): method __init__ (line 40) | def __init__(self): method forward (line 59) | def forward(self, x, edge_index, **kwargs): function train (line 81) | def train(model, dataloader): FILE: examples/contrib/pgm_explainer_node_classification.py class Net (line 22) | class Net(torch.nn.Module): method __init__ (line 23) | def __init__(self): method forward (line 28) | def forward(self, x, edge_index, edge_weight): FILE: examples/contrib/rbcd_attack.py class GCN (line 20) | class GCN(torch.nn.Module): method __init__ (line 21) | def __init__(self, in_channels, hidden_channels, out_channels): method reset_parameters (line 27) | def reset_parameters(self): method forward (line 31) | def forward(self, x, edge_index, edge_weight=None, **kwargs): class WeightedGATConv (line 47) | class WeightedGATConv(GATConv): method edge_update (line 49) | def edge_update(self, alpha_j: Tensor, alpha_i: Optional[Tensor], class GAT (line 69) | class GAT(torch.nn.Module): method __init__ (line 70) | def __init__(self, in_channels, hidden_channels, out_channels): method reset_parameters (line 78) | def reset_parameters(self): method forward (line 82) | def forward(self, x, edge_index, edge_weight=None): function train (line 89) | def train(model, data, epochs=200, lr=0.01, weight_decay=5e-4): function accuracy (line 100) | def accuracy(pred, y, mask): function test (line 105) | def test(model, data): function metric (line 112) | def metric(*args, **kwargs): FILE: examples/contrib/rbcd_attack_poisoning.py class PoisoningPRBCDAttack (line 43) | class PoisoningPRBCDAttack(PRBCDAttack): method _forward (line 44) | def _forward(self, x: Tensor, edge_index: Tensor, edge_weight: Tensor, method _forward_and_gradient (line 57) | def _forward_and_gradient(self, x: Tensor, labels: Tensor, FILE: examples/cora.py class Net (line 24) | class Net(torch.nn.Module): method __init__ (line 25) | def __init__(self): method forward (line 30) | def forward(self): function train (line 44) | def train(): function test (line 52) | def test(): FILE: examples/correct_and_smooth.py function train (line 33) | def train(): function test (line 44) | def test(out=None): FILE: examples/cpp/main.cpp function main (line 7) | int main(int argc, const char *argv[]) { FILE: examples/cpp/save_model.py class GIN (line 11) | class GIN(torch.nn.Module): method __init__ (line 12) | def __init__(self, in_channels: int, hidden_channels: int, method forward (line 30) | def forward( FILE: examples/datapipe.py function molecule_datapipe (line 29) | def molecule_datapipe() -> IterDataPipe: class MeshOpener (line 48) | class MeshOpener(IterDataPipe): method __init__ (line 50) | def __init__(self, dp: IterDataPipe) -> None: method __iter__ (line 62) | def __iter__(self): function mesh_datapipe (line 80) | def mesh_datapipe() -> IterDataPipe: FILE: examples/dgcnn_classification.py class Net (line 91) | class Net(torch.nn.Module): method __init__ (line 92) | def __init__(self, out_channels, k=20, aggr='max'): method forward (line 101) | def forward(self, data): function train (line 117) | def train(): function test (line 132) | def test(loader): FILE: examples/dgcnn_segmentation.py class Net (line 32) | class Net(torch.nn.Module): method __init__ (line 33) | def __init__(self, out_channels, k=30, aggr='max'): method forward (line 43) | def forward(self, data): function train (line 59) | def train(): function test (line 81) | def test(loader): FILE: examples/dir_gnn.py class DirGNN (line 42) | class DirGNN(torch.nn.Module): method __init__ (line 43) | def __init__(self, in_channels, hidden_channels, out_channels, alpha): method forward (line 51) | def forward(self, x, edge_index): function train (line 67) | def train(): function test (line 78) | def test(): FILE: examples/distributed/graphlearn_for_pytorch/dist_train_sage_supervised.py function test (line 18) | def test(model, test_loader, dataset_name): function run_training_proc (line 41) | def run_training_proc( FILE: examples/distributed/graphlearn_for_pytorch/partition_ogbn_dataset.py function partition_dataset (line 10) | def partition_dataset( FILE: examples/distributed/kuzu/papers_100M/train.py class GraphSAGE (line 62) | class GraphSAGE(torch.nn.Module): method __init__ (line 63) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method forward (line 87) | def forward(self, x, edge_index): FILE: examples/dna.py function gen_uniform_20_20_60_split (line 17) | def gen_uniform_20_20_60_split(data): class Net (line 29) | class Net(torch.nn.Module): method __init__ (line 30) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method reset_parameters (line 41) | def reset_parameters(self): method forward (line 47) | def forward(self, x, edge_index): function train (line 74) | def train(): function test (line 84) | def test(): FILE: examples/egc.py class Net (line 40) | class Net(torch.nn.Module): method __init__ (line 41) | def __init__(self, hidden_channels, num_layers, num_heads, num_bases): method forward (line 68) | def forward(self, x, adj_t, batch): function train (line 93) | def train(): function evaluate (line 113) | def evaluate(loader): FILE: examples/explain/captum_explainer.py class GCN (line 16) | class GCN(torch.nn.Module): method __init__ (line 17) | def __init__(self): method forward (line 22) | def forward(self, x, edge_index): FILE: examples/explain/captum_explainer_hetero_link.py class GNNEncoder (line 38) | class GNNEncoder(torch.nn.Module): method __init__ (line 39) | def __init__(self, hidden_channels, out_channels): method forward (line 44) | def forward(self, x, edge_index): class EdgeDecoder (line 50) | class EdgeDecoder(torch.nn.Module): method __init__ (line 51) | def __init__(self, hidden_channels): method forward (line 56) | def forward(self, z_dict, edge_label_index): class Model (line 65) | class Model(torch.nn.Module): method __init__ (line 66) | def __init__(self, hidden_channels): method forward (line 72) | def forward(self, x_dict, edge_index_dict, edge_label_index): FILE: examples/explain/gnn_explainer.py class GCN (line 16) | class GCN(torch.nn.Module): method __init__ (line 17) | def __init__(self): method forward (line 22) | def forward(self, x, edge_index): FILE: examples/explain/gnn_explainer_ba_shapes.py function train (line 32) | def train(): function test (line 44) | def test(): FILE: examples/explain/gnn_explainer_link_pred.py class GCN (line 30) | class GCN(torch.nn.Module): method __init__ (line 31) | def __init__(self, in_channels, hidden_channels, out_channels): method encode (line 36) | def encode(self, x, edge_index): method decode (line 41) | def decode(self, z, edge_label_index): method forward (line 45) | def forward(self, x, edge_index, edge_label_index): function train (line 54) | def train(): function test (line 67) | def test(data): FILE: examples/explain/graphmask_explainer.py class GCN (line 19) | class GCN(torch.nn.Module): method __init__ (line 20) | def __init__(self): method forward (line 25) | def forward(self, x, edge_index): class GAT (line 63) | class GAT(torch.nn.Module): method __init__ (line 64) | def __init__(self): method forward (line 69) | def forward(self, x, edge_index): FILE: examples/faust.py class Net (line 24) | class Net(torch.nn.Module): method __init__ (line 25) | def __init__(self): method forward (line 36) | def forward(self, data): function train (line 56) | def train(epoch): function test (line 69) | def test(): FILE: examples/film.py class Net (line 21) | class Net(torch.nn.Module): method __init__ (line 22) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method forward (line 37) | def forward(self, x, edge_index): function train (line 59) | def train(): function test (line 74) | def test(loader): FILE: examples/gat.py class GAT (line 38) | class GAT(torch.nn.Module): method __init__ (line 39) | def __init__(self, in_channels, hidden_channels, out_channels, heads): method forward (line 46) | def forward(self, x, edge_index): function train (line 59) | def train(): function test (line 70) | def test(): FILE: examples/gcn.py class GCN (line 49) | class GCN(torch.nn.Module): method __init__ (line 50) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 57) | def forward(self, x, edge_index, edge_weight=None): function train (line 77) | def train(): function test (line 88) | def test(): FILE: examples/gcn2_cora.py class Net (line 19) | class Net(torch.nn.Module): method __init__ (line 20) | def __init__(self, hidden_channels, num_layers, alpha, theta, method forward (line 36) | def forward(self, x, adj_t): function train (line 61) | def train(): function test (line 72) | def test(): FILE: examples/gcn2_ppi.py class Net (line 24) | class Net(torch.nn.Module): method __init__ (line 25) | def __init__(self, hidden_channels, num_layers, alpha, theta, method forward (line 41) | def forward(self, x, adj_t): function train (line 64) | def train(): function test (line 80) | def test(loader): FILE: examples/geniepath.py class Breadth (line 30) | class Breadth(torch.nn.Module): method __init__ (line 31) | def __init__(self, in_dim, out_dim): method forward (line 35) | def forward(self, x, edge_index): class Depth (line 40) | class Depth(torch.nn.Module): method __init__ (line 41) | def __init__(self, in_dim, hidden): method forward (line 45) | def forward(self, x, h, c): class GeniePathLayer (line 50) | class GeniePathLayer(torch.nn.Module): method __init__ (line 51) | def __init__(self, in_dim): method forward (line 56) | def forward(self, x, edge_index, h, c): class GeniePath (line 64) | class GeniePath(torch.nn.Module): method __init__ (line 65) | def __init__(self, in_dim, out_dim): method forward (line 72) | def forward(self, x, edge_index): class GeniePathLazy (line 82) | class GeniePathLazy(torch.nn.Module): method __init__ (line 83) | def __init__(self, in_dim, out_dim): method forward (line 92) | def forward(self, x, edge_index): function train (line 115) | def train(): function test (line 131) | def test(loader): FILE: examples/glnn.py function train_teacher (line 40) | def train_teacher(): function test_teacher (line 51) | def test_teacher(): function train_student (line 77) | def train_student(): function test_student (line 91) | def test_student(): FILE: examples/gpse.py function load_ZINC (line 25) | def load_ZINC(args): class IdentityNodeEncoder (line 66) | class IdentityNodeEncoder(torch.nn.Module): method __init__ (line 67) | def __init__(self, emb_dim): method forward (line 70) | def forward(self, batch): class LinearNodeEncoder (line 74) | class LinearNodeEncoder(torch.nn.Module): method __init__ (line 75) | def __init__(self, emb_dim, emb_pe_out, bias=True): method forward (line 79) | def forward(self, batch): class TypeDictNodeEncoder (line 85) | class TypeDictNodeEncoder(torch.nn.Module): method __init__ (line 86) | def __init__(self, emb_dim, num_types=28): method forward (line 95) | def forward(self, batch): class GNNStackStage (line 102) | class GNNStackStage(torch.nn.Module): method __init__ (line 115) | def __init__(self, dim_in, dim_out, num_layers, conv_type='gcn', method forward (line 131) | def forward(self, batch): class GPSEPlusGNN (line 145) | class GPSEPlusGNN(torch.nn.Module): method __init__ (line 177) | def __init__(self, dim_emb, dim_conv, conv_type, num_layers, dim_pe_in, method forward (line 194) | def forward(self, batch): function train (line 205) | def train(loader): function test (line 225) | def test(loader): FILE: examples/graph_gps.py class GPS (line 39) | class GPS(torch.nn.Module): method __init__ (line 40) | def __init__(self, channels: int, pe_dim: int, num_layers: int, method forward (line 71) | def forward(self, x, pe, edge_index, edge_attr, batch): class RedrawProjection (line 82) | class RedrawProjection: method __init__ (line 83) | def __init__(self, model: torch.nn.Module, method redraw_projections (line 89) | def redraw_projections(self): function train (line 113) | def train(): function test (line 131) | def test(loader): FILE: examples/graph_sage_unsup.py function train (line 43) | def train(): function test (line 63) | def test(): FILE: examples/graph_sage_unsup_ppi.py function train (line 48) | def train(): function encode (line 72) | def encode(loader): function test (line 84) | def test(): FILE: examples/graph_saint.py class Net (line 32) | class Net(torch.nn.Module): method __init__ (line 33) | def __init__(self, hidden_channels): method set_aggr (line 42) | def set_aggr(self, aggr): method forward (line 47) | def forward(self, x0, edge_index, edge_weight=None): function train (line 64) | def train(): function test (line 90) | def test(): FILE: examples/graph_unet.py class Net (line 16) | class Net(torch.nn.Module): method __init__ (line 17) | def __init__(self): method forward (line 23) | def forward(self): function train (line 38) | def train(): function test (line 46) | def test(): FILE: examples/hetero/bipartite_sage.py class MovieGNNEncoder (line 51) | class MovieGNNEncoder(torch.nn.Module): method __init__ (line 52) | def __init__(self, hidden_channels, out_channels): method forward (line 59) | def forward(self, x, edge_index): class UserGNNEncoder (line 65) | class UserGNNEncoder(torch.nn.Module): method __init__ (line 66) | def __init__(self, hidden_channels, out_channels): method forward (line 73) | def forward(self, x_dict, edge_index_dict): class EdgeDecoder (line 92) | class EdgeDecoder(torch.nn.Module): method __init__ (line 93) | def __init__(self, hidden_channels): method forward (line 98) | def forward(self, z_src, z_dst, edge_label_index): class Model (line 107) | class Model(torch.nn.Module): method __init__ (line 108) | def __init__(self, num_users, hidden_channels, out_channels): method forward (line 115) | def forward(self, x_dict, edge_index_dict, edge_label_index): function train (line 131) | def train(): function test (line 146) | def test(data): FILE: examples/hetero/bipartite_sage_unsup.py class ItemGNNEncoder (line 109) | class ItemGNNEncoder(torch.nn.Module): method __init__ (line 110) | def __init__(self, hidden_channels, out_channels): method forward (line 116) | def forward(self, x, edge_index): class UserGNNEncoder (line 122) | class UserGNNEncoder(torch.nn.Module): method __init__ (line 123) | def __init__(self, hidden_channels, out_channels): method forward (line 130) | def forward(self, x_dict, edge_index_dict): class EdgeDecoder (line 149) | class EdgeDecoder(torch.nn.Module): method __init__ (line 150) | def __init__(self, hidden_channels): method forward (line 155) | def forward(self, z_src, z_dst, edge_label_index): class Model (line 164) | class Model(torch.nn.Module): method __init__ (line 165) | def __init__(self, num_users, num_items, hidden_channels, out_channels): method forward (line 173) | def forward(self, x_dict, edge_index_dict, edge_label_index): function train (line 195) | def train(): function test (line 220) | def test(loader): FILE: examples/hetero/dmgi_unsup.py class DMGI (line 27) | class DMGI(torch.nn.Module): method __init__ (line 28) | def __init__(self, num_nodes, in_channels, out_channels, num_relations): method reset_parameters (line 36) | def reset_parameters(self): method forward (line 43) | def forward(self, x, edge_indices): method loss (line 59) | def loss(self, pos_hs, neg_hs, summaries): function train (line 90) | def train(): function test (line 103) | def test(): FILE: examples/hetero/han_imdb.py class HAN (line 22) | class HAN(nn.Module): method __init__ (line 23) | def __init__(self, in_channels: Union[int, Dict[str, int]], method forward (line 30) | def forward(self, x_dict, edge_index_dict): function train (line 51) | def train() -> float: function test (line 63) | def test() -> List[float]: FILE: examples/hetero/hetero_conv_dblp.py class HeteroGNN (line 17) | class HeteroGNN(torch.nn.Module): method __init__ (line 18) | def __init__(self, metadata, hidden_channels, out_channels, num_layers): method forward (line 31) | def forward(self, x_dict, edge_index_dict): function train (line 54) | def train(): function test (line 66) | def test(): FILE: examples/hetero/hetero_link_pred.py function weighted_mse_loss (line 50) | def weighted_mse_loss(pred, target, weight=None): class GNNEncoder (line 55) | class GNNEncoder(torch.nn.Module): method __init__ (line 56) | def __init__(self, hidden_channels, out_channels): method forward (line 61) | def forward(self, x, edge_index): class EdgeDecoder (line 67) | class EdgeDecoder(torch.nn.Module): method __init__ (line 68) | def __init__(self, hidden_channels): method forward (line 73) | def forward(self, z_dict, edge_label_index): class Model (line 82) | class Model(torch.nn.Module): method __init__ (line 83) | def __init__(self, hidden_channels): method forward (line 89) | def forward(self, x_dict, edge_index_dict, edge_label_index): function train (line 98) | def train(): function test (line 111) | def test(data): FILE: examples/hetero/hgt_dblp.py class HGT (line 17) | class HGT(torch.nn.Module): method __init__ (line 18) | def __init__(self, hidden_channels, out_channels, num_heads, num_layers): method forward (line 33) | def forward(self, x_dict, edge_index_dict): function train (line 60) | def train(): function test (line 72) | def test(): FILE: examples/hetero/hierarchical_sage.py class HierarchicalHeteroGraphSage (line 33) | class HierarchicalHeteroGraphSage(torch.nn.Module): method __init__ (line 34) | def __init__(self, edge_types, hidden_channels, out_channels, num_laye... method forward (line 48) | def forward(self, x_dict, edge_index_dict, num_sampled_edges_dict, function train (line 93) | def train(): function test (line 121) | def test(loader): FILE: examples/hetero/load_csv.py function load_node_csv (line 17) | def load_node_csv(path, index_col, encoders=None, **kwargs): function load_edge_csv (line 29) | def load_edge_csv(path, src_index_col, src_mapping, dst_index_col, dst_m... class SequenceEncoder (line 45) | class SequenceEncoder: method __init__ (line 47) | def __init__(self, model_name='all-MiniLM-L6-v2', device=None): method __call__ (line 52) | def __call__(self, df): class GenresEncoder (line 58) | class GenresEncoder: method __init__ (line 61) | def __init__(self, sep='|'): method __call__ (line 64) | def __call__(self, df): class IdentityEncoder (line 75) | class IdentityEncoder: method __init__ (line 78) | def __init__(self, dtype=None): method __call__ (line 81) | def __call__(self, df): FILE: examples/hetero/metapath2vec.py function train (line 37) | def train(epoch, log_steps=100, eval_steps=2000): function test (line 60) | def test(train_ratio=0.1): FILE: examples/hetero/recommender_system.py class GNN (line 93) | class GNN(torch.nn.Module): method __init__ (line 94) | def __init__(self, hidden_channels): method forward (line 100) | def forward(self, x, edge_index): class InnerProductDecoder (line 107) | class InnerProductDecoder(torch.nn.Module): method forward (line 108) | def forward(self, x_dict, edge_label_index): class Model (line 114) | class Model(torch.nn.Module): method __init__ (line 115) | def __init__(self, hidden_channels): method forward (line 121) | def forward(self, x_dict, edge_index_dict, edge_label_index): function train (line 130) | def train(): function test (line 156) | def test(edge_label_index, exclude_links): FILE: examples/hetero/temporal_link_pred.py class GNNEncoder (line 63) | class GNNEncoder(torch.nn.Module): method __init__ (line 64) | def __init__(self, hidden_channels, out_channels): method forward (line 69) | def forward(self, x, edge_index): class EdgeDecoder (line 75) | class EdgeDecoder(torch.nn.Module): method __init__ (line 76) | def __init__(self, hidden_channels): method forward (line 81) | def forward(self, z_dict, edge_label_index): class Model (line 90) | class Model(torch.nn.Module): method __init__ (line 91) | def __init__(self, hidden_channels): method forward (line 97) | def forward(self, x_dict, edge_index_dict, edge_label_index): function train (line 106) | def train(): function test (line 129) | def test(loader): FILE: examples/hetero/to_hetero_mag.py function init_params (line 53) | def init_params(): function train (line 60) | def train(): function test (line 80) | def test(loader): FILE: examples/hierarchical_sampling.py function train (line 32) | def train(trim=False): FILE: examples/infomax_inductive.py class Encoder (line 21) | class Encoder(torch.nn.Module): method __init__ (line 22) | def __init__(self, in_channels, hidden_channels): method forward (line 37) | def forward(self, x, edge_index, batch_size): function corruption (line 44) | def corruption(x, edge_index, batch_size): function train (line 57) | def train(epoch): function test (line 75) | def test(): FILE: examples/infomax_transductive.py class Encoder (line 13) | class Encoder(torch.nn.Module): method __init__ (line 14) | def __init__(self, in_channels, hidden_channels): method forward (line 19) | def forward(self, x, edge_index): function corruption (line 25) | def corruption(x, edge_index): function train (line 46) | def train(): function test (line 56) | def test(): FILE: examples/jit/film.py class FiLM (line 22) | class FiLM(torch.nn.Module): method __init__ (line 23) | def __init__(self, in_channels: int, hidden_channels: int, method forward (line 39) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function train (line 55) | def train(): function test (line 70) | def test(loader): FILE: examples/jit/gat.py class GAT (line 16) | class GAT(torch.nn.Module): method __init__ (line 17) | def __init__(self): method forward (line 24) | def forward(self, x, edge_index): function train (line 38) | def train(): function test (line 49) | def test(): FILE: examples/jit/gcn.py class GCN (line 17) | class GCN(torch.nn.Module): method __init__ (line 18) | def __init__(self, in_channels: int, hidden_channels: int, method forward (line 24) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function train (line 41) | def train(): function test (line 52) | def test(): FILE: examples/jit/gin.py class GIN (line 22) | class GIN(torch.nn.Module): method __init__ (line 23) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method forward (line 47) | def forward(self, x, edge_index, batch): function train (line 64) | def train(): function test (line 80) | def test(loader): FILE: examples/kge_fb15k_237.py function train (line 54) | def train(): function test (line 68) | def test(data): FILE: examples/lcm_aggr_2nd_min.py class RandomPermutation (line 18) | class RandomPermutation(BaseTransform): method forward (line 19) | def forward(self, data: Data) -> Data: class Random2ndMinimumDataset (line 24) | class Random2ndMinimumDataset(InMemoryDataset): method __init__ (line 29) | def __init__( method get_data (line 43) | def get_data( class BitwiseEmbedding (line 79) | class BitwiseEmbedding(torch.nn.Module): method __init__ (line 80) | def __init__(self, emb_dim: int): method forward (line 85) | def forward(self, x: Tensor) -> Tensor: class LCM (line 90) | class LCM(torch.nn.Module): method __init__ (line 91) | def __init__(self, emb_dim: int, dropout: float = 0.25): method forward (line 110) | def forward(self, x: Tensor, batch: Tensor) -> Tensor: function train (line 122) | def train(): function test (line 137) | def test(loader): FILE: examples/lightgcn.py function train (line 36) | def train(): function test (line 70) | def test(k: int): FILE: examples/link_pred.py class Net (line 32) | class Net(torch.nn.Module): method __init__ (line 33) | def __init__(self, in_channels, hidden_channels, out_channels): method encode (line 38) | def encode(self, x, edge_index): method decode (line 42) | def decode(self, z, edge_label_index): method decode_all (line 45) | def decode_all(self, z): function train (line 55) | def train(): function test (line 82) | def test(data): FILE: examples/linkx.py function train (line 26) | def train(): function test (line 38) | def test(): FILE: examples/llm/g_retriever.py function adjust_learning_rate (line 28) | def adjust_learning_rate(param_group: dict, LR: float, epoch: int, function save_params_dict (line 52) | def save_params_dict(model, save_path): function load_params_dict (line 77) | def load_params_dict(model, save_path): function normalize_batch_dtype (line 88) | def normalize_batch_dtype(batch): function get_loss (line 94) | def get_loss(model, batch, model_save_name="gnn+llm") -> Tensor: function inference_step (line 122) | def inference_step(model, batch, model_save_name="gnn+llm", FILE: examples/llm/git_mol.py function eval (line 20) | def eval(model, data_loader): function train (line 32) | def train( FILE: examples/llm/glem.py function get_n_params (line 32) | def get_n_params(model): function main (line 42) | def main(args): FILE: examples/llm/molecule_gpt.py function save_params_dict (line 20) | def save_params_dict(model, save_path): function eval (line 33) | def eval(model, data_loader): function train (line 45) | def train( FILE: examples/llm/protein_mpnn.py function loss_smoothed (line 17) | def loss_smoothed(y, logits, mask, weight=0.1): function loss_nll (line 30) | def loss_nll(y, logits, mask): class NoamOpt (line 41) | class NoamOpt: method __init__ (line 43) | def __init__(self, model_size, factor, warmup, optimizer, step): method param_groups (line 52) | def param_groups(self): method step (line 56) | def step(self): method rate (line 65) | def rate(self, step=None): method zero_grad (line 72) | def zero_grad(self): function train (line 76) | def train(model, optimizer, data_loader, device, scaler): function eval (line 130) | def eval(model, data_loader, device): function main (line 156) | def main(args): FILE: examples/llm/txt2kg_rag.py function parse_args (line 71) | def parse_args(): function _process_and_chunk_text (line 208) | def _process_and_chunk_text(text, chunk_size, doc_parsing_mode): function get_data (line 232) | def get_data(args): function index_kg (line 306) | def index_kg(args, context_docs): function update_data_lists (line 379) | def update_data_lists(args, data_lists): function make_dataset (line 428) | def make_dataset(args): function train (line 570) | def train(args, train_loader, val_loader): function test (line 720) | def test(model, test_loader, args): FILE: examples/lpformer.py function train_epoch (line 74) | def train_epoch(): function test (line 128) | def test(): function set_seeds (line 158) | def set_seeds(seed): FILE: examples/mem_pool.py class Net (line 27) | class Net(torch.nn.Module): method __init__ (line 28) | def __init__(self, in_channels, hidden_channels, out_channels, dropout): method forward (line 45) | def forward(self, x, edge_index, batch): function train (line 67) | def train(): function test (line 93) | def test(loader): FILE: examples/mixhop.py class MixHop (line 21) | class MixHop(torch.nn.Module): method __init__ (line 22) | def __init__(self): method forward (line 35) | def forward(self, x, edge_index): function train (line 59) | def train(): function test (line 71) | def test(): FILE: examples/mnist_graclus.py function normalized_cut_2d (line 33) | def normalized_cut_2d(edge_index, pos): class Net (line 39) | class Net(torch.nn.Module): method __init__ (line 40) | def __init__(self): method forward (line 47) | def forward(self, data): function train (line 70) | def train(epoch): function test (line 88) | def test(): FILE: examples/mnist_nn_conv.py function normalized_cut_2d (line 32) | def normalized_cut_2d(edge_index, pos): class Net (line 38) | class Net(torch.nn.Module): method __init__ (line 39) | def __init__(self): method forward (line 58) | def forward(self, data): function train (line 81) | def train(epoch): function test (line 99) | def test(): FILE: examples/mnist_voxel_grid.py class Net (line 24) | class Net(torch.nn.Module): method __init__ (line 25) | def __init__(self): method forward (line 33) | def forward(self, data): function train (line 63) | def train(epoch): function test (line 81) | def test(): FILE: examples/multi_gpu/distributed_batching.py class GIN (line 21) | class GIN(torch.nn.Module): method __init__ (line 22) | def __init__( method forward (line 47) | def forward( function run (line 66) | def run(rank: int, world_size: int, dataset_name: str, root: str) -> None: FILE: examples/multi_gpu/distributed_sampling.py class SAGE (line 18) | class SAGE(torch.nn.Module): method __init__ (line 19) | def __init__( method forward (line 33) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test (line 43) | def test( function run (line 61) | def run(rank: int, world_size: int, dataset: Reddit) -> None: FILE: examples/multi_gpu/distributed_sampling_multinode.py class SAGE (line 17) | class SAGE(torch.nn.Module): method __init__ (line 18) | def __init__( method forward (line 32) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: method inference (line 41) | def inference( function run (line 69) | def run(world_size: int, rank: int, local_rank: int): FILE: examples/multi_gpu/distributed_sampling_xpu.py class GAT (line 37) | class GAT(torch.nn.Module): method __init__ (line 38) | def __init__( method forward (line 67) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: method inference (line 75) | def inference( function train_step (line 108) | def train_step(model: Any, optimizer: Any, x: Tensor, edge_index: Tensor, function run (line 118) | def run(rank: int, world_size: int, dataset: PygNodePropPredDataset): function get_dist_params (line 188) | def get_dist_params() -> Tuple[int, int, str]: FILE: examples/multi_gpu/mag240m_graphsage.py function common_step (line 17) | def common_step(batch, model): function training_step (line 25) | def training_step(batch, acc, model): function validation_step (line 32) | def validation_step(batch, acc, model): class HeteroSAGEConv (line 37) | class HeteroSAGEConv(torch.nn.Module): method __init__ (line 38) | def __init__(self, in_channels, out_channels, dropout, node_types, method forward (line 55) | def forward(self, x_dict, edge_index_dict): class HeteroGraphSAGE (line 65) | class HeteroGraphSAGE(torch.nn.Module): method __init__ (line 66) | def __init__(self, in_channels, hidden_channels, num_layers, out_chann... method forward (line 98) | def forward(self, x_dict, edge_index_dict): function run (line 104) | def run( FILE: examples/multi_gpu/model_parallel.py class GCN (line 19) | class GCN(torch.nn.Module): method __init__ (line 20) | def __init__(self, in_channels, out_channels, device1, device2): method forward (line 28) | def forward(self, x, edge_index): function train (line 47) | def train(): function test (line 58) | def test(): FILE: examples/multi_gpu/papers100m_gcn.py function get_num_workers (line 18) | def get_num_workers(world_size): function run_train (line 30) | def run_train(rank, data, world_size, model, epochs, batch_size, fan_out, FILE: examples/multi_gpu/papers100m_gcn_multinode.py function get_num_workers (line 24) | def get_num_workers() -> int: function run (line 36) | def run(world_size, data, split_idx, model, acc, wall_clock_start): FILE: examples/multi_gpu/pcqm4m_ogb.py function ogb_from_smiles_wrapper (line 41) | def ogb_from_smiles_wrapper(smiles, *args, **kwargs): class GINConv (line 54) | class GINConv(MessagePassing): method __init__ (line 55) | def __init__(self, emb_dim): method forward (line 71) | def forward(self, x, edge_index, edge_attr): method message (line 77) | def message(self, x_j, edge_attr): method update (line 80) | def update(self, aggr_out): class GCNConv (line 84) | class GCNConv(MessagePassing): method __init__ (line 85) | def __init__(self, emb_dim): method forward (line 91) | def forward(self, x, edge_index, edge_attr): method message (line 103) | def message(self, x_j, edge_attr, norm): method update (line 106) | def update(self, aggr_out): class GNNNode (line 110) | class GNNNode(torch.nn.Module): method __init__ (line 111) | def __init__(self, num_layers, emb_dim, drop_ratio=0.5, JK="last", method forward (line 145) | def forward(self, batched_data): class GNNNodeVirtualNode (line 179) | class GNNNodeVirtualNode(torch.nn.Module): method __init__ (line 181) | def __init__(self, num_layers, emb_dim, drop_ratio=0.5, JK="last", method forward (line 221) | def forward(self, batched_data): class GNN (line 282) | class GNN(torch.nn.Module): method __init__ (line 283) | def __init__( method forward (line 361) | def forward(self, batched_data): function train (line 372) | def train(model, rank, device, loader, optimizer): function eval (line 388) | def eval(model, device, loader, evaluator): function test (line 406) | def test(model, device, loader): function run (line 420) | def run(rank, dataset, args): FILE: examples/multi_gpu/taobao.py class ItemGNNEncoder (line 23) | class ItemGNNEncoder(torch.nn.Module): method __init__ (line 24) | def __init__(self, hidden_channels, out_channels): method forward (line 30) | def forward(self, x, edge_index): class UserGNNEncoder (line 36) | class UserGNNEncoder(torch.nn.Module): method __init__ (line 37) | def __init__(self, hidden_channels, out_channels): method forward (line 44) | def forward(self, x_dict, edge_index_dict): class EdgeDecoder (line 63) | class EdgeDecoder(torch.nn.Module): method __init__ (line 64) | def __init__(self, hidden_channels): method forward (line 69) | def forward(self, z_src, z_dst, edge_label_index): class Model (line 78) | class Model(torch.nn.Module): method __init__ (line 79) | def __init__(self, num_users, num_items, hidden_channels, out_channels): method forward (line 87) | def forward(self, x_dict, edge_index_dict, edge_label_index): function run_train (line 100) | def run_train(rank, data, train_data, val_data, test_data, args, world_s... function pre_transform (line 240) | def pre_transform(data): FILE: examples/mutag_gin.py class GIN (line 48) | class GIN(torch.nn.Module): method __init__ (line 49) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method forward (line 61) | def forward(self, x, edge_index, batch): function train (line 77) | def train(): function test (line 93) | def test(loader): FILE: examples/node2vec.py function train (line 33) | def train(): function test (line 46) | def test(): function plot_points (line 66) | def plot_points(colors): FILE: examples/ogbn_proteins_deepgcn.py class DeeperGCN (line 32) | class DeeperGCN(torch.nn.Module): method __init__ (line 33) | def __init__(self, hidden_channels, num_layers): method forward (line 52) | def forward(self, x, edge_index, edge_attr): function train (line 74) | def train(epoch): function test (line 100) | def test(): FILE: examples/ogbn_train.py function get_loader (line 109) | def get_loader(input_nodes: dict[str, Tensor]) -> NeighborLoader: function train (line 127) | def train(epoch: int) -> tuple[Tensor, float]: function test (line 161) | def test(loader: NeighborLoader) -> float: function get_model (line 182) | def get_model(model_name: str) -> torch.nn.Module: FILE: examples/ogc.py class LinearNeuralNetwork (line 57) | class LinearNeuralNetwork(torch.nn.Module): method __init__ (line 58) | def __init__(self, num_features: int, num_classes: int, bias: bool = T... method forward (line 62) | def forward(self, x: Tensor) -> Tensor: method test (line 66) | def test(self, U: Tensor, y_one_hot: Tensor, data: Data): method update_W (line 82) | def update_W(self, U: Tensor, y_one_hot: Tensor, data: Data): function update_U (line 102) | def update_U(U: Tensor, y_one_hot: Tensor, pred: Tensor, W: Tensor): function ogc (line 116) | def ogc() -> float: FILE: examples/pmlp.py function train (line 28) | def train(): function test (line 39) | def test(): FILE: examples/pna.py class Net (line 36) | class Net(torch.nn.Module): method __init__ (line 37) | def __init__(self): method forward (line 59) | def forward(self, x, edge_index, edge_attr, batch): function train (line 82) | def train(epoch): function test (line 98) | def test(loader): FILE: examples/point_transformer_classification.py class TransformerBlock (line 32) | class TransformerBlock(torch.nn.Module): method __init__ (line 33) | def __init__(self, in_channels, out_channels): method forward (line 47) | def forward(self, x, pos, edge_index): class TransitionDown (line 54) | class TransitionDown(torch.nn.Module): method __init__ (line 58) | def __init__(self, in_channels, out_channels, ratio=0.25, k=16): method forward (line 64) | def forward(self, x, pos, batch): class Net (line 87) | class Net(torch.nn.Module): method __init__ (line 88) | def __init__(self, in_channels, out_channels, dim_model, k=16): method forward (line 117) | def forward(self, x, pos, batch=None): function train (line 144) | def train(): function test (line 160) | def test(loader): FILE: examples/point_transformer_segmentation.py class TransitionUp (line 35) | class TransitionUp(torch.nn.Module): method __init__ (line 39) | def __init__(self, in_channels, out_channels): method forward (line 44) | def forward(self, x, x_sub, pos, pos_sub, batch=None, batch_sub=None): class Net (line 57) | class Net(torch.nn.Module): method __init__ (line 58) | def __init__(self, in_channels, out_channels, dim_model, k=16): method forward (line 111) | def forward(self, x, pos, batch=None): function train (line 172) | def train(): function test (line 193) | def test(loader): FILE: examples/pointnet2_classification.py class SAModule (line 16) | class SAModule(torch.nn.Module): method __init__ (line 17) | def __init__(self, ratio, r, nn): method forward (line 23) | def forward(self, x, pos, batch): class GlobalSAModule (line 34) | class GlobalSAModule(torch.nn.Module): method __init__ (line 35) | def __init__(self, nn): method forward (line 39) | def forward(self, x, pos, batch): class Net (line 47) | class Net(torch.nn.Module): method __init__ (line 48) | def __init__(self): method forward (line 58) | def forward(self, data): function train (line 68) | def train(epoch): function test (line 79) | def test(loader): FILE: examples/pointnet2_segmentation.py class FPModule (line 37) | class FPModule(torch.nn.Module): method __init__ (line 38) | def __init__(self, k, nn): method forward (line 43) | def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip): class Net (line 51) | class Net(torch.nn.Module): method __init__ (line 52) | def __init__(self, num_classes): method forward (line 70) | def forward(self, data): function train (line 88) | def train(): function test (line 110) | def test(loader): FILE: examples/ppi.py class Net (line 21) | class Net(torch.nn.Module): method __init__ (line 22) | def __init__(self): method forward (line 30) | def forward(self, x, edge_index): function train (line 43) | def train(): function test (line 58) | def test(loader): FILE: examples/proteins_diff_pool.py class GNN (line 33) | class GNN(torch.nn.Module): method __init__ (line 34) | def __init__(self, in_channels, hidden_channels, out_channels, method bn (line 51) | def bn(self, i, x): method forward (line 59) | def forward(self, x, adj, mask=None): class Net (line 75) | class Net(torch.nn.Module): method __init__ (line 76) | def __init__(self): method forward (line 92) | def forward(self, x, adj, mask=None): function train (line 122) | def train(epoch): function test (line 138) | def test(loader): FILE: examples/proteins_dmon_pool.py class Net (line 26) | class Net(torch.nn.Module): method __init__ (line 27) | def __init__(self, in_channels, out_channels, hidden_channels=32): method forward (line 43) | def forward(self, x, edge_index, batch): function train (line 68) | def train(train_loader): function test (line 84) | def test(loader): FILE: examples/proteins_gmt.py class Net (line 25) | class Net(torch.nn.Module): method __init__ (line 26) | def __init__(self): method forward (line 38) | def forward(self, x0, edge_index, batch): function train (line 58) | def train(): function test (line 74) | def test(loader): FILE: examples/proteins_mincut_pool.py class Net (line 26) | class Net(torch.nn.Module): method __init__ (line 27) | def __init__(self, in_channels, out_channels, hidden_channels=32): method forward (line 43) | def forward(self, x, edge_index, batch): function train (line 70) | def train(epoch): function test (line 86) | def test(loader): FILE: examples/proteins_topk_pool.py class Net (line 22) | class Net(torch.nn.Module): method __init__ (line 23) | def __init__(self): method forward (line 37) | def forward(self, data): function train (line 67) | def train(epoch): function test (line 82) | def test(loader): FILE: examples/pytorch_ignite/gin.py class Model (line 16) | class Model(torch.nn.Module): method __init__ (line 17) | def __init__(self, in_channels: int, out_channels: int, method forward (line 28) | def forward(self, data): function main (line 35) | def main(): FILE: examples/pytorch_lightning/gin.py class Model (line 14) | class Model(pl.LightningModule): method __init__ (line 15) | def __init__(self, in_channels: int, out_channels: int, method forward (line 29) | def forward(self, x, edge_index, batch): method training_step (line 35) | def training_step(self, data, batch_idx): method validation_step (line 43) | def validation_step(self, data, batch_idx): method test_step (line 49) | def test_step(self, data, batch_idx): method configure_optimizers (line 55) | def configure_optimizers(self): FILE: examples/pytorch_lightning/graph_sage.py class Model (line 14) | class Model(pl.LightningModule): method __init__ (line 15) | def __init__(self, in_channels: int, out_channels: int, method forward (line 27) | def forward(self, x, edge_index): method training_step (line 30) | def training_step(self, data, batch_idx): method validation_step (line 39) | def validation_step(self, data, batch_idx): method test_step (line 46) | def test_step(self, data, batch_idx): method configure_optimizers (line 53) | def configure_optimizers(self): FILE: examples/pytorch_lightning/relational_gnn.py class GNN (line 20) | class GNN(torch.nn.Module): method __init__ (line 21) | def __init__(self, hidden_channels: int, out_channels: int, method forward (line 30) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class RelationalGNN (line 38) | class RelationalGNN(LightningModule): method __init__ (line 39) | def __init__( method forward (line 58) | def forward( method common_step (line 65) | def common_step(self, batch: Batch) -> Tuple[Tensor, Tensor]: method training_step (line 71) | def training_step(self, batch: Batch, batch_idx: int) -> Tensor: method validation_step (line 79) | def validation_step(self, batch: Batch, batch_idx: int): method test_step (line 85) | def test_step(self, batch: Batch, batch_idx: int): method configure_optimizers (line 91) | def configure_optimizers(self): function main (line 95) | def main(): FILE: examples/qm9_nn_conv.py class MyTransform (line 18) | class MyTransform: method __call__ (line 19) | def __call__(self, data): class Complete (line 25) | class Complete: method __call__ (line 26) | def __call__(self, data): class Net (line 71) | class Net(torch.nn.Module): method __init__ (line 72) | def __init__(self): method forward (line 84) | def forward(self, data): function train (line 107) | def train(epoch): function test (line 121) | def test(loader): FILE: examples/quiver/multi_gpu_quiver.py class SAGE (line 19) | class SAGE(torch.nn.Module): method __init__ (line 20) | def __init__(self, in_channels, hidden_channels, out_channels, method forward (line 31) | def forward(self, x, adjs): method inference (line 41) | def inference(self, x_all, device, subgraph_loader): function run (line 65) | def run(rank, world_size, dataset, quiver_feature, quiver_sampler): FILE: examples/quiver/single_gpu_quiver.py class SAGE (line 41) | class SAGE(torch.nn.Module): method __init__ (line 42) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 51) | def forward(self, x, adjs): method inference (line 66) | def inference(self, x_all): function train (line 109) | def train(epoch): function test (line 144) | def test(): FILE: examples/randlanet_classification.py class SharedMLP (line 32) | class SharedMLP(MLP): method __init__ (line 34) | def __init__(self, *args, **kwargs): class LocalFeatureAggregation (line 46) | class LocalFeatureAggregation(MessagePassing): method __init__ (line 48) | def __init__(self, channels): method forward (line 55) | def forward(self, edge_index, x, pos): method message (line 60) | def message(self, x_j: Tensor, pos_i: Tensor, pos_j: Tensor, class DilatedResidualBlock (line 92) | class DilatedResidualBlock(torch.nn.Module): method __init__ (line 93) | def __init__( method forward (line 116) | def forward(self, x, pos, batch): function decimate (line 129) | def decimate(tensors, ptr: Tensor, decimation_factor: int): class Net (line 136) | class Net(torch.nn.Module): method __init__ (line 137) | def __init__( method forward (line 158) | def forward(self, x, pos, batch, ptr): function train (line 175) | def train(epoch): function test (line 191) | def test(loader): FILE: examples/randlanet_segmentation.py class FPModule (line 54) | class FPModule(torch.nn.Module): method __init__ (line 56) | def __init__(self, k, nn): method forward (line 61) | def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip): class Net (line 68) | class Net(torch.nn.Module): method __init__ (line 69) | def __init__( method forward (line 102) | def forward(self, x, pos, batch, ptr): function train (line 143) | def train(): function test (line 165) | def test(loader): FILE: examples/rdl.py class GloveTextEmbedding (line 44) | class GloveTextEmbedding: method __init__ (line 46) | def __init__(self, device: Optional[torch.device] = None) -> None: method __call__ (line 52) | def __call__(self, sentences: List[str]) -> Tensor: class HeteroEncoder (line 56) | class HeteroEncoder(torch.nn.Module): method __init__ (line 72) | def __init__( method reset_parameters (line 106) | def reset_parameters(self) -> None: method forward (line 111) | def forward( class HeteroTemporalEncoder (line 131) | class HeteroTemporalEncoder(torch.nn.Module): method __init__ (line 155) | def __init__(self, node_types: List[NodeType], channels: int) -> None: method reset_parameters (line 168) | def reset_parameters(self) -> None: method forward (line 175) | def forward( class HeteroGraphSAGE (line 204) | class HeteroGraphSAGE(torch.nn.Module): method __init__ (line 226) | def __init__( method reset_parameters (line 255) | def reset_parameters(self) -> None: method forward (line 263) | def forward( class Model (line 285) | class Model(torch.nn.Module): method __init__ (line 307) | def __init__( method reset_parameters (line 346) | def reset_parameters(self) -> None: method forward (line 353) | def forward( class AttachTargetTransform (line 391) | class AttachTargetTransform: method __init__ (line 400) | def __init__(self, entity: str, target: Tensor) -> None: method __call__ (line 404) | def __call__(self, batch: HeteroData) -> HeteroData: class TrainingTableInput (line 409) | class TrainingTableInput(NamedTuple): function get_task_type_params (line 431) | def get_task_type_params( function to_unix_time (line 461) | def to_unix_time(ser: pd.Series) -> np.ndarray: function get_train_table_input (line 477) | def get_train_table_input( function train (line 523) | def train( function test (line 555) | def test( function main (line 572) | def main(): FILE: examples/reddit.py class SAGE (line 35) | class SAGE(torch.nn.Module): method __init__ (line 36) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 42) | def forward(self, x, edge_index): method inference (line 51) | def inference(self, x_all, subgraph_loader): function train (line 76) | def train(epoch): function test (line 101) | def test(): FILE: examples/renet.py function train (line 52) | def train(): function test (line 68) | def test(loader): FILE: examples/rev_gnn.py class GNNBlock (line 22) | class GNNBlock(torch.nn.Module): method __init__ (line 23) | def __init__(self, in_channels, out_channels): method reset_parameters (line 28) | def reset_parameters(self): method forward (line 32) | def forward(self, x, edge_index, dropout_mask=None): class RevGNN (line 39) | class RevGNN(torch.nn.Module): method __init__ (line 40) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay... method reset_parameters (line 59) | def reset_parameters(self): method forward (line 66) | def forward(self, x, edge_index): function train (line 112) | def train(epoch): function test (line 139) | def test(epoch): FILE: examples/rgat.py class RGAT (line 16) | class RGAT(torch.nn.Module): method __init__ (line 17) | def __init__(self, in_channels, hidden_channels, out_channels, method forward (line 24) | def forward(self, x, edge_index, edge_type): function train (line 37) | def train(): function test (line 48) | def test(): FILE: examples/rgcn.py class Net (line 42) | class Net(torch.nn.Module): method __init__ (line 43) | def __init__(self): method forward (line 50) | def forward(self, edge_index, edge_type): function train (line 68) | def train(): function test (line 79) | def test(): FILE: examples/rgcn_link_pred.py class RGCNEncoder (line 27) | class RGCNEncoder(torch.nn.Module): method __init__ (line 28) | def __init__(self, num_nodes, hidden_channels, num_relations): method reset_parameters (line 37) | def reset_parameters(self): method forward (line 42) | def forward(self, edge_index, edge_type): class DistMultDecoder (line 50) | class DistMultDecoder(torch.nn.Module): method __init__ (line 51) | def __init__(self, num_relations, hidden_channels): method reset_parameters (line 56) | def reset_parameters(self): method forward (line 59) | def forward(self, z, edge_index, edge_type): function negative_sampling (line 73) | def negative_sampling(edge_index, num_nodes): function train (line 86) | def train(): function test (line 111) | def test(): function compute_rank (line 122) | def compute_rank(ranks): function compute_mrr (line 132) | def compute_mrr(z, edge_index, edge_type): FILE: examples/seal_link_pred.py class SEALDataset (line 21) | class SEALDataset(InMemoryDataset): method __init__ (line 22) | def __init__(self, dataset, num_hops, split='train'): method processed_file_names (line 30) | def processed_file_names(self): method process (line 33) | def process(self): method extract_enclosing_subgraphs (line 70) | def extract_enclosing_subgraphs(self, edge_index, edge_label_index, y): method drnl_node_labeling (line 92) | def drnl_node_labeling(self, edge_index, src, dst, num_nodes=None): class DGCNN (line 139) | class DGCNN(torch.nn.Module): method __init__ (line 140) | def __init__(self, hidden_channels, num_layers, GNN=GCNConv, k=0.6): method forward (line 167) | def forward(self, x, edge_index, batch): function train (line 190) | def train(): function test (line 207) | def test(loader): FILE: examples/sgc.py class Net (line 15) | class Net(torch.nn.Module): method __init__ (line 16) | def __init__(self): method forward (line 25) | def forward(self): function train (line 42) | def train(): function test (line 50) | def test(): FILE: examples/shadow.py class GNN (line 23) | class GNN(torch.nn.Module): method __init__ (line 24) | def __init__(self, in_channels, hidden_channels, out_channels): method forward (line 31) | def forward(self, x, edge_index, batch, root_n_id): function train (line 51) | def train(): function test (line 67) | def test(loader): FILE: examples/sign.py class Net (line 26) | class Net(torch.nn.Module): method __init__ (line 27) | def __init__(self): method forward (line 35) | def forward(self, xs): function train (line 51) | def train(): function test (line 73) | def test(loader): FILE: examples/signed_gcn.py function train (line 37) | def train(): function test (line 47) | def test(): FILE: examples/super_gat.py class Net (line 17) | class Net(torch.nn.Module): method __init__ (line 18) | def __init__(self): method forward (line 29) | def forward(self, x, edge_index): function train (line 44) | def train(data): function test (line 55) | def test(data): FILE: examples/tagcn.py class Net (line 16) | class Net(torch.nn.Module): method __init__ (line 17) | def __init__(self): method forward (line 22) | def forward(self): function train (line 41) | def train(): function test (line 49) | def test(): FILE: examples/tensorboard_logging.py class Net (line 17) | class Net(torch.nn.Module): method __init__ (line 18) | def __init__(self): method forward (line 23) | def forward(self, x, edge_index): function train (line 41) | def train(): function test (line 52) | def test(): FILE: examples/tgn.py class GraphAttentionEmbedding (line 60) | class GraphAttentionEmbedding(torch.nn.Module): method __init__ (line 61) | def __init__(self, in_channels, out_channels, msg_dim, time_enc): method forward (line 68) | def forward(self, x, last_update, edge_index, t, msg): class LinkPredictor (line 75) | class LinkPredictor(torch.nn.Module): method __init__ (line 76) | def __init__(self, in_channels): method forward (line 82) | def forward(self, z_src, z_dst): function train (line 117) | def train(): function test (line 156) | def test(loader): FILE: examples/triangles_sag_pool.py class HandleNodeAttention (line 17) | class HandleNodeAttention: method __call__ (line 18) | def __call__(self, data): class Net (line 35) | class Net(torch.nn.Module): method __init__ (line 36) | def __init__(self, in_channels): method forward (line 47) | def forward(self, data): function train (line 74) | def train(): function test (line 90) | def test(loader): FILE: examples/unimp_arxiv.py class UniMP (line 15) | class UniMP(torch.nn.Module): method __init__ (line 16) | def __init__(self, in_channels, num_classes, hidden_channels, num_layers, method forward (line 39) | def forward(self, x, y, edge_index, label_mask): function train (line 59) | def train(label_rate=0.65): # How many labels to use for propagation. function test (line 75) | def test(): FILE: examples/upfd.py class Net (line 32) | class Net(torch.nn.Module): method __init__ (line 33) | def __init__(self, model, in_channels, hidden_channels, out_channels, method forward (line 51) | def forward(self, x, edge_index, batch): function train (line 74) | def train(): function test (line 91) | def test(loader): FILE: examples/wl_kernel.py class WL (line 27) | class WL(torch.nn.Module): method __init__ (line 28) | def __init__(self, num_layers): method forward (line 32) | def forward(self, x, edge_index, batch=None): FILE: graphgym/agg_batch.py function parse_args (line 6) | def parse_args(): FILE: graphgym/configs_gen.py function parse_args (line 19) | def parse_args(): function get_fname (line 44) | def get_fname(string): function grid2list (line 51) | def grid2list(grid): function lists_distance (line 62) | def lists_distance(l1, l2): function grid2list_sample (line 71) | def grid2list_sample(grid, sample=10): function load_config (line 82) | def load_config(fname): function load_search_file (line 90) | def load_search_file(fname): function load_alias_file (line 112) | def load_alias_file(fname): function exclude_list_id (line 120) | def exclude_list_id(list, id): function gen_grid (line 124) | def gen_grid(args, config, config_budget=None): function gen_grid_sample (line 166) | def gen_grid_sample(args, config, config_budget=None, compare_alias_list... FILE: graphgym/custom_graphgym/act/example.py class SWISH (line 10) | class SWISH(nn.Module): method __init__ (line 11) | def __init__(self, inplace=False): method forward (line 15) | def forward(self, x): FILE: graphgym/custom_graphgym/config/example.py function set_cfg_example (line 7) | def set_cfg_example(cfg): FILE: graphgym/custom_graphgym/encoder/example.py class ExampleNodeEncoder (line 11) | class ExampleNodeEncoder(torch.nn.Module): method __init__ (line 18) | def __init__(self, emb_dim, num_classes=None): method forward (line 24) | def forward(self, batch): class ExampleEdgeEncoder (line 32) | class ExampleEdgeEncoder(torch.nn.Module): method __init__ (line 33) | def __init__(self, emb_dim): method forward (line 44) | def forward(self, batch): FILE: graphgym/custom_graphgym/head/example.py class ExampleNodeHead (line 7) | class ExampleNodeHead(nn.Module): method __init__ (line 9) | def __init__(self, dim_in, dim_out): method _apply_index (line 13) | def _apply_index(self, batch): method forward (line 20) | def forward(self, batch): FILE: graphgym/custom_graphgym/layer/example.py class ExampleConv1 (line 17) | class ExampleConv1(MessagePassing): method __init__ (line 19) | def __init__(self, in_channels, out_channels, bias=True, **kwargs): method reset_parameters (line 34) | def reset_parameters(self): method forward (line 38) | def forward(self, batch): method message (line 46) | def message(self, x_j): method update (line 49) | def update(self, aggr_out): class ExampleConv2Layer (line 57) | class ExampleConv2Layer(MessagePassing): method __init__ (line 59) | def __init__(self, in_channels, out_channels, bias=True, **kwargs): method reset_parameters (line 74) | def reset_parameters(self): method forward (line 78) | def forward(self, x, edge_index): method message (line 83) | def message(self, x_j): method update (line 86) | def update(self, aggr_out): class ExampleConv2 (line 93) | class ExampleConv2(nn.Module): method __init__ (line 94) | def __init__(self, dim_in, dim_out, bias=False, **kwargs): method forward (line 98) | def forward(self, batch): FILE: graphgym/custom_graphgym/loader/example.py function load_dataset_example (line 6) | def load_dataset_example(format, name, dataset_dir): FILE: graphgym/custom_graphgym/loss/example.py function loss_example (line 8) | def loss_example(pred, true): FILE: graphgym/custom_graphgym/network/example.py class ExampleGNN (line 13) | class ExampleGNN(torch.nn.Module): method __init__ (line 14) | def __init__(self, dim_in, dim_out, num_layers=2, model_type='GCN'): method build_conv_model (line 26) | def build_conv_model(self, model_type): method forward (line 36) | def forward(self, batch): FILE: graphgym/custom_graphgym/optimizer/example.py function adagrad_optimizer (line 11) | def adagrad_optimizer(params: Iterator[Parameter], base_lr: float, function plateau_scheduler (line 17) | def plateau_scheduler(optimizer: Optimizer, patience: int, FILE: graphgym/custom_graphgym/pooling/example.py function global_example_pool (line 6) | def global_example_pool(x, batch, size=None): FILE: graphgym/custom_graphgym/stage/example.py function GNNLayer (line 9) | def GNNLayer(dim_in, dim_out, has_act=True): class GNNStackStage (line 14) | class GNNStackStage(nn.Module): method __init__ (line 16) | def __init__(self, dim_in, dim_out, num_layers): method forward (line 24) | def forward(self, batch): FILE: graphgym/custom_graphgym/train/example.py function train_epoch (line 17) | def train_epoch(logger, loader, model, optimizer, scheduler): function eval_epoch (line 36) | def eval_epoch(logger, loader, model): function train_example (line 51) | def train_example(loggers, loaders, model, optimizer, scheduler): FILE: test/conftest.py function load_dataset (line 14) | def load_dataset(root: str, name: str, *args, **kwargs) -> Dataset: function get_dataset (line 50) | def get_dataset() -> Callable: function enable_extensions (line 64) | def enable_extensions(): # Nothing to do. function disable_extensions (line 69) | def disable_extensions(): function without_extensions (line 89) | def without_extensions(request): function spawn_context (line 95) | def spawn_context(): FILE: test/contrib/explain/test_pgm_explainer.py class GCN (line 12) | class GCN(torch.nn.Module): method __init__ (line 13) | def __init__(self, model_config: ModelConfig): method forward (line 25) | def forward(self, x, edge_index, edge_weight=None, batch=None, **kwargs): function test_pgm_explainer_classification (line 59) | def test_pgm_explainer_classification(node_idx, task_level, perturbation... class DummyModel (line 93) | class DummyModel(torch.nn.Module): method forward (line 94) | def forward(self, x, edge_index, **kwargs): function test_batch_perturb_features_on_node (line 98) | def test_batch_perturb_features_on_node(): FILE: test/contrib/nn/models/test_rbcd_attack.py class GCN (line 10) | class GCN(torch.nn.Module): method __init__ (line 11) | def __init__(self): method forward (line 16) | def forward(self, x, edge_index, edge_weight): class GNN (line 22) | class GNN(torch.nn.Module): method __init__ (line 23) | def __init__(self): method forward (line 29) | def forward(self, x, edge_index, edge_weight, batch=None): function test_prbcd_attack (line 43) | def test_prbcd_attack(model, budget, loss, is_undirected, with_early_sto... function test_grbcd_attack (line 82) | def test_grbcd_attack(model, budget, is_undirected): FILE: test/data/lightning/test_datamodule.py class LinearGraphModule (line 35) | class LinearGraphModule(LightningModule): method __init__ (line 36) | def __init__(self, in_channels: int, hidden_channels: int, method forward (line 48) | def forward(self, x: Tensor, batch: Data) -> Tensor: method training_step (line 57) | def training_step(self, data: Data, batch_idx: int): method validation_step (line 65) | def validation_step(self, data: Data, batch_idx: int): method test_step (line 70) | def test_step(self, data: Data, batch_idx: int): method configure_optimizers (line 75) | def configure_optimizers(self): function test_lightning_dataset (line 84) | def test_lightning_dataset(get_dataset, strategy_type): class LinearNodeModule (line 164) | class LinearNodeModule(LightningModule): method __init__ (line 165) | def __init__(self, in_channels: int, out_channels: int): method forward (line 175) | def forward(self, x: Tensor) -> Tensor: method training_step (line 181) | def training_step(self, data: Data, batch_idx: int): method validation_step (line 190) | def validation_step(self, data: Data, batch_idx: int): method test_step (line 196) | def test_step(self, data: Data, batch_idx: int): method configure_optimizers (line 202) | def configure_optimizers(self): function test_lightning_node_data (line 213) | def test_lightning_node_data(get_dataset, strategy_type, loader): class LinearHeteroNodeModule (line 273) | class LinearHeteroNodeModule(LightningModule): method __init__ (line 274) | def __init__(self, in_channels: int, out_channels: int): method forward (line 284) | def forward(self, x: Tensor) -> Tensor: method training_step (line 290) | def training_step(self, data: HeteroData, batch_idx: int): method validation_step (line 299) | def validation_step(self, data: HeteroData, batch_idx: int): method test_step (line 305) | def test_step(self, data: HeteroData, batch_idx: int): method configure_optimizers (line 311) | def configure_optimizers(self): function preserve_context (line 316) | def preserve_context(): function test_lightning_hetero_node_data (line 328) | def test_lightning_hetero_node_data(preserve_context, get_dataset): function test_lightning_data_custom_sampler (line 353) | def test_lightning_data_custom_sampler(): function test_lightning_hetero_link_data (line 377) | def test_lightning_hetero_link_data(): function test_lightning_hetero_link_data_custom_store (line 437) | def test_lightning_hetero_link_data_custom_store(): function test_eval_loader_kwargs (line 475) | def test_eval_loader_kwargs(get_dataset): FILE: test/data/test_batch.py function test_batch_basic (line 15) | def test_batch_basic(): function test_index (line 111) | def test_index(): function test_edge_index (line 136) | def test_edge_index(): function test_batch_with_sparse_tensor (line 177) | def test_batch_with_sparse_tensor(): function test_batch_with_torch_coo_tensor (line 237) | def test_batch_with_torch_coo_tensor(): function test_batching_with_new_dimension (line 279) | def test_batching_with_new_dimension(): function test_pickling (line 321) | def test_pickling(tmp_path): function test_recursive_batch (line 341) | def test_recursive_batch(): function test_batching_of_batches (line 399) | def test_batching_of_batches(): function test_hetero_batch (line 412) | def test_hetero_batch(): function test_pair_data_batching (line 480) | def test_pair_data_batching(): function test_batch_with_empty_list (line 513) | def test_batch_with_empty_list(): function test_nested_follow_batch (line 524) | def test_nested_follow_batch(): function test_torch_sparse_batch (line 566) | def test_torch_sparse_batch(layout): function test_torch_nested_batch (line 588) | def test_torch_nested_batch(): FILE: test/data/test_data.py function test_data (line 13) | def test_data(): function test_data_attr_cache (line 151) | def test_data_attr_cache(): function test_data_attr_cache_not_shared (line 180) | def test_data_attr_cache_not_shared(): function test_to_heterogeneous_empty_edge_index (line 194) | def test_to_heterogeneous_empty_edge_index(): function test_data_subgraph (line 216) | def test_data_subgraph(): function test_data_subgraph_with_list_field (line 269) | def test_data_subgraph_with_list_field(): function test_data_empty_subgraph (line 285) | def test_data_empty_subgraph(): function test_copy_data (line 295) | def test_copy_data(): function test_data_sort (line 320) | def test_data_sort(): function test_debug_data (line 350) | def test_debug_data(): function run (line 364) | def run(rank, data_list): function test_data_share_memory (line 370) | def test_data_share_memory(): function test_data_setter_properties (line 384) | def test_data_setter_properties(): function test_data_update (line 410) | def test_data_update(): function test_data_connected_components (line 421) | def test_data_connected_components(): function test_data_find_parent (line 449) | def test_data_find_parent(): function test_data_union (line 465) | def test_data_union(): function test_basic_feature_store (line 514) | def test_basic_feature_store(): function test_basic_graph_store (line 550) | def test_basic_graph_store(): function test_data_generate_ids (line 587) | def test_data_generate_ids(): function test_data_with_tensor_frame (line 601) | def test_data_with_tensor_frame(): function test_data_time_handling (line 628) | def test_data_time_handling(num_nodes, num_edges): function test_data_inc (line 677) | def test_data_inc(): FILE: test/data/test_database.py function test_database_single_tensor (line 22) | def test_database_single_tensor(tmp_path, Database, batch_size): function test_database_schema (line 61) | def test_database_schema(tmp_path, Database): function test_index (line 153) | def test_index(tmp_path, Database): function test_edge_index (line 183) | def test_edge_index(tmp_path, Database): function test_database_syntactic_sugar (line 223) | def test_database_syntactic_sugar(tmp_path): function in_memory_get (line 273) | def in_memory_get(data): function db_get (line 277) | def db_get(db): FILE: test/data/test_datapipes.py function dataset_adapter (line 12) | def dataset_adapter() -> DatasetAdapter: function test_dataset_adapter (line 19) | def test_dataset_adapter(dataset_adapter): function test_datapipe_batch_graphs (line 32) | def test_datapipe_batch_graphs(dataset_adapter): function test_functional_transform (line 39) | def test_functional_transform(dataset_adapter): function test_datapipe_parse_smiles (line 46) | def test_datapipe_parse_smiles(): FILE: test/data/test_dataset.py class MyTestDataset (line 14) | class MyTestDataset(InMemoryDataset): method __init__ (line 15) | def __init__(self, data_list, transform=None): class MyStoredTestDataset (line 20) | class MyStoredTestDataset(InMemoryDataset): method __init__ (line 21) | def __init__(self, root, data_list, transform=None): method processed_file_names (line 27) | def processed_file_names(self) -> str: method process (line 30) | def process(self): function test_in_memory_dataset (line 34) | def test_in_memory_dataset(): function test_stored_in_memory_dataset (line 77) | def test_stored_in_memory_dataset(tmp_path): function test_stored_hetero_in_memory_dataset (line 102) | def test_stored_hetero_in_memory_dataset(tmp_path): function test_index (line 125) | def test_index(tmp_path): function test_edge_index (line 149) | def test_edge_index(tmp_path): function test_in_memory_num_classes (line 184) | def test_in_memory_num_classes(): function test_in_memory_dataset_copy (line 212) | def test_in_memory_dataset_copy(): function test_to_datapipe (line 229) | def test_to_datapipe(): function test_in_memory_sparse_tensor_dataset (line 247) | def test_in_memory_sparse_tensor_dataset(): function test_collate_with_new_dimension (line 264) | def test_collate_with_new_dimension(): function test_hetero_in_memory_dataset (line 295) | def test_hetero_in_memory_dataset(): function test_override_behavior (line 323) | def test_override_behavior(): function test_lists_of_tensors_in_memory_dataset (line 389) | def test_lists_of_tensors_in_memory_dataset(): function test_lists_of_sparse_tensors (line 410) | def test_lists_of_sparse_tensors(): function test_file_names_as_property_and_method (line 432) | def test_file_names_as_property_and_method(): function test_to_on_disk_dataset (line 460) | def test_to_on_disk_dataset(tmp_path): FILE: test/data/test_dataset_summary.py function check_stats (line 9) | def check_stats(stats: Stats, expected: Tensor): function test_dataset_summary (line 20) | def test_dataset_summary(): function test_dataset_summary_representation (line 35) | def test_dataset_summary_representation(): function test_dataset_summary_hetero (line 45) | def test_dataset_summary_hetero(): function test_dataset_summary_hetero_representation_length (line 58) | def test_dataset_summary_hetero_representation_length(): function test_dataset_summary_hetero_per_type_check (line 70) | def test_dataset_summary_hetero_per_type_check(): FILE: test/data/test_feature_store.py class MyTensorAttrNoGroupName (line 12) | class MyTensorAttrNoGroupName(TensorAttr): method __init__ (line 13) | def __init__(self, attr_name=_FieldStatus.UNSET, index=_FieldStatus.UN... class MyFeatureStoreNoGroupName (line 18) | class MyFeatureStoreNoGroupName(MyFeatureStore): method __init__ (line 19) | def __init__(self): function test_feature_store (line 24) | def test_feature_store(): function test_feature_store_override (line 94) | def test_feature_store_override(): FILE: test/data/test_graph_store.py function test_graph_store (line 13) | def test_graph_store(): function test_graph_store_conversion (line 43) | def test_graph_store_conversion(): FILE: test/data/test_hetero_data.py function test_init_hetero_data (line 34) | def test_init_hetero_data(): function test_hetero_data_to_from_dict (line 100) | def test_hetero_data_to_from_dict(): function test_hetero_data_functions (line 114) | def test_hetero_data_functions(): function test_hetero_data_set_value_dict (line 170) | def test_hetero_data_set_value_dict(): function test_hetero_data_rename (line 182) | def test_hetero_data_rename(): function test_dangling_types (line 203) | def test_dangling_types(): function test_hetero_data_subgraph (line 215) | def test_hetero_data_subgraph(): function test_hetero_data_empty_subgraph (line 336) | def test_hetero_data_empty_subgraph(): function test_copy_hetero_data (line 356) | def test_copy_hetero_data(): function test_to_homogeneous_and_vice_versa (line 388) | def test_to_homogeneous_and_vice_versa(): function test_to_homogeneous_padding (line 531) | def test_to_homogeneous_padding(): function test_hetero_data_to_canonical (line 547) | def test_hetero_data_to_canonical(): function test_hetero_data_invalid_names (line 563) | def test_hetero_data_invalid_names(): function test_hetero_data_update (line 576) | def test_hetero_data_update(): function test_hetero_data_connected_components (line 597) | def test_hetero_data_connected_components(): function test_hetero_data_connected_components_single_component (line 686) | def test_hetero_data_connected_components_single_component(): function test_hetero_data_find_parent (line 701) | def test_hetero_data_find_parent(): function test_hetero_data_union (line 716) | def test_hetero_data_union(): function test_basic_feature_store (line 764) | def test_basic_feature_store(): function test_hetero_data_with_tensor_frame (line 801) | def test_hetero_data_with_tensor_frame(): function test_basic_graph_store (line 854) | def test_basic_graph_store(): function test_generate_ids (line 896) | def test_generate_ids(): function test_invalid_keys (line 914) | def test_invalid_keys(): FILE: test/data/test_hypergraph_data.py function test_hypergraph_data (line 9) | def test_hypergraph_data(): function test_hypergraphdata_subgraph (line 138) | def test_hypergraphdata_subgraph(): FILE: test/data/test_inherit.py class MyData (line 6) | class MyData(Data): method __init__ (line 7) | def __init__(self, x=None, edge_index=None, arg=None): method random (line 10) | def random(self): class MyInMemoryDataset (line 14) | class MyInMemoryDataset(InMemoryDataset): method __init__ (line 15) | def __init__(self): method _download (line 25) | def _download(self): method _process (line 28) | def _process(self): class MyDataset (line 32) | class MyDataset(Dataset): method __init__ (line 33) | def __init__(self): method _download (line 36) | def _download(self): method _process (line 39) | def _process(self): method len (line 42) | def len(self): method get (line 45) | def get(self, idx): function test_inherit (line 52) | def test_inherit(): FILE: test/data/test_on_disk_dataset.py function test_pickle (line 11) | def test_pickle(tmp_path): function test_custom_schema (line 57) | def test_custom_schema(tmp_path): FILE: test/data/test_remote_backend_utils.py function test_num_nodes_size (line 15) | def test_num_nodes_size(FeatureStore, GraphStore): FILE: test/data/test_storage.py function test_base_storage (line 10) | def test_base_storage(): function test_storage_tensor_methods (line 60) | def test_storage_tensor_methods(): function test_setter_and_getter (line 93) | def test_setter_and_getter(): FILE: test/data/test_temporal.py function get_temporal_data (line 8) | def get_temporal_data(num_events, msg_channels): function test_temporal_data (line 18) | def test_temporal_data(): function test_train_val_test_split (line 84) | def test_train_val_test_split(): function test_temporal_indexing (line 98) | def test_temporal_indexing(): FILE: test/data/test_view.py function test_views (line 4) | def test_views(): FILE: test/datasets/graph_generator/test_ba_graph.py function test_ba_graph (line 4) | def test_ba_graph(): FILE: test/datasets/graph_generator/test_er_graph.py function test_er_graph (line 4) | def test_er_graph(): FILE: test/datasets/graph_generator/test_grid_graph.py function test_grid_graph (line 4) | def test_grid_graph(): FILE: test/datasets/graph_generator/test_tree_graph.py function test_tree_graph (line 7) | def test_tree_graph(undirected): FILE: test/datasets/motif_generator/test_custom_motif.py function test_custom_motif_pyg_data (line 9) | def test_custom_motif_pyg_data(): function test_custom_motif_networkx (line 22) | def test_custom_motif_networkx(): function test_custom_motif_unknown (line 36) | def test_custom_motif_unknown(): FILE: test/datasets/motif_generator/test_cycle_motif.py function test_cycle_motif (line 4) | def test_cycle_motif(): FILE: test/datasets/motif_generator/test_grid_motif.py function test_grid_motif (line 4) | def test_grid_motif(): FILE: test/datasets/motif_generator/test_house_motif.py function test_house_motif (line 4) | def test_house_motif(): FILE: test/datasets/test_ba_shapes.py function test_ba_shapes (line 4) | def test_ba_shapes(get_dataset): FILE: test/datasets/test_bzr.py function test_bzr (line 6) | def test_bzr(get_dataset): function test_bzr_with_node_attr (line 19) | def test_bzr_with_node_attr(get_dataset): FILE: test/datasets/test_elliptic.py function test_elliptic_bitcoin_dataset (line 6) | def test_elliptic_bitcoin_dataset(get_dataset): FILE: test/datasets/test_enzymes.py function test_enzymes (line 10) | def test_enzymes(get_dataset): function test_enzymes_with_node_attr (line 61) | def test_enzymes_with_node_attr(get_dataset): function test_cleaned_enzymes (line 69) | def test_cleaned_enzymes(get_dataset): FILE: test/datasets/test_explainer_dataset.py function test_explainer_dataset_ba_house (line 17) | def test_explainer_dataset_ba_house(graph_generator, motif_generator): function test_explainer_dataset_reproducibility (line 38) | def test_explainer_dataset_reproducibility(): FILE: test/datasets/test_fake.py function test_fake_dataset (line 9) | def test_fake_dataset(num_graphs, edge_dim, task): function test_fake_hetero_dataset (line 47) | def test_fake_hetero_dataset(num_graphs, edge_dim, task): FILE: test/datasets/test_git_mol_dataset.py function test_git_mol_dataset (line 16) | def test_git_mol_dataset(split: Tuple[int, int]) -> None: FILE: test/datasets/test_imdb_binary.py function test_imdb_binary (line 6) | def test_imdb_binary(get_dataset): FILE: test/datasets/test_infection_dataset.py class DummyGraph (line 9) | class DummyGraph(GraphGenerator): method __call__ (line 10) | def __call__(self) -> Data: function test_infection_dataset (line 18) | def test_infection_dataset(): function test_infection_dataset_reproducibility (line 45) | def test_infection_dataset_reproducibility(): FILE: test/datasets/test_karate.py function test_karate (line 1) | def test_karate(get_dataset): FILE: test/datasets/test_medshapenet.py function test_medshapenet (line 9) | def test_medshapenet(): FILE: test/datasets/test_molecule_gpt_dataset.py function test_molecule_gpt_dataset (line 7) | def test_molecule_gpt_dataset(): FILE: test/datasets/test_mutag.py function test_mutag (line 5) | def test_mutag(get_dataset): function test_mutag_with_node_attr (line 17) | def test_mutag_with_node_attr(get_dataset): FILE: test/datasets/test_planetoid.py function test_citeseer (line 7) | def test_citeseer(get_dataset): function test_citeseer_with_full_split (line 36) | def test_citeseer_with_full_split(get_dataset): function test_citeseer_with_random_split (line 47) | def test_citeseer_with_random_split(get_dataset): FILE: test/datasets/test_protein_mpnn_dataset.py function test_protein_mpnn_dataset (line 8) | def test_protein_mpnn_dataset(): FILE: test/datasets/test_snap_dataset.py function test_ego_facebook_snap_dataset (line 6) | def test_ego_facebook_snap_dataset(get_dataset): function test_soc_slashdot_snap_dataset (line 35) | def test_soc_slashdot_snap_dataset(get_dataset): function test_wiki_vote_snap_dataset (line 43) | def test_wiki_vote_snap_dataset(get_dataset): FILE: test/datasets/test_suite_sparse.py function test_suite_sparse_dataset (line 6) | def test_suite_sparse_dataset(get_dataset): function test_illc1850_suite_sparse_dataset (line 15) | def test_illc1850_suite_sparse_dataset(get_dataset): FILE: test/datasets/test_tag_dataset.py function test_tag_dataset (line 7) | def test_tag_dataset() -> None: FILE: test/datasets/test_teeth3ds.py function test_teeth3ds (line 7) | def test_teeth3ds(tmp_path) -> None: FILE: test/datasets/test_web_qsp_dataset.py function test_web_qsp_dataset (line 16) | def test_web_qsp_dataset(tmp_path): class MockSentenceTransformer (line 24) | class MockSentenceTransformer: method __init__ (line 25) | def __init__(self, *args, **kwargs): method to (line 28) | def to(self, device): method eval (line 31) | def eval(self): method encode (line 34) | def encode(self, sentences, batch_size=None, output_device=None): function create_mock_graphs (line 45) | def create_mock_graphs(tmp_path: str, train_size: int, val_size: int, function test_kgqa_base_dataset (line 144) | def test_kgqa_base_dataset(tmp_path, monkeypatch): FILE: test/distributed/test_dist_link_neighbor_loader.py function create_dist_data (line 22) | def create_dist_data(tmp_path: str, rank: int): function dist_link_neighbor_loader_homo (line 29) | def dist_link_neighbor_loader_homo( function dist_link_neighbor_loader_hetero (line 79) | def dist_link_neighbor_loader_hetero( function test_dist_link_neighbor_loader_homo (line 146) | def test_dist_link_neighbor_loader_homo( function test_dist_link_neighbor_loader_hetero (line 187) | def test_dist_link_neighbor_loader_hetero( FILE: test/distributed/test_dist_link_neighbor_sampler.py function create_data (line 27) | def create_data(rank, world_size, time_attr: Optional[str] = None): function create_hetero_data (line 78) | def create_hetero_data(tmp_path: str, rank: int): function dist_link_neighbor_sampler (line 86) | def dist_link_neighbor_sampler( function dist_link_neighbor_sampler_temporal (line 164) | def dist_link_neighbor_sampler_temporal( function dist_link_neighbor_sampler_hetero (line 252) | def dist_link_neighbor_sampler_hetero( function dist_link_neighbor_sampler_temporal_hetero (line 343) | def dist_link_neighbor_sampler_temporal_hetero( function test_dist_link_neighbor_sampler (line 441) | def test_dist_link_neighbor_sampler(disjoint): function test_dist_link_neighbor_sampler_temporal (line 459) | def test_dist_link_neighbor_sampler_temporal(seed_time, temporal_strategy): function test_dist_link_neighbor_sampler_edge_level_temporal (line 483) | def test_dist_link_neighbor_sampler_edge_level_temporal( function test_dist_link_neighbor_sampler_hetero (line 512) | def test_dist_link_neighbor_sampler_hetero(tmp_path, disjoint): function test_dist_link_neighbor_sampler_temporal_hetero (line 551) | def test_dist_link_neighbor_sampler_temporal_hetero( function test_dist_link_neighbor_sampler_edge_level_temporal_hetero (line 603) | def test_dist_link_neighbor_sampler_edge_level_temporal_hetero( FILE: test/distributed/test_dist_neighbor_loader.py function create_dist_data (line 22) | def create_dist_data(tmp_path: str, rank: int): function dist_neighbor_loader_homo (line 29) | def dist_neighbor_loader_homo( function dist_neighbor_loader_hetero (line 82) | def dist_neighbor_loader_hetero( function test_dist_neighbor_loader_homo (line 160) | def test_dist_neighbor_loader_homo( function test_dist_neighbor_loader_hetero (line 197) | def test_dist_neighbor_loader_hetero( FILE: test/distributed/test_dist_neighbor_sampler.py function create_data (line 25) | def create_data(rank: int, world_size: int, time_attr: Optional[str] = N... function create_hetero_data (line 76) | def create_hetero_data( function dist_neighbor_sampler (line 86) | def dist_neighbor_sampler( function dist_neighbor_sampler_temporal (line 152) | def dist_neighbor_sampler_temporal( function dist_neighbor_sampler_hetero (line 228) | def dist_neighbor_sampler_hetero( function dist_neighbor_sampler_temporal_hetero (line 307) | def dist_neighbor_sampler_temporal_hetero( function test_dist_neighbor_sampler (line 392) | def test_dist_neighbor_sampler(disjoint): function test_dist_neighbor_sampler_temporal (line 410) | def test_dist_neighbor_sampler_temporal(seed_time, temporal_strategy): function test_dist_neighbor_sampler_edge_level_temporal (line 434) | def test_dist_neighbor_sampler_edge_level_temporal( function test_dist_neighbor_sampler_hetero (line 463) | def test_dist_neighbor_sampler_hetero(tmp_path, disjoint): function test_dist_neighbor_sampler_temporal_hetero (line 501) | def test_dist_neighbor_sampler_temporal_hetero( function test_dist_neighbor_sampler_edge_level_temporal_hetero (line 553) | def test_dist_neighbor_sampler_edge_level_temporal_hetero( FILE: test/distributed/test_dist_utils.py function test_remove_duplicates (line 9) | def test_remove_duplicates(): function test_remove_duplicates_disjoint (line 22) | def test_remove_duplicates_disjoint(): FILE: test/distributed/test_local_feature_store.py function test_local_feature_store_global_id (line 8) | def test_local_feature_store_global_id(): function test_local_feature_store_utils (line 35) | def test_local_feature_store_utils(): function test_homogeneous_feature_store (line 64) | def test_homogeneous_feature_store(): function test_heterogeneous_feature_store (line 94) | def test_heterogeneous_feature_store(): FILE: test/distributed/test_local_graph_store.py function test_local_graph_store (line 8) | def test_local_graph_store(): function test_homogeneous_graph_store (line 39) | def test_homogeneous_graph_store(): function test_heterogeneous_graph_store (line 69) | def test_heterogeneous_graph_store(): function test_sorted_graph_store (line 101) | def test_sorted_graph_store(): FILE: test/distributed/test_partition.py function test_partition_data (line 18) | def test_partition_data(tmp_path): function test_partition_hetero_data (line 65) | def test_partition_hetero_data(tmp_path): function test_partition_data_temporal (line 100) | def test_partition_data_temporal(tmp_path): function test_partition_data_edge_level_temporal (line 121) | def test_partition_data_edge_level_temporal(tmp_path): function test_partition_hetero_data_temporal (line 144) | def test_partition_hetero_data_temporal(tmp_path): function test_partition_hetero_data_edge_level_temporal (line 168) | def test_partition_hetero_data_edge_level_temporal(tmp_path): function test_from_partition_data (line 198) | def test_from_partition_data(tmp_path): function test_from_partition_hetero_data (line 233) | def test_from_partition_hetero_data(tmp_path): function test_from_partition_temporal_data (line 261) | def test_from_partition_temporal_data(tmp_path): function test_from_partition_edge_level_temporal_data (line 287) | def test_from_partition_edge_level_temporal_data(tmp_path): function test_from_partition_hetero_temporal_data (line 315) | def test_from_partition_hetero_temporal_data(tmp_path): function test_from_partition_hetero_edge_level_temporal_data (line 348) | def test_from_partition_hetero_edge_level_temporal_data(tmp_path): FILE: test/distributed/test_rpc.py function run_rpc_feature_test (line 12) | def run_rpc_feature_test( function test_dist_feature_lookup (line 84) | def test_dist_feature_lookup(): FILE: test/explain/algorithm/test_attention_explainer.py class AttentionGNN (line 25) | class AttentionGNN(torch.nn.Module): method __init__ (line 26) | def __init__(self): method forward (line 32) | def forward(self, x, edge_index): class HeteroAttentionGNN (line 39) | class HeteroAttentionGNN(torch.nn.Module): method __init__ (line 40) | def __init__(self, metadata, model_config=None): method forward (line 69) | def forward(self, x_dict, edge_index_dict, **kwargs): class HeteroConvAttentionGNN (line 90) | class HeteroConvAttentionGNN(torch.nn.Module): method __init__ (line 91) | def __init__(self, metadata, model_config=None): method _initialize_layers (line 128) | def _initialize_layers(self, x_dict): method forward (line 145) | def forward(self, x_dict, edge_index_dict): function test_attention_explainer (line 184) | def test_attention_explainer(index, check_explanation): function test_attention_explainer_supports (line 203) | def test_attention_explainer_supports(explanation_type, node_mask_type): function test_attention_explainer_attentive_fp (line 219) | def test_attention_explainer_attentive_fp(check_explanation): function test_attention_explainer_hetero (line 239) | def test_attention_explainer_hetero(index, hetero_data, function test_attention_explainer_hetero_conv (line 277) | def test_attention_explainer_hetero_conv(index, hetero_data, FILE: test/explain/algorithm/test_captum.py function test_to_captum (line 36) | def test_to_captum(model, mask_type, output_idx): function test_captum_attribution_methods (line 65) | def test_captum_attribution_methods(mask_type, method): function test_custom_explain_message (line 109) | def test_custom_explain_message(): function test_to_captum_input (line 135) | def test_to_captum_input(mask_type): FILE: test/explain/algorithm/test_captum_explainer.py class GCN (line 38) | class GCN(torch.nn.Module): method __init__ (line 39) | def __init__(self, model_config: ModelConfig): method forward (line 54) | def forward(self, x, edge_index, batch=None, edge_label_index=None): function check_explanation (line 82) | def check_explanation( function test_unsupported_methods (line 100) | def test_unsupported_methods(method): function test_captum_explainer_binary_classification (line 120) | def test_captum_explainer_binary_classification( function test_captum_explainer_multiclass_classification (line 165) | def test_captum_explainer_multiclass_classification( function test_captum_hetero_data (line 218) | def test_captum_hetero_data(method, node_mask_type, edge_mask_type, index, function test_captum_explainer_supports (line 243) | def test_captum_explainer_supports(node_mask_type): FILE: test/explain/algorithm/test_captum_hetero.py function test_captum_attribution_methods_hetero (line 35) | def test_captum_attribution_methods_hetero(mask_type, method, hetero_data, FILE: test/explain/algorithm/test_explain_algorithm_utils.py class HeteroModel (line 10) | class HeteroModel(torch.nn.Module): method __init__ (line 11) | def __init__(self): class GraphSAGE (line 33) | class GraphSAGE(torch.nn.Module): method __init__ (line 34) | def __init__(self): method forward (line 39) | def forward(self, x, edge_index): function test_set_clear_mask (line 44) | def test_set_clear_mask(hetero_data): FILE: test/explain/algorithm/test_gnn_explainer.py class GNN (line 22) | class GNN(torch.nn.Module): method __init__ (line 23) | def __init__(self, Conv, model_config: ModelConfig): method forward (line 38) | def forward(self, x, edge_index, batch=None, edge_label_index=None): function test_gnn_explainer_binary_classification (line 90) | def test_gnn_explainer_binary_classification( function test_gnn_explainer_multiclass_classification (line 152) | def test_gnn_explainer_multiclass_classification( function test_gnn_explainer_regression (line 205) | def test_gnn_explainer_regression( function test_gnn_explainer_cheb_conv (line 250) | def test_gnn_explainer_cheb_conv(check_explanation): function test_gnn_explainer_attentive_fp (line 272) | def test_gnn_explainer_attentive_fp(check_explanation): function test_gnn_explainer_hetero (line 306) | def test_gnn_explainer_hetero( FILE: test/explain/algorithm/test_graphmask_explainer.py class GCN (line 15) | class GCN(torch.nn.Module): method __init__ (line 16) | def __init__(self, model_config: ModelConfig): method forward (line 28) | def forward(self, x, edge_index, batch=None, edge_label_index=None): function check_explanation (line 50) | def check_explanation( function test_graph_mask_explainer_binary_classification (line 99) | def test_graph_mask_explainer_binary_classification( function test_graph_mask_explainer_multiclass_classification (line 151) | def test_graph_mask_explainer_multiclass_classification( function test_graph_mask_explainer_regression (line 198) | def test_graph_mask_explainer_regression( FILE: test/explain/algorithm/test_pg_explainer.py class GCN (line 16) | class GCN(torch.nn.Module): method __init__ (line 17) | def __init__(self, model_config: ModelConfig): method forward (line 29) | def forward(self, x, edge_index, batch=None, edge_label_index=None): function test_pg_explainer_node (line 43) | def test_pg_explainer_node(device, check_explanation, mode): function test_pg_explainer_graph (line 90) | def test_pg_explainer_graph(device, check_explanation, mode): function test_pg_explainer_hetero (line 141) | def test_pg_explainer_hetero(device, hetero_data, hetero_model, function test_pg_explainer_supports (line 229) | def test_pg_explainer_supports(): function test_pg_explainer_native_hetero (line 287) | def test_pg_explainer_native_hetero(device, hetero_data, hetero_model_na... function test_pg_explainer_hetero_conv (line 387) | def test_pg_explainer_hetero_conv(device, hetero_data, hetero_model_custom, FILE: test/explain/conftest.py function data (line 27) | def data(): function hetero_data (line 36) | def hetero_data(): function hetero_model (line 52) | def hetero_model(): function hetero_model_custom (line 57) | def hetero_model_custom(): class GraphSAGE (line 61) | class GraphSAGE(torch.nn.Module): method __init__ (line 62) | def __init__(self): method forward (line 67) | def forward(self, x, edge_index): class HeteroSAGE (line 72) | class HeteroSAGE(torch.nn.Module): method __init__ (line 73) | def __init__(self, metadata, model_config: Optional[ModelConfig] = None): method forward (line 86) | def forward(self, x_dict, edge_index_dict, function check_explanation (line 106) | def check_explanation(): function check_explanation_hetero (line 138) | def check_explanation_hetero(): class NativeHeteroGNN (line 178) | class NativeHeteroGNN(torch.nn.Module): method __init__ (line 179) | def __init__(self, metadata, model_config: Optional[ModelConfig] = None, method _initialize_layers (line 210) | def _initialize_layers(self, x_dict): method forward (line 227) | def forward(self, x_dict, edge_index_dict): function hetero_model_native (line 259) | def hetero_model_native(): class HeteroConvModel (line 263) | class HeteroConvModel(torch.nn.Module): method __init__ (line 264) | def __init__(self, metadata, model_config: Optional[ModelConfig] = None): method forward (line 289) | def forward(self, x_dict, edge_index_dict): FILE: test/explain/metric/test_basic_metric.py function test_groundtruth_metrics (line 10) | def test_groundtruth_metrics(): function test_perfect_groundtruth_metrics (line 25) | def test_perfect_groundtruth_metrics(): function test_groundtruth_true_negative (line 39) | def test_groundtruth_true_negative(): FILE: test/explain/metric/test_faithfulness.py class DummyModel (line 12) | class DummyModel(torch.nn.Module): method __init__ (line 13) | def __init__(self, model_config: ModelConfig): method forward (line 17) | def forward(self, x, edge_index): function test_unfaithfulness (line 29) | def test_unfaithfulness(top_k, explanation_type, node_mask_type, return_... FILE: test/explain/metric/test_fidelity.py class DummyModel (line 13) | class DummyModel(torch.nn.Module): method forward (line 14) | def forward(self, x, edge_index): function test_fidelity (line 19) | def test_fidelity(explanation_type): function test_characterization_score (line 50) | def test_characterization_score(): function test_fidelity_curve_auc (line 60) | def test_fidelity_curve_auc(): FILE: test/explain/test_explain_config.py function test_threshold_config (line 17) | def test_threshold_config(threshold_pairs): function test_configuration_config (line 40) | def test_configuration_config(explanation_type, mask_type): FILE: test/explain/test_explainer.py class DummyModel (line 8) | class DummyModel(torch.nn.Module): method forward (line 9) | def forward(self, x, edge_index): function test_get_prediction (line 13) | def test_get_prediction(data): function test_forward (line 34) | def test_forward(data, target, explanation_type): function test_hard_threshold (line 70) | def test_hard_threshold(data, threshold_value, node_mask_type): function test_topk_threshold (line 96) | def test_topk_threshold(data, threshold_value, threshold_type, node_mask... FILE: test/explain/test_explanation.py function create_random_explanation (line 13) | def create_random_explanation( function test_available_explanations (line 49) | def test_available_explanations(data, node_mask_type, edge_mask_type): function test_validate_explanation (line 65) | def test_validate_explanation(data): function test_node_mask (line 85) | def test_node_mask(data): function test_edge_mask (line 111) | def test_edge_mask(data): function test_visualize_feature_importance (line 140) | def test_visualize_feature_importance(tmp_path, data, top_k, node_mask_t... FILE: test/explain/test_hetero_explainer.py class DummyModel (line 12) | class DummyModel(torch.nn.Module): method forward (line 13) | def forward(self, x_dict, edge_index_dict, *args) -> torch.Tensor: function test_get_prediction (line 17) | def test_get_prediction(hetero_data): function test_forward (line 39) | def test_forward(hetero_data, target, explanation_type): function test_hard_threshold (line 74) | def test_hard_threshold(hetero_data, threshold_value, node_mask_type): function test_topk_threshold (line 100) | def test_topk_threshold(hetero_data, threshold_value, threshold_type, FILE: test/explain/test_hetero_explanation.py function create_random_explanation (line 13) | def create_random_explanation( function test_available_explanations (line 46) | def test_available_explanations(hetero_data, node_mask_type, edge_mask_t... function test_validate_explanation (line 62) | def test_validate_explanation(hetero_data): function test_node_mask (line 82) | def test_node_mask(): function test_edge_mask (line 98) | def test_edge_mask(): function test_visualize_feature_importance (line 126) | def test_visualize_feature_importance( function test_hetero_visualize_graph (line 148) | def test_hetero_visualize_graph(tmp_path, hetero_data): FILE: test/graphgym/test_config.py class MyConfig (line 7) | class MyConfig: function my_func (line 12) | def my_func(a: int, b: int = 2) -> str: function test_from_config (line 16) | def test_from_config(): FILE: test/graphgym/test_graphgym.py function trivial_metric (line 38) | def trivial_metric(true, pred, task_type): function test_run_single_graphgym (line 49) | def test_run_single_graphgym(tmp_path, capfd, auto_resume, skip_train_eval, function test_graphgym_module (line 114) | def test_graphgym_module(tmp_path): function destroy_process_group (line 167) | def destroy_process_group(): function test_train (line 176) | def test_train(destroy_process_group, tmp_path, capfd): FILE: test/graphgym/test_logger.py function test_logger_callback (line 8) | def test_logger_callback(): FILE: test/graphgym/test_register.py function identity_act (line 8) | def identity_act(x: torch.Tensor) -> torch.Tensor: function test_register (line 13) | def test_register(): FILE: test/io/test_fs.py function tmp_fs_path (line 20) | def tmp_fs_path(request, tmp_path) -> str: function test_get_fs (line 28) | def test_get_fs(): function test_normpath (line 34) | def test_normpath(): function test_exists (line 39) | def test_exists(tmp_fs_path): function test_makedirs (line 47) | def test_makedirs(tmp_fs_path): function test_ls (line 55) | def test_ls(tmp_fs_path, detail): function test_cp (line 68) | def test_cp(tmp_fs_path): function test_extract (line 95) | def test_extract(tmp_fs_path): function test_torch_save_load (line 126) | def test_torch_save_load(tmp_fs_path): FILE: test/io/test_off.py function test_read_off (line 12) | def test_read_off(): function test_write_off (line 26) | def test_write_off(): FILE: test/llm/conftest.py function pytest_collection_modifyitems (line 8) | def pytest_collection_modifyitems(items): FILE: test/llm/models/test_g_retriever.py function test_g_retriever (line 16) | def test_g_retriever(use_lora: bool) -> None: function test_g_retriever_many_tokens (line 63) | def test_g_retriever_many_tokens() -> None: class DummyHFModel (line 110) | class DummyHFModel(nn.Module): method __init__ (line 111) | def __init__(self, vocab_size=10): method forward (line 116) | def forward(self, inputs_embeds=None, **kwargs): class DummyLLM (line 128) | class DummyLLM: method __init__ (line 129) | def __init__(self, hidden_dim): method _get_embeds (line 135) | def _get_embeds(self, question, *args): class DummyGNN (line 146) | class DummyGNN(nn.Module): method __init__ (line 148) | def __init__(self, in_channels=4, out_channels=8): method forward (line 154) | def forward(self, *args, **kwargs): function test_gretriever_prefix_embedding_injection (line 160) | def test_gretriever_prefix_embedding_injection(batch_size): FILE: test/llm/models/test_git_mol.py function test_git_mol (line 8) | def test_git_mol(): FILE: test/llm/models/test_glem.py function test_deal_nan_tensor_replaces_nans (line 7) | def test_deal_nan_tensor_replaces_nans(): function test_deal_nan_non_tensor_passthrough (line 17) | def test_deal_nan_non_tensor_passthrough(): function test_deal_nan_tensor_dtypes (line 23) | def test_deal_nan_tensor_dtypes(dtype): function test_deal_nan_is_non_mutating (line 43) | def test_deal_nan_is_non_mutating(): FILE: test/llm/models/test_llm.py function test_get_llm_kwargs (line 12) | def test_get_llm_kwargs(): function test_llm (line 22) | def test_llm(sys_prompt, context, use_embedding) -> None: class DummyBatch (line 47) | class DummyBatch(dict): method to (line 49) | def to(self, device): class DummyTokenizer (line 53) | class DummyTokenizer: method __call__ (line 57) | def __call__(self, texts, return_tensors=None, padding=True): class DummyModel (line 75) | class DummyModel(torch.nn.Module): method get_input_embeddings (line 76) | def get_input_embeddings(self): method forward (line 79) | def forward(self, inputs_embeds=None, attention_mask=None, **kwargs): function dummy_llm (line 91) | def dummy_llm(): function test_llm_prepare_inputs (line 100) | def test_llm_prepare_inputs(dummy_llm): function test_llm_single_prompt (line 120) | def test_llm_single_prompt(dummy_llm): function test_llm_variable_lengths (line 126) | def test_llm_variable_lengths(dummy_llm): FILE: test/llm/models/test_llm_judge.py function test_llm_judge (line 6) | def test_llm_judge(): FILE: test/llm/models/test_molecule_gpt.py function test_molecule_gpt (line 12) | def test_molecule_gpt() -> None: FILE: test/llm/models/test_protein_mpnn.py function test_protein_mpnn (line 8) | def test_protein_mpnn(): FILE: test/llm/models/test_sentence_transformer.py function test_sentence_transformer (line 17) | def test_sentence_transformer(batch_size, pooling_strategy, device, verb... function test_mean_pooling (line 48) | def test_mean_pooling(): function test_last_pooling (line 58) | def test_last_pooling(mask): FILE: test/llm/models/test_txt2kg.py function test_init_local_lm_flag (line 16) | def test_init_local_lm_flag(): function test_parse_n_check_triples_formats (line 22) | def test_parse_n_check_triples_formats(): function test_chunk_text_simple_sentence (line 29) | def test_chunk_text_simple_sentence(): class DummyLLM (line 36) | class DummyLLM: method __init__ (line 37) | def __init__(self): method inference (line 40) | def inference(self, *args, **kwargs): function test_local_lm_integration (line 44) | def test_local_lm_integration(monkeypatch): function test_add_doc_empty (line 60) | def test_add_doc_empty(monkeypatch): function test_add_doc_to_KG (line 67) | def test_add_doc_to_KG(monkeypatch): function test_merge_triples_deterministically_basic (line 83) | def test_merge_triples_deterministically_basic(): function test_merge_triples_deterministically_unicode_and_nonstring (line 101) | def test_merge_triples_deterministically_unicode_and_nonstring(): function test_merge_triples_deterministically_empty (line 119) | def test_merge_triples_deterministically_empty(): function test_merge_triples_deterministically_singleton (line 127) | def test_merge_triples_deterministically_singleton(): function test_chunk_to_triples_str_cloud (line 135) | def test_chunk_to_triples_str_cloud(monkeypatch): function dummy_multiproc_helper (line 177) | def dummy_multiproc_helper( function test_extract_relevant_triples_cloud (line 191) | def test_extract_relevant_triples_cloud(monkeypatch): function test_multiproc_helper_success (line 202) | def test_multiproc_helper_success(monkeypatch): function test_multiproc_helper_retry (line 233) | def test_multiproc_helper_retry(monkeypatch): function test_add_doc_empty_text (line 262) | def test_add_doc_empty_text(): function test_add_doc_empty_text_with_QA_pair (line 275) | def test_add_doc_empty_text_with_QA_pair(): function kg_cpu (line 287) | def kg_cpu(): function test_add_doc_empty_text_cpu (line 292) | def test_add_doc_empty_text_cpu(kg_cpu): function test_add_doc_empty_text_with_QA_pair_cpu (line 300) | def test_add_doc_empty_text_with_QA_pair_cpu(kg_cpu): function test_add_doc_nonempty_text_placeholder (line 308) | def test_add_doc_nonempty_text_placeholder(kg_cpu, monkeypatch): FILE: test/llm/models/test_vision_transformer.py function test_vision_transformer (line 10) | def test_vision_transformer(device): FILE: test/llm/test_large_graph_indexer.py function featurize (line 30) | def featurize(s: str) -> int: function sample_triplets (line 34) | def sample_triplets(amount: int = 1) -> List[TripletLike]: function test_basic_collate (line 43) | def test_basic_collate(): function test_large_graph_index (line 70) | def test_large_graph_index(): function test_save_load (line 155) | def test_save_load(tmp_path): FILE: test/llm/test_rag_loader.py class MockRAGFeatureStore (line 22) | class MockRAGFeatureStore: method __init__ (line 24) | def __init__(self): method retrieve_seed_nodes (line 28) | def retrieve_seed_nodes(self, query: Any, **kwargs): method config (line 35) | def config(self) -> Dict[str, Any]: method config (line 39) | def config(self, config: Dict[str, Any]): method retrieve_seed_edges (line 46) | def retrieve_seed_edges(self, query: Any, **kwargs): method load_subgraph (line 50) | def load_subgraph(self, sample): class MockRAGGraphStore (line 58) | class MockRAGGraphStore: method __init__ (line 60) | def __init__(self): method sample_subgraph (line 64) | def sample_subgraph(self, seed_nodes, seed_edges=None, **kwargs): method config (line 71) | def config(self) -> Dict[str, Any]: method config (line 75) | def config(self, config: Dict[str, Any]): method register_feature_store (line 82) | def register_feature_store(self, feature_store): class TestRAGQueryLoader (line 86) | class TestRAGQueryLoader: method setup_method (line 88) | def setup_method(self): method test_initialization_basic (line 97) | def test_initialization_basic(self): method test_initialization_with_all_params (line 108) | def test_initialization_with_all_params(self): method test_bad_config (line 126) | def test_bad_config(self): method test_config_propagation (line 133) | def test_config_propagation(self): method test_basic_query_without_vector_retriever (line 140) | def test_basic_query_without_vector_retriever(self): method test_query_with_vector_retriever (line 156) | def test_query_with_vector_retriever(self): method test_query_with_subgraph_filter (line 177) | def test_query_with_subgraph_filter(self): function test_rag_loader_integration (line 204) | def test_rag_loader_integration(tmp_path): FILE: test/llm/utils/test_rag_backend_utils.py function test_preprocess_triplet (line 19) | def test_preprocess_triplet(): function test_batch_knn (line 25) | def test_batch_knn(): function create_mock_data (line 43) | def create_mock_data(num_nodes=3, num_edges=2): function test_empty_graph (line 67) | def test_empty_graph(): function test_topk_zero (line 83) | def test_topk_zero(): class MockSentenceTransformer (line 96) | class MockSentenceTransformer: method encode (line 97) | def encode(self, sentences, **kwargs): function create_mock_graph_and_triples (line 101) | def create_mock_graph_and_triples(): function test_apply_retrieval_via_pcst_isolated_node (line 121) | def test_apply_retrieval_via_pcst_isolated_node(): class MockEmbeddingModel (line 141) | class MockEmbeddingModel: method __init__ (line 143) | def __init__(self, embed_dim: int = 64): method __call__ (line 146) | def __call__(self, texts: List[str], **kwargs) -> torch.Tensor: class TestCreateGraphFromTriples (line 162) | class TestCreateGraphFromTriples: method setup_method (line 164) | def setup_method(self): method test_create_graph_basic_functionality (line 171) | def test_create_graph_basic_functionality(self): method test_create_graph_empty_triples (line 192) | def test_create_graph_empty_triples(self): class TestCreateRemoteBackendFromGraphData (line 205) | class TestCreateRemoteBackendFromGraphData: method setup_method (line 207) | def setup_method(self): method test_create_backend_data_load (line 219) | def test_create_backend_data_load(self): FILE: test/llm/utils/test_rag_feature_store.py class TestKNNRAGFeatureStore (line 11) | class TestKNNRAGFeatureStore: method setup_method (line 13) | def setup_method(self): method test_bad_config (line 24) | def test_bad_config(self): method create_feature_store (line 30) | def create_feature_store(self): method test_retrieve_seed_nodes_single_query (line 43) | def test_retrieve_seed_nodes_single_query(self): method test_retrieve_seed_nodes_multiple_queries (line 78) | def test_retrieve_seed_nodes_multiple_queries(self): method test_load_subgraph_valid_sample (line 112) | def test_load_subgraph_valid_sample(self, induced): FILE: test/llm/utils/test_rag_graph_store.py function setup_test_fixtures (line 11) | def setup_test_fixtures(): function test_sample_subgraph_with_valid_tensor_input (line 18) | def test_sample_subgraph_with_valid_tensor_input(): function test_bad_config (line 65) | def test_bad_config(): FILE: test/llm/utils/test_vectorrag.py function sample_documents (line 8) | def sample_documents(): function sample_model (line 18) | def sample_model(): function test_save_load (line 34) | def test_save_load(sample_documents, sample_model, tmp_path): function test_query (line 47) | def test_query(sample_documents, sample_model): FILE: test/loader/test_cache.py function test_cached_loader (line 11) | def test_cached_loader(device): function test_cached_loader_transform (line 46) | def test_cached_loader_transform(device): FILE: test/loader/test_cluster.py function test_cluster_gcn (line 11) | def test_cluster_gcn(): function test_keep_inter_cluster_edges (line 111) | def test_keep_inter_cluster_edges(): function test_cluster_gcn_correctness (line 156) | def test_cluster_gcn_correctness(get_dataset, sparse_format): FILE: test/loader/test_dataloader.py function test_dataloader (line 28) | def test_dataloader(num_workers, device): function test_dataloader_on_disk_dataset (line 78) | def test_dataloader_on_disk_dataset(tmp_path, num_workers): function test_dataloader_fallbacks (line 94) | def test_dataloader_fallbacks(): function test_multiprocessing (line 144) | def test_multiprocessing(): function test_pin_memory (line 159) | def test_pin_memory(): function test_heterogeneous_dataloader (line 171) | def test_heterogeneous_dataloader(num_workers): function test_index_dataloader (line 193) | def test_index_dataloader(num_workers): function test_edge_index_dataloader (line 216) | def test_edge_index_dataloader(num_workers, sort_order): function test_dataloader_tensor_frame (line 247) | def test_dataloader_tensor_frame(): function test_dataloader_sparse (line 266) | def test_dataloader_sparse(): FILE: test/loader/test_dynamic_batch_sampler.py function test_dataloader_with_dynamic_batches (line 10) | def test_dataloader_with_dynamic_batches(): FILE: test/loader/test_graph_saint.py function test_graph_saint (line 13) | def test_graph_saint(): FILE: test/loader/test_hgt_loader.py function is_subset (line 16) | def is_subset(subedge_index, edge_index, src_idx, dst_idx): function test_hgt_loader (line 25) | def test_hgt_loader(): function test_hgt_loader_on_cora (line 141) | def test_hgt_loader_on_cora(get_dataset): function test_hgt_loader_disconnected (line 192) | def test_hgt_loader_disconnected(): FILE: test/loader/test_ibmb_loader.py function test_ibmb_batch_loader (line 20) | def test_ibmb_batch_loader(use_sparse_tensor, kwargs): function test_ibmb_node_loader (line 49) | def test_ibmb_node_loader(use_sparse_tensor, kwargs): FILE: test/loader/test_imbalanced_sampler.py function test_dataloader_with_imbalanced_sampler (line 15) | def test_dataloader_with_imbalanced_sampler(): function test_in_memory_dataset_imbalanced_sampler (line 51) | def test_in_memory_dataset_imbalanced_sampler(): function test_neighbor_loader_with_imbalanced_sampler (line 67) | def test_neighbor_loader_with_imbalanced_sampler(): function test_hetero_neighbor_loader_with_imbalanced_sampler (line 98) | def test_hetero_neighbor_loader_with_imbalanced_sampler(): FILE: test/loader/test_link_neighbor_loader.py function unique_edge_pairs (line 16) | def unique_edge_pairs(edge_index): function test_homo_link_neighbor_loader_basic (line 25) | def test_homo_link_neighbor_loader_basic(device, subgraph_type, function test_hetero_link_neighbor_loader_basic (line 112) | def test_hetero_link_neighbor_loader_basic(subgraph_type, neg_sampling_r... function test_hetero_link_neighbor_loader_loop (line 157) | def test_hetero_link_neighbor_loader_loop(subgraph_type): function test_link_neighbor_loader_edge_label (line 187) | def test_link_neighbor_loader_edge_label(): function test_temporal_homo_link_neighbor_loader (line 219) | def test_temporal_homo_link_neighbor_loader(batch_size): function test_temporal_hetero_link_neighbor_loader (line 250) | def test_temporal_hetero_link_neighbor_loader(): function test_custom_hetero_link_neighbor_loader (line 296) | def test_custom_hetero_link_neighbor_loader(): function test_homo_link_neighbor_loader_no_edges (line 363) | def test_homo_link_neighbor_loader_no_edges(): function test_hetero_link_neighbor_loader_no_edges (line 379) | def test_hetero_link_neighbor_loader_no_edges(): function test_homo_link_neighbor_loader_triplet (line 402) | def test_homo_link_neighbor_loader_triplet(disjoint, temporal, amount): function test_hetero_link_neighbor_loader_triplet (line 483) | def test_hetero_link_neighbor_loader_triplet(disjoint, temporal, amount): function test_link_neighbor_loader_mapping (line 583) | def test_link_neighbor_loader_mapping(): FILE: test/loader/test_mixin.py function test_cpu_affinity_neighbor_loader (line 19) | def test_cpu_affinity_neighbor_loader(loader_cores, spawn_context): function init_fn (line 41) | def init_fn(worker_id): function test_multithreading_neighbor_loader (line 49) | def test_multithreading_neighbor_loader(spawn_context): FILE: test/loader/test_neighbor_loader.py function is_subset (line 56) | def is_subset(subedge_index, edge_index, src_idx, dst_idx): function test_homo_neighbor_loader_basic (line 69) | def test_homo_neighbor_loader_basic( function test_hetero_neighbor_loader_basic (line 148) | def test_hetero_neighbor_loader_basic(subgraph_type, dtype): function test_homo_neighbor_loader_on_karate (line 316) | def test_homo_neighbor_loader_on_karate(get_dataset, subgraph_type): function test_hetero_neighbor_loader_on_karate (line 362) | def test_hetero_neighbor_loader_on_karate(get_dataset, subgraph_type): function test_temporal_hetero_neighbor_loader_on_karate (line 408) | def test_temporal_hetero_neighbor_loader_on_karate(get_dataset): function test_custom_neighbor_loader (line 426) | def test_custom_neighbor_loader(): function test_custom_hetero_neighbor_loader (line 471) | def test_custom_hetero_neighbor_loader(): function test_custom_hetero_neighbor_loader_duplicate (line 552) | def test_custom_hetero_neighbor_loader_duplicate(): function test_temporal_custom_neighbor_loader_on_karate (line 583) | def test_temporal_custom_neighbor_loader_on_karate(get_dataset): function test_pyg_lib_and_torch_sparse_homo_equality (line 644) | def test_pyg_lib_and_torch_sparse_homo_equality(): function test_pyg_lib_and_torch_sparse_hetero_equality (line 665) | def test_pyg_lib_and_torch_sparse_hetero_equality(): function test_memmap_neighbor_loader (line 716) | def test_memmap_neighbor_loader(tmp_path): function test_homo_neighbor_loader_sampled_info (line 737) | def test_homo_neighbor_loader_sampled_info(): function test_hetero_neighbor_loader_sampled_info (line 758) | def test_hetero_neighbor_loader_sampled_info(): function test_neighbor_loader_mapping (line 798) | def test_neighbor_loader_mapping(): function test_weighted_homo_neighbor_loader (line 823) | def test_weighted_homo_neighbor_loader(): function test_weighted_hetero_neighbor_loader (line 853) | def test_weighted_hetero_neighbor_loader(): function test_edge_level_temporal_homo_neighbor_loader (line 887) | def test_edge_level_temporal_homo_neighbor_loader(): function test_edge_level_temporal_hetero_neighbor_loader (line 914) | def test_edge_level_temporal_hetero_neighbor_loader(): function test_neighbor_loader_with_tensor_frame (line 944) | def test_neighbor_loader_with_tensor_frame(device): function test_neighbor_loader_input_id (line 972) | def test_neighbor_loader_input_id(): function test_temporal_neighbor_loader_single_link (line 1001) | def test_temporal_neighbor_loader_single_link(): FILE: test/loader/test_neighbor_sampler.py function test_neighbor_sampler_basic (line 12) | def test_neighbor_sampler_basic(): function test_neighbor_sampler_invalid_kwargs (line 44) | def test_neighbor_sampler_invalid_kwargs(): function test_neighbor_sampler_on_cora (line 52) | def test_neighbor_sampler_on_cora(get_dataset): FILE: test/loader/test_prefetch.py function test_prefetch_loader (line 9) | def test_prefetch_loader(device): FILE: test/loader/test_random_node_loader.py function test_random_node_loader (line 8) | def test_random_node_loader(): function test_heterogeneous_random_node_loader (line 27) | def test_heterogeneous_random_node_loader(): FILE: test/loader/test_shadow.py function test_shadow_k_hop_sampler (line 10) | def test_shadow_k_hop_sampler(): FILE: test/loader/test_temporal_dataloader.py function test_temporal_dataloader (line 9) | def test_temporal_dataloader(batch_size, drop_last): FILE: test/loader/test_utils.py function test_index_select (line 7) | def test_index_select(): function test_index_select_out_of_range (line 14) | def test_index_select_out_of_range(): FILE: test/loader/test_zip_loader.py function test_zip_loader (line 11) | def test_zip_loader(filter_per_worker): FILE: test/metrics/test_link_pred_metric.py function test_precision (line 28) | def test_precision(num_src_nodes, num_dst_nodes, num_edges, batch_size, k): function test_recall (line 70) | def test_recall(): function test_f1 (line 108) | def test_f1(): function test_map (line 124) | def test_map(): function test_ndcg (line 140) | def test_ndcg(): function test_mrr (line 177) | def test_mrr(): function test_hit_ratio (line 194) | def test_hit_ratio(): function test_coverage (line 211) | def test_coverage(): function test_diversity (line 234) | def test_diversity(): function test_personalization (line 249) | def test_personalization(device): function test_average_popularity (line 270) | def test_average_popularity(): function test_metric_collection (line 287) | def test_metric_collection(num_src_nodes, num_dst_nodes, num_edges): function test_empty_ground_truth (line 331) | def test_empty_ground_truth(): FILE: test/nn/aggr/test_aggr_utils.py function test_multihead_attention_block (line 13) | def test_multihead_attention_block(device: torch.device): function test_multihead_attention_block_dropout (line 34) | def test_multihead_attention_block_dropout(device: torch.device): function test_set_attention_block (line 41) | def test_set_attention_block(): function test_induced_set_attention_block (line 57) | def test_induced_set_attention_block(): function test_pooling_by_multihead_attention (line 72) | def test_pooling_by_multihead_attention(): FILE: test/nn/aggr/test_attention.py function test_attentional_aggregation (line 10) | def test_attentional_aggregation(dim): FILE: test/nn/aggr/test_basic.py function test_validate (line 18) | def test_validate(): function test_basic_aggregation (line 44) | def test_basic_aggregation(Aggregation): function test_var_aggregation (line 66) | def test_var_aggregation(): function test_empty_std_aggregation (line 78) | def test_empty_std_aggregation(): function test_learnable_aggregation (line 94) | def test_learnable_aggregation(Aggregation, learn): function test_learnable_channels_aggregation (line 122) | def test_learnable_channels_aggregation(Aggregation): FILE: test/nn/aggr/test_deep_sets.py function test_deep_sets_aggregation (line 6) | def test_deep_sets_aggregation(): FILE: test/nn/aggr/test_equilibrium.py function test_equilibrium (line 9) | def test_equilibrium(iter, alpha): function test_equilibrium_batch (line 28) | def test_equilibrium_batch(iter, alpha): FILE: test/nn/aggr/test_fused.py function test_fused_aggregation (line 18) | def test_fused_aggregation(aggrs): function test_empty_fused_std_aggregation (line 45) | def test_empty_fused_std_aggregation(): function naive_aggr (line 75) | def naive_aggr(x, index, dim_size): function fused_aggr (line 79) | def fused_aggr(x, index, dim_size): FILE: test/nn/aggr/test_gmt.py function test_graph_multiset_transformer (line 7) | def test_graph_multiset_transformer(): FILE: test/nn/aggr/test_gru.py function test_gru_aggregation (line 6) | def test_gru_aggregation(): FILE: test/nn/aggr/test_lcm.py function test_lcm_aggregation_with_project (line 10) | def test_lcm_aggregation_with_project(): function test_lcm_aggregation_without_project (line 21) | def test_lcm_aggregation_without_project(): function test_lcm_aggregation_error_handling (line 32) | def test_lcm_aggregation_error_handling(): FILE: test/nn/aggr/test_lstm.py function test_lstm_aggregation (line 7) | def test_lstm_aggregation(): FILE: test/nn/aggr/test_mlp_aggr.py function test_mlp_aggregation (line 6) | def test_mlp_aggregation(): FILE: test/nn/aggr/test_multi.py function test_multi_aggr (line 21) | def test_multi_aggr(multi_aggr_tuple): FILE: test/nn/aggr/test_patch_transformer.py function test_patch_transformer_aggregation (line 8) | def test_patch_transformer_aggregation(device: torch.device) -> None: FILE: test/nn/aggr/test_quantile.py function test_quantile_aggregation (line 12) | def test_quantile_aggregation(q, interpolation, dim, dim_size, fill_value): function test_median_aggregation (line 41) | def test_median_aggregation(): function test_quantile_aggregation_multi (line 80) | def test_quantile_aggregation_multi(): function test_quantile_aggregation_validate (line 103) | def test_quantile_aggregation_validate(): FILE: test/nn/aggr/test_scaler.py function test_degree_scaler_aggregation (line 8) | def test_degree_scaler_aggregation(train_norm): FILE: test/nn/aggr/test_set2set.py function test_set2set (line 6) | def test_set2set(): FILE: test/nn/aggr/test_set_transformer.py function test_set_transformer_aggregation (line 10) | def test_set_transformer_aggregation(): FILE: test/nn/aggr/test_sort.py function test_sort_aggregation (line 6) | def test_sort_aggregation(): function test_sort_aggregation_smaller_than_k (line 30) | def test_sort_aggregation_smaller_than_k(): function test_sort_aggregation_dim_size (line 56) | def test_sort_aggregation_dim_size(): FILE: test/nn/aggr/test_variance_preserving.py function test_variance_preserving (line 12) | def test_variance_preserving(): FILE: test/nn/attention/test_performer_attention.py function test_performer_attention (line 6) | def test_performer_attention(): FILE: test/nn/attention/test_polynormer_attention.py function test_performer_attention (line 6) | def test_performer_attention(): FILE: test/nn/attention/test_qformer.py function test_qformer (line 6) | def test_qformer(): FILE: test/nn/conv/cugraph/test_cugraph_gat_conv.py function test_gat_conv_equality (line 17) | def test_gat_conv_equality(bias, bipartite, concat, edge_attr, heads, FILE: test/nn/conv/cugraph/test_cugraph_rgcn_conv.py function test_rgcn_conv_equality (line 18) | def test_rgcn_conv_equality(aggr, bias, bipartite, max_num_neighbors, FILE: test/nn/conv/cugraph/test_cugraph_sage_conv.py function test_sage_conv_equality (line 17) | def test_sage_conv_equality(aggr, bias, bipartite, max_num_neighbors, FILE: test/nn/conv/test_agnn_conv.py function test_agnn_conv (line 12) | def test_agnn_conv(requires_grad): FILE: test/nn/conv/test_antisymmetric_conv.py function test_antisymmetric_conv (line 9) | def test_antisymmetric_conv(): FILE: test/nn/conv/test_appnp.py function test_appnp (line 10) | def test_appnp(): function test_appnp_dropout (line 45) | def test_appnp_dropout(): FILE: test/nn/conv/test_arma_conv.py function test_arma_conv (line 11) | def test_arma_conv(): function test_lazy_arma_conv (line 35) | def test_lazy_arma_conv(): FILE: test/nn/conv/test_cg_conv.py function test_cg_conv (line 12) | def test_cg_conv(batch_norm): function test_cg_conv_with_edge_features (line 55) | def test_cg_conv_with_edge_features(): FILE: test/nn/conv/test_cheb_conv.py function test_cheb_conv (line 8) | def test_cheb_conv(): function test_cheb_conv_batch (line 50) | def test_cheb_conv_batch(): FILE: test/nn/conv/test_cluster_gcn_conv.py function test_cluster_gcn_conv (line 10) | def test_cluster_gcn_conv(): FILE: test/nn/conv/test_create_gnn.py class GCNConv (line 7) | class GCNConv(MessagePassing): method __init__ (line 8) | def __init__(self, in_channels, out_channels): method forward (line 12) | def forward(self, x, edge_index): method message (line 24) | def message(self, x_j, norm): method update (line 27) | def update(self, aggr_out): function test_create_gnn (line 31) | def test_create_gnn(): FILE: test/nn/conv/test_dir_gnn_conv.py function test_dir_gnn_conv (line 6) | def test_dir_gnn_conv(): function test_static_dir_gnn_conv (line 17) | def test_static_dir_gnn_conv(): FILE: test/nn/conv/test_dna_conv.py function test_dna_conv (line 13) | def test_dna_conv(channels, num_layers): function test_dna_conv_sparse_tensor (line 48) | def test_dna_conv_sparse_tensor(channels, num_layers): FILE: test/nn/conv/test_edge_conv.py function test_edge_conv_conv (line 13) | def test_edge_conv_conv(): function test_dynamic_edge_conv (line 66) | def test_dynamic_edge_conv(): FILE: test/nn/conv/test_eg_conv.py function test_eg_conv_with_error (line 11) | def test_eg_conv_with_error(): function test_eg_conv (line 24) | def test_eg_conv(aggregators, add_self_loops): function test_eg_conv_with_sparse_input_feature (line 63) | def test_eg_conv_with_sparse_input_feature(): FILE: test/nn/conv/test_fa_conv.py function test_fa_conv (line 13) | def test_fa_conv(): FILE: test/nn/conv/test_feast_conv.py function test_feast_conv (line 10) | def test_feast_conv(): FILE: test/nn/conv/test_film_conv.py function test_film_conv (line 9) | def test_film_conv(): FILE: test/nn/conv/test_fused_gat_conv.py function test_to_graph_format (line 7) | def test_to_graph_format() -> None: function test_fused_gat_conv (line 26) | def test_fused_gat_conv() -> None: FILE: test/nn/conv/test_gat_conv.py function test_gat_conv (line 15) | def test_gat_conv(residual): function test_gat_conv_with_edge_attr (line 157) | def test_gat_conv_with_edge_attr(): function test_gat_conv_empty_edge_index (line 195) | def test_gat_conv_empty_edge_index(device): FILE: test/nn/conv/test_gated_graph_conv.py function test_gated_graph_conv (line 10) | def test_gated_graph_conv(): FILE: test/nn/conv/test_gatv2_conv.py function test_gatv2_conv (line 15) | def test_gatv2_conv(residual): function test_gatv2_conv_with_edge_attr (line 143) | def test_gatv2_conv_with_edge_attr(): FILE: test/nn/conv/test_gcn2_conv.py function test_gcn2_conv (line 10) | def test_gcn2_conv(): FILE: test/nn/conv/test_gcn_conv.py function test_gcn_conv (line 12) | def test_gcn_conv(): function test_gcn_conv_with_decomposed_layers (line 57) | def test_gcn_conv_with_decomposed_layers(): function test_gcn_conv_with_sparse_input_feature (line 81) | def test_gcn_conv_with_sparse_input_feature(): function test_static_gcn_conv (line 93) | def test_static_gcn_conv(): function test_gcn_conv_error (line 102) | def test_gcn_conv_error(): function test_gcn_conv_flow (line 107) | def test_gcn_conv_flow(): function test_gcn_norm_gradient (line 120) | def test_gcn_norm_gradient(requires_grad, layout): FILE: test/nn/conv/test_gen_conv.py function test_gen_conv (line 16) | def test_gen_conv(aggr): FILE: test/nn/conv/test_general_conv.py function test_general_conv (line 19) | def test_general_conv(kwargs): FILE: test/nn/conv/test_gin_conv.py function test_gin_conv (line 13) | def test_gin_conv(): function test_gine_conv (line 71) | def test_gine_conv(): function test_gine_conv_edge_dim (line 126) | def test_gine_conv_edge_dim(): function test_static_gin_conv (line 142) | def test_static_gin_conv(): function test_static_gine_conv (line 152) | def test_static_gine_conv(): FILE: test/nn/conv/test_gmm_conv.py function test_gmm_conv (line 12) | def test_gmm_conv(separate_gaussians): function test_lazy_gmm_conv (line 77) | def test_lazy_gmm_conv(separate_gaussians): FILE: test/nn/conv/test_gps_conv.py function test_gps_conv (line 12) | def test_gps_conv(norm, attn_type): FILE: test/nn/conv/test_graph_conv.py function test_graph_conv (line 11) | def test_graph_conv(): class EdgeGraphConv (line 132) | class EdgeGraphConv(GraphConv): method message (line 133) | def message(self, x_j, edge_weight): function test_inheritance (line 137) | def test_inheritance(): FILE: test/nn/conv/test_gravnet_conv.py function test_gravnet_conv (line 8) | def test_gravnet_conv(): FILE: test/nn/conv/test_han_conv.py function test_han_conv (line 9) | def test_han_conv(): function test_han_conv_lazy (line 71) | def test_han_conv_lazy(): function test_han_conv_empty_tensor (line 117) | def test_han_conv_empty_tensor(): FILE: test/nn/conv/test_heat_conv.py function test_heat_conv (line 11) | def test_heat_conv(concat): FILE: test/nn/conv/test_hetero_conv.py function test_hetero_conv (line 27) | def test_hetero_conv(aggr): function test_gcn2_hetero_conv (line 74) | def test_gcn2_hetero_conv(): class CustomConv (line 99) | class CustomConv(MessagePassing): method __init__ (line 100) | def __init__(self, out_channels): method forward (line 104) | def forward(self, x, edge_index, y, z): method message (line 107) | def message(self, x_j, y_j, z_j): function test_hetero_conv_with_custom_conv (line 111) | def test_hetero_conv_with_custom_conv(): class MessagePassingLoops (line 136) | class MessagePassingLoops(MessagePassing): method __init__ (line 137) | def __init__(self): function test_hetero_conv_self_loop_error (line 142) | def test_hetero_conv_self_loop_error(): function test_hetero_conv_with_dot_syntax_node_types (line 148) | def test_hetero_conv_with_dot_syntax_node_types(): function test_compile_hetero_conv_graph_breaks (line 184) | def test_compile_hetero_conv_graph_breaks(device): function gen_args (line 221) | def gen_args(): class HeteroGNN (line 225) | class HeteroGNN(torch.nn.Module): method __init__ (line 226) | def __init__(self, channels: int = 32, num_layers: int = 2): method forward (line 253) | def forward(self, x_dict, edge_index_dict): FILE: test/nn/conv/test_hgt_conv.py function test_hgt_conv_same_dimensions (line 12) | def test_hgt_conv_same_dimensions(): function test_hgt_conv_different_dimensions (line 62) | def test_hgt_conv_different_dimensions(): function test_hgt_conv_lazy (line 112) | def test_hgt_conv_lazy(): function test_hgt_conv_out_of_place (line 159) | def test_hgt_conv_out_of_place(): function test_hgt_conv_missing_dst_node_type (line 181) | def test_hgt_conv_missing_dst_node_type(): function test_hgt_conv_missing_input_node_type (line 199) | def test_hgt_conv_missing_input_node_type(): function test_hgt_conv_missing_edge_type (line 217) | def test_hgt_conv_missing_edge_type(): FILE: test/nn/conv/test_hypergraph_conv.py function test_hypergraph_conv_with_more_nodes_than_edges (line 6) | def test_hypergraph_conv_with_more_nodes_than_edges(): function test_hypergraph_conv_with_more_edges_than_nodes (line 35) | def test_hypergraph_conv_with_more_edges_than_nodes(): FILE: test/nn/conv/test_le_conv.py function test_le_conv (line 10) | def test_le_conv(): FILE: test/nn/conv/test_lg_conv.py function test_lg_conv (line 10) | def test_lg_conv(): FILE: test/nn/conv/test_meshcnn_conv.py function test_meshcnn_conv (line 15) | def test_meshcnn_conv(in_channels: int, out_channels: int): FILE: test/nn/conv/test_message_passing.py class MyConv (line 28) | class MyConv(MessagePassing): method __init__ (line 29) | def __init__(self, in_channels: Union[int, Tuple[int, int]], method forward (line 39) | def forward( method message (line 61) | def message(self, x_j: Tensor, edge_weight: Optional[Tensor]) -> Tensor: method message_and_aggregate (line 64) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: class MyConvWithSelfLoops (line 68) | class MyConvWithSelfLoops(MessagePassing): method __init__ (line 69) | def __init__(self, aggr: str = 'add'): method forward (line 72) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test_my_conv_basic (line 79) | def test_my_conv_basic(): function test_my_conv_save (line 132) | def test_my_conv_save(tmp_path): function test_my_conv_edge_index (line 148) | def test_my_conv_edge_index(): class MyCommentedConv (line 159) | class MyCommentedConv(MessagePassing): method __init__ (line 161) | def __init__(self) -> None: method forward (line 164) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test_my_commented_conv (line 169) | def test_my_commented_conv(): class MyKwargsConv (line 182) | class MyKwargsConv(MessagePassing): method __init__ (line 183) | def __init__(self) -> None: method forward (line 186) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test_my_kwargs_conv (line 190) | def test_my_kwargs_conv(): function test_my_conv_out_of_bounds (line 201) | def test_my_conv_out_of_bounds(): function test_my_conv_jit (line 216) | def test_my_conv_jit(): function test_my_conv_jit_save (line 257) | def test_my_conv_jit_save(tmp_path): function test_my_conv_aggr (line 267) | def test_my_conv_aggr(aggr): function test_my_static_graph_conv (line 277) | def test_my_static_graph_conv(): class MyMultipleAggrConv (line 306) | class MyMultipleAggrConv(MessagePassing): method __init__ (line 307) | def __init__(self, **kwargs): method forward (line 310) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: function test_my_multiple_aggr_conv (line 319) | def test_my_multiple_aggr_conv(multi_aggr_tuple): function test_my_multiple_aggr_conv_jit (line 338) | def test_my_multiple_aggr_conv_jit(): function test_copy (line 353) | def test_copy(): class MyEdgeConv (line 371) | class MyEdgeConv(MessagePassing): method __init__ (line 372) | def __init__(self): method forward (line 375) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: method edge_update (line 383) | def edge_update(self, x_j: Tensor, x_i: Tensor) -> Tensor: method message (line 386) | def message(self, edge_attr: Tensor) -> Tensor: function test_my_edge_conv (line 390) | def test_my_edge_conv(): function test_my_edge_conv_jit (line 409) | def test_my_edge_conv_jit(): function test_message_passing_hooks (line 428) | def test_message_passing_hooks(): function test_modified_message_passing_hook (line 517) | def test_modified_message_passing_hook(): class MyDefaultArgConv (line 539) | class MyDefaultArgConv(MessagePassing): method __init__ (line 540) | def __init__(self): method forward (line 544) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: method message (line 547) | def message(self, x_j, zeros: bool = True): function test_my_default_arg_conv (line 551) | def test_my_default_arg_conv(): class MyMultipleOutputConv (line 569) | class MyMultipleOutputConv(MessagePassing): method __init__ (line 570) | def __init__(self): method forward (line 573) | def forward(self, x: Tensor, edge_index: Tensor) -> Tuple[Tensor, Tens... method message (line 577) | def message(self, x_j: Tensor) -> Tuple[Tensor, Tensor]: method aggregate (line 580) | def aggregate(self, inputs: Tuple[Tensor, Tensor], method update (line 585) | def update(self, inputs: Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tensor]: function test_tuple_output (line 589) | def test_tuple_output(): function test_tuple_output_jit (line 599) | def test_tuple_output_jit(): class MyExplainConv (line 615) | class MyExplainConv(MessagePassing): method __init__ (line 616) | def __init__(self): method forward (line 619) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: function test_explain_message (line 623) | def test_explain_message(): class MyAggregatorConv (line 652) | class MyAggregatorConv(MessagePassing): method __init__ (line 653) | def __init__(self, **kwargs): method forward (line 656) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: function test_message_passing_with_aggr_module (line 669) | def test_message_passing_with_aggr_module(aggr_module): function test_message_passing_int32_edge_index (line 686) | def test_message_passing_int32_edge_index(): function test_traceable_my_conv_with_self_loops (line 706) | def test_traceable_my_conv_with_self_loops(num_nodes): function test_pickle (line 733) | def test_pickle(tmp_path): class MyOptionalEdgeAttrConv (line 744) | class MyOptionalEdgeAttrConv(MessagePassing): method __init__ (line 745) | def __init__(self): method forward (line 748) | def forward(self, x, edge_index, edge_attr=None): method message (line 751) | def message(self, x_j, edge_attr=None): function test_my_optional_edge_attr_conv (line 755) | def test_my_optional_edge_attr_conv(): FILE: test/nn/conv/test_mf_conv.py function test_mf_conv (line 9) | def test_mf_conv(): FILE: test/nn/conv/test_mixhop_conv.py function test_mixhop_conv (line 10) | def test_mixhop_conv(): FILE: test/nn/conv/test_nn_conv.py function test_nn_conv (line 14) | def test_nn_conv(device): FILE: test/nn/conv/test_pan_conv.py function test_pan_conv (line 11) | def test_pan_conv(): FILE: test/nn/conv/test_pdn_conv.py function test_pdn_conv (line 9) | def test_pdn_conv(): function test_pdn_conv_with_sparse_node_input_feature (line 32) | def test_pdn_conv_with_sparse_node_input_feature(): FILE: test/nn/conv/test_pna_conv.py function test_pna_conv (line 18) | def test_pna_conv(divide_input): function test_pna_conv_get_degree_histogram_neighbor_loader (line 44) | def test_pna_conv_get_degree_histogram_neighbor_loader(): function test_pna_conv_get_degree_histogram_dataloader (line 58) | def test_pna_conv_get_degree_histogram_dataloader(): FILE: test/nn/conv/test_point_conv.py function test_point_net_conv (line 13) | def test_point_net_conv(): FILE: test/nn/conv/test_point_gnn_conv.py function test_point_gnn_conv (line 10) | def test_point_gnn_conv(): FILE: test/nn/conv/test_point_transformer_conv.py function test_point_transformer_conv (line 11) | def test_point_transformer_conv(): FILE: test/nn/conv/test_ppf_conv.py function test_ppf_conv (line 14) | def test_ppf_conv(): FILE: test/nn/conv/test_res_gated_graph_conv.py function test_res_gated_graph_conv (line 12) | def test_res_gated_graph_conv(edge_dim): FILE: test/nn/conv/test_rgat_conv.py function test_rgat_conv (line 27) | def test_rgat_conv(mod, attention_mechanism, attention_mode, concat, edg... function test_rgat_conv_jit (line 101) | def test_rgat_conv_jit(): FILE: test/nn/conv/test_rgcn_conv.py function test_rgcn_conv_equality (line 15) | def test_rgcn_conv_equality(conf, device): function test_rgcn_conv_basic (line 52) | def test_rgcn_conv_basic(cls, conf, device): FILE: test/nn/conv/test_sage_conv.py function test_sage_conv (line 18) | def test_sage_conv(project, aggr): function test_lazy_sage_conv (line 60) | def test_lazy_sage_conv(project): function test_lstm_aggr_sage_conv (line 75) | def test_lstm_aggr_sage_conv(): function test_mlp_sage_conv (line 90) | def test_mlp_sage_conv(): function test_multi_aggr_sage_conv (line 116) | def test_multi_aggr_sage_conv(aggr_kwargs): function test_compile_multi_aggr_sage_conv (line 130) | def test_compile_multi_aggr_sage_conv(device): FILE: test/nn/conv/test_sg_conv.py function test_sg_conv (line 10) | def test_sg_conv(): FILE: test/nn/conv/test_signed_conv.py function test_signed_conv (line 10) | def test_signed_conv(): FILE: test/nn/conv/test_simple_conv.py function test_simple_conv (line 17) | def test_simple_conv(aggr, combine_root): FILE: test/nn/conv/test_spline_conv.py function test_spline_conv (line 12) | def test_spline_conv(): function test_lazy_spline_conv (line 68) | def test_lazy_spline_conv(): FILE: test/nn/conv/test_ssg_conv.py function test_ssg_conv (line 10) | def test_ssg_conv(): FILE: test/nn/conv/test_static_graph.py class MyConv (line 7) | class MyConv(MessagePassing): method forward (line 8) | def forward(self, x, edge_index): function test_static_graph (line 12) | def test_static_graph(): FILE: test/nn/conv/test_supergat_conv.py function test_supergat_conv (line 10) | def test_supergat_conv(att_type): FILE: test/nn/conv/test_tag_conv.py function test_tag_conv (line 10) | def test_tag_conv(): function test_static_tag_conv (line 44) | def test_static_tag_conv(): FILE: test/nn/conv/test_transformer_conv.py function test_transformer_conv (line 16) | def test_transformer_conv(edge_dim, concat): FILE: test/nn/conv/test_wl_conv.py function test_wl_conv (line 9) | def test_wl_conv(): FILE: test/nn/conv/test_wl_conv_continuous.py function test_wl_conv (line 9) | def test_wl_conv(): FILE: test/nn/conv/test_x_conv.py function test_x_conv (line 8) | def test_x_conv(): FILE: test/nn/conv/utils/test_gnn_cheatsheet.py function test_gnn_cheatsheet (line 4) | def test_gnn_cheatsheet(): FILE: test/nn/dense/test_dense_gat_conv.py function test_dense_gat_conv (line 10) | def test_dense_gat_conv(heads, concat): function test_dense_gat_conv_with_broadcasting (line 53) | def test_dense_gat_conv_with_broadcasting(): FILE: test/nn/dense/test_dense_gcn_conv.py function test_dense_gcn_conv (line 7) | def test_dense_gcn_conv(): function test_dense_gcn_conv_with_broadcasting (line 51) | def test_dense_gcn_conv_with_broadcasting(): FILE: test/nn/dense/test_dense_gin_conv.py function test_dense_gin_conv (line 10) | def test_dense_gin_conv(): function test_dense_gin_conv_with_broadcasting (line 57) | def test_dense_gin_conv_with_broadcasting(): FILE: test/nn/dense/test_dense_graph_conv.py function test_dense_graph_conv (line 10) | def test_dense_graph_conv(aggr): function test_dense_graph_conv_batch (line 41) | def test_dense_graph_conv_batch(aggr): function test_dense_graph_conv_with_broadcasting (line 81) | def test_dense_graph_conv_with_broadcasting(aggr): FILE: test/nn/dense/test_dense_sage_conv.py function test_dense_sage_conv (line 7) | def test_dense_sage_conv(): function test_dense_sage_conv_with_broadcasting (line 51) | def test_dense_sage_conv_with_broadcasting(): FILE: test/nn/dense/test_diff_pool.py function test_dense_diff_pool (line 10) | def test_dense_diff_pool(): FILE: test/nn/dense/test_dmon_pool.py function test_dmon_pooling (line 8) | def test_dmon_pooling(): FILE: test/nn/dense/test_linear.py function test_linear (line 25) | def test_linear(weight, bias, device): function test_lazy_linear (line 36) | def test_lazy_linear(weight, bias, device): function test_load_lazy_linear (line 57) | def test_load_lazy_linear(dim1, dim2, bias, device): function test_identical_linear_default_initialization (line 89) | def test_identical_linear_default_initialization(lazy): function test_copy_unintialized_parameter (line 104) | def test_copy_unintialized_parameter(): function test_copy_linear (line 111) | def test_copy_linear(lazy, device): function test_hetero_linear_basic (line 135) | def test_hetero_linear_basic(device): function test_hetero_linear_initializer (line 149) | def test_hetero_linear_initializer(): function test_hetero_linear_amp (line 162) | def test_hetero_linear_amp(device, use_segment_matmul): function test_lazy_hetero_linear (line 180) | def test_lazy_hetero_linear(device): function test_hetero_dict_linear (line 193) | def test_hetero_dict_linear(bias, device): function test_hetero_dict_linear_jit (line 223) | def test_hetero_dict_linear_jit(): function test_lazy_hetero_dict_linear (line 236) | def test_lazy_hetero_dict_linear(device): function test_hetero_linear_sort (line 257) | def test_hetero_linear_sort(type_vec, device): function get_xs (line 287) | def get_xs(mean: float, std: float, num_types: int, function sequential (line 299) | def sequential(xs: List[Tensor], weights: List[Tensor]) -> List[Tensor]: function nested (line 302) | def nested(xs: List[Tensor], weights: List[Tensor]) -> List[Tensor]: function grouped (line 307) | def grouped(x: Tensor, ptr: Tensor, weight: Tensor) -> Tensor: function padded (line 310) | def padded(x: Tensor, weight: Tensor) -> Tensor: function dgl_mm (line 313) | def dgl_mm(x: Tensor, count: Tensor, weight: Tensor) -> Tensor: FILE: test/nn/dense/test_mincut_pool.py function test_dense_mincut_pool (line 9) | def test_dense_mincut_pool(): FILE: test/nn/functional/test_bro.py function test_bro (line 6) | def test_bro(): FILE: test/nn/functional/test_gini.py function test_gini (line 6) | def test_gini(): FILE: test/nn/kge/test_complex.py function test_complex_scoring (line 6) | def test_complex_scoring(): function test_complex (line 40) | def test_complex(): FILE: test/nn/kge/test_distmult.py function test_distmult (line 6) | def test_distmult(): FILE: test/nn/kge/test_rotate.py function test_rotate (line 6) | def test_rotate(): FILE: test/nn/kge/test_transe.py function test_transe (line 6) | def test_transe(): FILE: test/nn/models/test_attentive_fp.py function test_attentive_fp (line 7) | def test_attentive_fp(): FILE: test/nn/models/test_attract_repel.py function test_ar_link_predictor (line 6) | def test_ar_link_predictor(): function test_ar_link_predictor_with_custom_ratio (line 31) | def test_ar_link_predictor_with_custom_ratio(): FILE: test/nn/models/test_autoencoder.py function test_gae (line 10) | def test_gae(): function test_vgae (line 52) | def test_vgae(): function test_arga (line 68) | def test_arga(): function test_argva (line 85) | def test_argva(): function test_init (line 100) | def test_init(): FILE: test/nn/models/test_basic_gnn.py function test_gcn (line 36) | def test_gcn(out_dim, dropout, act, norm, jk): function test_graph_sage (line 52) | def test_graph_sage(out_dim, dropout, act, norm, jk): function test_gin (line 68) | def test_gin(out_dim, dropout, act, norm, jk): function test_gat (line 84) | def test_gat(out_dim, dropout, act, norm, jk): function test_pna (line 106) | def test_pna(out_dim, dropout, act, norm, jk): function test_edge_cnn (line 128) | def test_edge_cnn(out_dim, dropout, act, norm, jk): function test_jit (line 139) | def test_jit(): function test_one_layer_gnn (line 151) | def test_one_layer_gnn(out_dim, jk): function test_batch (line 166) | def test_batch(norm): function test_basic_gnn_inference (line 185) | def test_basic_gnn_inference(get_dataset, jk): function test_compile_basic (line 208) | def test_compile_basic(device): function test_packaging (line 220) | def test_packaging(): function test_onnx (line 254) | def test_onnx(tmp_path: str) -> None: function test_trim_to_layer (line 315) | def test_trim_to_layer(): function test_compile_graph_breaks (line 350) | def test_compile_graph_breaks(Model, device): function test_basic_gnn_cache (line 378) | def test_basic_gnn_cache(): function gen_args (line 416) | def gen_args(): FILE: test/nn/models/test_correct_and_smooth.py function test_correct_and_smooth (line 10) | def test_correct_and_smooth(): FILE: test/nn/models/test_deep_graph_infomax.py function test_infomax (line 8) | def test_infomax(device): function test_infomax_predefined_model (line 47) | def test_infomax_predefined_model(device): FILE: test/nn/models/test_deepgcn.py function test_deepgcn (line 13) | def test_deepgcn(block_tuple, ckpt_grad): FILE: test/nn/models/test_dimenet.py function test_dimenet_modules (line 14) | def test_dimenet_modules(): function test_dimenet (line 32) | def test_dimenet(Model): FILE: test/nn/models/test_gnnff.py function test_gnnff (line 9) | def test_gnnff(): FILE: test/nn/models/test_gpse.py function test_gpse_training (line 16) | def test_gpse_training(): function test_gpse_from_pretrained (line 49) | def test_gpse_from_pretrained(): function test_gpse_node_encoder (line 65) | def test_gpse_node_encoder(expand_x): FILE: test/nn/models/test_graph_mixer.py function test_node_encoder (line 10) | def test_node_encoder(): function test_link_encoder (line 34) | def test_link_encoder(): function test_latest_k_edge_attr (line 58) | def test_latest_k_edge_attr(): FILE: test/nn/models/test_graph_unet.py function test_graph_unet (line 8) | def test_graph_unet(): FILE: test/nn/models/test_jumping_knowledge.py function test_jumping_knowledge (line 7) | def test_jumping_knowledge(): function test_hetero_jumping_knowledge (line 43) | def test_hetero_jumping_knowledge(): FILE: test/nn/models/test_label_prop.py function test_label_prop (line 8) | def test_label_prop(): FILE: test/nn/models/test_lightgcn.py function test_lightgcn_ranking (line 11) | def test_lightgcn_ranking(embedding_dim, with_edge_weight, lambda_reg, a... function test_lightgcn_link_prediction (line 47) | def test_lightgcn_link_prediction(embedding_dim, with_edge_weight, alpha): FILE: test/nn/models/test_linkx.py function test_linkx (line 11) | def test_linkx(num_edge_layers): FILE: test/nn/models/test_lpformer.py function test_lpformer (line 9) | def test_lpformer(): FILE: test/nn/models/test_mask_label.py function test_mask_label (line 6) | def test_mask_label(): function test_ratio_mask (line 24) | def test_ratio_mask(): FILE: test/nn/models/test_meta.py function test_meta_layer (line 16) | def test_meta_layer(): function test_meta_layer_example (line 41) | def test_meta_layer_example(): FILE: test/nn/models/test_metapath2vec.py function test_metapath2vec (line 8) | def test_metapath2vec(device): function test_metapath2vec_empty_edges (line 45) | def test_metapath2vec_empty_edges(): FILE: test/nn/models/test_mlp.py function test_mlp (line 10) | def test_mlp(norm, act_first, plain_last): function test_batch (line 46) | def test_batch(norm): function test_mlp_return_emb (line 67) | def test_mlp_return_emb(): function test_fine_grained_mlp (line 82) | def test_fine_grained_mlp(plain_last): FILE: test/nn/models/test_neural_fingerprint.py function test_neural_fingerprint (line 11) | def test_neural_fingerprint(batch): FILE: test/nn/models/test_node2vec.py function test_node2vec (line 18) | def test_node2vec(device, p, q): FILE: test/nn/models/test_pmlp.py function test_pmlp (line 7) | def test_pmlp(): FILE: test/nn/models/test_polynormer.py function test_polynormer (line 11) | def test_polynormer(local_attn, qk_shared, pre_ln, post_bn): FILE: test/nn/models/test_re_net.py class MyTestEventDataset (line 9) | class MyTestEventDataset(EventDataset): method __init__ (line 10) | def __init__(self, root, seq_len): method num_nodes (line 15) | def num_nodes(self): method num_rels (line 19) | def num_rels(self): method processed_file_names (line 23) | def processed_file_names(self): method _download (line 26) | def _download(self): method process_events (line 29) | def process_events(self): method process (line 36) | def process(self): function test_re_net (line 41) | def test_re_net(tmp_path): FILE: test/nn/models/test_rect.py function test_rect (line 9) | def test_rect(): FILE: test/nn/models/test_rev_gnn.py function test_revgnn_forward_inverse (line 10) | def test_revgnn_forward_inverse(num_groups): function test_revgnn_backward (line 35) | def test_revgnn_backward(num_groups): function test_revgnn_multi_backward (line 50) | def test_revgnn_multi_backward(num_groups): function test_revgnn_diable (line 69) | def test_revgnn_diable(num_groups): function test_revgnn_with_args (line 90) | def test_revgnn_with_args(num_groups): FILE: test/nn/models/test_schnet.py function generate_data (line 10) | def generate_data(): function test_schnet (line 21) | def test_schnet(use_interaction_graph, use_atomref): function test_schnet_batch (line 53) | def test_schnet_batch(): FILE: test/nn/models/test_sgformer.py function test_sgformer (line 6) | def test_sgformer(): FILE: test/nn/models/test_signed_gcn.py function test_signed_gcn (line 8) | def test_signed_gcn(): FILE: test/nn/models/test_tgn.py function test_tgn (line 15) | def test_tgn(neg_sampling_ratio): FILE: test/nn/models/test_visnet.py function test_visnet (line 13) | def test_visnet(kwargs): FILE: test/nn/norm/test_batch_norm.py function test_batch_norm (line 10) | def test_batch_norm(device, conf): function test_batch_norm_single_element (line 26) | def test_batch_norm_single_element(): function test_hetero_batch_norm (line 44) | def test_hetero_batch_norm(device, conf): FILE: test/nn/norm/test_diff_group_norm.py function test_diff_group_norm (line 8) | def test_diff_group_norm(device): function test_group_distance_ratio (line 31) | def test_group_distance_ratio(): FILE: test/nn/norm/test_graph_norm.py function test_graph_norm (line 8) | def test_graph_norm(device): FILE: test/nn/norm/test_graph_size_norm.py function test_graph_size_norm (line 7) | def test_graph_size_norm(): FILE: test/nn/norm/test_instance_norm.py function test_instance_norm (line 10) | def test_instance_norm(conf, device): FILE: test/nn/norm/test_layer_norm.py function test_layer_norm (line 11) | def test_layer_norm(device, affine, mode): function test_hetero_layer_norm (line 32) | def test_hetero_layer_norm(device, affine): FILE: test/nn/norm/test_mean_subtraction_norm.py function test_mean_subtraction_norm (line 7) | def test_mean_subtraction_norm(): FILE: test/nn/norm/test_msg_norm.py function test_message_norm (line 8) | def test_message_norm(device): FILE: test/nn/norm/test_pair_norm.py function test_pair_norm (line 9) | def test_pair_norm(scale_individually): FILE: test/nn/pool/connect/test_filter_edges.py function test_filter_edges (line 8) | def test_filter_edges(): FILE: test/nn/pool/select/test_select_topk.py function test_topk_ratio (line 12) | def test_topk_ratio(): function test_select_topk (line 39) | def test_select_topk(min_score): FILE: test/nn/pool/test_approx_knn.py function to_set (line 9) | def to_set(edge_index): function test_approx_knn (line 15) | def test_approx_knn(): function test_approx_knn_graph (line 45) | def test_approx_knn_graph(): FILE: test/nn/pool/test_asap.py function test_asap (line 10) | def test_asap(): function test_asap_jit_save (line 42) | def test_asap_jit_save(): FILE: test/nn/pool/test_avg_pool.py function test_avg_pool_x (line 8) | def test_avg_pool_x(): function test_avg_pool (line 46) | def test_avg_pool(): function test_avg_pool_neighbor_x (line 81) | def test_avg_pool_neighbor_x(): FILE: test/nn/pool/test_cluster_pool.py function test_cluster_pooling (line 14) | def test_cluster_pooling(edge_score_method): FILE: test/nn/pool/test_consecutive.py function test_consecutive_cluster (line 6) | def test_consecutive_cluster(): FILE: test/nn/pool/test_decimation.py function test_decimation_basic (line 6) | def test_decimation_basic(): function test_decimation_single_cloud (line 20) | def test_decimation_single_cloud(): function test_decimation_almost_empty (line 32) | def test_decimation_almost_empty(): FILE: test/nn/pool/test_edge_pool.py function test_compute_edge_score_softmax (line 8) | def test_compute_edge_score_softmax(): function test_compute_edge_score_tanh (line 26) | def test_compute_edge_score_tanh(): function test_compute_edge_score_sigmoid (line 39) | def test_compute_edge_score_sigmoid(): function test_edge_pooling (line 52) | def test_edge_pooling(): FILE: test/nn/pool/test_glob.py function test_global_pool (line 10) | def test_global_pool(): function test_permuted_global_pool (line 43) | def test_permuted_global_pool(): function test_dense_global_pool (line 70) | def test_dense_global_pool(): FILE: test/nn/pool/test_graclus.py function test_graclus (line 8) | def test_graclus(): FILE: test/nn/pool/test_knn.py function test_l2 (line 16) | def test_l2(device, k): function test_mips (line 40) | def test_mips(device, k): function test_approx_l2 (line 65) | def test_approx_l2(device, k, reserve): function test_approx_mips (line 89) | def test_approx_mips(device, k, reserve): function test_mips_exclude (line 112) | def test_mips_exclude(device, k): FILE: test/nn/pool/test_max_pool.py function test_max_pool_x (line 8) | def test_max_pool_x(): function test_max_pool (line 46) | def test_max_pool(): function test_max_pool_neighbor_x (line 81) | def test_max_pool_neighbor_x(): FILE: test/nn/pool/test_mem_pool.py function test_mem_pool (line 7) | def test_mem_pool(): FILE: test/nn/pool/test_pan_pool.py function test_pan_pooling (line 8) | def test_pan_pooling(): FILE: test/nn/pool/test_pool.py function test_radius_graph_jit (line 12) | def test_radius_graph_jit(): FILE: test/nn/pool/test_sag_pool.py function test_sag_pooling (line 13) | def test_sag_pooling(): FILE: test/nn/pool/test_topk_pool.py function test_filter_adj (line 8) | def test_filter_adj(): function test_topk_pooling (line 26) | def test_topk_pooling(): FILE: test/nn/pool/test_voxel_grid.py function test_voxel_grid (line 9) | def test_voxel_grid(): function test_single_voxel_grid (line 30) | def test_single_voxel_grid(): FILE: test/nn/test_compile_basic.py function gather_scatter (line 14) | def gather_scatter(x, edge_index, reduce='sum'): function gather_cat_scatter (line 20) | def gather_cat_scatter(x, edge_index, reduce='sum'): function gather_weight_scatter (line 26) | def gather_weight_scatter(x, edge_index, edge_weight, reduce='sum'): function gather_transform_scatter (line 32) | def gather_transform_scatter(x, edge_index, matrix, reduce='sum'): function fused_gather_scatter (line 38) | def fused_gather_scatter(x, edge_index, reduce=('sum', 'mean', 'max')): function test_torch_compile (line 49) | def test_torch_compile(device): FILE: test/nn/test_compile_conv.py class MySAGEConv (line 17) | class MySAGEConv(torch.nn.Module): method __init__ (line 18) | def __init__(self, in_channels: int, out_channels: int): method forward (line 23) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test_compile_conv (line 34) | def test_compile_conv(device, Conv): function test_compile_conv_edge_index (line 57) | def test_compile_conv_edge_index(device, Conv): FILE: test/nn/test_compile_dynamic.py class MySAGEConv (line 16) | class MySAGEConv(torch.nn.Module): method __init__ (line 17) | def __init__(self, in_channels: int, out_channels: int): method forward (line 22) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test_dynamic_torch_compile (line 32) | def test_dynamic_torch_compile(device): FILE: test/nn/test_data_parallel.py function test_data_parallel_single_gpu (line 10) | def test_data_parallel_single_gpu(): function test_data_parallel_multi_gpu (line 20) | def test_data_parallel_multi_gpu(): FILE: test/nn/test_encoding.py function test_positional_encoding (line 8) | def test_positional_encoding(device): function test_temporal_encoding (line 17) | def test_temporal_encoding(device): FILE: test/nn/test_fvcore.py function test_fvcore (line 8) | def test_fvcore(): FILE: test/nn/test_fx.py function test_dropout (line 6) | def test_dropout(): FILE: test/nn/test_inits.py function test_inits (line 16) | def test_inits(): function test_reset (line 51) | def test_reset(): FILE: test/nn/test_model_hub.py class DummyModel (line 18) | class DummyModel(GCN, PyGModelHubMixin): method __init__ (line 19) | def __init__(self, model_name, dataset_name, model_kwargs): function model (line 25) | def model(): function test_model_init (line 30) | def test_model_init(): function test_save_pretrained (line 39) | def test_save_pretrained(model, tmp_path): function test_save_pretrained_internal (line 48) | def test_save_pretrained_internal(model, tmp_path): function test_save_pretrained_with_push_to_hub (line 56) | def test_save_pretrained_with_push_to_hub(model, tmp_path): function test_from_pretrained (line 87) | def test_from_pretrained(model, tmp_path): function test_from_pretrained_internal (line 96) | def test_from_pretrained_internal(model, monkeypatch): FILE: test/nn/test_model_summary.py class GraphSAGE (line 14) | class GraphSAGE(torch.nn.Module): method __init__ (line 15) | def __init__(self): method forward (line 21) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class ModuleDictModel (line 28) | class ModuleDictModel(nn.Module): method __init__ (line 29) | def __init__(self): method forward (line 36) | def forward(self, x: torch.Tensor, act_type: str) -> torch.Tensor: function gcn (line 41) | def gcn(): function test_summary_basic (line 56) | def test_summary_basic(gcn): function test_summary_with_sparse_tensor (line 76) | def test_summary_with_sparse_tensor(gcn): function test_lazy_gcn (line 96) | def test_lazy_gcn(): function test_summary_with_max_depth (line 120) | def test_summary_with_max_depth(gcn): function test_summary_with_leaf_module (line 141) | def test_summary_with_leaf_module(gcn): function test_summary_with_reusing_layers (line 170) | def test_summary_with_reusing_layers(): function test_summary_with_to_hetero_model (line 186) | def test_summary_with_to_hetero_model(): function test_summary_with_module_dict_model (line 220) | def test_summary_with_module_dict_model(): function test_summary_with_jit_model (line 238) | def test_summary_with_jit_model(): FILE: test/nn/test_module_dict.py function test_internal_external_key_conversion (line 6) | def test_internal_external_key_conversion(): function test_dot_syntax_keys (line 21) | def test_dot_syntax_keys(): function test_tuple_keys (line 39) | def test_tuple_keys(): function test_reserved_keys (line 56) | def test_reserved_keys(): FILE: test/nn/test_parameter_dict.py function test_internal_external_key_conversion (line 6) | def test_internal_external_key_conversion(): function test_dot_syntax_keys (line 21) | def test_dot_syntax_keys(): function test_tuple_keys (line 40) | def test_tuple_keys(): function test_reserved_keys (line 58) | def test_reserved_keys(): FILE: test/nn/test_reshape.py function test_reshape (line 6) | def test_reshape(): FILE: test/nn/test_resolver.py function test_activation_resolver (line 15) | def test_activation_resolver(): function test_aggregation_resolver (line 37) | def test_aggregation_resolver(aggr_tuple): function test_multi_aggregation_resolver (line 43) | def test_multi_aggregation_resolver(): function test_normalization_resolver (line 66) | def test_normalization_resolver(norm_tuple): function test_optimizer_resolver (line 74) | def test_optimizer_resolver(): function test_lr_scheduler_resolver (line 99) | def test_lr_scheduler_resolver(scheduler_args): FILE: test/nn/test_sequential.py function test_sequential_basic (line 20) | def test_sequential_basic(): function test_sequential_jit (line 70) | def test_sequential_jit(): function symbolic_trace (line 96) | def symbolic_trace(module): function test_sequential_tracable (line 105) | def test_sequential_tracable(): function test_sequential_with_multiple_return_values (line 117) | def test_sequential_with_multiple_return_values(): function test_sequential_with_ordered_dict (line 132) | def test_sequential_with_ordered_dict(): function test_sequential_to_hetero (line 149) | def test_sequential_to_hetero(): FILE: test/nn/test_to_fixed_size_transformer.py class Model (line 7) | class Model(torch.nn.Module): method __init__ (line 8) | def __init__(self): method forward (line 12) | def forward(self, x, batch): function test_to_fixed_size (line 16) | def test_to_fixed_size(): FILE: test/nn/test_to_hetero_module.py function test_to_hetero_linear (line 13) | def test_to_hetero_linear(LinearCls): function test_to_hetero_message_passing (line 32) | def test_to_hetero_message_passing(): FILE: test/nn/test_to_hetero_transformer.py class Net1 (line 34) | class Net1(torch.nn.Module): method __init__ (line 35) | def __init__(self): method forward (line 40) | def forward(self, x: Tensor, edge_attr: Tensor) -> Tuple[Tensor, Tensor]: class Net2 (line 46) | class Net2(torch.nn.Module): method __init__ (line 47) | def __init__(self): method forward (line 53) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net3 (line 60) | class Net3(torch.nn.Module): method __init__ (line 61) | def __init__(self): method forward (line 66) | def forward(self, x: Tensor, edge_index: Tensor, class Net4 (line 72) | class Net4(torch.nn.Module): method __init__ (line 73) | def __init__(self): method forward (line 79) | def forward(self, x0: Tensor, edge_index: Tensor) -> Tensor: class Net5 (line 85) | class Net5(torch.nn.Module): method __init__ (line 86) | def __init__(self, num_layers): method forward (line 94) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net6 (line 100) | class Net6(torch.nn.Module): method __init__ (line 101) | def __init__(self, num_layers): method forward (line 109) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net7 (line 115) | class Net7(torch.nn.Module): method __init__ (line 116) | def __init__(self): method forward (line 121) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net8 (line 127) | class Net8(torch.nn.Module): method __init__ (line 128) | def __init__(self): method forward (line 132) | def forward(self, x: Tensor) -> Tensor: class Net9 (line 137) | class Net9(torch.nn.Module): method __init__ (line 138) | def __init__(self): method forward (line 142) | def forward(self, x: Tensor) -> Tensor: class Net10 (line 146) | class Net10(torch.nn.Module): method __init__ (line 147) | def __init__(self): method forward (line 151) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net11 (line 157) | class Net11(torch.nn.Module): method __init__ (line 158) | def __init__(self): method forward (line 163) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net12 (line 170) | class Net12(torch.nn.Module): method __init__ (line 171) | def __init__(self): method forward (line 175) | def forward(self, x: Tensor) -> Tensor: function test_to_hetero_basic (line 179) | def test_to_hetero_basic(): class GCN (line 311) | class GCN(torch.nn.Module): method __init__ (line 312) | def __init__(self): method forward (line 317) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test_to_hetero_with_gcn (line 323) | def test_to_hetero_with_gcn(): function test_to_hetero_with_basic_model (line 340) | def test_to_hetero_with_basic_model(): class GraphConv (line 367) | class GraphConv(MessagePassing): method __init__ (line 368) | def __init__(self, in_channels, out_channels): method reset_parameters (line 372) | def reset_parameters(self): method forward (line 375) | def forward(self, x, edge_index): class RGCN (line 381) | class RGCN(torch.nn.Module): method __init__ (line 382) | def __init__(self, in_channels, out_channels): method forward (line 387) | def forward(self, x, edge_index): function test_to_hetero_and_rgcn_equal_output (line 391) | def test_to_hetero_and_rgcn_equal_output(): class GraphLevelGNN (line 453) | class GraphLevelGNN(torch.nn.Module): method __init__ (line 454) | def __init__(self): method forward (line 460) | def forward(self, x: Tensor, edge_index: Tensor, batch: Tensor) -> Ten... function test_graph_level_to_hetero (line 467) | def test_graph_level_to_hetero(): class MessagePassingLoops (line 491) | class MessagePassingLoops(MessagePassing): method __init__ (line 492) | def __init__(self): method forward (line 496) | def forward(self, x): class ModelLoops (line 500) | class ModelLoops(torch.nn.Module): method __init__ (line 501) | def __init__(self): method forward (line 505) | def forward(self, x): function test_hetero_transformer_self_loop_error (line 509) | def test_hetero_transformer_self_loop_error(): function test_to_hetero_validate (line 516) | def test_to_hetero_validate(): function test_to_hetero_on_static_graphs (line 524) | def test_to_hetero_on_static_graphs(): function test_to_hetero_lazy_cuda (line 544) | def test_to_hetero_lazy_cuda(): FILE: test/nn/test_to_hetero_with_bases_transformer.py class Net1 (line 20) | class Net1(torch.nn.Module): method __init__ (line 21) | def __init__(self): method forward (line 26) | def forward(self, x: Tensor, edge_attr: Tensor) -> Tuple[Tensor, Tensor]: class Net2 (line 32) | class Net2(torch.nn.Module): method __init__ (line 33) | def __init__(self): method forward (line 39) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net3 (line 46) | class Net3(torch.nn.Module): method __init__ (line 47) | def __init__(self): method forward (line 52) | def forward(self, x: Tensor, edge_index: Tensor, class Net4 (line 58) | class Net4(torch.nn.Module): method __init__ (line 59) | def __init__(self): method forward (line 65) | def forward(self, x0: Tensor, edge_index: Tensor) -> Tensor: class Net5 (line 71) | class Net5(torch.nn.Module): method __init__ (line 72) | def __init__(self, num_layers): method forward (line 80) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net6 (line 86) | class Net6(torch.nn.Module): method __init__ (line 87) | def __init__(self, num_layers): method forward (line 95) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: class Net7 (line 101) | class Net7(torch.nn.Module): method __init__ (line 102) | def __init__(self): method forward (line 107) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: function test_to_hetero_with_bases (line 113) | def test_to_hetero_with_bases(): class GraphConv (line 204) | class GraphConv(MessagePassing): method __init__ (line 205) | def __init__(self, in_channels, out_channels): method reset_parameters (line 209) | def reset_parameters(self): method forward (line 212) | def forward(self, x, edge_index): class RGCN (line 218) | class RGCN(torch.nn.Module): method __init__ (line 219) | def __init__(self, in_channels, out_channels): method forward (line 224) | def forward(self, x, edge_index): function test_to_hetero_with_bases_and_rgcn_equal_output (line 228) | def test_to_hetero_with_bases_and_rgcn_equal_output(): function test_to_hetero_with_bases_validate (line 291) | def test_to_hetero_with_bases_validate(): function test_to_hetero_with_bases_on_static_graphs (line 299) | def test_to_hetero_with_bases_on_static_graphs(): function test_to_hetero_with_bases_save (line 319) | def test_to_hetero_with_bases_save(tmp_path): FILE: test/nn/unpool/test_knn_interpolate.py function test_knn_interpolate (line 8) | def test_knn_interpolate(): FILE: test/profile/test_benchmark.py function test_benchmark (line 8) | def test_benchmark(capfd): FILE: test/profile/test_nvtx.py function _setup_mock (line 6) | def _setup_mock(torch_cuda_mock): function test_nvtxit_base (line 14) | def test_nvtxit_base(torch_cuda_mock): function test_nvtxit_rename (line 48) | def test_nvtxit_rename(torch_cuda_mock): function test_nvtxit_iters (line 82) | def test_nvtxit_iters(torch_cuda_mock): function test_nvtxit_warmups (line 111) | def test_nvtxit_warmups(torch_cuda_mock): FILE: test/profile/test_profile.py function test_timeit (line 29) | def test_timeit(device): function test_profileit_cuda (line 49) | def test_profileit_cuda(get_dataset): function test_profileit_xpu (line 91) | def test_profileit_xpu(get_dataset): function test_torch_profile (line 135) | def test_torch_profile(capfd, get_dataset, device): function test_xpu_profile (line 157) | def test_xpu_profile(capfd, get_dataset, export_chrome_trace): FILE: test/profile/test_profile_utils.py function test_count_parameters (line 22) | def test_count_parameters(): function test_get_model_size (line 26) | def test_get_model_size(): function test_get_data_size (line 31) | def test_get_data_size(): function test_get_data_size_with_sparse_tensor (line 40) | def test_get_data_size_with_sparse_tensor(): function test_get_cpu_memory_from_gc (line 50) | def test_get_cpu_memory_from_gc(): function test_get_gpu_memory_from_gc (line 58) | def test_get_gpu_memory_from_gc(): function test_get_gpu_memory_from_nvidia_smi (line 66) | def test_get_gpu_memory_from_nvidia_smi(): function test_get_gpu_memory_from_ipex (line 73) | def test_get_gpu_memory_from_ipex(): function test_bytes_function (line 80) | def test_bytes_function(): FILE: test/profile/test_profiler.py function test_profiler (line 10) | def test_profiler(capfd, get_dataset, device): FILE: test/sampler/test_sampler_base.py function test_homogeneous_num_neighbors (line 14) | def test_homogeneous_num_neighbors(): function _init_merge_sampler_outputs (line 52) | def _init_merge_sampler_outputs(hetero=False, disjoint=False): function test_homogeneous_merge (line 87) | def test_homogeneous_merge(disjoint, bidirectional): function test_homogeneous_merge_no_replace (line 116) | def test_homogeneous_merge_no_replace(disjoint, bidirectional): function _init_collate_sampler_outputs (line 146) | def _init_collate_sampler_outputs(disjoint=False): function test_homogeneous_collate (line 166) | def test_homogeneous_collate(disjoint, replace): function test_homogeneous_collate_empty (line 175) | def test_homogeneous_collate_empty(): function test_homogeneous_collate_single (line 181) | def test_homogeneous_collate_single(): function test_homogeneous_collate_missing_fields (line 187) | def test_homogeneous_collate_missing_fields(): function test_heterogeneous_num_neighbors_list (line 196) | def test_heterogeneous_num_neighbors_list(): function test_heterogeneous_num_neighbors_dict_and_default (line 208) | def test_heterogeneous_num_neighbors_dict_and_default(): function test_heterogeneous_num_neighbors_empty_dict (line 227) | def test_heterogeneous_num_neighbors_empty_dict(): function test_homogeneous_to_bidirectional (line 239) | def test_homogeneous_to_bidirectional(): function test_heterogeneous_to_bidirectional (line 252) | def test_heterogeneous_to_bidirectional(): function test_homogeneous_sampler_output_global_fields (line 287) | def test_homogeneous_sampler_output_global_fields(): function test_heterogeneous_sampler_output_global_fields (line 336) | def test_heterogeneous_sampler_output_global_fields(): FILE: test/sampler/test_sampler_neighbor_sampler.py function _init_sample_graph (line 17) | def _init_sample_graph(hetero=False): function _init_graph_to_sample (line 63) | def _init_graph_to_sample(graph_dtype, hetero=False, reverse=False): function test_homogeneous_neighbor_sampler_basic (line 138) | def test_homogeneous_neighbor_sampler_basic(input_type): function test_heterogeneous_neighbor_sampler_basic (line 177) | def test_heterogeneous_neighbor_sampler_basic(input_type): function test_homogeneous_neighbor_sampler_backwards (line 234) | def test_homogeneous_neighbor_sampler_backwards(input_type): function test_homogeneous_neighbor_sampler_weighted_backwards (line 302) | def test_homogeneous_neighbor_sampler_weighted_backwards(input_type): function test_homogeneous_neighbor_sampler_temporal_backwards (line 370) | def test_homogeneous_neighbor_sampler_temporal_backwards( function test_heterogeneous_neighbor_sampler_backwards (line 413) | def test_heterogeneous_neighbor_sampler_backwards(input_type): function test_bidirectional_neighbor_sampler (line 508) | def test_bidirectional_neighbor_sampler(input_type): function test_bidirectional_neighbor_sampler_hetero (line 631) | def test_bidirectional_neighbor_sampler_hetero(input_type): function test_neighbor_sampler_backwards_not_supported (line 638) | def test_neighbor_sampler_backwards_not_supported(input_type, hetero): FILE: test/test_config_mixin.py function teardown_once (line 12) | def teardown_once(): class Dataclass (line 18) | class Dataclass: class Base (line 23) | class Base(torch.nn.Module, ConfigMixin): class Module (line 28) | class Module(Base): method __init__ (line 29) | def __init__(self, x: int, data: Dataclass): class SubModule (line 36) | class SubModule(Base): method __init__ (line 37) | def __init__(self, p: float): class CompoundModule (line 43) | class CompoundModule(torch.nn.Module, ConfigMixin): method __init__ (line 44) | def __init__( function test_config_mixin (line 58) | def test_config_mixin() -> None: function test_config_mixin_compound (line 116) | def test_config_mixin_compound() -> None: FILE: test/test_config_store.py function teardown_function (line 17) | def teardown_function(): function test_to_dataclass (line 21) | def test_to_dataclass(): function test_map_annotation (line 48) | def test_map_annotation(): function test_register (line 62) | def test_register(): function test_fill_config_store (line 79) | def test_fill_config_store(): function test_hydra_config_store (line 92) | def test_hydra_config_store(): FILE: test/test_debug.py function test_debug (line 4) | def test_debug(): FILE: test/test_edge_index.py function test_basic (line 45) | def test_basic(dtype, device): function test_identity (line 84) | def test_identity(dtype, device, is_undirected): function test_sparse_tensor (line 104) | def test_sparse_tensor(dtype, device): function test_set_tuple_item (line 127) | def test_set_tuple_item(): function test_validate (line 141) | def test_validate(): function test_undirected (line 170) | def test_undirected(dtype, device): function test_fill_cache_ (line 196) | def test_fill_cache_(dtype, device, is_undirected): function test_clone (line 239) | def test_clone(dtype, device, is_undirected): function test_to_function (line 261) | def test_to_function(dtype, device, is_undirected): function test_cpu_cuda (line 305) | def test_cpu_cuda(dtype): function test_share_memory (line 321) | def test_share_memory(dtype, device): function test_pin_memory (line 336) | def test_pin_memory(dtype): function test_contiguous (line 345) | def test_contiguous(dtype, device): function test_sort_by (line 360) | def test_sort_by(dtype, device, is_undirected): function test_cat (line 407) | def test_cat(dtype, device, is_undirected): function test_flip (line 446) | def test_flip(dtype, device, is_undirected): function test_index_select (line 476) | def test_index_select(dtype, device, is_undirected): function test_narrow (line 503) | def test_narrow(dtype, device, is_undirected): function test_getitem (line 521) | def test_getitem(dtype, device, is_undirected): function test_select (line 569) | def test_select(dtype, device): function test_unbind (line 631) | def test_unbind(dtype, device): function test_to_dense (line 659) | def test_to_dense(dtype, device, value_dtype): function test_to_sparse_coo (line 690) | def test_to_sparse_coo(dtype, device): function test_to_sparse_csr (line 733) | def test_to_sparse_csr(dtype, device): function test_to_sparse_csc (line 754) | def test_to_sparse_csc(dtype, device): function test_to_sparse_tensor (line 775) | def test_to_sparse_tensor(device): function test_add (line 789) | def test_add(dtype, device, is_undirected): function test_sub (line 836) | def test_sub(dtype, device, is_undirected): function test_torch_sparse_spmm (line 885) | def test_torch_sparse_spmm(device, reduce, transpose, is_undirected): function test_torch_spmm (line 936) | def test_torch_spmm(device, reduce, transpose, is_undirected): function test_spmm (line 1000) | def test_spmm(without_extensions, device, reduce, transpose, is_undirect... function test_spspmm (line 1059) | def test_spspmm(device, reduce, transpose, is_undirected): function test_matmul (line 1087) | def test_matmul(without_extensions, device): function test_sparse_row_narrow (line 1116) | def test_sparse_row_narrow(dtype, device): function test_sparse_col_narrow (line 1147) | def test_sparse_col_narrow(dtype, device): function test_sparse_resize (line 1178) | def test_sparse_resize(dtype, device): function test_tolist (line 1216) | def test_tolist(): function test_numpy (line 1222) | def test_numpy(): function test_global_mapping (line 1230) | def test_global_mapping(device, dtype): function test_to_vector (line 1242) | def test_to_vector(device, dtype): function test_save_and_load (line 1251) | def test_save_and_load(dtype, device, tmp_path): function _collate_fn (line 1269) | def _collate_fn(edge_indices: List[EdgeIndex]) -> List[EdgeIndex]: function test_data_loader (line 1276) | def test_data_loader(dtype, num_workers, pin_memory): function test_torch_script (line 1301) | def test_torch_script(): function test_compile_basic (line 1343) | def test_compile_basic(): function test_compile_create_edge_index (line 1375) | def test_compile_create_edge_index(): function edge_index_mm (line 1420) | def edge_index_mm(edge_index, x, reduce): function torch_sparse_mm (line 1423) | def torch_sparse_mm(adj, x): function sparse_tensor_mm (line 1426) | def sparse_tensor_mm(adj, x, reduce): function scatter_mm (line 1429) | def scatter_mm(edge_index, x, reduce): FILE: test/test_experimental.py function test_experimental_mode (line 11) | def test_experimental_mode(options): FILE: test/test_hash_tensor.py function test_basic (line 37) | def test_basic(dtype, device): function test_empty (line 65) | def test_empty(dtype, device): function test_string_key (line 93) | def test_string_key(device): function test_clone (line 101) | def test_clone(device): function test_share_memory (line 121) | def test_share_memory(device): function test_pin_memory (line 135) | def test_pin_memory(): function test_detach (line 148) | def test_detach(device): function test_contiguous (line 166) | def test_contiguous(device): function test_save_and_load (line 180) | def test_save_and_load(device, tmp_path): function test_to_function (line 197) | def test_to_function(device): function test_unsqueeze (line 228) | def test_unsqueeze(device): function test_squeeze (line 279) | def test_squeeze(num_keys, device): function test_slice (line 329) | def test_slice(device): function test_index_select (line 375) | def test_index_select(dtype, device): function test_select (line 427) | def test_select(device): function test_tolist (line 460) | def test_tolist(): function test_numpy (line 467) | def test_numpy(): function _collate_fn (line 473) | def _collate_fn(hash_tensors: List[HashTensor]) -> List[HashTensor]: function test_data_loader (line 479) | def test_data_loader(num_workers, pin_memory): function test_getitem (line 506) | def test_getitem(dtype, device): function test_compile_basic (line 560) | def test_compile_basic(): FILE: test/test_home.py function test_home (line 8) | def test_home(): FILE: test/test_index.py function test_basic (line 20) | def test_basic(dtype, device): function test_identity (line 49) | def test_identity(dtype, device): function test_validate (line 66) | def test_validate(): function test_fill_cache_ (line 85) | def test_fill_cache_(dtype, device): function test_dim_resize (line 101) | def test_dim_resize(dtype, device): function test_clone (line 123) | def test_clone(dtype, device): function test_to_function (line 144) | def test_to_function(dtype, device): function test_cpu_cuda (line 185) | def test_cpu_cuda(dtype): function test_share_memory (line 201) | def test_share_memory(dtype, device): function test_pin_memory (line 216) | def test_pin_memory(dtype): function test_contiguous (line 225) | def test_contiguous(dtype, device): function test_sort (line 237) | def test_sort(dtype, device): function test_sort_stable (line 262) | def test_sort_stable(dtype, device): function test_cat (line 289) | def test_cat(dtype, device): function test_flip (line 326) | def test_flip(dtype, device): function test_index_select (line 339) | def test_index_select(dtype, device): function test_narrow (line 360) | def test_narrow(dtype, device): function test_getitem (line 373) | def test_getitem(dtype, device): function test_add (line 435) | def test_add(dtype, device): function test_sub (line 475) | def test_sub(dtype, device): function test_to_list (line 513) | def test_to_list(): function test_numpy (line 519) | def test_numpy(): function test_save_and_load (line 527) | def test_save_and_load(dtype, device, tmp_path): function _collate_fn (line 543) | def _collate_fn(indices: List[Index]) -> List[Index]: function test_data_loader (line 550) | def test_data_loader(dtype, num_workers, pin_memory): FILE: test/test_inspector.py function test_eval_type (line 12) | def test_eval_type() -> None: function test_type_repr (line 21) | def test_type_repr() -> None: function test_inspector_sage_conv (line 44) | def test_inspector_sage_conv() -> None: function test_inspector_gat_conv (line 71) | def test_inspector_gat_conv() -> None: function test_get_params_from_method_call (line 102) | def test_get_params_from_method_call() -> None: FILE: test/test_isinstance.py function test_basic (line 7) | def test_basic(): function test_compile (line 13) | def test_compile(): FILE: test/test_onnx.py class SimpleModel (line 17) | class SimpleModel(torch.nn.Module): method __init__ (line 19) | def __init__(self) -> None: method forward (line 23) | def forward(self, x: torch.Tensor) -> torch.Tensor: function test_is_in_onnx_export (line 27) | def test_is_in_onnx_export() -> None: function test_safe_onnx_export_ci_resilient (line 32) | def test_safe_onnx_export_ci_resilient() -> None: function test_safe_onnx_export_success (line 62) | def test_safe_onnx_export_success() -> None: function test_safe_onnx_export_with_skip_on_error (line 107) | def test_safe_onnx_export_with_skip_on_error() -> None: function test_serde_error_patterns (line 134) | def test_serde_error_patterns() -> None: function test_non_serde_error_reraise (line 173) | def test_non_serde_error_reraise() -> None: function test_dynamo_fallback (line 197) | def test_dynamo_fallback() -> None: function test_opset_fallback (line 233) | def test_opset_fallback() -> None: function test_all_strategies_fail (line 272) | def test_all_strategies_fail() -> None: function test_pytest_environment_detection (line 303) | def test_pytest_environment_detection() -> None: function test_warnings_emitted (line 335) | def test_warnings_emitted() -> None: function test_args_conversion (line 385) | def test_args_conversion(args_input: Any) -> None: FILE: test/test_seed.py function test_seed_everything (line 9) | def test_seed_everything(): FILE: test/test_typing.py function test_edge_type_str (line 6) | def test_edge_type_str(): FILE: test/test_warnings.py function test_warn (line 9) | def test_warn(): function test_no_warn_if_compiling (line 15) | def test_no_warn_if_compiling(_): function test_warning_cache (line 22) | def test_warning_cache(): FILE: test/testing/test_decorators.py function test_enable_extensions (line 5) | def test_enable_extensions(): function test_disable_extensions (line 26) | def test_disable_extensions(): FILE: test/transforms/test_add_gpse.py function test_gpse (line 12) | def test_gpse(): FILE: test/transforms/test_add_metapaths.py function generate_data (line 9) | def generate_data() -> HeteroData: function test_add_metapaths (line 22) | def test_add_metapaths() -> None: function test_add_metapaths_max_sample (line 75) | def test_add_metapaths_max_sample() -> None: function test_add_weighted_metapaths (line 87) | def test_add_weighted_metapaths() -> None: function test_add_random_metapaths (line 145) | def test_add_random_metapaths() -> None: FILE: test/transforms/test_add_positional_encoding.py function test_add_laplacian_eigenvector_pe (line 12) | def test_add_laplacian_eigenvector_pe(): function test_eigenvector_permutation_invariance (line 56) | def test_eigenvector_permutation_invariance(): function test_add_random_walk_pe (line 79) | def test_add_random_walk_pe(): FILE: test/transforms/test_add_remaining_self_loops.py function test_add_remaining_self_loops (line 7) | def test_add_remaining_self_loops(): function test_add_remaining_self_loops_all_loops_exist (line 46) | def test_add_remaining_self_loops_all_loops_exist(): function test_hetero_add_remaining_self_loops (line 61) | def test_hetero_add_remaining_self_loops(): FILE: test/transforms/test_add_self_loops.py function test_add_self_loops (line 7) | def test_add_self_loops(): function test_add_self_loops_with_existing_self_loops (line 39) | def test_add_self_loops_with_existing_self_loops(): function test_hetero_add_self_loops (line 47) | def test_hetero_add_self_loops(): FILE: test/transforms/test_cartesian.py function test_cartesian (line 7) | def test_cartesian(): FILE: test/transforms/test_center.py function test_center (line 7) | def test_center(): FILE: test/transforms/test_compose.py function test_compose (line 7) | def test_compose(): function test_compose_data_list (line 24) | def test_compose_data_list(): function test_compose_filters (line 39) | def test_compose_filters(): FILE: test/transforms/test_constant.py function test_constant (line 7) | def test_constant(): FILE: test/transforms/test_delaunay.py function assert_one_point (line 8) | def assert_one_point(transform: Delaunay) -> None: function assert_two_points (line 15) | def assert_two_points(transform: Delaunay) -> None: function assert_three_points (line 22) | def assert_three_points(transform: Delaunay) -> None: function assert_four_points (line 29) | def assert_four_points(transform: Delaunay) -> None: function test_qhull_delaunay (line 46) | def test_qhull_delaunay() -> None: function test_shull_delaunay (line 57) | def test_shull_delaunay() -> None: FILE: test/transforms/test_distance.py function test_distance (line 7) | def test_distance(): FILE: test/transforms/test_face_to_edge.py function test_2d_face_to_edge (line 7) | def test_2d_face_to_edge() -> None: function test_3d_face_to_edge (line 23) | def test_3d_face_to_edge() -> None: FILE: test/transforms/test_feature_propagation.py function test_feature_propagation (line 7) | def test_feature_propagation(): FILE: test/transforms/test_fixed_points.py function test_fixed_points (line 7) | def test_fixed_points(): FILE: test/transforms/test_gcn_norm.py function test_gcn_norm (line 9) | def test_gcn_norm(): FILE: test/transforms/test_gdc.py function test_gdc (line 10) | def test_gdc(): FILE: test/transforms/test_generate_mesh_normals.py function test_generate_mesh_normals (line 7) | def test_generate_mesh_normals(): FILE: test/transforms/test_grid_sampling.py function test_grid_sampling (line 9) | def test_grid_sampling(): FILE: test/transforms/test_half_hop.py function test_half_hop (line 7) | def test_half_hop(): FILE: test/transforms/test_knn_graph.py function test_knn_graph (line 9) | def test_knn_graph(): FILE: test/transforms/test_laplacian_lambda_max.py function test_laplacian_lambda_max (line 9) | def test_laplacian_lambda_max(): FILE: test/transforms/test_largest_connected_components.py function test_largest_connected_components (line 9) | def test_largest_connected_components(): FILE: test/transforms/test_line_graph.py function test_line_graph (line 7) | def test_line_graph(): FILE: test/transforms/test_linear_transformation.py function test_linear_transformation (line 12) | def test_linear_transformation(matrix): FILE: test/transforms/test_local_cartesian.py function test_local_cartesian (line 7) | def test_local_cartesian(): FILE: test/transforms/test_local_degree_profile.py function test_target_indegree (line 7) | def test_target_indegree(): FILE: test/transforms/test_mask_transform.py function test_index_to_mask (line 7) | def test_index_to_mask(): function test_mask_to_index (line 38) | def test_mask_to_index(): function test_hetero_index_to_mask (line 61) | def test_hetero_index_to_mask(): function test_hetero_mask_to_index (line 79) | def test_hetero_mask_to_index(): FILE: test/transforms/test_node_property_split.py function test_node_property_split (line 15) | def test_node_property_split(property_name): FILE: test/transforms/test_normalize_features.py function test_normalize_scale (line 7) | def test_normalize_scale(): function test_hetero_normalize_scale (line 19) | def test_hetero_normalize_scale(): FILE: test/transforms/test_normalize_rotation.py function test_normalize_rotation (line 9) | def test_normalize_rotation(): FILE: test/transforms/test_normalize_scale.py function test_normalize_scale (line 7) | def test_normalize_scale(): FILE: test/transforms/test_one_hot_degree.py function test_one_hot_degree (line 7) | def test_one_hot_degree(): FILE: test/transforms/test_pad.py function fake_data (line 20) | def fake_data() -> Data: function fake_hetero_data (line 24) | def fake_hetero_data(node_types=2, edge_types=5) -> HeteroData: function _generate_homodata_node_attrs (line 30) | def _generate_homodata_node_attrs(data: Data) -> Generator[str, None, No... function _generate_homodata_edge_attrs (line 36) | def _generate_homodata_edge_attrs(data: Data) -> Generator[str, None, No... function _generate_heterodata_nodes (line 42) | def _generate_heterodata_nodes( function _generate_heterodata_edges (line 50) | def _generate_heterodata_edges( function _check_homo_data_nodes (line 58) | def _check_homo_data_nodes( function _check_homo_data_edges (line 99) | def _check_homo_data_edges( function _check_hetero_data_nodes (line 157) | def _check_hetero_data_nodes( function _check_hetero_data_edges (line 209) | def _check_hetero_data_edges( function _check_data (line 284) | def _check_data( function test_pad_repr (line 320) | def test_pad_repr(): function test_pad_auto_edges (line 330) | def test_pad_auto_edges(data, num_nodes, add_pad_mask): function test_pad_data_explicit_edges (line 340) | def test_pad_data_explicit_edges(num_nodes, num_edges, add_pad_mask): function test_pad_heterodata_explicit_edges (line 353) | def test_pad_heterodata_explicit_edges(num_nodes, num_edges, add_pad_mask): function test_pad_data_pad_values (line 366) | def test_pad_data_pad_values(node_pad_value, edge_pad_value): function test_pad_heterodata_pad_values (line 393) | def test_pad_heterodata_pad_values(node_pad_value, edge_pad_value): function test_pad_data_exclude_keys (line 411) | def test_pad_data_exclude_keys(data, add_pad_mask, exclude_keys): function test_pad_invalid_max_num_nodes (line 422) | def test_pad_invalid_max_num_nodes(is_hetero): function test_pad_invalid_max_num_edges (line 435) | def test_pad_invalid_max_num_edges(is_hetero): function test_pad_num_nodes_not_complete (line 448) | def test_pad_num_nodes_not_complete(): function test_pad_invalid_padding_type (line 456) | def test_pad_invalid_padding_type(): function test_pad_data_non_tensor_attr (line 463) | def test_pad_data_non_tensor_attr(): function test_pad_node_additional_attr_mask (line 478) | def test_pad_node_additional_attr_mask(mask_pad_value): function test_uniform_padding (line 500) | def test_uniform_padding(): function test_attr_name_padding (line 513) | def test_attr_name_padding(): function test_attr_name_padding_invalid (line 530) | def test_attr_name_padding_invalid(): function test_node_edge_type_padding (line 549) | def test_node_edge_type_padding(store_type): function test_edge_padding_invalid (line 590) | def test_edge_padding_invalid(): FILE: test/transforms/test_point_pair_features.py function test_point_pair_features (line 9) | def test_point_pair_features(): FILE: test/transforms/test_polar.py function test_polar (line 9) | def test_polar(): FILE: test/transforms/test_radius_graph.py function test_radius_graph (line 10) | def test_radius_graph(): FILE: test/transforms/test_random_flip.py function test_random_flip (line 7) | def test_random_flip(): FILE: test/transforms/test_random_jitter.py function test_random_jitter (line 7) | def test_random_jitter(): FILE: test/transforms/test_random_link_split.py function test_random_link_split (line 14) | def test_random_link_split(): function test_random_link_split_with_to_sparse_tensor (line 88) | def test_random_link_split_with_to_sparse_tensor(): function test_random_link_split_with_label (line 102) | def test_random_link_split_with_label(): function test_random_link_split_increment_label (line 133) | def test_random_link_split_increment_label(): function test_random_link_split_on_hetero_data (line 154) | def test_random_link_split_on_hetero_data(): function test_random_link_split_on_undirected_hetero_data (line 253) | def test_random_link_split_on_undirected_hetero_data(): function test_random_link_split_insufficient_negative_edges (line 274) | def test_random_link_split_insufficient_negative_edges(): function test_random_link_split_non_contiguous (line 290) | def test_random_link_split_non_contiguous(): function test_random_link_split_on_dataset (line 313) | def test_random_link_split_on_dataset(get_dataset): FILE: test/transforms/test_random_node_split.py function test_random_node_split (line 9) | def test_random_node_split(num_splits): function test_random_node_split_on_hetero_data (line 142) | def test_random_node_split_on_hetero_data(): FILE: test/transforms/test_random_rotate.py function test_random_rotate (line 7) | def test_random_rotate(): FILE: test/transforms/test_random_scale.py function test_random_scale (line 7) | def test_random_scale(): FILE: test/transforms/test_random_shear.py function test_random_shear (line 7) | def test_random_shear(): FILE: test/transforms/test_remove_duplicated_edges.py function test_remove_duplicated_edges (line 7) | def test_remove_duplicated_edges(): FILE: test/transforms/test_remove_isolated_nodes.py function test_remove_isolated_nodes (line 7) | def test_remove_isolated_nodes(): function test_remove_isolated_nodes_in_hetero_data (line 23) | def test_remove_isolated_nodes_in_hetero_data(): FILE: test/transforms/test_remove_self_loops.py function test_remove_self_loops (line 7) | def test_remove_self_loops(): function test_hetero_remove_self_loops (line 35) | def test_hetero_remove_self_loops(): FILE: test/transforms/test_remove_training_classes.py function test_remove_training_classes (line 7) | def test_remove_training_classes(): FILE: test/transforms/test_rooted_subgraph.py function test_rooted_ego_nets (line 9) | def test_rooted_ego_nets(): function test_rooted_rw_subgraph (line 43) | def test_rooted_rw_subgraph(): function test_rooted_subgraph_minibatch (line 64) | def test_rooted_subgraph_minibatch(): FILE: test/transforms/test_sample_points.py function test_sample_points (line 7) | def test_sample_points(): FILE: test/transforms/test_sign.py function test_sign (line 7) | def test_sign(): FILE: test/transforms/test_spherical.py function test_spherical (line 9) | def test_spherical(): FILE: test/transforms/test_svd_feature_reduction.py function test_svd_feature_reduction (line 7) | def test_svd_feature_reduction(): FILE: test/transforms/test_target_indegree.py function test_target_indegree (line 7) | def test_target_indegree(): FILE: test/transforms/test_to_dense.py function test_to_dense (line 7) | def test_to_dense(): FILE: test/transforms/test_to_device.py function test_to_device (line 9) | def test_to_device(device): FILE: test/transforms/test_to_sparse_tensor.py function test_to_sparse_tensor_basic (line 10) | def test_to_sparse_tensor_basic(layout): function test_to_sparse_tensor_and_keep_edge_index (line 47) | def test_to_sparse_tensor_and_keep_edge_index(): function test_hetero_to_sparse_tensor (line 65) | def test_hetero_to_sparse_tensor(layout): function test_to_sparse_tensor_num_nodes_equals_num_edges (line 108) | def test_to_sparse_tensor_num_nodes_equals_num_edges(): FILE: test/transforms/test_to_superpixels.py function test_to_superpixels (line 19) | def test_to_superpixels(tmp_path): FILE: test/transforms/test_to_undirected.py function test_to_undirected (line 7) | def test_to_undirected(): function test_to_undirected_with_duplicates (line 26) | def test_to_undirected_with_duplicates(): function test_hetero_to_undirected (line 38) | def test_hetero_to_undirected(): FILE: test/transforms/test_two_hop.py function test_two_hop (line 7) | def test_two_hop(): FILE: test/transforms/test_virtual_node.py function test_virtual_node (line 7) | def test_virtual_node(): FILE: test/utils/conftest.py class GraphSAGE (line 10) | class GraphSAGE(torch.nn.Module): method __init__ (line 11) | def __init__(self): method forward (line 16) | def forward(self, x, edge_index): class HeteroSAGE (line 21) | class HeteroSAGE(torch.nn.Module): method __init__ (line 22) | def __init__(self, metadata, model_config=None): method forward (line 35) | def forward(self, x_dict, edge_index_dict, function hetero_data (line 55) | def hetero_data(): function hetero_model (line 71) | def hetero_model(): FILE: test/utils/test_assortativity.py function test_assortativity (line 9) | def test_assortativity(): FILE: test/utils/test_augmentation.py function test_shuffle_node (line 13) | def test_shuffle_node(): function test_mask_feature (line 36) | def test_mask_feature(): function test_add_random_edge (line 75) | def test_add_random_edge(): FILE: test/utils/test_coalesce.py function test_coalesce (line 9) | def test_coalesce(): function test_coalesce_without_duplicates (line 35) | def test_coalesce_without_duplicates(): function test_coalesce_jit (line 56) | def test_coalesce_jit(): FILE: test/utils/test_convert.py function test_to_scipy_sparse_matrix (line 25) | def test_to_scipy_sparse_matrix(): function test_from_scipy_sparse_matrix (line 47) | def test_from_scipy_sparse_matrix(): function test_to_networkx (line 57) | def test_to_networkx(): function test_from_networkx_set_node_attributes (line 82) | def test_from_networkx_set_node_attributes(): function test_to_networkx_undirected (line 103) | def test_to_networkx_undirected(): function test_to_networkx_undirected_options (line 142) | def test_to_networkx_undirected_options(): function test_to_networkx_hetero (line 158) | def test_to_networkx_hetero(): function test_from_networkx (line 195) | def test_from_networkx(): function test_from_networkx_group_attrs (line 212) | def test_from_networkx_group_attrs(): function test_networkx_vice_versa_convert (line 234) | def test_networkx_vice_versa_convert(): function test_from_networkx_non_consecutive (line 248) | def test_from_networkx_non_consecutive(): function test_from_networkx_inverse (line 265) | def test_from_networkx_inverse(): function test_from_networkx_non_numeric_labels (line 284) | def test_from_networkx_non_numeric_labels(): function test_from_networkx_without_edges (line 300) | def test_from_networkx_without_edges(): function test_from_networkx_with_same_node_and_edge_attributes (line 313) | def test_from_networkx_with_same_node_and_edge_attributes(): function test_from_networkx_subgraph_convert (line 335) | def test_from_networkx_subgraph_convert(): function test_from_networkx_sbm (line 353) | def test_from_networkx_sbm(n, p, q): function test_to_networkit_vice_versa (line 369) | def test_to_networkit_vice_versa(): function test_to_networkit (line 385) | def test_to_networkit(directed, edge_weight, num_nodes): function test_from_networkit (line 411) | def test_from_networkit(directed, weighted): function test_trimesh_vice_versa (line 441) | def test_trimesh_vice_versa(): function test_to_trimesh (line 455) | def test_to_trimesh(): function test_from_trimesh (line 472) | def test_from_trimesh(): function test_to_cugraph (line 489) | def test_to_cugraph(edge_weight, directed, relabel_nodes): function test_from_cugraph (line 531) | def test_from_cugraph(edge_weight, directed, relabel_nodes): function test_to_dgl_graph (line 574) | def test_to_dgl_graph(): function test_to_dgl_hetero_graph (line 590) | def test_to_dgl_hetero_graph(): function test_to_dgl_sparse (line 611) | def test_to_dgl_sparse(): function test_from_dgl_graph (line 630) | def test_from_dgl_graph(): function test_from_dgl_hetero_graph (line 646) | def test_from_dgl_hetero_graph(): FILE: test/utils/test_cross_entropy.py function test_sparse_cross_entropy_multiclass (line 11) | def test_sparse_cross_entropy_multiclass( function test_sparse_cross_entropy_multilabel (line 38) | def test_sparse_cross_entropy_multilabel( function test_sparse_cross_entropy_negative_weight (line 69) | def test_sparse_cross_entropy_negative_weight( FILE: test/utils/test_degree.py function test_degree (line 6) | def test_degree(): FILE: test/utils/test_dropout.py function test_dropout_adj (line 13) | def test_dropout_adj(): function test_dropout_node (line 38) | def test_dropout_node(): function test_dropout_edge (line 56) | def test_dropout_edge(): function test_dropout_path (line 75) | def test_dropout_path(): FILE: test/utils/test_embedding.py class GNN (line 9) | class GNN(torch.nn.Module): method __init__ (line 10) | def __init__(self): method forward (line 15) | def forward(self, x0, edge_index): function test_get_embeddings (line 21) | def test_get_embeddings(): function test_get_embeddings_hetero (line 38) | def test_get_embeddings_hetero(hetero_data, hetero_model): FILE: test/utils/test_functions.py function test_cumsum (line 6) | def test_cumsum(): FILE: test/utils/test_geodesic.py function test_geodesic_distance (line 10) | def test_geodesic_distance(): FILE: test/utils/test_grid.py function test_grid (line 7) | def test_grid(): FILE: test/utils/test_hetero.py function test_construct_bipartite_edge_index (line 7) | def test_construct_bipartite_edge_index(): FILE: test/utils/test_homophily.py function test_homophily (line 9) | def test_homophily(): FILE: test/utils/test_index_sort.py function test_index_sort_stable (line 8) | def test_index_sort_stable(device): FILE: test/utils/test_isolated.py function test_contains_isolated_nodes (line 10) | def test_contains_isolated_nodes(): function test_remove_isolated_nodes (line 24) | def test_remove_isolated_nodes(): FILE: test/utils/test_laplacian.py function test_get_laplacian (line 7) | def test_get_laplacian(): FILE: test/utils/test_lexsort.py function test_lexsort (line 7) | def test_lexsort(): FILE: test/utils/test_loop.py function test_contains_self_loops (line 15) | def test_contains_self_loops(): function test_remove_self_loops (line 23) | def test_remove_self_loops(): function test_segregate_self_loops (line 62) | def test_segregate_self_loops(): function test_add_self_loops (line 109) | def test_add_self_loops(): function test_add_self_loops_bipartite (line 201) | def test_add_self_loops_bipartite(): function test_add_remaining_self_loops (line 220) | def test_add_remaining_self_loops(): function test_add_remaining_self_loops_without_initial_loops (line 269) | def test_add_remaining_self_loops_without_initial_loops(): function test_get_self_loop_attr (line 294) | def test_get_self_loop_attr(): FILE: test/utils/test_map.py function test_map_index (line 12) | def test_map_index(device, max_index): function test_map_index_na (line 25) | def test_map_index_na(device, max_index): function trivial_map (line 46) | def trivial_map(src, index, max_index, inclusive): FILE: test/utils/test_mask.py function test_mask_select (line 6) | def test_mask_select(): function test_index_to_mask (line 18) | def test_index_to_mask(): function test_mask_to_index (line 28) | def test_mask_to_index(): FILE: test/utils/test_mesh_laplacian.py function test_get_mesh_laplacian_of_cube (line 6) | def test_get_mesh_laplacian_of_cube(): function test_get_mesh_laplacian_of_irregular_triangular_prism (line 57) | def test_get_mesh_laplacian_of_irregular_triangular_prism(): FILE: test/utils/test_negative_sampling.py function is_negative (line 18) | def is_negative(edge_index, neg_edge_index, size, bipartite): function test_edge_index_to_vector_and_vice_versa (line 32) | def test_edge_index_to_vector_and_vice_versa(): function test_negative_sampling (line 65) | def test_negative_sampling(): function test_bipartite_negative_sampling (line 97) | def test_bipartite_negative_sampling(): function test_batched_negative_sampling (line 110) | def test_batched_negative_sampling(): function test_bipartite_batched_negative_sampling (line 139) | def test_bipartite_batched_negative_sampling(): function test_structured_negative_sampling (line 160) | def test_structured_negative_sampling(): function test_structured_negative_sampling_feasible (line 183) | def test_structured_negative_sampling_feasible(): FILE: test/utils/test_nested.py function test_to_nested_tensor (line 7) | def test_to_nested_tensor(): function test_from_nested_tensor (line 28) | def test_from_nested_tensor(): function test_to_and_from_nested_tensor_autograd (line 49) | def test_to_and_from_nested_tensor_autograd(): FILE: test/utils/test_noise_scheduler.py function test_get_smld_sigma_schedule (line 10) | def test_get_smld_sigma_schedule(): function test_get_diffusion_beta_schedule (line 27) | def test_get_diffusion_beta_schedule(schedule_type): FILE: test/utils/test_normalize_edge_index.py function test_normalize_edge_index (line 9) | def test_normalize_edge_index(add_self_loops: bool, symmetric: bool): FILE: test/utils/test_normalized_cut.py function test_normalized_cut (line 7) | def test_normalized_cut(): FILE: test/utils/test_num_nodes.py function test_maybe_num_nodes (line 10) | def test_maybe_num_nodes(): function test_maybe_num_nodes_dict (line 21) | def test_maybe_num_nodes_dict(): FILE: test/utils/test_one_hot.py function test_one_hot (line 6) | def test_one_hot(): FILE: test/utils/test_ppr.py function test_get_ppr (line 11) | def test_get_ppr(target): FILE: test/utils/test_random.py function test_erdos_renyi_graph (line 11) | def test_erdos_renyi_graph(): function test_stochastic_blockmodel_graph (line 26) | def test_stochastic_blockmodel_graph(): function test_barabasi_albert_graph (line 51) | def test_barabasi_albert_graph(): FILE: test/utils/test_repeat.py function test_repeat (line 4) | def test_repeat(): FILE: test/utils/test_scatter.py function test_scatter_validate (line 13) | def test_scatter_validate(): function test_scatter (line 30) | def test_scatter(reduce, device): function test_scatter_backward (line 59) | def test_scatter_backward(reduce, device): function test_scatter_any (line 76) | def test_scatter_any(device): function test_group_argsort (line 90) | def test_group_argsort(num_groups, descending, device): function test_scatter_argmax (line 112) | def test_scatter_argmax(device): function test_group_cat (line 124) | def test_group_cat(device): function pytorch_scatter (line 177) | def pytorch_scatter(x, index, dim_size, reduce): function pytorch_index_add (line 188) | def pytorch_index_add(x, index, dim_size, reduce): function own_scatter (line 195) | def own_scatter(x, index, dim_size, reduce): function optimized_scatter (line 199) | def optimized_scatter(x, index, dim_size, reduce): FILE: test/utils/test_segment.py function test_segment (line 16) | def test_segment(device, without_extensions, reduce): function test_segment_logsumexp (line 36) | def test_segment_logsumexp(device, without_extensions) -> None: function pytorch_segment (line 83) | def pytorch_segment(x, ptr, reduce): function own_segment (line 88) | def own_segment(x, ptr, reduce): function optimized_scatter (line 91) | def optimized_scatter(x, index, reduce, dim_size): function optimized_segment (line 94) | def optimized_segment(x, index, reduce): FILE: test/utils/test_select.py function test_select (line 6) | def test_select(): function test_narrow (line 18) | def test_narrow(): FILE: test/utils/test_smiles.py function test_from_to_smiles (line 22) | def test_from_to_smiles(smiles): function test_from_to_rdmol (line 29) | def test_from_to_rdmol(smiles): FILE: test/utils/test_softmax.py function test_softmax (line 12) | def test_softmax(): function test_softmax_backward (line 30) | def test_softmax_backward(): function test_softmax_dim (line 47) | def test_softmax_dim(): function dense_softmax (line 90) | def dense_softmax(x, index): FILE: test/utils/test_sort_edge_index.py function test_sort_edge_index (line 10) | def test_sort_edge_index(): function test_sort_edge_index_jit (line 41) | def test_sort_edge_index_jit(): FILE: test/utils/test_sparse.py function test_dense_to_sparse (line 24) | def test_dense_to_sparse(): function test_dense_to_sparse_bipartite (line 84) | def test_dense_to_sparse_bipartite(): function test_is_torch_sparse_tensor (line 90) | def test_is_torch_sparse_tensor(): function test_is_sparse (line 100) | def test_is_sparse(): function test_to_torch_coo_tensor (line 110) | def test_to_torch_coo_tensor(): function test_to_torch_csr_tensor (line 149) | def test_to_torch_csr_tensor(): function test_to_torch_csc_tensor (line 178) | def test_to_torch_csc_tensor(): function test_to_torch_coo_tensor_save_load (line 218) | def test_to_torch_coo_tensor_save_load(tmp_path): function test_to_edge_index (line 232) | def test_to_edge_index(): function test_cat (line 257) | def test_cat(layout, dim, device): FILE: test/utils/test_spmm.py function test_spmm_basic (line 18) | def test_spmm_basic(device, reduce): function test_spmm_reduce (line 44) | def test_spmm_reduce(device, reduce): function test_spmm_layout (line 64) | def test_spmm_layout(device, layout, reduce): function test_spmm_jit (line 86) | def test_spmm_jit(reduce): function test_spmm_edge_index (line 112) | def test_spmm_edge_index(device, reduce): FILE: test/utils/test_subgraph.py function test_get_num_hops (line 14) | def test_get_num_hops(): function test_subgraph (line 30) | def test_subgraph(): function test_subgraph_large_index (line 55) | def test_subgraph_large_index(device): function test_bipartite_subgraph (line 62) | def test_bipartite_subgraph(): function test_bipartite_subgraph_large_index (line 87) | def test_bipartite_subgraph_large_index(device): function test_k_hop_subgraph (line 99) | def test_k_hop_subgraph(): FILE: test/utils/test_to_dense_adj.py function test_to_dense_adj (line 7) | def test_to_dense_adj(): function test_to_dense_adj_with_empty_edge_index (line 67) | def test_to_dense_adj_with_empty_edge_index(): function test_to_dense_adj_with_duplicate_entries (line 84) | def test_to_dense_adj_with_duplicate_entries(): FILE: test/utils/test_to_dense_batch.py function test_to_dense_batch (line 13) | def test_to_dense_batch(fill): function test_to_dense_batch_disable_dynamic_shapes (line 65) | def test_to_dense_batch_disable_dynamic_shapes(): function test_to_dense_batch_jit (line 94) | def test_to_dense_batch_jit(): FILE: test/utils/test_total_influence.py class GNN (line 8) | class GNN(torch.nn.Module): method __init__ (line 9) | def __init__(self): method forward (line 14) | def forward(self, x0, edge_index): function test_total_influence_smoke (line 20) | def test_total_influence_smoke(): FILE: test/utils/test_train_test_split_edges.py function test_train_test_split_edges (line 8) | def test_train_test_split_edges(): FILE: test/utils/test_tree_decomposition.py function test_tree_decomposition (line 12) | def test_tree_decomposition(smiles): FILE: test/utils/test_trim_to_layer.py function test_trim_sparse_tensor (line 17) | def test_trim_sparse_tensor(): function test_trim_to_layer_basic (line 28) | def test_trim_to_layer_basic(): function test_trim_to_layer_hetero (line 128) | def test_trim_to_layer_hetero(): class GNN (line 149) | class GNN(torch.nn.Module): method __init__ (line 150) | def __init__(self, num_layers: int): method forward (line 156) | def forward( function test_trim_to_layer_with_neighbor_loader (line 174) | def test_trim_to_layer_with_neighbor_loader(): FILE: test/utils/test_unbatch.py function test_unbatch (line 6) | def test_unbatch(): function test_unbatch_edge_index (line 16) | def test_unbatch_edge_index(): FILE: test/utils/test_undirected.py function test_is_undirected (line 7) | def test_is_undirected(): function test_to_undirected (line 29) | def test_to_undirected(): FILE: test/visualization/test_graph_visualization.py function test_visualize_graph_via_graphviz (line 12) | def test_visualize_graph_via_graphviz(tmp_path, backend): function test_visualize_graph_via_graphviz_with_node_labels (line 26) | def test_visualize_graph_via_graphviz_with_node_labels(tmp_path, backend): function test_visualize_graph_via_networkx (line 41) | def test_visualize_graph_via_networkx(tmp_path, backend): FILE: test/visualization/test_influence.py class Net (line 8) | class Net(torch.nn.Module): method __init__ (line 9) | def __init__(self, in_channels, out_channels): method forward (line 14) | def forward(self, x, edge_index): function test_influence (line 20) | def test_influence(): FILE: torch_geometric/_compile.py function is_compiling (line 9) | def is_compiling() -> bool: function compile (line 20) | def compile( FILE: torch_geometric/_onnx.py function is_in_onnx_export (line 10) | def is_in_onnx_export() -> bool: function safe_onnx_export (line 21) | def safe_onnx_export( function _apply_onnx_allowzero_workaround (line 103) | def _apply_onnx_allowzero_workaround( FILE: torch_geometric/backend.py function use_segment_matmul_heuristic (line 16) | def use_segment_matmul_heuristic( FILE: torch_geometric/config_mixin.py class ConfigMixin (line 15) | class ConfigMixin: method config (line 17) | def config(self) -> Any: method from_config (line 33) | def from_config(cls, cfg: Any, *args: Any, **kwargs: Any) -> Any: function _recursive_config (line 71) | def _recursive_config(value: Any) -> Any: function _recursive_from_config (line 83) | def _recursive_from_config(value: Any) -> Any: function _locate_cls (line 105) | def _locate_cls(qualname: str) -> Any: FILE: torch_geometric/config_store.py function get_node (line 31) | def get_node(cls: Union[str, Any]) -> Optional[Any]: function dataclass_from_class (line 58) | def dataclass_from_class(cls: Union[str, Any]) -> Optional[Any]: function class_from_dataclass (line 65) | def class_from_dataclass(cls: Union[str, Any]) -> Optional[Any]: class Singleton (line 74) | class Singleton(type): method __call__ (line 77) | def __call__(cls, *args: Any, **kwargs: Any) -> Any: class Metadata (line 85) | class Metadata: class ConfigNode (line 89) | class ConfigNode: class ConfigStore (line 95) | class ConfigStore(metaclass=Singleton): method __init__ (line 96) | def __init__(self) -> None: method instance (line 100) | def instance(cls, *args: Any, **kwargs: Any) -> 'ConfigStore': method store (line 103) | def store( function get_node (line 119) | def get_node(cls: Union[str, Any]) -> Optional[ConfigNode]: function dataclass_from_class (line 146) | def dataclass_from_class(cls: Union[str, Any]) -> Optional[Any]: function class_from_dataclass (line 153) | def class_from_dataclass(cls: Union[str, Any]) -> Optional[Any]: function map_annotation (line 161) | def map_annotation( function to_dataclass (line 190) | def to_dataclass( function get_config_store (line 310) | def get_config_store() -> ConfigStore: function clear_config_store (line 315) | def clear_config_store() -> ConfigStore: function register (line 324) | def register( class Transform (line 377) | class Transform: class Dataset (line 382) | class Dataset: class Model (line 387) | class Model: class Optimizer (line 392) | class Optimizer: class LRScheduler (line 397) | class LRScheduler: class Config (line 402) | class Config: function fill_config_store (line 409) | def fill_config_store() -> None: FILE: torch_geometric/contrib/explain/pgm_explainer.py class PGMExplainer (line 15) | class PGMExplainer(ExplainerAlgorithm): method __init__ (line 47) | def __init__( method _perturb_features_on_nodes (line 68) | def _perturb_features_on_nodes( method _batch_perturb_features_on_node (line 108) | def _batch_perturb_features_on_node( method _explain_graph (line 171) | def _explain_graph( method _explain_node (line 258) | def _explain_node( method forward (line 375) | def forward( method supports (line 425) | def supports(self) -> bool: FILE: torch_geometric/contrib/nn/models/rbcd_attack.py class PRBCDAttack (line 17) | class PRBCDAttack(torch.nn.Module): method __init__ (line 92) | def __init__( method attack (line 134) | def attack( method _prepare (line 198) | def _prepare(self, budget: int) -> Iterable[int]: method _update (line 215) | def _update(self, epoch: int, gradient: Tensor, x: Tensor, labels: Ten... method _close (line 275) | def _close(self, x: Tensor, labels: Tensor, budget: int, method _forward (line 292) | def _forward(self, x: Tensor, edge_index: Tensor, edge_weight: Tensor, method _forward_and_gradient (line 297) | def _forward_and_gradient(self, x: Tensor, labels: Tensor, method _get_modified_adj (line 320) | def _get_modified_adj(self, edge_index: Tensor, edge_weight: Tensor, method _filter_self_loops_in_block (line 345) | def _filter_self_loops_in_block(self, with_weight: bool): method _sample_random_block (line 352) | def _sample_random_block(self, budget: int = 0): method _resample_random_block (line 376) | def _resample_random_block(self, budget: int): method _sample_final_edges (line 422) | def _sample_final_edges(self, x: Tensor, labels: Tensor, budget: int, method _update_edge_weights (line 465) | def _update_edge_weights(self, budget: int, block_edge_weight: Tensor, method _project (line 476) | def _project(budget: int, values: Tensor, eps: float = 1e-7) -> Tensor: method _bisection (line 488) | def _bisection(edge_weights: Tensor, a: float, b: float, n_pert: int, method _num_possible_edges (line 510) | def _num_possible_edges(n: int, is_undirected: bool) -> int: method _linear_to_triu_idx (line 518) | def _linear_to_triu_idx(n: int, lin_idx: Tensor) -> Tensor: method _linear_to_full_idx (line 532) | def _linear_to_full_idx(n: int, lin_idx: Tensor) -> Tensor: method _margin_loss (line 539) | def _margin_loss(score: Tensor, labels: Tensor, method _tanh_margin_loss (line 578) | def _tanh_margin_loss(prediction: Tensor, labels: Tensor, method _probability_margin_loss (line 597) | def _probability_margin_loss(prediction: Tensor, labels: Tensor, method _masked_cross_entropy (line 617) | def _masked_cross_entropy(log_prob: Tensor, labels: Tensor, method _append_statistics (line 641) | def _append_statistics(self, mapping: Dict[str, Any]): method __repr__ (line 645) | def __repr__(self) -> str: class GRBCDAttack (line 649) | class GRBCDAttack(PRBCDAttack): method __init__ (line 688) | def __init__( method _prepare (line 702) | def _prepare(self, budget: int) -> List[int]: method _update (line 721) | def _update(self, step_size: int, gradient: Tensor, *args, method _close (line 761) | def _close(self, *args, **kwargs) -> Tuple[Tensor, Tensor]: FILE: torch_geometric/data/batch.py class DynamicInheritance (line 16) | class DynamicInheritance(type): method __call__ (line 20) | def __call__(cls, *args: Any, **kwargs: Any) -> Any: class DynamicInheritanceGetter (line 52) | class DynamicInheritanceGetter: method __call__ (line 53) | def __call__(self, cls: Type, base_cls: Type) -> Self: class Batch (line 57) | class Batch(metaclass=DynamicInheritance): method from_data_list (line 83) | def from_data_list( method get_example (line 112) | def get_example(self, idx: int) -> BaseData: method index_select (line 135) | def index_select(self, idx: IndexType) -> List[BaseData]: method __getitem__ (line 173) | def __getitem__(self, idx: Union[int, np.integer, str, IndexType]) -> ... method to_data_list (line 185) | def to_data_list(self) -> List[BaseData]: method num_graphs (line 196) | def num_graphs(self) -> int: method batch_size (line 208) | def batch_size(self) -> int: method __len__ (line 212) | def __len__(self) -> int: method __reduce__ (line 215) | def __reduce__(self) -> Any: FILE: torch_geometric/data/collate.py function collate (line 37) | def collate( function _collate (line 149) | def _collate( function _batch_and_ptr (line 279) | def _batch_and_ptr( function repeat_interleave (line 315) | def repeat_interleave( function get_incs (line 323) | def get_incs(key, values: List[Any], data_list: List[BaseData], FILE: torch_geometric/data/data.py class BaseData (line 48) | class BaseData: method __getattr__ (line 49) | def __getattr__(self, key: str) -> Any: method __setattr__ (line 52) | def __setattr__(self, key: str, value: Any): method __delattr__ (line 55) | def __delattr__(self, key: str): method __getitem__ (line 58) | def __getitem__(self, key: str) -> Any: method __setitem__ (line 61) | def __setitem__(self, key: str, value: Any): method __delitem__ (line 64) | def __delitem__(self, key: str): method __copy__ (line 67) | def __copy__(self): method __deepcopy__ (line 70) | def __deepcopy__(self, memo): method __repr__ (line 73) | def __repr__(self) -> str: method stores_as (line 76) | def stores_as(self, data: Self): method stores (line 80) | def stores(self) -> List[BaseStorage]: method node_stores (line 84) | def node_stores(self) -> List[NodeStorage]: method edge_stores (line 88) | def edge_stores(self) -> List[EdgeStorage]: method to_dict (line 91) | def to_dict(self) -> Dict[str, Any]: method to_namedtuple (line 95) | def to_namedtuple(self) -> NamedTuple: method update (line 99) | def update(self, data: Self) -> Self: method concat (line 105) | def concat(self, data: Self) -> Self: method __cat_dim__ (line 114) | def __cat_dim__(self, key: str, value: Any, *args, **kwargs) -> Any: method __inc__ (line 127) | def __inc__(self, key: str, value: Any, *args, **kwargs) -> Any: method debug (line 140) | def debug(self): method keys (line 145) | def keys(self) -> List[str]: method __len__ (line 152) | def __len__(self) -> int: method __contains__ (line 156) | def __contains__(self, key: str) -> bool: method __getstate__ (line 162) | def __getstate__(self) -> Dict[str, Any]: method __setstate__ (line 165) | def __setstate__(self, mapping: Dict[str, Any]): method num_nodes (line 170) | def num_nodes(self) -> Optional[int]: method size (line 192) | def size(self) -> Tuple[Optional[int], Optional[int]]: method size (line 196) | def size(self, dim: int) -> Optional[int]: method size (line 199) | def size( method num_edges (line 207) | def num_edges(self) -> int: method node_attrs (line 214) | def node_attrs(self) -> List[str]: method edge_attrs (line 218) | def edge_attrs(self) -> List[str]: method node_offsets (line 223) | def node_offsets(self) -> Dict[NodeType, int]: method generate_ids (line 231) | def generate_ids(self): method is_sorted (line 240) | def is_sorted(self, sort_by_row: bool = True) -> bool: method sort (line 251) | def sort(self, sort_by_row: bool = True) -> Self: method is_coalesced (line 265) | def is_coalesced(self) -> bool: method coalesce (line 271) | def coalesce(self) -> Self: method is_sorted_by_time (line 280) | def is_sorted_by_time(self) -> bool: method sort_by_time (line 284) | def sort_by_time(self) -> Self: method snapshot (line 291) | def snapshot( method up_to (line 305) | def up_to(self, end_time: Union[float, int]) -> Self: method has_isolated_nodes (line 314) | def has_isolated_nodes(self) -> bool: method has_self_loops (line 318) | def has_self_loops(self) -> bool: method is_undirected (line 322) | def is_undirected(self) -> bool: method is_directed (line 326) | def is_directed(self) -> bool: method apply_ (line 330) | def apply_(self, func: Callable, *args: str): method apply (line 338) | def apply(self, func: Callable, *args: str): method clone (line 346) | def clone(self, *args: str): method contiguous (line 352) | def contiguous(self, *args: str): method to (line 358) | def to(self, device: Union[int, str, torch.device], *args: str, method cpu (line 366) | def cpu(self, *args: str): method cuda (line 372) | def cuda(self, device: Optional[Union[int, str]] = None, *args: str, method pin_memory (line 382) | def pin_memory(self, *args: str): method share_memory_ (line 388) | def share_memory_(self, *args: str): method detach_ (line 394) | def detach_(self, *args: str): method detach (line 400) | def detach(self, *args: str): method requires_grad_ (line 407) | def requires_grad_(self, *args: str, requires_grad: bool = True): method record_stream (line 414) | def record_stream(self, stream: torch.cuda.Stream, *args: str): method is_cuda (line 422) | def is_cuda(self) -> bool: method contains_isolated_nodes (line 435) | def contains_isolated_nodes(self) -> bool: method contains_self_loops (line 439) | def contains_self_loops(self) -> bool: class DataTensorAttr (line 447) | class DataTensorAttr(TensorAttr): method __init__ (line 449) | def __init__( class DataEdgeAttr (line 458) | class DataEdgeAttr(EdgeAttr): method __init__ (line 460) | def __init__( class Data (line 472) | class Data(BaseData, FeatureStore, GraphStore): method __init__ (line 519) | def __init__( method __getattr__ (line 555) | def __getattr__(self, key: str) -> Any: method __setattr__ (line 564) | def __setattr__(self, key: str, value: Any): method __delattr__ (line 571) | def __delattr__(self, key: str): method __getitem__ (line 577) | def __getitem__(self, key: str) -> Any: method __setitem__ (line 580) | def __setitem__(self, key: str, value: Any): method __delitem__ (line 583) | def __delitem__(self, key: str): method __copy__ (line 587) | def __copy__(self): method __deepcopy__ (line 595) | def __deepcopy__(self, memo): method __repr__ (line 602) | def __repr__(self) -> str: method num_nodes (line 616) | def num_nodes(self) -> Optional[int]: method num_nodes (line 620) | def num_nodes(self, num_nodes: Optional[int]): method stores_as (line 623) | def stores_as(self, data: Self): method stores (line 627) | def stores(self) -> List[BaseStorage]: method node_stores (line 631) | def node_stores(self) -> List[NodeStorage]: method edge_stores (line 635) | def edge_stores(self) -> List[EdgeStorage]: method to_dict (line 638) | def to_dict(self) -> Dict[str, Any]: method to_namedtuple (line 641) | def to_namedtuple(self) -> NamedTuple: method update (line 644) | def update(self, data: Union[Self, Dict[str, Any]]) -> Self: method __cat_dim__ (line 649) | def __cat_dim__(self, key: str, value: Any, *args, **kwargs) -> Any: method __inc__ (line 657) | def __inc__(self, key: str, value: Any, *args, **kwargs) -> Any: method validate (line 673) | def validate(self, raise_on_error: bool = True) -> bool: method debug (line 709) | def debug(self): method is_node_attr (line 712) | def is_node_attr(self, key: str) -> bool: method is_edge_attr (line 718) | def is_edge_attr(self, key: str) -> bool: method subgraph (line 724) | def subgraph(self, subset: Tensor) -> Self: method edge_subgraph (line 766) | def edge_subgraph(self, subset: Tensor) -> Self: method to_heterogeneous (line 784) | def to_heterogeneous( method connected_components (line 908) | def connected_components(self) -> List[Self]: method from_dict (line 965) | def from_dict(cls, mapping: Dict[str, Any]) -> Self: method num_node_features (line 972) | def num_node_features(self) -> int: method num_features (line 977) | def num_features(self) -> int: method num_edge_features (line 984) | def num_edge_features(self) -> int: method num_node_types (line 989) | def num_node_types(self) -> int: method num_edge_types (line 994) | def num_edge_types(self) -> int: method __iter__ (line 998) | def __iter__(self) -> Iterable: method __call__ (line 1004) | def __call__(self, *args: str) -> Iterable: method x (line 1012) | def x(self) -> Optional[Tensor]: method x (line 1016) | def x(self, x: Optional[Tensor]): method edge_index (line 1020) | def edge_index(self) -> Optional[Tensor]: method edge_index (line 1024) | def edge_index(self, edge_index: Optional[Tensor]): method edge_weight (line 1028) | def edge_weight(self) -> Optional[Tensor]: method edge_weight (line 1032) | def edge_weight(self, edge_weight: Optional[Tensor]): method edge_attr (line 1036) | def edge_attr(self) -> Optional[Tensor]: method edge_attr (line 1040) | def edge_attr(self, edge_attr: Optional[Tensor]): method y (line 1044) | def y(self) -> Optional[Union[Tensor, int, float]]: method y (line 1048) | def y(self, y: Optional[Tensor]): method pos (line 1052) | def pos(self) -> Optional[Tensor]: method pos (line 1056) | def pos(self, pos: Optional[Tensor]): method batch (line 1060) | def batch(self) -> Optional[Tensor]: method batch (line 1064) | def batch(self, batch: Optional[Tensor]): method time (line 1068) | def time(self) -> Optional[Tensor]: method time (line 1072) | def time(self, time: Optional[Tensor]): method face (line 1076) | def face(self) -> Optional[Tensor]: method face (line 1080) | def face(self, face: Optional[Tensor]): method num_faces (line 1087) | def num_faces(self) -> Optional[int]: method _put_tensor (line 1095) | def _put_tensor(self, tensor: FeatureTensorType, attr: TensorAttr) -> ... method _get_tensor (line 1104) | def _get_tensor(self, attr: TensorAttr) -> Optional[FeatureTensorType]: method _remove_tensor (line 1114) | def _remove_tensor(self, attr: TensorAttr) -> bool: method _get_tensor_size (line 1120) | def _get_tensor_size(self, attr: TensorAttr) -> Tuple: method get_all_tensor_attrs (line 1123) | def get_all_tensor_attrs(self) -> List[TensorAttr]: method _put_edge_index (line 1132) | def _put_edge_index(self, edge_index: EdgeTensorType, method _get_edge_index (line 1161) | def _get_edge_index(self, edge_attr: EdgeAttr) -> Optional[EdgeTensorT... method _remove_edge_index (line 1176) | def _remove_edge_index(self, edge_attr: EdgeAttr) -> bool: method get_all_edge_attrs (line 1194) | def get_all_edge_attrs(self) -> List[EdgeAttr]: method _find_parent (line 1210) | def _find_parent(self, node: int) -> int: method _union (line 1228) | def _union(self, node1: int, node2: int): function size_repr (line 1255) | def size_repr(key: Any, value: Any, indent: int = 0) -> str: function warn_or_raise (line 1290) | def warn_or_raise(msg: str, raise_on_error: bool = True): FILE: torch_geometric/data/database.py class TensorInfo (line 18) | class TensorInfo(CastMixin): method __post_init__ (line 24) | def __post_init__(self) -> None: function maybe_cast_to_tensor_info (line 36) | def maybe_cast_to_tensor_info(value: Any) -> Union[Any, TensorInfo]: class Database (line 59) | class Database(ABC): method __init__ (line 107) | def __init__(self, schema: Schema = object) -> None: method connect (line 115) | def connect(self) -> None: method close (line 122) | def close(self) -> None: method insert (line 127) | def insert(self, index: int, data: Any) -> None: method multi_insert (line 136) | def multi_insert( method _multi_insert (line 173) | def _multi_insert( method get (line 184) | def get(self, index: int) -> Any: method multi_get (line 192) | def multi_get( method _multi_get (line 217) | def _multi_get(self, indices: Union[Sequence[int], Tensor]) -> List[Any]: method _to_dict (line 225) | def _to_dict( method slice_to_range (line 235) | def slice_to_range(self, indices: slice) -> range: method __len__ (line 244) | def __len__(self) -> int: method __getitem__ (line 247) | def __getitem__( method __setitem__ (line 257) | def __setitem__( method __repr__ (line 267) | def __repr__(self) -> str: class SQLiteDatabase (line 274) | class SQLiteDatabase(Database): method __init__ (line 292) | def __init__(self, path: str, name: str, schema: Schema = object) -> N... method connect (line 319) | def connect(self) -> None: method close (line 324) | def close(self) -> None: method connection (line 332) | def connection(self) -> Any: method cursor (line 338) | def cursor(self) -> Any: method insert (line 343) | def insert(self, index: int, data: Any) -> None: method _multi_insert (line 350) | def _multi_insert( method get (line 367) | def get(self, index: int) -> Any: method multi_get (line 373) | def multi_get( method __len__ (line 422) | def __len__(self) -> int: method _col_names (line 430) | def _col_names(self) -> List[str]: method _joined_col_names (line 434) | def _joined_col_names(self) -> str: method _dummies (line 438) | def _dummies(self) -> str: method _to_sql_type (line 441) | def _to_sql_type(self, type_info: Any) -> str: method _serialize (line 451) | def _serialize(self, row: Any) -> List[Any]: method _deserialize (line 509) | def _deserialize(self, row: Tuple[Any]) -> Any: class RocksDatabase (line 584) | class RocksDatabase(Database): method __init__ (line 605) | def __init__(self, path: str, schema: Schema = object) -> None: method connect (line 616) | def connect(self) -> None: method close (line 623) | def close(self) -> None: method db (line 629) | def db(self) -> Any: method to_key (line 635) | def to_key(index: int) -> bytes: method insert (line 638) | def insert(self, index: int, data: Any) -> None: method get (line 641) | def get(self, index: int) -> Any: method _multi_get (line 644) | def _multi_get(self, indices: Union[Sequence[int], Tensor]) -> List[Any]: method _serialize (line 652) | def _serialize(self, row: Any) -> bytes: method _deserialize (line 660) | def _deserialize(self, row: bytes) -> Any: FILE: torch_geometric/data/datapipes.py function functional_datapipe (line 15) | def functional_datapipe(name: str) -> Callable: # type: ignore class Batcher (line 20) | class Batcher(IterBatcher): method __init__ (line 21) | def __init__( class SMILESParser (line 36) | class SMILESParser(IterDataPipe): method __init__ (line 37) | def __init__( method __iter__ (line 48) | def __iter__(self) -> Iterator: class DatasetAdapter (line 67) | class DatasetAdapter(IterDataPipe): method __init__ (line 68) | def __init__(self, dataset: Sequence[Any]) -> None: method is_shardable (line 73) | def is_shardable(self) -> bool: method apply_sharding (line 76) | def apply_sharding(self, num_shards: int, shard_idx: int) -> None: method __iter__ (line 79) | def __iter__(self) -> Iterator: method __len__ (line 83) | def __len__(self) -> int: function functional_transform (line 87) | def functional_transform(name: str) -> Callable: FILE: torch_geometric/data/dataset.py class Dataset (line 30) | class Dataset(torch.utils.data.Dataset): method raw_file_names (line 61) | def raw_file_names(self) -> Union[str, List[str], Tuple[str, ...]]: method processed_file_names (line 68) | def processed_file_names(self) -> Union[str, List[str], Tuple[str, ...]]: method download (line 74) | def download(self) -> None: method process (line 78) | def process(self) -> None: method len (line 82) | def len(self) -> int: method get (line 86) | def get(self, idx: int) -> BaseData: method __init__ (line 90) | def __init__( method indices (line 118) | def indices(self) -> Sequence: method raw_dir (line 122) | def raw_dir(self) -> str: method processed_dir (line 126) | def processed_dir(self) -> str: method num_node_features (line 130) | def num_node_features(self) -> int: method num_features (line 143) | def num_features(self) -> int: method num_edge_features (line 150) | def num_edge_features(self) -> int: method _infer_num_classes (line 162) | def _infer_num_classes(self, y: Optional[Tensor]) -> int: method num_classes (line 180) | def num_classes(self) -> int: method raw_paths (line 198) | def raw_paths(self) -> List[str]: method processed_paths (line 210) | def processed_paths(self) -> List[str]: method has_download (line 222) | def has_download(self) -> bool: method _download (line 226) | def _download(self): method has_process (line 234) | def has_process(self) -> bool: method _process (line 238) | def _process(self): method __len__ (line 276) | def __len__(self) -> int: method __getitem__ (line 280) | def __getitem__( method __iter__ (line 302) | def __iter__(self) -> Iterator[BaseData]: method index_select (line 306) | def index_select(self, idx: IndexType) -> 'Dataset': method shuffle (line 352) | def shuffle( method __repr__ (line 367) | def __repr__(self) -> str: method get_summary (line 371) | def get_summary(self) -> Any: method print_summary (line 376) | def print_summary(self, fmt: str = "psql") -> None: method to_datapipe (line 386) | def to_datapipe(self) -> Any: function overrides_method (line 411) | def overrides_method(cls, method_name: str) -> bool: function to_list (line 424) | def to_list(value: Any) -> Sequence: function files_exist (line 431) | def files_exist(files: List[str]) -> bool: function _repr (line 437) | def _repr(obj: Any) -> str: function _get_flattened_data_list (line 443) | def _get_flattened_data_list(data_list: Iterable[Any]) -> List[BaseData]: FILE: torch_geometric/data/download.py function download_url (line 13) | def download_url( function download_google_url (line 60) | def download_google_url( FILE: torch_geometric/data/extract.py function maybe_log (line 10) | def maybe_log(path: str, log: bool = True) -> None: function extract_tar (line 15) | def extract_tar( function extract_zip (line 35) | def extract_zip(path: str, folder: str, log: bool = True) -> None: function extract_bz2 (line 49) | def extract_bz2(path: str, folder: str, log: bool = True) -> None: function extract_gz (line 65) | def extract_gz(path: str, folder: str, log: bool = True) -> None: FILE: torch_geometric/data/feature_store.py class _FieldStatus (line 41) | class _FieldStatus(Enum): class TensorAttr (line 46) | class TensorAttr(CastMixin): method is_set (line 68) | def is_set(self, key: str) -> bool: method is_fully_specified (line 73) | def is_fully_specified(self) -> bool: method update (line 77) | def update(self, attr: 'TensorAttr') -> 'TensorAttr': class AttrView (line 87) | class AttrView(CastMixin): method __init__ (line 112) | def __init__(self, store: 'FeatureStore', attr: TensorAttr): method __getattr__ (line 118) | def __getattr__(self, key: Any) -> Union['AttrView', FeatureTensorType]: method __getitem__ (line 154) | def __getitem__(self, key: Any) -> Union['AttrView', FeatureTensorType]: method __setattr__ (line 173) | def __setattr__(self, key: str, value: Any): method __setitem__ (line 194) | def __setitem__(self, key: str, value: Any): method __call__ (line 212) | def __call__(self) -> FeatureTensorType: method __copy__ (line 232) | def __copy__(self) -> 'AttrView': method __eq__ (line 239) | def __eq__(self, obj: Any) -> bool: method __repr__ (line 248) | def __repr__(self) -> str: class FeatureStore (line 260) | class FeatureStore(ABC): method __init__ (line 271) | def __init__(self, tensor_attr_cls: Optional[Any] = None): method _put_tensor (line 278) | def _put_tensor(self, tensor: FeatureTensorType, attr: TensorAttr) -> ... method put_tensor (line 281) | def put_tensor(self, tensor: FeatureTensorType, *args, **kwargs) -> bool: method _get_tensor (line 303) | def _get_tensor(self, attr: TensorAttr) -> Optional[FeatureTensorType]: method get_tensor (line 306) | def get_tensor( method _multi_get_tensor (line 337) | def _multi_get_tensor( method multi_get_tensor (line 344) | def multi_get_tensor( method _remove_tensor (line 387) | def _remove_tensor(self, attr: TensorAttr) -> bool: method remove_tensor (line 390) | def remove_tensor(self, *args, **kwargs) -> bool: method update_tensor (line 409) | def update_tensor(self, tensor: FeatureTensorType, *args, method _get_tensor_size (line 431) | def _get_tensor_size(self, attr: TensorAttr) -> Optional[Tuple[int, ..... method get_tensor_size (line 434) | def get_tensor_size(self, *args, **kwargs) -> Optional[Tuple[int, ...]]: method get_all_tensor_attrs (line 444) | def get_all_tensor_attrs(self) -> List[TensorAttr]: method view (line 449) | def view(self, *args, **kwargs) -> AttrView: method _to_type (line 459) | def _to_type( method __setitem__ (line 471) | def __setitem__(self, key: TensorAttr, value: FeatureTensorType): method __getitem__ (line 479) | def __getitem__(self, key: TensorAttr) -> Any: method __delitem__ (line 498) | def __delitem__(self, attr: TensorAttr): method __iter__ (line 509) | def __iter__(self): method __eq__ (line 512) | def __eq__(self, obj: object) -> bool: method __repr__ (line 515) | def __repr__(self) -> str: FILE: torch_geometric/data/graph_store.py class EdgeLayout (line 46) | class EdgeLayout(Enum): class EdgeAttr (line 53) | class EdgeAttr(CastMixin): method __init__ (line 79) | def __init__( class GraphStore (line 101) | class GraphStore(ABC): method __init__ (line 109) | def __init__(self, edge_attr_cls: Optional[Any] = None): method _put_edge_index (line 116) | def _put_edge_index(self, edge_index: EdgeTensorType, method put_edge_index (line 120) | def put_edge_index(self, edge_index: EdgeTensorType, *args, method _get_edge_index (line 137) | def _get_edge_index(self, edge_attr: EdgeAttr) -> Optional[EdgeTensorT... method get_edge_index (line 140) | def get_edge_index(self, *args, **kwargs) -> EdgeTensorType: method _remove_edge_index (line 159) | def _remove_edge_index(self, edge_attr: EdgeAttr) -> bool: method remove_edge_index (line 162) | def remove_edge_index(self, *args, **kwargs) -> bool: method get_all_edge_attrs (line 175) | def get_all_edge_attrs(self) -> List[EdgeAttr]: method coo (line 180) | def coo( method csr (line 196) | def csr( method csc (line 212) | def csc( method __setitem__ (line 230) | def __setitem__(self, key: EdgeAttr, value: EdgeTensorType): method __getitem__ (line 233) | def __getitem__(self, key: EdgeAttr) -> Optional[EdgeTensorType]: method __delitem__ (line 236) | def __delitem__(self, key: EdgeAttr): method __repr__ (line 239) | def __repr__(self) -> str: method _edge_to_layout (line 244) | def _edge_to_layout( method _edges_to_layout (line 299) | def _edges_to_layout( FILE: torch_geometric/data/hetero_data.py class HeteroData (line 43) | class HeteroData(BaseData, FeatureStore, GraphStore): method __init__ (line 119) | def __init__(self, _mapping: Optional[Dict[str, Any]] = None, **kwargs): method from_dict (line 136) | def from_dict(cls, mapping: Dict[str, Any]) -> Self: method __getattr__ (line 153) | def __getattr__(self, key: str) -> Any: method __setattr__ (line 165) | def __setattr__(self, key: str, value: Any): method __delattr__ (line 173) | def __delattr__(self, key: str): method __getitem__ (line 176) | def __getitem__(self, *args: QueryType) -> Any: method __setitem__ (line 192) | def __setitem__(self, key: str, value: Any): method __delitem__ (line 199) | def __delitem__(self, *args: QueryType): method __copy__ (line 209) | def __copy__(self): method __deepcopy__ (line 225) | def __deepcopy__(self, memo): method __repr__ (line 236) | def __repr__(self) -> str: method stores_as (line 244) | def stores_as(self, data: Self): method stores (line 252) | def stores(self) -> List[BaseStorage]: method node_types (line 258) | def node_types(self) -> List[NodeType]: method node_stores (line 263) | def node_stores(self) -> List[NodeStorage]: method edge_types (line 268) | def edge_types(self) -> List[EdgeType]: method edge_stores (line 273) | def edge_stores(self) -> List[EdgeStorage]: method node_items (line 277) | def node_items(self) -> List[Tuple[NodeType, NodeStorage]]: method edge_items (line 281) | def edge_items(self) -> List[Tuple[EdgeType, EdgeStorage]]: method input_type (line 286) | def input_type(self) -> Optional[Union[NodeType, EdgeType]]: method to_dict (line 300) | def to_dict(self) -> Dict[str, Any]: method to_namedtuple (line 308) | def to_namedtuple(self) -> NamedTuple: method set_value_dict (line 322) | def set_value_dict( method update (line 346) | def update(self, data: Self) -> Self: method __cat_dim__ (line 352) | def __cat_dim__(self, key: str, value: Any, method __inc__ (line 361) | def __inc__(self, key: str, value: Any, method num_nodes (line 374) | def num_nodes(self) -> Optional[int]: method num_node_features (line 379) | def num_node_features(self) -> Dict[NodeType, int]: method num_features (line 387) | def num_features(self) -> Dict[NodeType, int]: method num_edge_features (line 394) | def num_edge_features(self) -> Dict[EdgeType, int]: method has_isolated_nodes (line 401) | def has_isolated_nodes(self) -> bool: method is_undirected (line 406) | def is_undirected(self) -> bool: method validate (line 411) | def validate(self, raise_on_error: bool = True) -> bool: method connected_components (line 490) | def connected_components(self) -> List[Self]: method debug (line 561) | def debug(self): method _to_canonical (line 566) | def _to_canonical(self, *args: QueryType) -> NodeOrEdgeType: method metadata (line 603) | def metadata(self) -> Tuple[List[NodeType], List[EdgeType]]: method collect (line 619) | def collect( method _check_type_name (line 655) | def _check_type_name(self, name: str): method get_node_store (line 667) | def get_node_store(self, key: NodeType) -> NodeStorage: method get_edge_store (line 686) | def get_edge_store(self, src: str, rel: str, dst: str) -> EdgeStorage: method rename (line 706) | def rename(self, name: NodeType, new_name: NodeType) -> Self: method subgraph (line 724) | def subgraph(self, subset_dict: Dict[NodeType, Tensor]) -> Self: method edge_subgraph (line 815) | def edge_subgraph( method node_type_subgraph (line 842) | def node_type_subgraph(self, node_types: List[NodeType]) -> Self: method edge_type_subgraph (line 858) | def edge_type_subgraph(self, edge_types: List[EdgeType]) -> Self: method to_homogeneous (line 877) | def to_homogeneous( method _put_tensor (line 1081) | def _put_tensor(self, tensor: FeatureTensorType, attr: TensorAttr) -> ... method _get_tensor (line 1099) | def _get_tensor(self, attr: TensorAttr) -> Optional[FeatureTensorType]: method _remove_tensor (line 1110) | def _remove_tensor(self, attr: TensorAttr) -> bool: method _get_tensor_size (line 1117) | def _get_tensor_size(self, attr: TensorAttr) -> Tuple: method get_all_tensor_attrs (line 1120) | def get_all_tensor_attrs(self) -> List[TensorAttr]: method _put_edge_index (line 1130) | def _put_edge_index(self, edge_index: EdgeTensorType, method _get_edge_index (line 1160) | def _get_edge_index(self, edge_attr: EdgeAttr) -> Optional[EdgeTensorT... method _remove_edge_index (line 1181) | def _remove_edge_index(self, edge_attr: EdgeAttr) -> bool: method get_all_edge_attrs (line 1201) | def get_all_edge_attrs(self) -> List[EdgeAttr]: method _find_parent (line 1224) | def _find_parent(self, node: Tuple[str, int]) -> Tuple[str, int]: method _union (line 1243) | def _union(self, node1: Tuple[str, int], node2: Tuple[str, int]): function get_node_slices (line 1271) | def get_node_slices(num_nodes: Dict[str, int]) -> Dict[str, Tuple[int, i... function offset_edge_index (line 1281) | def offset_edge_index( function to_homogeneous_edge_index (line 1295) | def to_homogeneous_edge_index( FILE: torch_geometric/data/hypergraph_data.py class HyperGraphData (line 15) | class HyperGraphData(Data): method __init__ (line 54) | def __init__( method num_edges (line 73) | def num_edges(self) -> int: method num_nodes (line 80) | def num_nodes(self) -> Optional[int]: method num_nodes (line 92) | def num_nodes(self, num_nodes: Optional[int]) -> None: method is_edge_attr (line 95) | def is_edge_attr(self, key: str) -> bool: method __inc__ (line 101) | def __inc__(self, key: str, value: Any, *args: Any, **kwargs: Any) -> ... method subgraph (line 107) | def subgraph(self, subset: Tensor) -> 'HyperGraphData': method edge_subgraph (line 159) | def edge_subgraph(self, subset: Tensor) -> Self: method to_heterogeneous (line 162) | def to_heterogeneous( method has_isolated_nodes (line 171) | def has_isolated_nodes(self) -> bool: method is_directed (line 176) | def is_directed(self) -> bool: method is_undirected (line 179) | def is_undirected(self) -> bool: method has_self_loops (line 182) | def has_self_loops(self) -> bool: method validate (line 185) | def validate(self, raise_on_error: bool = True) -> bool: function warn_or_raise (line 222) | def warn_or_raise(msg: str, raise_on_error: bool = True) -> None: FILE: torch_geometric/data/in_memory_dataset.py class InMemoryDataset (line 32) | class InMemoryDataset(Dataset): method raw_file_names (line 65) | def raw_file_names(self) -> Union[str, List[str], Tuple[str, ...]]: method processed_file_names (line 69) | def processed_file_names(self) -> Union[str, List[str], Tuple[str, ...]]: method __init__ (line 72) | def __init__( method num_classes (line 89) | def num_classes(self) -> int: method len (line 94) | def len(self) -> int: method get (line 101) | def get(self, idx: int) -> BaseData: method save (line 124) | def save(cls, data_list: Sequence[BaseData], path: str) -> None: method load (line 129) | def load(self, path: str, data_cls: Type[BaseData] = Data) -> None: method collate (line 145) | def collate( method copy (line 164) | def copy(self, idx: Optional[IndexType] = None) -> 'InMemoryDataset': method to_on_disk_dataset (line 182) | def to_on_disk_dataset( method data (line 276) | def data(self) -> Any: method data (line 305) | def data(self, value: Any): method __getattr__ (line 309) | def __getattr__(self, key: str) -> Any: method to (line 321) | def to(self, device: Union[int, str]) -> 'InMemoryDataset': method cpu (line 331) | def cpu(self, *args: str) -> 'InMemoryDataset': method cuda (line 335) | def cuda( function nested_iter (line 347) | def nested_iter(node: Union[Mapping, Sequence]) -> Iterable: FILE: torch_geometric/data/lightning/datamodule.py class LightningDataModule (line 26) | class LightningDataModule(_LightningDataModule): method __init__ (line 27) | def __init__(self, has_val: bool, has_test: bool, **kwargs: Any) -> None: method __repr__ (line 58) | def __repr__(self) -> str: class LightningData (line 62) | class LightningData(LightningDataModule): method __init__ (line 63) | def __init__( method train_shuffle (line 182) | def train_shuffle(self) -> bool: method prepare_data (line 187) | def prepare_data(self) -> None: method full_dataloader (line 204) | def full_dataloader(self, **kwargs: Any) -> torch.utils.data.DataLoader: method __repr__ (line 214) | def __repr__(self) -> str: class LightningDataset (line 219) | class LightningDataset(LightningDataModule): method __init__ (line 256) | def __init__( method dataloader (line 275) | def dataloader(self, dataset: Dataset, **kwargs: Any) -> DataLoader: method train_dataloader (line 278) | def train_dataloader(self) -> DataLoader: method val_dataloader (line 291) | def val_dataloader(self) -> DataLoader: method test_dataloader (line 300) | def test_dataloader(self) -> DataLoader: method predict_dataloader (line 309) | def predict_dataloader(self) -> DataLoader: method __repr__ (line 318) | def __repr__(self) -> str: class LightningNodeData (line 329) | class LightningNodeData(LightningData): method __init__ (line 403) | def __init__( method dataloader (line 459) | def dataloader( method train_dataloader (line 481) | def train_dataloader(self) -> torch.utils.data.DataLoader: method val_dataloader (line 491) | def val_dataloader(self) -> torch.utils.data.DataLoader: method test_dataloader (line 501) | def test_dataloader(self) -> torch.utils.data.DataLoader: method predict_dataloader (line 511) | def predict_dataloader(self) -> torch.utils.data.DataLoader: class LightningLinkData (line 522) | class LightningLinkData(LightningData): method __init__ (line 591) | def __init__( method dataloader (line 641) | def dataloader( method train_dataloader (line 665) | def train_dataloader(self) -> torch.utils.data.DataLoader: method val_dataloader (line 676) | def val_dataloader(self) -> torch.utils.data.DataLoader: method test_dataloader (line 687) | def test_dataloader(self) -> torch.utils.data.DataLoader: method predict_dataloader (line 698) | def predict_dataloader(self) -> torch.utils.data.DataLoader: function infer_input_nodes (line 714) | def infer_input_nodes(data: Union[Data, HeteroData], split: str) -> Inpu... function kwargs_repr (line 741) | def kwargs_repr(**kwargs: Any) -> str: function split_kwargs (line 745) | def split_kwargs( FILE: torch_geometric/data/makedirs.py function makedirs (line 6) | def makedirs(path: str): FILE: torch_geometric/data/on_disk_dataset.py class OnDiskDataset (line 12) | class OnDiskDataset(Dataset): method __init__ (line 52) | def __init__( method processed_file_names (line 75) | def processed_file_names(self) -> str: method db (line 79) | def db(self) -> Database: method close (line 95) | def close(self) -> None: method serialize (line 100) | def serialize(self, data: BaseData) -> Any: method deserialize (line 111) | def deserialize(self, data: Any) -> BaseData: method append (line 122) | def append(self, data: BaseData) -> None: method extend (line 128) | def extend( method get (line 140) | def get(self, idx: int) -> BaseData: method multi_get (line 144) | def multi_get( method __getitems__ (line 160) | def __getitems__(self, indices: List[int]) -> List[BaseData]: method len (line 163) | def len(self) -> int: method __repr__ (line 168) | def __repr__(self) -> str: FILE: torch_geometric/data/remote_backend_utils.py function _internal_num_nodes (line 11) | def _internal_num_nodes( function _internal_num_nodes (line 20) | def _internal_num_nodes( function _internal_num_nodes (line 30) | def _internal_num_nodes( function num_nodes (line 104) | def num_nodes( function size (line 115) | def size( FILE: torch_geometric/data/separate.py function separate (line 15) | def separate( function _separate (line 59) | def _separate( FILE: torch_geometric/data/storage.py class AttrType (line 48) | class AttrType(Enum): class BaseStorage (line 54) | class BaseStorage(MutableMapping): method __init__ (line 66) | def __init__( method _key (line 79) | def _key(self) -> Any: method _pop_cache (line 82) | def _pop_cache(self, key: str) -> None: method __len__ (line 86) | def __len__(self) -> int: method __getattr__ (line 89) | def __getattr__(self, key: str) -> Any: method __setattr__ (line 100) | def __setattr__(self, key: str, value: Any) -> None: method __delattr__ (line 111) | def __delattr__(self, key: str) -> None: method __getitem__ (line 117) | def __getitem__(self, key: str) -> Any: method __setitem__ (line 120) | def __setitem__(self, key: str, value: Any) -> None: method __delitem__ (line 127) | def __delitem__(self, key: str) -> None: method __iter__ (line 132) | def __iter__(self) -> Iterator[Any]: method __copy__ (line 135) | def __copy__(self) -> Self: method __deepcopy__ (line 143) | def __deepcopy__(self, memo: Optional[Dict[int, Any]]) -> Self: method __getstate__ (line 150) | def __getstate__(self) -> Dict[str, Any]: method __setstate__ (line 159) | def __setstate__(self, mapping: Dict[str, Any]) -> None: method __repr__ (line 167) | def __repr__(self) -> str: method keys (line 179) | def keys(self, *args: str) -> KeysView: # type: ignore method values (line 182) | def values(self, *args: str) -> ValuesView: # type: ignore method items (line 185) | def items(self, *args: str) -> ItemsView: # type: ignore method apply_ (line 188) | def apply_(self, func: Callable, *args: str) -> Self: method apply (line 196) | def apply(self, func: Callable, *args: str) -> Self: method get (line 206) | def get(self, key: str, value: Optional[Any] = None) -> Any: method to_dict (line 209) | def to_dict(self) -> Dict[str, Any]: method to_namedtuple (line 219) | def to_namedtuple(self) -> NamedTuple: method clone (line 226) | def clone(self, *args: str) -> Self: method contiguous (line 230) | def contiguous(self, *args: str) -> Self: method to (line 236) | def to( method cpu (line 248) | def cpu(self, *args: str) -> Self: method cuda (line 254) | def cuda( method pin_memory (line 266) | def pin_memory(self, *args: str) -> Self: method share_memory_ (line 272) | def share_memory_(self, *args: str) -> Self: method detach_ (line 278) | def detach_(self, *args: str) -> Self: method detach (line 284) | def detach(self, *args: str) -> Self: method requires_grad_ (line 291) | def requires_grad_(self, *args: str, requires_grad: bool = True) -> Self: method record_stream (line 298) | def record_stream(self, stream: torch.cuda.Stream, *args: str) -> Self: method _cat_dims (line 307) | def _cat_dims(self, keys: Iterable[str]) -> Dict[str, int]: method _select (line 313) | def _select( method concat (line 324) | def concat(self, other: Self) -> Self: method is_sorted_by_time (line 348) | def is_sorted_by_time(self) -> bool: method sort_by_time (line 353) | def sort_by_time(self) -> Self: method snapshot (line 369) | def snapshot( method up_to (line 391) | def up_to(self, time: Union[float, int]) -> Self: class NodeStorage (line 397) | class NodeStorage(BaseStorage): method _key (line 400) | def _key(self) -> NodeType: method can_infer_num_nodes (line 407) | def can_infer_num_nodes(self) -> bool: method num_nodes (line 421) | def num_nodes(self) -> Optional[int]: method num_nodes (line 469) | def num_nodes(self, num_nodes: Optional[int]) -> None: method num_node_features (line 473) | def num_node_features(self) -> int: method num_features (line 491) | def num_features(self) -> int: method is_node_attr (line 494) | def is_node_attr(self, key: str) -> bool: method is_edge_attr (line 526) | def is_edge_attr(self, key: str) -> bool: method node_attrs (line 529) | def node_attrs(self) -> List[str]: class EdgeStorage (line 533) | class EdgeStorage(BaseStorage): method _key (line 551) | def _key(self) -> EdgeType: method edge_index (line 558) | def edge_index(self) -> Tensor: method edge_index (line 570) | def edge_index(self, edge_index: Optional[Tensor]) -> None: method num_edges (line 574) | def num_edges(self) -> int: method num_edge_features (line 604) | def num_edge_features(self) -> int: method num_features (line 615) | def num_features(self) -> int: method size (line 619) | def size(self) -> Tuple[Optional[int], Optional[int]]: method size (line 623) | def size(self, dim: int) -> Optional[int]: method size (line 626) | def size( method is_node_attr (line 639) | def is_node_attr(self, key: str) -> bool: method is_edge_attr (line 642) | def is_edge_attr(self, key: str) -> bool: method edge_attrs (line 674) | def edge_attrs(self) -> List[str]: method is_sorted (line 677) | def is_sorted(self, sort_by_row: bool = True) -> bool: method sort (line 683) | def sort(self, sort_by_row: bool = True) -> Self: method is_coalesced (line 694) | def is_coalesced(self) -> bool: method coalesce (line 709) | def coalesce(self, reduce: str = 'sum') -> Self: method has_isolated_nodes (line 726) | def has_isolated_nodes(self) -> bool: method has_self_loops (line 735) | def has_self_loops(self) -> bool: method is_undirected (line 741) | def is_undirected(self) -> bool: method is_directed (line 752) | def is_directed(self) -> bool: method is_bipartite (line 755) | def is_bipartite(self) -> bool: class GlobalStorage (line 759) | class GlobalStorage(NodeStorage, EdgeStorage): method _key (line 762) | def _key(self) -> Any: method num_features (line 766) | def num_features(self) -> int: method size (line 770) | def size(self) -> Tuple[Optional[int], Optional[int]]: method size (line 774) | def size(self, dim: int) -> Optional[int]: method size (line 777) | def size( method is_node_attr (line 783) | def is_node_attr(self, key: str) -> bool: method is_edge_attr (line 833) | def is_edge_attr(self, key: str) -> bool: function recursive_apply_ (line 884) | def recursive_apply_(data: Any, func: Callable) -> Any: function recursive_apply (line 903) | def recursive_apply(data: Any, func: Callable) -> Any: FILE: torch_geometric/data/summary.py class Stats (line 14) | class Stats: method from_data (line 24) | def from_data( class Summary (line 44) | class Summary: method from_dataset (line 53) | def from_dataset( method format (line 120) | def format(self, fmt: str = "psql") -> str: method __repr__ (line 170) | def __repr__(self) -> str: FILE: torch_geometric/data/temporal.py class TemporalData (line 26) | class TemporalData(BaseData): method __init__ (line 89) | def __init__( method from_dict (line 109) | def from_dict(cls, mapping: Dict[str, Any]) -> 'TemporalData': method index_select (line 115) | def index_select(self, idx: Any) -> 'TemporalData': method __getitem__ (line 123) | def __getitem__(self, idx: Any) -> Any: method __setitem__ (line 128) | def __setitem__(self, key: str, value: Any): method __delitem__ (line 132) | def __delitem__(self, key: str): method __getattr__ (line 136) | def __getattr__(self, key: str) -> Any: method __setattr__ (line 145) | def __setattr__(self, key: str, value: Any): method __delattr__ (line 148) | def __delattr__(self, key: str): method __iter__ (line 151) | def __iter__(self) -> Iterable: method __len__ (line 155) | def __len__(self) -> int: method __call__ (line 158) | def __call__(self, *args: List[str]) -> Iterable: method __copy__ (line 161) | def __copy__(self): method __deepcopy__ (line 169) | def __deepcopy__(self, memo): method stores_as (line 176) | def stores_as(self, data: 'TemporalData'): method stores (line 180) | def stores(self) -> List[BaseStorage]: method node_stores (line 184) | def node_stores(self) -> List[NodeStorage]: method edge_stores (line 188) | def edge_stores(self) -> List[EdgeStorage]: method to_dict (line 191) | def to_dict(self) -> Dict[str, Any]: method to_namedtuple (line 194) | def to_namedtuple(self) -> NamedTuple: method debug (line 197) | def debug(self): method num_nodes (line 201) | def num_nodes(self) -> int: method num_events (line 206) | def num_events(self) -> int: method num_edges (line 217) | def num_edges(self) -> int: method edge_index (line 222) | def edge_index(self) -> Tensor: method size (line 231) | def size( method __cat_dim__ (line 238) | def __cat_dim__(self, key: str, value: Any, *args, **kwargs) -> Any: method __inc__ (line 241) | def __inc__(self, key: str, value: Any, *args, **kwargs) -> Any: method __repr__ (line 249) | def __repr__(self) -> str: method train_val_test_split (line 256) | def train_val_test_split(self, val_ratio: float = 0.15, method coalesce (line 279) | def coalesce(self): method has_isolated_nodes (line 282) | def has_isolated_nodes(self) -> bool: method has_self_loops (line 285) | def has_self_loops(self) -> bool: method is_undirected (line 288) | def is_undirected(self) -> bool: method is_directed (line 291) | def is_directed(self) -> bool: function prepare_idx (line 298) | def prepare_idx(idx): FILE: torch_geometric/data/view.py class MappingView (line 4) | class MappingView: method __init__ (line 5) | def __init__(self, mapping: Mapping[str, Any], *args: str): method _keys (line 9) | def _keys(self) -> List[str]: method __len__ (line 15) | def __len__(self) -> int: method __repr__ (line 18) | def __repr__(self) -> str: class KeysView (line 25) | class KeysView(MappingView): method __iter__ (line 26) | def __iter__(self) -> Iterator[str]: class ValuesView (line 30) | class ValuesView(MappingView): method __iter__ (line 31) | def __iter__(self) -> Iterator[Any]: class ItemsView (line 36) | class ItemsView(MappingView): method __iter__ (line 37) | def __iter__(self) -> Iterator[Tuple[str, Any]]: FILE: torch_geometric/datasets/actor.py class Actor (line 10) | class Actor(InMemoryDataset): method __init__ (line 50) | def __init__( method raw_file_names (line 62) | def raw_file_names(self) -> List[str]: method processed_file_names (line 67) | def processed_file_names(self) -> str: method download (line 70) | def download(self) -> None: method process (line 76) | def process(self) -> None: FILE: torch_geometric/datasets/airfrans.py class AirfRANS (line 14) | class AirfRANS(InMemoryDataset): method __init__ (line 88) | def __init__( method raw_file_names (line 110) | def raw_file_names(self) -> List[str]: method processed_file_names (line 114) | def processed_file_names(self) -> str: method download (line 117) | def download(self) -> None: method process (line 122) | def process(self) -> None: method __repr__ (line 143) | def __repr__(self) -> str: FILE: torch_geometric/datasets/airports.py class Airports (line 10) | class Airports(InMemoryDataset): method __init__ (line 37) | def __init__( method raw_dir (line 52) | def raw_dir(self) -> str: method processed_dir (line 56) | def processed_dir(self) -> str: method raw_file_names (line 60) | def raw_file_names(self) -> List[str]: method processed_file_names (line 67) | def processed_file_names(self) -> str: method download (line 70) | def download(self) -> None: method process (line 74) | def process(self) -> None: method __repr__ (line 98) | def __repr__(self) -> str: FILE: torch_geometric/datasets/amazon.py class Amazon (line 8) | class Amazon(InMemoryDataset): method __init__ (line 54) | def __init__( method raw_dir (line 69) | def raw_dir(self) -> str: method processed_dir (line 73) | def processed_dir(self) -> str: method raw_file_names (line 77) | def raw_file_names(self) -> str: method processed_file_names (line 81) | def processed_file_names(self) -> str: method download (line 84) | def download(self) -> None: method process (line 87) | def process(self) -> None: method __repr__ (line 92) | def __repr__(self) -> str: FILE: torch_geometric/datasets/amazon_book.py class AmazonBook (line 8) | class AmazonBook(InMemoryDataset): method __init__ (line 31) | def __init__( method raw_file_names (line 43) | def raw_file_names(self) -> List[str]: method processed_file_names (line 47) | def processed_file_names(self) -> str: method download (line 50) | def download(self) -> None: method process (line 54) | def process(self) -> None: FILE: torch_geometric/datasets/amazon_products.py class AmazonProducts (line 11) | class AmazonProducts(InMemoryDataset): method __init__ (line 47) | def __init__( method raw_file_names (line 59) | def raw_file_names(self) -> List[str]: method processed_file_names (line 63) | def processed_file_names(self) -> str: method download (line 66) | def download(self) -> None: method process (line 72) | def process(self) -> None: FILE: torch_geometric/datasets/aminer.py class AMiner (line 17) | class AMiner(InMemoryDataset): method __init__ (line 42) | def __init__( method raw_file_names (line 54) | def raw_file_names(self) -> List[str]: method processed_file_names (line 61) | def processed_file_names(self) -> str: method download (line 64) | def download(self) -> None: method process (line 74) | def process(self) -> None: FILE: torch_geometric/datasets/aqsol.py class AQSOL (line 17) | class AQSOL(InMemoryDataset): method __init__ (line 69) | def __init__( method raw_file_names (line 85) | def raw_file_names(self) -> List[str]: method processed_file_names (line 92) | def processed_file_names(self) -> List[str]: method download (line 95) | def download(self) -> None: method process (line 102) | def process(self) -> None: method atoms (line 132) | def atoms(self) -> List[str]: method bonds (line 142) | def bonds(self) -> List[str]: FILE: torch_geometric/datasets/attributed_graph_dataset.py class AttributedGraphDataset (line 16) | class AttributedGraphDataset(InMemoryDataset): method __init__ (line 112) | def __init__( method raw_dir (line 127) | def raw_dir(self) -> str: method processed_dir (line 131) | def processed_dir(self) -> str: method raw_file_names (line 135) | def raw_file_names(self) -> List[str]: method processed_file_names (line 139) | def processed_file_names(self) -> str: method download (line 142) | def download(self) -> None: method process (line 154) | def process(self) -> None: method __repr__ (line 191) | def __repr__(self) -> str: FILE: torch_geometric/datasets/ba2motif_dataset.py class BA2MotifDataset (line 9) | class BA2MotifDataset(InMemoryDataset): method __init__ (line 83) | def __init__( method raw_file_names (line 95) | def raw_file_names(self) -> str: method processed_file_names (line 99) | def processed_file_names(self) -> str: method download (line 102) | def download(self) -> None: method process (line 105) | def process(self) -> None: FILE: torch_geometric/datasets/ba_multi_shapes.py class BAMultiShapesDataset (line 10) | class BAMultiShapesDataset(InMemoryDataset): method __init__ (line 62) | def __init__( method raw_file_names (line 75) | def raw_file_names(self) -> str: method processed_file_names (line 79) | def processed_file_names(self) -> str: method download (line 82) | def download(self) -> None: method process (line 85) | def process(self) -> None: FILE: torch_geometric/datasets/ba_shapes.py function house (line 11) | def house() -> Tuple[Tensor, Tensor]: class BAShapes (line 20) | class BAShapes(InMemoryDataset): method __init__ (line 42) | def __init__( FILE: torch_geometric/datasets/bitcoin_otc.py class BitcoinOTC (line 15) | class BitcoinOTC(InMemoryDataset): method __init__ (line 57) | def __init__( method raw_file_names (line 71) | def raw_file_names(self) -> str: method processed_file_names (line 75) | def processed_file_names(self) -> str: method num_nodes (line 79) | def num_nodes(self) -> int: method download (line 84) | def download(self) -> None: method process (line 89) | def process(self) -> None: FILE: torch_geometric/datasets/brca_tgca.py class BrcaTcga (line 17) | class BrcaTcga(InMemoryDataset): method __init__ (line 63) | def __init__( method raw_file_names (line 76) | def raw_file_names(self) -> List[str]: method processed_file_names (line 80) | def processed_file_names(self) -> str: method download (line 83) | def download(self) -> None: method process (line 90) | def process(self) -> None: FILE: torch_geometric/datasets/citation_full.py class CitationFull (line 8) | class CitationFull(InMemoryDataset): method __init__ (line 73) | def __init__( method raw_dir (line 90) | def raw_dir(self) -> str: method processed_dir (line 94) | def processed_dir(self) -> str: method raw_file_names (line 98) | def raw_file_names(self) -> str: method processed_file_names (line 102) | def processed_file_names(self) -> str: method download (line 106) | def download(self) -> None: method process (line 109) | def process(self) -> None: method __repr__ (line 114) | def __repr__(self) -> str: class CoraFull (line 118) | class CoraFull(CitationFull): method __init__ (line 137) | def __init__( method download (line 145) | def download(self) -> None: method process (line 148) | def process(self) -> None: FILE: torch_geometric/datasets/city.py class CityNetwork (line 13) | class CityNetwork(InMemoryDataset): method __init__ (line 79) | def __init__( method raw_dir (line 102) | def raw_dir(self) -> str: method processed_dir (line 106) | def processed_dir(self) -> str: method raw_file_names (line 110) | def raw_file_names(self) -> str: method processed_file_names (line 114) | def processed_file_names(self) -> str: method download (line 117) | def download(self) -> None: method process (line 123) | def process(self) -> None: method __repr__ (line 153) | def __repr__(self) -> str: FILE: torch_geometric/datasets/coauthor.py class Coauthor (line 8) | class Coauthor(InMemoryDataset): method __init__ (line 56) | def __init__( method raw_dir (line 71) | def raw_dir(self) -> str: method processed_dir (line 75) | def processed_dir(self) -> str: method raw_file_names (line 79) | def raw_file_names(self) -> str: method processed_file_names (line 83) | def processed_file_names(self) -> str: method download (line 86) | def download(self) -> None: method process (line 89) | def process(self) -> None: method __repr__ (line 94) | def __repr__(self) -> str: FILE: torch_geometric/datasets/coma.py class CoMA (line 11) | class CoMA(InMemoryDataset): method __init__ (line 81) | def __init__( method raw_file_names (line 96) | def raw_file_names(self) -> str: method processed_file_names (line 100) | def processed_file_names(self) -> List[str]: method download (line 103) | def download(self) -> None: method process (line 108) | def process(self) -> None: FILE: torch_geometric/datasets/cornell.py class CornellTemporalHyperGraphDataset (line 10) | class CornellTemporalHyperGraphDataset(InMemoryDataset): method __init__ (line 67) | def __init__( method raw_dir (line 99) | def raw_dir(self) -> str: method raw_file_names (line 103) | def raw_file_names(self) -> List[str]: method processed_dir (line 107) | def processed_dir(self) -> str: method processed_file_names (line 111) | def processed_file_names(self) -> List[str]: method download (line 114) | def download(self) -> None: method process (line 119) | def process(self) -> None: FILE: torch_geometric/datasets/dblp.py class DBLP (line 17) | class DBLP(InMemoryDataset): method __init__ (line 84) | def __init__( method raw_file_names (line 96) | def raw_file_names(self) -> List[str]: method processed_file_names (line 103) | def processed_file_names(self) -> str: method download (line 106) | def download(self) -> None: method process (line 111) | def process(self) -> None: method __repr__ (line 162) | def __repr__(self) -> str: FILE: torch_geometric/datasets/dbp15k.py class DBP15K (line 18) | class DBP15K(InMemoryDataset): method __init__ (line 44) | def __init__( method raw_file_names (line 59) | def raw_file_names(self) -> List[str]: method processed_file_names (line 63) | def processed_file_names(self) -> str: method download (line 66) | def download(self) -> None: method process (line 73) | def process(self) -> None: method process_graph (line 104) | def process_graph( method process_y (line 138) | def process_y(self, path: str, assoc1: Tensor, assoc2: Tensor) -> Tensor: method __repr__ (line 143) | def __repr__(self) -> str: FILE: torch_geometric/datasets/deezer_europe.py class DeezerEurope (line 9) | class DeezerEurope(InMemoryDataset): method __init__ (line 33) | def __init__( method raw_file_names (line 45) | def raw_file_names(self) -> str: method processed_file_names (line 49) | def processed_file_names(self) -> str: method download (line 52) | def download(self) -> None: method process (line 55) | def process(self) -> None: FILE: torch_geometric/datasets/dgraph.py class DGraphFin (line 11) | class DGraphFin(InMemoryDataset): method __init__ (line 56) | def __init__( method download (line 67) | def download(self) -> None: method raw_file_names (line 73) | def raw_file_names(self) -> str: method processed_file_names (line 77) | def processed_file_names(self) -> str: method num_classes (line 81) | def num_classes(self) -> int: method process (line 84) | def process(self) -> None: FILE: torch_geometric/datasets/dynamic_faust.py class DynamicFAUST (line 9) | class DynamicFAUST(InMemoryDataset): method __init__ (line 71) | def __init__( method raw_file_names (line 99) | def raw_file_names(self) -> List[str]: method processed_file_names (line 103) | def processed_file_names(self) -> str: method download (line 110) | def download(self) -> None: method process (line 117) | def process(self) -> None: FILE: torch_geometric/datasets/elliptic.py class EllipticBitcoinDataset (line 9) | class EllipticBitcoinDataset(InMemoryDataset): method __init__ (line 55) | def __init__( method raw_file_names (line 67) | def raw_file_names(self) -> List[str]: method processed_file_names (line 75) | def processed_file_names(self) -> str: method download (line 78) | def download(self) -> None: method _process_df (line 82) | def _process_df(self, feat_df: Any, edge_df: Any, method process (line 86) | def process(self) -> None: method num_classes (line 130) | def num_classes(self) -> int: FILE: torch_geometric/datasets/elliptic_temporal.py class EllipticBitcoinTemporalDataset (line 6) | class EllipticBitcoinTemporalDataset(EllipticBitcoinDataset): method __init__ (line 57) | def __init__( method processed_file_names (line 73) | def processed_file_names(self) -> str: method _process_df (line 76) | def _process_df(self, feat_df: Any, edge_df: Any, FILE: torch_geometric/datasets/email_eu_core.py class EmailEUCore (line 14) | class EmailEUCore(InMemoryDataset): method __init__ (line 42) | def __init__( method raw_file_names (line 54) | def raw_file_names(self) -> List[str]: method processed_file_names (line 58) | def processed_file_names(self) -> str: method download (line 61) | def download(self) -> None: method process (line 67) | def process(self) -> None: FILE: torch_geometric/datasets/entities.py class Entities (line 19) | class Entities(InMemoryDataset): method __init__ (line 78) | def __init__( method raw_dir (line 98) | def raw_dir(self) -> str: method processed_dir (line 102) | def processed_dir(self) -> str: method num_relations (line 106) | def num_relations(self) -> int: method num_classes (line 110) | def num_classes(self) -> int: method raw_file_names (line 114) | def raw_file_names(self) -> List[str]: method processed_file_names (line 123) | def processed_file_names(self) -> str: method download (line 126) | def download(self) -> None: method process (line 131) | def process(self) -> None: method __repr__ (line 216) | def __repr__(self) -> str: class hide_stdout (line 220) | class hide_stdout: method __enter__ (line 221) | def __enter__(self) -> None: method __exit__ (line 225) | def __exit__(self, *args: Any) -> None: FILE: torch_geometric/datasets/explainer_dataset.py class ExplainerDataset (line 12) | class ExplainerDataset(InMemoryDataset): method __init__ (line 68) | def __init__( method get_graph (line 98) | def get_graph(self) -> Explanation: method __repr__ (line 139) | def __repr__(self) -> str: FILE: torch_geometric/datasets/facebook.py class FacebookPagePage (line 9) | class FacebookPagePage(InMemoryDataset): method __init__ (line 32) | def __init__( method raw_file_names (line 44) | def raw_file_names(self) -> str: method processed_file_names (line 48) | def processed_file_names(self) -> str: method download (line 51) | def download(self) -> None: method process (line 54) | def process(self) -> None: FILE: torch_geometric/datasets/fake.py class FakeDataset (line 13) | class FakeDataset(InMemoryDataset): method __init__ (line 43) | def __init__( method generate_data (line 75) | def generate_data(self) -> Data: class FakeHeteroDataset (line 111) | class FakeHeteroDataset(InMemoryDataset): method __init__ (line 143) | def __init__( method generate_data (line 192) | def generate_data(self) -> HeteroData: function get_num_nodes (line 237) | def get_num_nodes(avg_num_nodes: int, avg_degree: float) -> int: function get_num_channels (line 243) | def get_num_channels(num_channels: int) -> int: function get_edge_index (line 249) | def get_edge_index( FILE: torch_geometric/datasets/faust.py class FAUST (line 10) | class FAUST(InMemoryDataset): method __init__ (line 66) | def __init__( method raw_file_names (line 81) | def raw_file_names(self) -> str: method processed_file_names (line 85) | def processed_file_names(self) -> List[str]: method download (line 88) | def download(self) -> None: method process (line 93) | def process(self) -> None: FILE: torch_geometric/datasets/flickr.py class Flickr (line 11) | class Flickr(InMemoryDataset): method __init__ (line 49) | def __init__( method raw_file_names (line 61) | def raw_file_names(self) -> List[str]: method processed_file_names (line 65) | def processed_file_names(self) -> str: method download (line 68) | def download(self) -> None: method process (line 74) | def process(self) -> None: FILE: torch_geometric/datasets/freebase.py class FB15k_237 (line 8) | class FB15k_237(InMemoryDataset): method __init__ (line 43) | def __init__( method raw_file_names (line 61) | def raw_file_names(self) -> List[str]: method processed_file_names (line 65) | def processed_file_names(self) -> List[str]: method download (line 68) | def download(self) -> None: method process (line 72) | def process(self) -> None: FILE: torch_geometric/datasets/gdelt.py class GDELT (line 11) | class GDELT(EventDataset): method __init__ (line 41) | def __init__( method num_nodes (line 57) | def num_nodes(self) -> int: method num_rels (line 61) | def num_rels(self) -> int: method raw_file_names (line 65) | def raw_file_names(self) -> List[str]: method processed_file_names (line 69) | def processed_file_names(self) -> List[str]: method download (line 72) | def download(self) -> None: method process_events (line 76) | def process_events(self) -> Tensor: method process (line 84) | def process(self) -> None: FILE: torch_geometric/datasets/gdelt_lite.py class GDELTLite (line 15) | class GDELTLite(InMemoryDataset): method __init__ (line 57) | def __init__( method raw_file_names (line 69) | def raw_file_names(self) -> List[str]: method processed_file_names (line 73) | def processed_file_names(self) -> str: method download (line 76) | def download(self) -> None: method process (line 81) | def process(self) -> None: FILE: torch_geometric/datasets/ged_dataset.py class GEDDataset (line 20) | class GEDDataset(InMemoryDataset): method __init__ (line 132) | def __init__( method raw_file_names (line 154) | def raw_file_names(self) -> List[str]: method processed_file_names (line 159) | def processed_file_names(self) -> List[str]: method download (line 163) | def download(self) -> None: method process (line 178) | def process(self) -> None: method __repr__ (line 257) | def __repr__(self) -> str: FILE: torch_geometric/datasets/gemsec.py class GemsecDeezer (line 10) | class GemsecDeezer(InMemoryDataset): method __init__ (line 36) | def __init__( method raw_dir (line 51) | def raw_dir(self) -> str: method processed_dir (line 55) | def processed_dir(self) -> str: method raw_file_names (line 59) | def raw_file_names(self) -> str: method processed_file_names (line 63) | def processed_file_names(self) -> str: method download (line 66) | def download(self) -> None: method process (line 69) | def process(self) -> None: FILE: torch_geometric/datasets/geometry.py class GeometricShapes (line 17) | class GeometricShapes(InMemoryDataset): method __init__ (line 70) | def __init__( method raw_file_names (line 85) | def raw_file_names(self) -> str: method processed_file_names (line 89) | def processed_file_names(self) -> List[str]: method download (line 92) | def download(self) -> None: method process (line 97) | def process(self) -> None: method process_set (line 101) | def process_set(self, dataset: str) -> List[Data]: FILE: torch_geometric/datasets/git_mol_dataset.py function safe_index (line 17) | def safe_index(lst: List[Any], e: int) -> int: class GitMolDataset (line 21) | class GitMolDataset(InMemoryDataset): method __init__ (line 48) | def __init__( method raw_file_names (line 84) | def raw_file_names(self) -> List[str]: method processed_file_names (line 88) | def processed_file_names(self) -> str: method download (line 91) | def download(self) -> None: method process (line 99) | def process(self) -> None: FILE: torch_geometric/datasets/github.py class GitHub (line 9) | class GitHub(InMemoryDataset): method __init__ (line 47) | def __init__( method raw_file_names (line 59) | def raw_file_names(self) -> str: method processed_file_names (line 63) | def processed_file_names(self) -> str: method download (line 66) | def download(self) -> None: method process (line 69) | def process(self) -> None: FILE: torch_geometric/datasets/gnn_benchmark_dataset.py class GNNBenchmarkDataset (line 19) | class GNNBenchmarkDataset(InMemoryDataset): method __init__ (line 114) | def __init__( method raw_dir (line 150) | def raw_dir(self) -> str: method processed_dir (line 154) | def processed_dir(self) -> str: method raw_file_names (line 158) | def raw_file_names(self) -> List[str]: method processed_file_names (line 169) | def processed_file_names(self) -> List[str]: method download (line 175) | def download(self) -> None: method process (line 180) | def process(self) -> None: method process_CSL (line 197) | def process_CSL(self) -> List[Data]: method __repr__ (line 216) | def __repr__(self) -> str: FILE: torch_geometric/datasets/graph_generator/ba_graph.py class BAGraph (line 6) | class BAGraph(GraphGenerator): method __init__ (line 15) | def __init__(self, num_nodes: int, num_edges: int): method __call__ (line 20) | def __call__(self) -> Data: method __repr__ (line 24) | def __repr__(self) -> str: FILE: torch_geometric/datasets/graph_generator/base.py class GraphGenerator (line 8) | class GraphGenerator(ABC): method __call__ (line 11) | def __call__(self) -> Data: method resolve (line 16) | def resolve(query: Any, *args: Any, **kwargs: Any) -> 'GraphGenerator': method __repr__ (line 25) | def __repr__(self) -> str: FILE: torch_geometric/datasets/graph_generator/er_graph.py class ERGraph (line 6) | class ERGraph(GraphGenerator): method __init__ (line 14) | def __init__(self, num_nodes: int, edge_prob: float): method __call__ (line 19) | def __call__(self) -> Data: method __repr__ (line 23) | def __repr__(self) -> str: FILE: torch_geometric/datasets/graph_generator/grid_graph.py class GridGraph (line 10) | class GridGraph(GraphGenerator): method __init__ (line 20) | def __init__( method __call__ (line 31) | def __call__(self) -> Data: method __repr__ (line 36) | def __repr__(self) -> str: FILE: torch_geometric/datasets/graph_generator/tree_graph.py function tree (line 11) | def tree( class TreeGraph (line 52) | class TreeGraph(GraphGenerator): method __init__ (line 62) | def __init__( method __call__ (line 73) | def __call__(self) -> Data: method __repr__ (line 78) | def __repr__(self) -> str: FILE: torch_geometric/datasets/heterophilous_graph_dataset.py class HeterophilousGraphDataset (line 11) | class HeterophilousGraphDataset(InMemoryDataset): method __init__ (line 74) | def __init__( method raw_dir (line 96) | def raw_dir(self) -> str: method processed_dir (line 100) | def processed_dir(self) -> str: method raw_file_names (line 104) | def raw_file_names(self) -> str: method processed_file_names (line 108) | def processed_file_names(self) -> str: method download (line 111) | def download(self) -> None: method process (line 114) | def process(self) -> None: method __repr__ (line 132) | def __repr__(self) -> str: FILE: torch_geometric/datasets/hgb_dataset.py class HGBDataset (line 17) | class HGBDataset(InMemoryDataset): method __init__ (line 59) | def __init__( method raw_dir (line 74) | def raw_dir(self) -> str: method processed_dir (line 78) | def processed_dir(self) -> str: method raw_file_names (line 82) | def raw_file_names(self) -> List[str]: method processed_file_names (line 87) | def processed_file_names(self) -> str: method download (line 90) | def download(self) -> None: method process (line 96) | def process(self) -> None: method __repr__ (line 210) | def __repr__(self) -> str: FILE: torch_geometric/datasets/hm.py class HM (line 8) | class HM(InMemoryDataset): method __init__ (line 35) | def __init__( method raw_file_names (line 49) | def raw_file_names(self) -> List[str]: method processed_file_names (line 56) | def processed_file_names(self) -> str: method download (line 62) | def download(self) -> None: method process (line 67) | def process(self) -> None: FILE: torch_geometric/datasets/hydro_net.py class HydroNet (line 23) | class HydroNet(InMemoryDataset): method __init__ (line 57) | def __init__( method raw_file_names (line 78) | def raw_file_names(self) -> List[str]: method processed_file_names (line 82) | def processed_file_names(self) -> List[str]: method download (line 85) | def download(self) -> None: method process (line 110) | def process(self) -> None: method _unpack_processed (line 124) | def _unpack_processed(self) -> None: method _create_partitions (line 130) | def _create_partitions(self, file: str) -> 'Partition': method select_clusters (line 135) | def select_clusters( method _validate_name (line 158) | def _validate_name( method _dataset (line 172) | def _dataset(self) -> Union[ConcatDataset, Subset]: method _load_small_split (line 180) | def _load_small_split(self, dataset: ConcatDataset) -> Subset: method len (line 188) | def len(self) -> int: method get (line 191) | def get(self, idx: int) -> Data: function get_num_clusters (line 195) | def get_num_clusters(filepath: str) -> int: function read_energy (line 200) | def read_energy(file: str, chunk_size: int) -> np.ndarray: function read_atoms (line 219) | def read_atoms(file: str, chunk_size: int) -> Tuple[np.ndarray, np.ndarr... class RemoteFile (line 245) | class RemoteFile: method unpack_to (line 249) | def unpack_to(self, dest_folder: str) -> None: method raw_dataset (line 255) | def raw_dataset() -> 'RemoteFile': method processed_dataset (line 261) | def processed_dataset() -> 'RemoteFile': method hydronet_splits (line 267) | def hydronet_splits() -> 'RemoteFile': class Partition (line 273) | class Partition(InMemoryDataset): method __init__ (line 274) | def __init__( method raw_file_names (line 288) | def raw_file_names(self) -> List[str]: method processed_file_names (line 292) | def processed_file_names(self) -> List[str]: method process (line 295) | def process(self) -> None: method _load (line 303) | def _load(self) -> None: method num_graphs (line 317) | def num_graphs(self) -> int: method len (line 321) | def len(self) -> int: method get (line 324) | def get(self, idx: int) -> Data: FILE: torch_geometric/datasets/icews.py class EventDataset (line 10) | class EventDataset(InMemoryDataset): method __init__ (line 11) | def __init__( method num_nodes (line 23) | def num_nodes(self) -> int: method num_rels (line 27) | def num_rels(self) -> int: method process_events (line 30) | def process_events(self) -> Tensor: method _process_data_list (line 33) | def _process_data_list(self) -> List[Data]: class ICEWS18 (line 49) | class ICEWS18(EventDataset): method __init__ (line 79) | def __init__( method num_nodes (line 95) | def num_nodes(self) -> int: method num_rels (line 99) | def num_rels(self) -> int: method raw_file_names (line 103) | def raw_file_names(self) -> List[str]: method processed_file_names (line 107) | def processed_file_names(self) -> List[str]: method download (line 110) | def download(self) -> None: method process_events (line 114) | def process_events(self) -> Tensor: method process (line 122) | def process(self) -> None: FILE: torch_geometric/datasets/igmc_dataset.py class IGMCDataset (line 10) | class IGMCDataset(InMemoryDataset): method __init__ (line 37) | def __init__( method raw_dir (line 53) | def raw_dir(self) -> str: method processed_dir (line 57) | def processed_dir(self) -> str: method raw_file_names (line 61) | def raw_file_names(self) -> str: method processed_file_names (line 65) | def processed_file_names(self) -> str: method download (line 68) | def download(self) -> None: method load_matlab_file (line 73) | def load_matlab_file(path_file: str, name: str) -> Tensor: method process (line 83) | def process(self) -> None: method __repr__ (line 127) | def __repr__(self) -> str: FILE: torch_geometric/datasets/imdb.py class IMDB (line 17) | class IMDB(InMemoryDataset): method __init__ (line 43) | def __init__( method raw_file_names (line 55) | def raw_file_names(self) -> List[str]: method processed_file_names (line 62) | def processed_file_names(self) -> str: method download (line 65) | def download(self) -> None: method process (line 70) | def process(self) -> None: method __repr__ (line 112) | def __repr__(self) -> str: FILE: torch_geometric/datasets/infection_dataset.py class InfectionDataset (line 11) | class InfectionDataset(InMemoryDataset): method __init__ (line 65) | def __init__( method get_graph (line 129) | def get_graph(self, num_infected_nodes: int, method __repr__ (line 162) | def __repr__(self) -> str: FILE: torch_geometric/datasets/instruct_mol_dataset.py class InstructMolDataset (line 13) | class InstructMolDataset(InMemoryDataset): method __init__ (line 37) | def __init__( method raw_file_names (line 50) | def raw_file_names(self) -> List[str]: method processed_file_names (line 54) | def processed_file_names(self) -> List[str]: method download (line 57) | def download(self) -> None: method process (line 61) | def process(self) -> None: FILE: torch_geometric/datasets/jodie.py class JODIEDataset (line 9) | class JODIEDataset(InMemoryDataset): method __init__ (line 65) | def __init__( method raw_dir (line 81) | def raw_dir(self) -> str: method processed_dir (line 85) | def processed_dir(self) -> str: method raw_file_names (line 89) | def raw_file_names(self) -> str: method processed_file_names (line 93) | def processed_file_names(self) -> str: method download (line 96) | def download(self) -> None: method process (line 99) | def process(self) -> None: method __repr__ (line 118) | def __repr__(self) -> str: FILE: torch_geometric/datasets/karate.py class KarateClub (line 8) | class KarateClub(InMemoryDataset): method __init__ (line 41) | def __init__(self, transform: Optional[Callable] = None): FILE: torch_geometric/datasets/last_fm.py class LastFM (line 17) | class LastFM(InMemoryDataset): method __init__ (line 42) | def __init__( method raw_file_names (line 54) | def raw_file_names(self) -> List[str]: method processed_file_names (line 61) | def processed_file_names(self) -> str: method download (line 64) | def download(self) -> None: method process (line 69) | def process(self) -> None: method __repr__ (line 119) | def __repr__(self) -> str: FILE: torch_geometric/datasets/lastfm_asia.py class LastFMAsia (line 9) | class LastFMAsia(InMemoryDataset): method __init__ (line 32) | def __init__( method raw_file_names (line 44) | def raw_file_names(self) -> str: method processed_file_names (line 48) | def processed_file_names(self) -> str: method download (line 51) | def download(self) -> None: method process (line 54) | def process(self) -> None: FILE: torch_geometric/datasets/linkx_dataset.py class LINKXDataset (line 12) | class LINKXDataset(InMemoryDataset): method __init__ (line 78) | def __init__( method raw_dir (line 93) | def raw_dir(self) -> str: method processed_dir (line 97) | def processed_dir(self) -> str: method raw_file_names (line 101) | def raw_file_names(self) -> List[str]: method processed_file_names (line 108) | def processed_file_names(self) -> str: method download (line 111) | def download(self) -> None: method _process_wiki (line 117) | def _process_wiki(self) -> Data: method _process_facebook (line 125) | def _process_facebook(self) -> Data: method _process_genius (line 162) | def _process_genius(self) -> Data: method process (line 172) | def process(self) -> None: method __repr__ (line 188) | def __repr__(self) -> str: FILE: torch_geometric/datasets/lrgb.py class LRGBDataset (line 18) | class LRGBDataset(InMemoryDataset): method __init__ (line 127) | def __init__( method raw_dir (line 147) | def raw_dir(self) -> str: method processed_dir (line 151) | def processed_dir(self) -> str: method raw_file_names (line 155) | def raw_file_names(self) -> List[str]: method processed_file_names (line 162) | def processed_file_names(self) -> List[str]: method download (line 165) | def download(self) -> None: method process (line 173) | def process(self) -> None: method label_remap_coco (line 243) | def label_remap_coco(self) -> Dict[int, int]: method process_pcqm_contact (line 260) | def process_pcqm_contact(self) -> None: FILE: torch_geometric/datasets/malnet_tiny.py class MalNetTiny (line 17) | class MalNetTiny(InMemoryDataset): method __init__ (line 52) | def __init__( method raw_file_names (line 80) | def raw_file_names(self) -> List[str]: method processed_file_names (line 84) | def processed_file_names(self) -> List[str]: method download (line 87) | def download(self) -> None: method process (line 96) | def process(self) -> None: FILE: torch_geometric/datasets/md17.py class MD17 (line 17) | class MD17(InMemoryDataset): method __init__ (line 338) | def __init__( method mean (line 370) | def mean(self) -> float: method raw_dir (line 375) | def raw_dir(self) -> str: method processed_dir (line 381) | def processed_dir(self) -> str: method raw_file_names (line 385) | def raw_file_names(self) -> Union[str, List[str]]: method processed_file_names (line 394) | def processed_file_names(self) -> List[str]: method download (line 400) | def download(self) -> None: method process (line 412) | def process(self) -> None: method __repr__ (line 439) | def __repr__(self) -> str: FILE: torch_geometric/datasets/medshapenet.py class MedShapeNet (line 11) | class MedShapeNet(InMemoryDataset): method __init__ (line 46) | def __init__( method raw_file_names (line 63) | def raw_file_names(self) -> List[str]: method processed_file_names (line 70) | def processed_file_names(self) -> List[str]: method raw_paths (line 74) | def raw_paths(self) -> List[str]: method process (line 80) | def process(self) -> None: FILE: torch_geometric/datasets/mixhop_synthetic_dataset.py class MixHopSyntheticDataset (line 11) | class MixHopSyntheticDataset(InMemoryDataset): method __init__ (line 40) | def __init__( method raw_dir (line 56) | def raw_dir(self) -> str: method processed_dir (line 60) | def processed_dir(self) -> str: method raw_file_names (line 64) | def raw_file_names(self) -> List[str]: method processed_file_names (line 69) | def processed_file_names(self) -> str: method download (line 72) | def download(self) -> None: method process (line 76) | def process(self) -> None: method __repr__ (line 104) | def __repr__(self) -> str: FILE: torch_geometric/datasets/mnist_superpixels.py class MNISTSuperpixels (line 13) | class MNISTSuperpixels(InMemoryDataset): method __init__ (line 59) | def __init__( method raw_file_names (line 74) | def raw_file_names(self) -> str: method processed_file_names (line 78) | def processed_file_names(self) -> List[str]: method download (line 81) | def download(self) -> None: method process (line 86) | def process(self) -> None: FILE: torch_geometric/datasets/modelnet.py class ModelNet (line 17) | class ModelNet(InMemoryDataset): method __init__ (line 86) | def __init__( method raw_file_names (line 104) | def raw_file_names(self) -> List[str]: method processed_file_names (line 111) | def processed_file_names(self) -> List[str]: method download (line 114) | def download(self) -> None: method process (line 127) | def process(self) -> None: method process_set (line 131) | def process_set(self, dataset: str) -> List[Data]: method __repr__ (line 152) | def __repr__(self) -> str: FILE: torch_geometric/datasets/molecule_gpt_dataset.py function clean_up_description (line 21) | def clean_up_description(description: str) -> str: function extract_name (line 125) | def extract_name( class MoleculeGPTDataset (line 174) | class MoleculeGPTDataset(InMemoryDataset): method __init__ (line 209) | def __init__( method raw_file_names (line 229) | def raw_file_names(self) -> List[str]: method processed_file_names (line 233) | def processed_file_names(self) -> List[str]: method download (line 236) | def download(self) -> None: method process (line 318) | def process(self, use_mp: bool = False) -> None: FILE: torch_geometric/datasets/molecule_net.py class MoleculeNet (line 13) | class MoleculeNet(InMemoryDataset): method __init__ (line 152) | def __init__( method raw_dir (line 170) | def raw_dir(self) -> str: method processed_dir (line 174) | def processed_dir(self) -> str: method raw_file_names (line 178) | def raw_file_names(self) -> str: method processed_file_names (line 182) | def processed_file_names(self) -> str: method download (line 185) | def download(self) -> None: method process (line 192) | def process(self) -> None: method __repr__ (line 228) | def __repr__(self) -> str: FILE: torch_geometric/datasets/motif_generator/base.py class MotifGenerator (line 8) | class MotifGenerator(ABC): method __call__ (line 11) | def __call__(self) -> Data: method resolve (line 15) | def resolve(query: Any, *args: Any, **kwargs: Any) -> 'MotifGenerator': method __repr__ (line 24) | def __repr__(self) -> str: FILE: torch_geometric/datasets/motif_generator/custom.py class CustomMotif (line 8) | class CustomMotif(MotifGenerator): method __init__ (line 16) | def __init__(self, structure: Any): method __call__ (line 36) | def __call__(self) -> Data: FILE: torch_geometric/datasets/motif_generator/cycle.py class CycleMotif (line 7) | class CycleMotif(CustomMotif): method __init__ (line 15) | def __init__(self, num_nodes: int): method __repr__ (line 29) | def __repr__(self) -> str: FILE: torch_geometric/datasets/motif_generator/grid.py class GridMotif (line 7) | class GridMotif(CustomMotif): method __init__ (line 12) | def __init__(self) -> None: FILE: torch_geometric/datasets/motif_generator/house.py class HouseMotif (line 7) | class HouseMotif(CustomMotif): method __init__ (line 14) | def __init__(self) -> None: FILE: torch_geometric/datasets/movie_lens.py class MovieLens (line 15) | class MovieLens(InMemoryDataset): method __init__ (line 40) | def __init__( method raw_file_names (line 54) | def raw_file_names(self) -> List[str]: method processed_file_names (line 61) | def processed_file_names(self) -> str: method download (line 64) | def download(self) -> None: method process (line 69) | def process(self) -> None: FILE: torch_geometric/datasets/movie_lens_100k.py class MovieLens100K (line 25) | class MovieLens100K(InMemoryDataset): method __init__ (line 73) | def __init__( method raw_file_names (line 85) | def raw_file_names(self) -> List[str]: method processed_file_names (line 89) | def processed_file_names(self) -> str: method download (line 92) | def download(self) -> None: method process (line 100) | def process(self) -> None: FILE: torch_geometric/datasets/movie_lens_1m.py class MovieLens1M (line 20) | class MovieLens1M(InMemoryDataset): method __init__ (line 68) | def __init__( method raw_file_names (line 80) | def raw_file_names(self) -> List[str]: method processed_file_names (line 84) | def processed_file_names(self) -> str: method download (line 87) | def download(self) -> None: method process (line 95) | def process(self) -> None: FILE: torch_geometric/datasets/myket.py class MyketDataset (line 9) | class MyketDataset(InMemoryDataset): method __init__ (line 49) | def __init__( method raw_file_names (line 61) | def raw_file_names(self) -> List[str]: method processed_file_names (line 65) | def processed_file_names(self) -> str: method download (line 68) | def download(self) -> None: method process (line 72) | def process(self) -> None: FILE: torch_geometric/datasets/nell.py class NELL (line 9) | class NELL(InMemoryDataset): method __init__ (line 53) | def __init__( method raw_file_names (line 65) | def raw_file_names(self) -> List[str]: method processed_file_names (line 70) | def processed_file_names(self) -> str: method download (line 73) | def download(self) -> None: method process (line 80) | def process(self) -> None: FILE: torch_geometric/datasets/neurograph.py class NeuroGraphDataset (line 14) | class NeuroGraphDataset(InMemoryDataset): method __init__ (line 68) | def __init__( method raw_dir (line 85) | def raw_dir(self) -> str: method raw_file_names (line 89) | def raw_file_names(self) -> str: method processed_dir (line 93) | def processed_dir(self) -> str: method processed_file_names (line 97) | def processed_file_names(self) -> str: method download (line 100) | def download(self) -> None: method process (line 110) | def process(self) -> None: FILE: torch_geometric/datasets/ogb_mag.py class OGB_MAG (line 18) | class OGB_MAG(InMemoryDataset): method __init__ (line 58) | def __init__( method num_classes (line 74) | def num_classes(self) -> int: method raw_dir (line 79) | def raw_dir(self) -> str: method processed_dir (line 83) | def processed_dir(self) -> str: method raw_file_names (line 87) | def raw_file_names(self) -> List[str]: method processed_file_names (line 99) | def processed_file_names(self) -> str: method download (line 105) | def download(self) -> None: method process (line 123) | def process(self) -> None: method __repr__ (line 182) | def __repr__(self) -> str: FILE: torch_geometric/datasets/omdb.py class OMDB (line 10) | class OMDB(InMemoryDataset): method __init__ (line 36) | def __init__( method raw_file_names (line 51) | def raw_file_names(self) -> str: method processed_file_names (line 55) | def processed_file_names(self) -> List[str]: method download (line 58) | def download(self) -> None: method process (line 63) | def process(self) -> None: FILE: torch_geometric/datasets/opf.py class OPFDataset (line 18) | class OPFDataset(InMemoryDataset): method __init__ (line 69) | def __init__( method raw_dir (line 109) | def raw_dir(self) -> str: method processed_dir (line 113) | def processed_dir(self) -> str: method raw_file_names (line 118) | def raw_file_names(self) -> List[str]: method processed_file_names (line 122) | def processed_file_names(self) -> List[str]: method download (line 125) | def download(self) -> None: method process (line 131) | def process(self) -> None: method __repr__ (line 221) | def __repr__(self) -> str: function extract_edge_index (line 228) | def extract_edge_index(obj: Dict, edge_name: str) -> Tensor: function extract_edge_index_rev (line 235) | def extract_edge_index_rev(obj: Dict, edge_name: str) -> Tensor: FILE: torch_geometric/datasets/ose_gvcs.py class OSE_GVCS (line 16) | class OSE_GVCS(InMemoryDataset): method __init__ (line 66) | def __init__( method raw_file_names (line 78) | def raw_file_names(self) -> List[str]: method processed_file_names (line 85) | def processed_file_names(self) -> str: method download (line 88) | def download(self) -> None: method process (line 93) | def process(self) -> None: FILE: torch_geometric/datasets/particle.py class TrackingData (line 12) | class TrackingData(Data): method __inc__ (line 13) | def __inc__(self, key: str, value: Any, *args: Any, **kwargs: Any) -> ... class TrackMLParticleTrackingDataset (line 20) | class TrackMLParticleTrackingDataset(Dataset): method __init__ (line 35) | def __init__( method raw_file_names (line 46) | def raw_file_names(self) -> List[str]: method download (line 55) | def download(self) -> None: method len (line 60) | def len(self) -> int: method get (line 63) | def get(self, i: int) -> TrackingData: FILE: torch_geometric/datasets/pascal.py class PascalVOCKeypoints (line 22) | class PascalVOCKeypoints(InMemoryDataset): method __init__ (line 79) | def __init__( method raw_dir (line 102) | def raw_dir(self) -> str: method processed_dir (line 106) | def processed_dir(self) -> str: method raw_file_names (line 110) | def raw_file_names(self) -> List[str]: method processed_file_names (line 114) | def processed_file_names(self) -> List[str]: method download (line 117) | def download(self) -> None: method process (line 132) | def process(self) -> None: method __repr__ (line 295) | def __repr__(self) -> str: FILE: torch_geometric/datasets/pascal_pf.py class PascalPF (line 17) | class PascalPF(InMemoryDataset): method __init__ (line 55) | def __init__( method raw_file_names (line 72) | def raw_file_names(self) -> List[str]: method processed_file_names (line 76) | def processed_file_names(self) -> List[str]: method download (line 79) | def download(self) -> None: method process (line 85) | def process(self) -> None: method __repr__ (line 125) | def __repr__(self) -> str: FILE: torch_geometric/datasets/pcpnet_dataset.py class PCPNetDataset (line 16) | class PCPNetDataset(InMemoryDataset): method __init__ (line 77) | def __init__( method raw_file_names (line 105) | def raw_file_names(self) -> str: method processed_file_names (line 114) | def processed_file_names(self) -> str: method download (line 117) | def download(self) -> None: method process (line 122) | def process(self) -> None: method __repr__ (line 147) | def __repr__(self) -> str: FILE: torch_geometric/datasets/pcqm4m.py class PCQM4Mv2 (line 14) | class PCQM4Mv2(OnDiskDataset): method __init__ (line 55) | def __init__( method raw_file_names (line 80) | def raw_file_names(self) -> List[str]: method download (line 86) | def download(self) -> None: method process (line 91) | def process(self) -> None: method serialize (line 107) | def serialize(self, data: BaseData) -> Dict[str, Any]: method deserialize (line 117) | def deserialize(self, data: Dict[str, Any]) -> Data: FILE: torch_geometric/datasets/planetoid.py class Planetoid (line 11) | class Planetoid(InMemoryDataset): method __init__ (line 85) | def __init__( method raw_dir (line 132) | def raw_dir(self) -> str: method processed_dir (line 138) | def processed_dir(self) -> str: method raw_file_names (line 144) | def raw_file_names(self) -> List[str]: method processed_file_names (line 149) | def processed_file_names(self) -> str: method download (line 152) | def download(self) -> None: method process (line 160) | def process(self) -> None: method __repr__ (line 178) | def __repr__(self) -> str: FILE: torch_geometric/datasets/polblogs.py class PolBlogs (line 14) | class PolBlogs(InMemoryDataset): method __init__ (line 57) | def __init__( method raw_file_names (line 69) | def raw_file_names(self) -> List[str]: method processed_file_names (line 73) | def processed_file_names(self) -> str: method download (line 76) | def download(self) -> None: method process (line 81) | def process(self) -> None: FILE: torch_geometric/datasets/ppi.py class PPI (line 19) | class PPI(InMemoryDataset): method __init__ (line 66) | def __init__( method raw_file_names (line 89) | def raw_file_names(self) -> List[str]: method processed_file_names (line 95) | def processed_file_names(self) -> List[str]: method download (line 98) | def download(self) -> None: method process (line 103) | def process(self) -> None: FILE: torch_geometric/datasets/protein_mpnn_dataset.py class ProteinMPNNDataset (line 19) | class ProteinMPNNDataset(InMemoryDataset): method __init__ (line 75) | def __init__( method raw_file_names (line 105) | def raw_file_names(self) -> List[str]: method processed_file_names (line 112) | def processed_file_names(self) -> List[str]: method download (line 115) | def download(self) -> None: method process (line 120) | def process(self) -> None: method _process_split (line 178) | def _process_split(self) -> Dict[int, List[Tuple[str, int]]]: method _process_pdb1 (line 219) | def _process_pdb1(self, chain_id: str) -> Dict[str, Any]: method _process_pdb2 (line 317) | def _process_pdb2(self, t: Dict[str, Any]) -> Dict[str, Any]: method _process_pdb3 (line 374) | def _process_pdb3( FILE: torch_geometric/datasets/qm7.py class QM7b (line 8) | class QM7b(InMemoryDataset): method __init__ (line 50) | def __init__( method raw_file_names (line 63) | def raw_file_names(self) -> str: method processed_file_names (line 67) | def processed_file_names(self) -> str: method download (line 70) | def download(self) -> None: method process (line 73) | def process(self) -> None: FILE: torch_geometric/datasets/qm9.py class QM9 (line 49) | class QM9(InMemoryDataset): method __init__ (line 146) | def __init__( method mean (line 158) | def mean(self, target: int) -> float: method std (line 162) | def std(self, target: int) -> float: method atomref (line 166) | def atomref(self, target: int) -> Optional[Tensor]: method raw_file_names (line 174) | def raw_file_names(self) -> List[str]: method processed_file_names (line 182) | def processed_file_names(self) -> str: method download (line 185) | def download(self) -> None: method process (line 200) | def process(self) -> None: FILE: torch_geometric/datasets/rcdd.py class RCDD (line 16) | class RCDD(InMemoryDataset): method __init__ (line 41) | def __init__( method raw_file_names (line 53) | def raw_file_names(self) -> List[str]: method processed_file_names (line 62) | def processed_file_names(self) -> str: method download (line 65) | def download(self) -> None: method num_classes (line 71) | def num_classes(self) -> int: method process (line 74) | def process(self) -> None: FILE: torch_geometric/datasets/reddit.py class Reddit (line 17) | class Reddit(InMemoryDataset): method __init__ (line 53) | def __init__( method raw_file_names (line 65) | def raw_file_names(self) -> List[str]: method processed_file_names (line 69) | def processed_file_names(self) -> str: method download (line 72) | def download(self) -> None: method process (line 77) | def process(self) -> None: FILE: torch_geometric/datasets/reddit2.py class Reddit2 (line 11) | class Reddit2(InMemoryDataset): method __init__ (line 57) | def __init__( method raw_file_names (line 69) | def raw_file_names(self) -> List[str]: method processed_file_names (line 73) | def processed_file_names(self) -> str: method download (line 76) | def download(self) -> None: method process (line 82) | def process(self) -> None: FILE: torch_geometric/datasets/rel_link_pred_dataset.py class RelLinkPredDataset (line 9) | class RelLinkPredDataset(InMemoryDataset): method __init__ (line 50) | def __init__( method num_relations (line 65) | def num_relations(self) -> int: method raw_dir (line 69) | def raw_dir(self) -> str: method processed_dir (line 73) | def processed_dir(self) -> str: method processed_file_names (line 77) | def processed_file_names(self) -> str: method raw_file_names (line 81) | def raw_file_names(self) -> List[str]: method download (line 87) | def download(self) -> None: method process (line 91) | def process(self) -> None: method __repr__ (line 125) | def __repr__(self) -> str: FILE: torch_geometric/datasets/s3dis.py class S3DIS (line 17) | class S3DIS(InMemoryDataset): method __init__ (line 53) | def __init__( method raw_file_names (line 71) | def raw_file_names(self) -> List[str]: method processed_file_names (line 75) | def processed_file_names(self) -> List[str]: method download (line 78) | def download(self) -> None: method process (line 86) | def process(self) -> None: FILE: torch_geometric/datasets/sbm_dataset.py class StochasticBlockModelDataset (line 12) | class StochasticBlockModelDataset(InMemoryDataset): method __init__ (line 44) | def __init__( method processed_dir (line 83) | def processed_dir(self) -> str: method processed_file_names (line 87) | def processed_file_names(self) -> str: method process (line 96) | def process(self) -> None: class RandomPartitionGraphDataset (line 131) | class RandomPartitionGraphDataset(StochasticBlockModelDataset): method __init__ (line 165) | def __init__( method processed_file_names (line 202) | def processed_file_names(self) -> str: method process (line 207) | def process(self) -> None: FILE: torch_geometric/datasets/shapenet.py class ShapeNet (line 17) | class ShapeNet(InMemoryDataset): method __init__ (line 122) | def __init__( method num_classes (line 165) | def num_classes(self) -> int: method raw_file_names (line 169) | def raw_file_names(self) -> List[str]: method processed_file_names (line 173) | def processed_file_names(self) -> List[str]: method download (line 180) | def download(self) -> None: method process_filenames (line 188) | def process_filenames(self, filenames: List[str]) -> List[Data]: method process (line 211) | def process(self) -> None: method __repr__ (line 227) | def __repr__(self) -> str: FILE: torch_geometric/datasets/shrec2016.py class SHREC2016 (line 12) | class SHREC2016(InMemoryDataset): method __init__ (line 65) | def __init__( method ref (line 87) | def ref(self) -> str: method raw_file_names (line 94) | def raw_file_names(self) -> List[str]: method processed_file_names (line 98) | def processed_file_names(self) -> List[str]: method download (line 102) | def download(self) -> None: method process (line 116) | def process(self) -> None: method __repr__ (line 155) | def __repr__(self) -> str: FILE: torch_geometric/datasets/snap_dataset.py class EgoData (line 14) | class EgoData(Data): method __inc__ (line 15) | def __inc__(self, key: str, value: Any, *args: Any, **kwargs: Any) -> ... function read_ego (line 23) | def read_ego(files: List[str], name: str) -> List[EgoData]: function read_soc (line 122) | def read_soc(files: List[str], name: str) -> List[Data]: function read_wiki (line 138) | def read_wiki(files: List[str], name: str) -> List[Data]: class SNAPDataset (line 160) | class SNAPDataset(InMemoryDataset): method __init__ (line 199) | def __init__( method raw_dir (line 215) | def raw_dir(self) -> str: method processed_dir (line 219) | def processed_dir(self) -> str: method processed_file_names (line 223) | def processed_file_names(self) -> str: method _download (line 226) | def _download(self) -> None: method download (line 233) | def download(self) -> None: method process (line 237) | def process(self) -> None: method __repr__ (line 263) | def __repr__(self) -> str: FILE: torch_geometric/datasets/suite_sparse.py class SuiteSparseMatrixCollection (line 11) | class SuiteSparseMatrixCollection(InMemoryDataset): method __init__ (line 34) | def __init__( method raw_dir (line 50) | def raw_dir(self) -> str: method processed_dir (line 54) | def processed_dir(self) -> str: method raw_file_names (line 58) | def raw_file_names(self) -> str: method processed_file_names (line 62) | def processed_file_names(self) -> str: method download (line 65) | def download(self) -> None: method process (line 68) | def process(self) -> None: method __repr__ (line 95) | def __repr__(self) -> str: FILE: torch_geometric/datasets/tag_dataset.py class TAGDataset (line 25) | class TAGDataset(InMemoryDataset): method __init__ (line 71) | def __init__( method num_classes (line 150) | def num_classes(self) -> int: method raw_file_names (line 154) | def raw_file_names(self) -> List[str]: method processed_file_names (line 162) | def processed_file_names(self) -> List[str]: method token (line 169) | def token(self) -> Dict[str, Tensor]: method llm_explanation_token (line 175) | def llm_explanation_token(self) -> Dict[str, Tensor]: method all_token (line 183) | def all_token(self) -> Dict[str, Tensor]: method is_gold (line 191) | def is_gold(self) -> Tensor: method get_n_id (line 197) | def get_n_id(self, node_idx: IndexType) -> Tensor: method load_gold_mask (line 205) | def load_gold_mask(self) -> Tensor: method get_gold (line 218) | def get_gold(self, node_idx: IndexType) -> Tensor: method get_idx_split (line 228) | def get_idx_split(self) -> Dict[str, Tensor]: method download (line 231) | def download(self) -> None: method process (line 248) | def process(self) -> None: method save_node_text (line 302) | def save_node_text(self, text: List[str]) -> None: method tokenize_graph (line 313) | def tokenize_graph(self, batch_size: int = 256, method __repr__ (line 386) | def __repr__(self) -> str: class TextDataset (line 389) | class TextDataset(torch.utils.data.Dataset): method __init__ (line 397) | def __init__(self, tag_dataset: 'TAGDataset', method get_token (line 413) | def get_token(self, node_idx: IndexType) -> Dict[str, Tensor]: method __getitem__ (line 425) | def __getitem__( method __len__ (line 448) | def __len__(self) -> int: method get (line 452) | def get(self, idx: int) -> BaseData: method __repr__ (line 455) | def __repr__(self) -> str: method to_text_dataset (line 458) | def to_text_dataset(self, text_type: str = 'raw_text') -> TextDataset: FILE: torch_geometric/datasets/taobao.py class Taobao (line 15) | class Taobao(InMemoryDataset): method __init__ (line 45) | def __init__( method raw_file_names (line 57) | def raw_file_names(self) -> str: method processed_file_names (line 61) | def processed_file_names(self) -> str: method download (line 64) | def download(self) -> None: method process (line 69) | def process(self) -> None: FILE: torch_geometric/datasets/teeth3ds.py class Teeth3DS (line 19) | class Teeth3DS(InMemoryDataset): method __init__ (line 80) | def __init__( method processed_dir (line 100) | def processed_dir(self) -> str: method raw_file_names (line 104) | def raw_file_names(self) -> List[str]: method processed_file_names (line 108) | def processed_file_names(self) -> List[str]: method download (line 128) | def download(self) -> None: method process_file (line 144) | def process_file(self, file_path: str) -> Optional[Data]: method process (line 249) | def process(self) -> None: method len (line 258) | def len(self) -> int: method get (line 261) | def get(self, idx: int) -> Data: method __repr__ (line 267) | def __repr__(self) -> str: FILE: torch_geometric/datasets/tosca.py class TOSCA (line 17) | class TOSCA(InMemoryDataset): method __init__ (line 65) | def __init__( method raw_file_names (line 84) | def raw_file_names(self) -> List[str]: method processed_file_names (line 88) | def processed_file_names(self) -> str: method download (line 92) | def download(self) -> None: method process (line 97) | def process(self) -> None: FILE: torch_geometric/datasets/tu_dataset.py class TUDataset (line 8) | class TUDataset(InMemoryDataset): method __init__ (line 115) | def __init__( method raw_dir (line 161) | def raw_dir(self) -> str: method processed_dir (line 166) | def processed_dir(self) -> str: method num_node_labels (line 171) | def num_node_labels(self) -> int: method num_node_attributes (line 175) | def num_node_attributes(self) -> int: method num_edge_labels (line 179) | def num_edge_labels(self) -> int: method num_edge_attributes (line 183) | def num_edge_attributes(self) -> int: method raw_file_names (line 187) | def raw_file_names(self) -> List[str]: method processed_file_names (line 192) | def processed_file_names(self) -> str: method download (line 195) | def download(self) -> None: method process (line 202) | def process(self) -> None: method __repr__ (line 223) | def __repr__(self) -> str: FILE: torch_geometric/datasets/twitch.py class Twitch (line 10) | class Twitch(InMemoryDataset): method __init__ (line 78) | def __init__( method raw_dir (line 93) | def raw_dir(self) -> str: method processed_dir (line 97) | def processed_dir(self) -> str: method raw_file_names (line 101) | def raw_file_names(self) -> str: method processed_file_names (line 105) | def processed_file_names(self) -> str: method download (line 108) | def download(self) -> None: method process (line 111) | def process(self) -> None: FILE: torch_geometric/datasets/upfd.py class UPFD (line 18) | class UPFD(InMemoryDataset): method __init__ (line 82) | def __init__( method raw_dir (line 107) | def raw_dir(self) -> str: method processed_dir (line 111) | def processed_dir(self) -> str: method raw_file_names (line 115) | def raw_file_names(self) -> List[str]: method processed_file_names (line 122) | def processed_file_names(self) -> List[str]: method download (line 125) | def download(self) -> None: method process (line 131) | def process(self) -> None: method __repr__ (line 170) | def __repr__(self) -> str: FILE: torch_geometric/datasets/utils/cheatsheet.py function paper_link (line 7) | def paper_link(cls: str) -> Optional[str]: function get_stats_table (line 15) | def get_stats_table(cls: str) -> str: function has_stats (line 23) | def has_stats(cls: str) -> bool: function get_type (line 27) | def get_type(cls: str) -> str: function get_stat (line 31) | def get_stat(cls: str, name: str, child: Optional[str] = None, function get_children (line 58) | def get_children(cls: str) -> List[str]: FILE: torch_geometric/datasets/web_qsp_dataset.py class KGQABaseDataset (line 24) | class KGQABaseDataset(InMemoryDataset): method __init__ (line 48) | def __init__( method raw_file_names (line 96) | def raw_file_names(self) -> List[str]: method processed_file_names (line 100) | def processed_file_names(self) -> List[str]: method download (line 103) | def download(self) -> None: method _get_trips (line 118) | def _get_trips(self) -> Iterator[TripletLike]: method _build_graph (line 132) | def _build_graph(self) -> None: method _retrieve_subgraphs (line 159) | def _retrieve_subgraphs(self) -> None: method process (line 221) | def process(self) -> None: class WebQSPDataset (line 256) | class WebQSPDataset(KGQABaseDataset): method __init__ (line 279) | def __init__( class CWQDataset (line 304) | class CWQDataset(KGQABaseDataset): method __init__ (line 327) | def __init__( FILE: torch_geometric/datasets/webkb.py class WebKB (line 11) | class WebKB(InMemoryDataset): method __init__ (line 65) | def __init__( method raw_dir (line 81) | def raw_dir(self) -> str: method processed_dir (line 85) | def processed_dir(self) -> str: method raw_file_names (line 89) | def raw_file_names(self) -> List[str]: method processed_file_names (line 95) | def processed_file_names(self) -> str: method download (line 98) | def download(self) -> None: method process (line 104) | def process(self) -> None: method __repr__ (line 136) | def __repr__(self) -> str: FILE: torch_geometric/datasets/wikics.py class WikiCS (line 12) | class WikiCS(InMemoryDataset): method __init__ (line 36) | def __init__( method raw_file_names (line 57) | def raw_file_names(self) -> List[str]: method processed_file_names (line 61) | def processed_file_names(self) -> str: method download (line 64) | def download(self) -> None: method process (line 68) | def process(self) -> None: FILE: torch_geometric/datasets/wikidata.py class Wikidata5M (line 16) | class Wikidata5M(InMemoryDataset): method __init__ (line 44) | def __init__( method raw_file_names (line 74) | def raw_file_names(self) -> List[str]: method processed_file_names (line 84) | def processed_file_names(self) -> str: method download (line 87) | def download(self) -> None: method process (line 94) | def process(self) -> None: FILE: torch_geometric/datasets/wikipedia_network.py class WikipediaNetwork (line 11) | class WikipediaNetwork(InMemoryDataset): method __init__ (line 50) | def __init__( method raw_dir (line 70) | def raw_dir(self) -> str: method processed_dir (line 77) | def processed_dir(self) -> str: method raw_file_names (line 84) | def raw_file_names(self) -> Union[List[str], str]: method processed_file_names (line 92) | def processed_file_names(self) -> str: method download (line 95) | def download(self) -> None: method process (line 106) | def process(self) -> None: FILE: torch_geometric/datasets/willow_object_class.py class WILLOWObjectClass (line 20) | class WILLOWObjectClass(InMemoryDataset): method __init__ (line 57) | def __init__( method raw_dir (line 78) | def raw_dir(self) -> str: method processed_dir (line 82) | def processed_dir(self) -> str: method raw_file_names (line 86) | def raw_file_names(self) -> List[str]: method processed_file_names (line 90) | def processed_file_names(self) -> str: method download (line 93) | def download(self) -> None: method process (line 102) | def process(self) -> None: method __repr__ (line 187) | def __repr__(self) -> str: FILE: torch_geometric/datasets/word_net.py class WordNet18 (line 10) | class WordNet18(InMemoryDataset): method __init__ (line 44) | def __init__( method raw_file_names (line 56) | def raw_file_names(self) -> List[str]: method processed_file_names (line 60) | def processed_file_names(self) -> str: method download (line 63) | def download(self) -> None: method process (line 67) | def process(self) -> None: class WordNet18RR (line 112) | class WordNet18RR(InMemoryDataset): method __init__ (line 148) | def __init__( method raw_file_names (line 160) | def raw_file_names(self) -> List[str]: method processed_file_names (line 164) | def processed_file_names(self) -> str: method download (line 167) | def download(self) -> None: method process (line 171) | def process(self) -> None: FILE: torch_geometric/datasets/yelp.py class Yelp (line 11) | class Yelp(InMemoryDataset): method __init__ (line 49) | def __init__( method raw_file_names (line 61) | def raw_file_names(self) -> List[str]: method processed_file_names (line 65) | def processed_file_names(self) -> str: method download (line 68) | def download(self) -> None: method process (line 74) | def process(self) -> None: FILE: torch_geometric/datasets/zinc.py class ZINC (line 18) | class ZINC(InMemoryDataset): method __init__ (line 91) | def __init__( method raw_file_names (line 109) | def raw_file_names(self) -> List[str]: method processed_dir (line 116) | def processed_dir(self) -> str: method processed_file_names (line 121) | def processed_file_names(self) -> List[str]: method download (line 124) | def download(self) -> None: method process (line 134) | def process(self) -> None: FILE: torch_geometric/debug.py function is_debug_enabled (line 6) | def is_debug_enabled() -> bool: function set_debug_enabled (line 11) | def set_debug_enabled(mode: bool) -> None: class debug (line 15) | class debug: method __init__ (line 24) | def __init__(self) -> None: method __enter__ (line 27) | def __enter__(self) -> None: method __exit__ (line 30) | def __exit__(self, *args: Any) -> None: class set_debug (line 34) | class set_debug: method __init__ (line 43) | def __init__(self, mode: bool) -> None: method __enter__ (line 47) | def __enter__(self) -> None: method __exit__ (line 50) | def __exit__(self, *args: Any) -> None: FILE: torch_geometric/deprecation.py function deprecated (line 7) | def deprecated( FILE: torch_geometric/device.py function is_mps_available (line 6) | def is_mps_available() -> bool: function is_xpu_available (line 17) | def is_xpu_available() -> bool: function device (line 28) | def device(device: Any) -> torch.device: FILE: torch_geometric/distributed/dist_context.py class DistRole (line 5) | class DistRole(Enum): class DistContext (line 10) | class DistContext: method worker_name (line 20) | def worker_name(self) -> str: FILE: torch_geometric/distributed/dist_link_neighbor_loader.py class DistLinkNeighborLoader (line 18) | class DistLinkNeighborLoader(LinkLoader, DistLoader): method __init__ (line 42) | def __init__( method __repr__ (line 133) | def __repr__(self) -> str: FILE: torch_geometric/distributed/dist_loader.py class DistLoader (line 19) | class DistLoader: method __init__ (line 45) | def __init__( method channel_get (line 94) | def channel_get(self, out: Any) -> Any: method reset_channel (line 100) | def reset_channel(self, channel=None): method worker_init_fn (line 111) | def worker_init_fn(self, worker_id: int): method __repr__ (line 145) | def __repr__(self) -> str: method __enter__ (line 148) | def __enter__(self) -> DataLoaderIterator: method __exit__ (line 155) | def __exit__(self, *args) -> None: FILE: torch_geometric/distributed/dist_neighbor_loader.py class DistNeighborLoader (line 18) | class DistNeighborLoader(NodeLoader, DistLoader): method __init__ (line 42) | def __init__( method __repr__ (line 123) | def __repr__(self) -> str: FILE: torch_geometric/distributed/dist_neighbor_sampler.py class RPCSamplingCallee (line 50) | class RPCSamplingCallee(RPCCallBase): method __init__ (line 54) | def __init__(self, sampler: NeighborSampler): method rpc_async (line 58) | def rpc_async(self, *args, **kwargs) -> Any: method rpc_sync (line 61) | def rpc_sync(self, *args, **kwargs) -> Any: class DistNeighborSampler (line 65) | class DistNeighborSampler: method __init__ (line 70) | def __init__( method init_sampler_instance (line 114) | def init_sampler_instance(self): method register_sampler_rpc (line 131) | def register_sampler_rpc(self) -> None: method init_event_loop (line 143) | def init_event_loop(self) -> None: method sample_from_nodes (line 151) | def sample_from_nodes( method sample_from_edges (line 172) | def sample_from_edges( method _sample_from (line 194) | async def _sample_from( method node_sample (line 212) | async def node_sample( method edge_sample (line 512) | async def edge_sample( method _get_sampler_output (line 772) | def _get_sampler_output( method _merge_sampler_outputs (line 804) | def _merge_sampler_outputs( method sample_one_hop (line 875) | async def sample_one_hop( method _sample_one_hop (line 949) | def _sample_one_hop( method _collate_fn (line 1003) | async def _collate_fn( method edge_permutation (line 1079) | def edge_permutation(self) -> None: method __repr__ (line 1082) | def __repr__(self) -> str: FILE: torch_geometric/distributed/event_loop.py function to_asyncio_future (line 14) | def to_asyncio_future(future: torch.futures.Future) -> asyncio.futures.F... class ConcurrentEventLoop (line 32) | class ConcurrentEventLoop: method __init__ (line 38) | def __init__(self, concurrency: int): method start_loop (line 57) | def start_loop(self): method wait_all (line 61) | def wait_all(self): method add_task (line 68) | def add_task(self, coro, callback: Optional[Callable] = None): method run_task (line 92) | def run_task(self, coro): method _run_loop (line 102) | def _run_loop(self): FILE: torch_geometric/distributed/local_feature_store.py class RPCCallFeatureLookup (line 22) | class RPCCallFeatureLookup(RPCCallBase): method __init__ (line 24) | def __init__(self, dist_feature: FeatureStore): method rpc_async (line 28) | def rpc_async(self, *args, **kwargs): method rpc_sync (line 31) | def rpc_sync(self, *args, **kwargs): class LocalTensorAttr (line 36) | class LocalTensorAttr(TensorAttr): method __init__ (line 38) | def __init__( class LocalFeatureStore (line 47) | class LocalFeatureStore(FeatureStore): method __init__ (line 51) | def __init__(self): method key (line 74) | def key(attr: TensorAttr) -> Tuple[str, str]: method put_global_id (line 77) | def put_global_id( method get_global_id (line 86) | def get_global_id( method remove_global_id (line 92) | def remove_global_id(self, group_name: Union[NodeType, EdgeType]) -> b... method _set_global_id_to_index (line 95) | def _set_global_id_to_index(self, group_name: Union[NodeType, EdgeType]): method _put_tensor (line 107) | def _put_tensor(self, tensor: Tensor, attr: TensorAttr) -> bool: method _get_tensor (line 112) | def _get_tensor(self, attr: TensorAttr) -> Optional[Tensor]: method _remove_tensor (line 123) | def _remove_tensor(self, attr: TensorAttr) -> bool: method get_tensor_from_global_id (line 127) | def get_tensor_from_global_id(self, *args, **kwargs) -> Optional[Tensor]: method _get_tensor_size (line 136) | def _get_tensor_size(self, attr: TensorAttr) -> Tuple[int, ...]: method get_all_tensor_attrs (line 139) | def get_all_tensor_attrs(self) -> List[LocalTensorAttr]: method set_rpc_router (line 142) | def set_rpc_router(self, rpc_router: RPCRouter): method has_edge_attr (line 153) | def has_edge_attr(self) -> bool: method lookup_features (line 163) | def lookup_features( method _local_lookup_features (line 196) | def _local_lookup_features( method _remote_lookup_features (line 236) | def _remote_lookup_features( method _rpc_local_feature_get (line 283) | def _rpc_local_feature_get( method from_data (line 316) | def from_data( method from_hetero_data (line 353) | def from_hetero_data( method from_partition (line 400) | def from_partition(cls, root: str, pid: int) -> 'LocalFeatureStore': FILE: torch_geometric/distributed/local_graph_store.py class LocalGraphStore (line 14) | class LocalGraphStore(GraphStore): method __init__ (line 18) | def __init__(self): method key (line 36) | def key(attr: EdgeAttr) -> Tuple: method get_partition_ids_from_nids (line 39) | def get_partition_ids_from_nids( method get_partition_ids_from_eids (line 50) | def get_partition_ids_from_eids(self, eids: torch.Tensor, method put_edge_id (line 58) | def put_edge_id(self, edge_id: Tensor, *args, **kwargs) -> bool: method get_edge_id (line 63) | def get_edge_id(self, *args, **kwargs) -> Optional[EdgeTensorType]: method remove_edge_id (line 67) | def remove_edge_id(self, *args, **kwargs) -> bool: method _put_edge_index (line 71) | def _put_edge_index(self, edge_index: EdgeTensorType, method _get_edge_index (line 77) | def _get_edge_index(self, edge_attr: EdgeAttr) -> Optional[EdgeTensorT... method _remove_edge_index (line 80) | def _remove_edge_index(self, edge_attr: EdgeAttr) -> bool: method get_all_edge_attrs (line 84) | def get_all_edge_attrs(self) -> List[EdgeAttr]: method from_data (line 90) | def from_data( method from_hetero_data (line 130) | def from_hetero_data( method from_partition (line 172) | def from_partition(cls, root: str, pid: int) -> 'LocalGraphStore': FILE: torch_geometric/distributed/partition.py class Partitioner (line 18) | class Partitioner: method __init__ (line 72) | def __init__( method is_hetero (line 87) | def is_hetero(self) -> bool: method is_node_level_time (line 91) | def is_node_level_time(self) -> bool: method is_edge_level_time (line 101) | def is_edge_level_time(self) -> bool: method node_types (line 114) | def node_types(self) -> Optional[List[NodeType]]: method edge_types (line 118) | def edge_types(self) -> Optional[List[EdgeType]]: method generate_partition (line 121) | def generate_partition(self): function load_partition_info (line 370) | def load_partition_info( FILE: torch_geometric/distributed/rpc.py function _is_current_rpc_agent_set (line 14) | def _is_current_rpc_agent_set() -> bool: function rpc_is_initialized (line 21) | def rpc_is_initialized() -> bool: function rpc_require_initialized (line 25) | def rpc_require_initialized(func: Callable) -> Callable: function global_all_gather (line 32) | def global_all_gather(obj, timeout: Optional[int] = None) -> Any: function global_barrier (line 40) | def global_barrier(timeout: Optional[int] = None) -> None: function init_rpc (line 48) | def init_rpc( function shutdown_rpc (line 81) | def shutdown_rpc(id: str = None, graceful: bool = True, class RPCRouter (line 92) | class RPCRouter: method __init__ (line 94) | def __init__(self, partition_to_workers: List[List[str]]): method get_to_worker (line 101) | def get_to_worker(self, partition_idx: int) -> str: function rpc_partition_to_workers (line 111) | def rpc_partition_to_workers( class RPCCallBase (line 128) | class RPCCallBase(ABC): method rpc_sync (line 131) | def rpc_sync(self, *args, **kwargs): method rpc_async (line 135) | def rpc_async(self, *args, **kwargs): function rpc_register (line 145) | def rpc_register(call: RPCCallBase) -> int: function _rpc_async_call (line 159) | def _rpc_async_call(call_id: int, *args, **kwargs): function rpc_async (line 165) | def rpc_async(worker_name: str, call_id: int, args=None, kwargs=None): function _rpc_sync_call (line 175) | def _rpc_sync_call(call_id: int, *args, **kwargs): function rpc_sync (line 181) | def rpc_sync(worker_name: str, call_id: int, args=None, kwargs=None): FILE: torch_geometric/distributed/utils.py class DistEdgeHeteroSamplerInput (line 16) | class DistEdgeHeteroSamplerInput: class NodeDict (line 37) | class NodeDict: method __init__ (line 43) | def __init__(self, node_types, num_hops): class BatchDict (line 62) | class BatchDict: method __init__ (line 70) | def __init__(self, node_types, num_hops): function remove_duplicates (line 85) | def remove_duplicates( function filter_dist_store (line 118) | def filter_dist_store( function as_str (line 171) | def as_str(inputs: Union[NodeType, EdgeType]) -> str: function reverse_edge_type (line 179) | def reverse_edge_type(etype: EdgeType) -> EdgeType: FILE: torch_geometric/edge_index.py class SortOrder (line 47) | class SortOrder(Enum): class CatMetadata (line 52) | class CatMetadata(NamedTuple): function implements (line 59) | def implements(torch_function: Callable) -> Callable: function set_tuple_item (line 69) | def set_tuple_item( function maybe_add (line 81) | def maybe_add( function maybe_sub (line 96) | def maybe_sub( function assert_valid_dtype (line 111) | def assert_valid_dtype(tensor: Tensor) -> None: function assert_two_dimensional (line 118) | def assert_two_dimensional(tensor: Tensor) -> None: function assert_contiguous (line 127) | def assert_contiguous(tensor: Tensor) -> None: function assert_symmetric (line 133) | def assert_symmetric(size: Tuple[Optional[int], Optional[int]]) -> None: function assert_sorted (line 140) | def assert_sorted(func: Callable) -> Callable: class EdgeIndex (line 153) | class EdgeIndex(Tensor): method __new__ (line 253) | def __new__( method validate (line 366) | def validate(self) -> 'EdgeIndex': method sparse_size (line 421) | def sparse_size(self) -> Tuple[Optional[int], Optional[int]]: method sparse_size (line 425) | def sparse_size(self, dim: int) -> Optional[int]: method sparse_size (line 428) | def sparse_size( method num_rows (line 445) | def num_rows(self) -> Optional[int]: method num_cols (line 450) | def num_cols(self) -> Optional[int]: method sort_order (line 455) | def sort_order(self) -> Optional[str]: method is_sorted (line 462) | def is_sorted(self) -> bool: method is_sorted_by_row (line 467) | def is_sorted_by_row(self) -> bool: method is_sorted_by_col (line 472) | def is_sorted_by_col(self) -> bool: method is_undirected (line 477) | def is_undirected(self) -> bool: method dtype (line 482) | def dtype(self) -> torch.dtype: # type: ignore method get_sparse_size (line 489) | def get_sparse_size(self) -> torch.Size: method get_sparse_size (line 493) | def get_sparse_size(self, dim: int) -> int: method get_sparse_size (line 496) | def get_sparse_size( method sparse_resize_ (line 525) | def sparse_resize_( # type: ignore method get_num_rows (line 576) | def get_num_rows(self) -> int: method get_num_cols (line 582) | def get_num_cols(self) -> int: method get_indptr (line 589) | def get_indptr(self) -> Tensor: method _sort_by_transpose (line 605) | def _sort_by_transpose(self) -> Tuple[Tuple[Tensor, Tensor], Tensor]: method get_csr (line 626) | def get_csr(self) -> Tuple[Tuple[Tensor, Tensor], Optional[Tensor]]: method get_csc (line 646) | def get_csc(self) -> Tuple[Tuple[Tensor, Tensor], Optional[Tensor]]: method _get_value (line 665) | def _get_value(self, dtype: Optional[torch.dtype] = None) -> Tensor: method fill_cache_ (line 677) | def fill_cache_(self, no_transpose: bool = False) -> 'EdgeIndex': method share_memory_ (line 700) | def share_memory_(self) -> 'EdgeIndex': method is_shared (line 717) | def is_shared(self) -> bool: method as_tensor (line 721) | def as_tensor(self) -> Tensor: method sort_by (line 727) | def sort_by( method to_dense (line 778) | def to_dense( # type: ignore method to_sparse_coo (line 813) | def to_sparse_coo(self, value: Optional[Tensor] = None) -> Tensor: method to_sparse_csr (line 845) | def to_sparse_csr( # type: ignore method to_sparse_csc (line 872) | def to_sparse_csc( # type: ignore method to_sparse (line 899) | def to_sparse( # type: ignore method to_sparse_tensor (line 925) | def to_sparse_tensor( method matmul (line 949) | def matmul( # type: ignore method matmul (line 960) | def matmul( method matmul (line 970) | def matmul( method sparse_narrow (line 1028) | def sparse_narrow( method to_vector (line 1135) | def to_vector(self) -> Tensor: method __tensor_flatten__ (line 1148) | def __tensor_flatten__(self) -> Tuple[List[str], Tuple[Any, ...]]: method __tensor_unflatten__ (line 1168) | def __tensor_unflatten__( method __torch_dispatch__ (line 1192) | def __torch_dispatch__( # type: ignore method __repr__ (line 1219) | def __repr__(self) -> str: # type: ignore method tolist (line 1245) | def tolist(self) -> List[Any]: method numpy (line 1249) | def numpy(self, *, force: bool = False) -> np.ndarray: method _shallow_copy (line 1255) | def _shallow_copy(self) -> 'EdgeIndex': method _clear_metadata (line 1268) | def _clear_metadata(self) -> 'EdgeIndex': class SortReturnType (line 1281) | class SortReturnType(NamedTuple): function apply_ (line 1286) | def apply_( function _clone (line 1331) | def _clone( function _to_copy (line 1342) | def _to_copy( function _alias (line 1365) | def _alias(tensor: EdgeIndex) -> EdgeIndex: function _pin_memory (line 1370) | def _pin_memory(tensor: EdgeIndex) -> EdgeIndex: function _cat (line 1377) | def _cat( function _flip (line 1431) | def _flip( function _index_select (line 1461) | def _index_select( function _slice (line 1477) | def _slice( function _index (line 1512) | def _index( function _select (line 1543) | def _select(input: EdgeIndex, dim: int, index: int) -> Union[Tensor, Ind... function _unbind (line 1566) | def _unbind( function _add (line 1582) | def _add( function add_ (line 1631) | def add_( function _sub (line 1679) | def _sub( function sub_ (line 1723) | def sub_( function _torch_sparse_spmm (line 1768) | def _torch_sparse_spmm( class _TorchSPMM (line 1815) | class _TorchSPMM(torch.autograd.Function): method forward (line 1817) | def forward( method backward (line 1849) | def backward( function _scatter_spmm (line 1903) | def _scatter_spmm( function _spmm (line 1925) | def _spmm( function matmul (line 1973) | def matmul( function _mm (line 2036) | def _mm( function _addmm (line 2044) | def _addmm( function _mm_reduce (line 2060) | def _mm_reduce( FILE: torch_geometric/experimental.py function get_options (line 18) | def get_options(options: Options) -> List[str]: function is_experimental_mode_enabled (line 26) | def is_experimental_mode_enabled(options: Options = None) -> bool: function set_experimental_mode_enabled (line 37) | def set_experimental_mode_enabled(mode: bool, options: Options = None) -... class experimental_mode (line 42) | class experimental_mode: method __init__ (line 55) | def __init__(self, options: Options = None) -> None: method __enter__ (line 62) | def __enter__(self) -> None: method __exit__ (line 65) | def __exit__(self, *args: Any) -> None: class set_experimental_mode (line 70) | class set_experimental_mode: method __init__ (line 79) | def __init__(self, mode: bool, options: Options = None) -> None: method __enter__ (line 87) | def __enter__(self) -> None: method __exit__ (line 90) | def __exit__(self, *args: Any) -> None: function disable_dynamic_shapes (line 95) | def disable_dynamic_shapes(required_args: List[str]) -> Callable: FILE: torch_geometric/explain/algorithm/attention_explainer.py class AttentionExplainer (line 14) | class AttentionExplainer(ExplainerAlgorithm): method __init__ (line 27) | def __init__(self, reduce: str = 'max'): method forward (line 33) | def forward( method forward (line 46) | def forward( method forward (line 58) | def forward( method _collect_attention_coefficients (line 84) | def _collect_attention_coefficients( method _collect_attention_coefficients (line 94) | def _collect_attention_coefficients( method _collect_attention_coefficients (line 103) | def _collect_attention_coefficients( method _process_attention_coefficients (line 196) | def _process_attention_coefficients( method _create_homo_explanation (line 227) | def _create_homo_explanation( method _create_hetero_explanation (line 252) | def _create_hetero_explanation( method supports (line 289) | def supports(self) -> bool: FILE: torch_geometric/explain/algorithm/base.py class ExplainerAlgorithm (line 19) | class ExplainerAlgorithm(torch.nn.Module): method forward (line 22) | def forward( method supports (line 49) | def supports(self) -> bool: method explainer_config (line 57) | def explainer_config(self) -> ExplainerConfig: method model_config (line 68) | def model_config(self) -> ModelConfig: method connect (line 78) | def connect( method _post_process_mask (line 97) | def _post_process_mask( method _get_hard_masks (line 119) | def _get_hard_masks( method _num_hops (line 145) | def _num_hops(model: torch.nn.Module) -> int: method _flow (line 156) | def _flow(model: torch.nn.Module) -> str: method _loss_binary_classification (line 163) | def _loss_binary_classification(self, y_hat: Tensor, y: Tensor) -> Ten... method _loss_multiclass_classification (line 173) | def _loss_multiclass_classification( method _loss_regression (line 190) | def _loss_regression(self, y_hat: Tensor, y: Tensor) -> Tensor: method __repr__ (line 194) | def __repr__(self) -> str: FILE: torch_geometric/explain/algorithm/captum.py class MaskLevelType (line 20) | class MaskLevelType(Enum): method with_edge (line 27) | def with_edge(self) -> bool: class CaptumModel (line 31) | class CaptumModel(torch.nn.Module): method __init__ (line 32) | def __init__( method forward (line 46) | def forward(self, mask, *args): method postprocess (line 77) | def postprocess(self, x: Tensor) -> Tensor: class CaptumHeteroModel (line 98) | class CaptumHeteroModel(CaptumModel): method __init__ (line 99) | def __init__( method _captum_data_to_hetero_data (line 113) | def _captum_data_to_hetero_data( method forward (line 144) | def forward(self, *args): function _to_edge_mask (line 174) | def _to_edge_mask(edge_index: Tensor) -> Tensor: function to_captum_input (line 179) | def to_captum_input( function captum_output_to_dicts (line 245) | def captum_output_to_dicts( function convert_captum_output (line 296) | def convert_captum_output( FILE: torch_geometric/explain/algorithm/captum_explainer.py class CaptumExplainer (line 22) | class CaptumExplainer(ExplainerAlgorithm): method __init__ (line 53) | def __init__( method _get_mask_type (line 84) | def _get_mask_type(self) -> MaskLevelType: method _get_attribute_parameters (line 99) | def _get_attribute_parameters(self) -> Dict[str, Any]: method _needs_baseline (line 104) | def _needs_baseline(self) -> bool: method _is_supported_attribution_method (line 113) | def _is_supported_attribution_method(self) -> bool: method forward (line 122) | def forward( method supports (line 192) | def supports(self) -> bool: FILE: torch_geometric/explain/algorithm/dummy_explainer.py class DummyExplainer (line 13) | class DummyExplainer(ExplainerAlgorithm): method forward (line 17) | def forward( method supports (line 71) | def supports(self) -> bool: FILE: torch_geometric/explain/algorithm/gnn_explainer.py class GNNExplainer (line 24) | class GNNExplainer(ExplainerAlgorithm): method __init__ (line 74) | def __init__(self, epochs: int = 100, lr: float = 0.01, **kwargs): method forward (line 86) | def forward( method forward (line 99) | def forward( method forward (line 111) | def forward( method _create_explanation (line 127) | def _create_explanation(self) -> Union[Explanation, HeteroExplanation]: method supports (line 173) | def supports(self) -> bool: method _train (line 177) | def _train( method _train (line 190) | def _train( method _train (line 202) | def _train( method _collect_parameters (line 245) | def _collect_parameters(self, model, edge_index): method _forward_with_masks (line 271) | def _forward_with_masks( method _forward_with_masks (line 281) | def _forward_with_masks( method _forward_with_masks (line 290) | def _forward_with_masks( method _initialize_masks (line 318) | def _initialize_masks( method _initialize_node_mask (line 357) | def _initialize_node_mask( method _initialize_edge_mask (line 385) | def _initialize_edge_mask(self, edge_mask_type, edge_type, E, N, device): method _initialize_homogeneous_masks (line 398) | def _initialize_homogeneous_masks(self, node_mask_type, edge_mask_type... method _collect_gradients (line 423) | def _collect_gradients(self) -> None: method _collect_hetero_gradients (line 429) | def _collect_hetero_gradients(self): method _collect_homo_gradients (line 452) | def _collect_homo_gradients(self): method _loss (line 470) | def _loss(self, y_hat: Tensor, y: Tensor) -> Tensor: method _calculate_base_loss (line 484) | def _calculate_base_loss(self, y_hat, y): method _apply_hetero_regularization (line 495) | def _apply_hetero_regularization(self, loss): method _apply_homo_regularization (line 518) | def _apply_homo_regularization(self, loss): method _add_mask_regularization (line 540) | def _add_mask_regularization(self, loss, mask, hard_mask, size_coeff, method _clean_model (line 554) | def _clean_model(self, model): class GNNExplainer_ (line 560) | class GNNExplainer_: method __init__ (line 578) | def __init__( method get_initial_prediction (line 607) | def get_initial_prediction(self, *args, **kwargs) -> Tensor: method explain_graph (line 621) | def explain_graph( method explain_node (line 638) | def explain_node( method _convert_output (line 657) | def _convert_output(self, explanation, edge_index, index=None, x=None): FILE: torch_geometric/explain/algorithm/graphmask_explainer.py function explain_message (line 16) | def explain_message(self, out: Tensor, x_i: Tensor, x_j: Tensor) -> Tensor: class GraphMaskExplainer (line 39) | class GraphMaskExplainer(ExplainerAlgorithm): method __init__ (line 83) | def __init__( method forward (line 112) | def forward( method supports (line 137) | def supports(self) -> bool: method _hard_concrete (line 140) | def _hard_concrete( method _set_masks (line 182) | def _set_masks( method _enable_layer (line 246) | def _enable_layer(self, layer: int): method reset_parameters (line 254) | def reset_parameters(self, input_dims: List[int], h_dim: List[int]): method _loss (line 269) | def _loss(self, y_hat: Tensor, y: Tensor, penalty: float) -> Tensor: method _freeze_model (line 293) | def _freeze_model(self, module: torch.nn.Module): method _set_flags (line 300) | def _set_flags(self, model: torch.nn.Module): method _inject_messages (line 310) | def _inject_messages( method _train_explainer (line 331) | def _train_explainer( method _explain (line 474) | def _explain( FILE: torch_geometric/explain/algorithm/pg_explainer.py class PGExplainer (line 26) | class PGExplainer(ExplainerAlgorithm): method __init__ (line 77) | def __init__(self, epochs: int, lr: float = 0.003, **kwargs): method reset_parameters (line 92) | def reset_parameters(self): method train (line 97) | def train( method train (line 111) | def train( method train (line 124) | def train( method forward (line 227) | def forward( method forward (line 240) | def forward( method forward (line 252) | def forward( method supports (line 330) | def supports(self) -> bool: method _get_embeddings (line 356) | def _get_embeddings(self, model: torch.nn.Module, x: Union[Tensor, method _generate_edge_masks (line 390) | def _generate_edge_masks( method _get_inputs (line 426) | def _get_inputs(self, embedding: Tensor, edge_index: Tensor, method _get_inputs_hetero (line 434) | def _get_inputs_hetero(self, embedding_dict: Dict[NodeType, Tensor], method _get_temperature (line 455) | def _get_temperature(self, epoch: int) -> float: method _concrete_sample (line 459) | def _concrete_sample(self, logits: Tensor, method _loss (line 465) | def _loss(self, y_hat: Tensor, y: Tensor, method _calculate_base_loss (line 478) | def _calculate_base_loss(self, y_hat: Tensor, y: Tensor) -> Tensor: method _apply_hetero_regularization (line 490) | def _apply_hetero_regularization( method _apply_homo_regularization (line 498) | def _apply_homo_regularization(self, loss: Tensor, method _add_mask_regularization (line 503) | def _add_mask_regularization(self, loss: Tensor, mask: Tensor) -> Tensor: FILE: torch_geometric/explain/algorithm/utils.py function set_masks (line 11) | def set_masks( function set_hetero_masks (line 40) | def set_hetero_masks( function clear_masks (line 67) | def clear_masks(model: torch.nn.Module): FILE: torch_geometric/explain/config.py class ExplanationType (line 8) | class ExplanationType(Enum): class MaskType (line 14) | class MaskType(Enum): class ModelMode (line 21) | class ModelMode(Enum): class ModelTaskLevel (line 28) | class ModelTaskLevel(Enum): class ModelReturnType (line 35) | class ModelReturnType(Enum): class ThresholdType (line 42) | class ThresholdType(Enum): class ExplainerConfig (line 51) | class ExplainerConfig(CastMixin): method __init__ (line 87) | def __init__( class ModelConfig (line 112) | class ModelConfig(CastMixin): method __init__ (line 149) | def __init__( class ThresholdConfig (line 176) | class ThresholdConfig(CastMixin): method __init__ (line 202) | def __init__( FILE: torch_geometric/explain/explainer.py class Explainer (line 29) | class Explainer: method __init__ (line 69) | def __init__( method get_prediction (line 97) | def get_prediction(self, *args, **kwargs) -> Tensor: method get_masked_prediction (line 121) | def get_masked_prediction( method __call__ (line 147) | def __call__( method get_target (line 253) | def get_target(self, prediction: Tensor) -> Tensor: FILE: torch_geometric/explain/explanation.py class ExplanationMixin (line 17) | class ExplanationMixin: method available_explanations (line 19) | def available_explanations(self) -> List[str]: method validate_masks (line 23) | def validate_masks(self, raise_on_error: bool = True) -> bool: method _threshold_mask (line 72) | def _threshold_mask( method threshold (line 108) | def threshold( class Explanation (line 139) | class Explanation(Data, ExplanationMixin): method validate (line 154) | def validate(self, raise_on_error: bool = True) -> bool: method get_explanation_subgraph (line 160) | def get_explanation_subgraph(self) -> 'Explanation': method get_complement_subgraph (line 172) | def get_complement_subgraph(self) -> 'Explanation': method _apply_masks (line 184) | def _apply_masks( method visualize_feature_importance (line 203) | def visualize_feature_importance( method visualize_graph (line 238) | def visualize_graph( class HeteroExplanation (line 267) | class HeteroExplanation(HeteroData, ExplanationMixin): method validate (line 274) | def validate(self, raise_on_error: bool = True) -> bool: method get_explanation_subgraph (line 280) | def get_explanation_subgraph(self) -> 'HeteroExplanation': method get_complement_subgraph (line 295) | def get_complement_subgraph(self) -> 'HeteroExplanation': method _apply_masks (line 310) | def _apply_masks( method visualize_feature_importance (line 326) | def visualize_feature_importance( method visualize_graph (line 368) | def visualize_graph( function _visualize_score (line 450) | def _visualize_score( FILE: torch_geometric/explain/metric/basic.py function groundtruth_metrics (line 8) | def groundtruth_metrics( FILE: torch_geometric/explain/metric/faithfulness.py function unfaithfulness (line 10) | def unfaithfulness( FILE: torch_geometric/explain/metric/fidelity.py function fidelity (line 10) | def fidelity( function characterization_score (line 103) | def characterization_score( function fidelity_curve_auc (line 135) | def fidelity_curve_auc( function auc (line 165) | def auc(x: Tensor, y: Tensor) -> Tensor: FILE: torch_geometric/graphgym/checkpoint.py function load_ckpt (line 16) | def load_ckpt( function save_ckpt (line 39) | def save_ckpt( function remove_ckpt (line 57) | def remove_ckpt(epoch: int = -1): function clean_ckpt (line 62) | def clean_ckpt(): function get_ckpt_dir (line 71) | def get_ckpt_dir() -> str: function get_ckpt_path (line 75) | def get_ckpt_path(epoch: Union[int, str]) -> str: function get_ckpt_epochs (line 79) | def get_ckpt_epochs() -> List[int]: function get_ckpt_epoch (line 84) | def get_ckpt_epoch(epoch: int) -> int: FILE: torch_geometric/graphgym/cmd_args.py function parse_args (line 4) | def parse_args() -> argparse.Namespace: FILE: torch_geometric/graphgym/config.py function set_cfg (line 24) | def set_cfg(cfg): function assert_cfg (line 454) | def assert_cfg(cfg): function dump_cfg (line 480) | def dump_cfg(cfg): function load_cfg (line 493) | def load_cfg(cfg, args): function makedirs_rm_exist (line 505) | def makedirs_rm_exist(dir): function get_fname (line 511) | def get_fname(fname): function set_out_dir (line 525) | def set_out_dir(out_dir, fname): function set_run_dir (line 541) | def set_run_dir(out_dir): function from_config (line 558) | def from_config(func): FILE: torch_geometric/graphgym/contrib/layer/generalconv.py class GeneralConvLayer (line 10) | class GeneralConvLayer(MessagePassing): method __init__ (line 12) | def __init__(self, in_channels, out_channels, improved=False, cached=F... method reset_parameters (line 34) | def reset_parameters(self): method norm (line 43) | def norm(edge_index, num_nodes, edge_weight=None, improved=False, method forward (line 60) | def forward(self, x, edge_index, edge_weight=None, edge_feature=None): method message (line 96) | def message(self, x_j, norm, edge_feature): method update (line 104) | def update(self, aggr_out): method __repr__ (line 109) | def __repr__(self): class GeneralEdgeConvLayer (line 114) | class GeneralEdgeConvLayer(MessagePassing): method __init__ (line 116) | def __init__(self, in_channels, out_channels, edge_dim, improved=False, method reset_parameters (line 153) | def reset_parameters(self): method norm (line 159) | def norm(edge_index, num_nodes, edge_weight=None, improved=False, method forward (line 176) | def forward(self, x, edge_index, edge_weight=None, edge_feature=None): method message (line 208) | def message(self, x_i, x_j, norm, edge_feature): method update (line 216) | def update(self, aggr_out): method __repr__ (line 221) | def __repr__(self): FILE: torch_geometric/graphgym/init.py function init_weights (line 4) | def init_weights(m): FILE: torch_geometric/graphgym/loader.py function planetoid_dataset (line 41) | def planetoid_dataset(name: str) -> Callable: function load_pyg (line 51) | def load_pyg(name, dataset_dir): function set_dataset_attr (line 95) | def set_dataset_attr(dataset, name, value, size): function load_ogb (line 102) | def load_ogb(name, dataset_dir): function load_dataset (line 171) | def load_dataset(): function set_dataset_info (line 196) | def set_dataset_info(dataset): function create_dataset (line 228) | def create_dataset(): function get_loader (line 240) | def get_loader(dataset, sampler, batch_size, shuffle=True): function create_loader (line 305) | def create_loader(): FILE: torch_geometric/graphgym/logger.py function set_printing (line 17) | def set_printing(): class Logger (line 35) | class Logger: method __init__ (line 36) | def __init__(self, name='train', task_type=None): method __getitem__ (line 51) | def __getitem__(self, key): method __setitem__ (line 54) | def __setitem__(self, key, value): method reset (line 57) | def reset(self): method basic (line 69) | def basic(self): method custom (line 82) | def custom(self): method _get_pred_int (line 90) | def _get_pred_int(self, pred_score): method classification_binary (line 97) | def classification_binary(self): method classification_multi (line 120) | def classification_multi(self): method regression (line 127) | def regression(self): method time_iter (line 140) | def time_iter(self): method eta (line 143) | def eta(self, epoch_current): method update_stats (line 148) | def update_stats(self, true, pred, loss, lr, time_used, params, **kwar... method write_iter (line 166) | def write_iter(self): method write_epoch (line 169) | def write_epoch(self, cur_epoch): method close (line 221) | def close(self): function infer_task (line 226) | def infer_task(): function create_logger (line 238) | def create_logger(): class LoggerCallback (line 247) | class LoggerCallback(Callback): method __init__ (line 248) | def __init__(self): method train_logger (line 255) | def train_logger(self) -> Any: method val_logger (line 259) | def val_logger(self) -> Any: method test_logger (line 263) | def test_logger(self) -> Any: method close (line 266) | def close(self): method _get_stats (line 270) | def _get_stats( method on_train_epoch_start (line 285) | def on_train_epoch_start( method on_validation_epoch_start (line 292) | def on_validation_epoch_start( method on_test_epoch_start (line 299) | def on_test_epoch_start( method on_train_batch_end (line 306) | def on_train_batch_end( method on_validation_batch_end (line 318) | def on_validation_batch_end( method on_test_batch_end (line 330) | def on_test_batch_end( method on_train_epoch_end (line 342) | def on_train_epoch_end( method on_validation_epoch_end (line 349) | def on_validation_epoch_end( method on_test_epoch_end (line 356) | def on_test_epoch_end( method on_fit_end (line 363) | def on_fit_end(self, trainer, pl_module): FILE: torch_geometric/graphgym/loss.py function compute_loss (line 8) | def compute_loss(pred, true): FILE: torch_geometric/graphgym/model_builder.py class GraphGymModule (line 16) | class GraphGymModule(LightningModule): method __init__ (line 17) | def __init__(self, dim_in, dim_out, cfg): method forward (line 23) | def forward(self, *args, **kwargs): method configure_optimizers (line 26) | def configure_optimizers(self) -> Tuple[Any, Any]: method _shared_step (line 31) | def _shared_step(self, batch, split: str) -> Dict: method training_step (line 39) | def training_step(self, batch, *args, **kwargs): method validation_step (line 42) | def validation_step(self, batch, *args, **kwargs): method test_step (line 45) | def test_step(self, batch, *args, **kwargs): method encoder (line 49) | def encoder(self) -> torch.nn.Module: method mp (line 53) | def mp(self) -> torch.nn.Module: method post_mp (line 57) | def post_mp(self) -> torch.nn.Module: method pre_mp (line 61) | def pre_mp(self) -> torch.nn.Module: method lr_scheduler_step (line 64) | def lr_scheduler_step(self, *args, **kwargs): function create_model (line 70) | def create_model(to_device=True, dim_in=None, dim_out=None) -> GraphGymM... FILE: torch_geometric/graphgym/models/act.py function relu (line 7) | def relu(): function selu (line 11) | def selu(): function prelu (line 15) | def prelu(): function elu (line 19) | def elu(): function lrelu_01 (line 23) | def lrelu_01(): function lrelu_025 (line 27) | def lrelu_025(): function lrelu_05 (line 31) | def lrelu_05(): FILE: torch_geometric/graphgym/models/encoder.py class IntegerFeatureEncoder (line 10) | class IntegerFeatureEncoder(torch.nn.Module): method __init__ (line 23) | def __init__(self, emb_dim: int, num_classes: int): method forward (line 29) | def forward(self, batch): class AtomEncoder (line 37) | class AtomEncoder(torch.nn.Module): method __init__ (line 49) | def __init__(self, emb_dim, *args, **kwargs): method forward (line 61) | def forward(self, batch): class BondEncoder (line 71) | class BondEncoder(torch.nn.Module): method __init__ (line 83) | def __init__(self, emb_dim: int): method forward (line 95) | def forward(self, batch): FILE: torch_geometric/graphgym/models/gnn.py function GNNLayer (line 16) | def GNNLayer(dim_in: int, dim_out: int, has_act: bool = True) -> General... function GNNPreMP (line 39) | def GNNPreMP(dim_in: int, dim_out: int, num_layers: int) -> GeneralMulti... class GNNStackStage (line 64) | class GNNStackStage(torch.nn.Module): method __init__ (line 72) | def __init__(self, dim_in, dim_out, num_layers): method forward (line 83) | def forward(self, batch): class FeatureEncoder (line 97) | class FeatureEncoder(torch.nn.Module): method __init__ (line 104) | def __init__(self, dim_in: int): method forward (line 140) | def forward(self, batch): class GNN (line 146) | class GNN(torch.nn.Module): method __init__ (line 165) | def __init__(self, dim_in: int, dim_out: int, **kwargs): method forward (line 184) | def forward(self, batch): FILE: torch_geometric/graphgym/models/head.py class GNNNodeHead (line 10) | class GNNNodeHead(torch.nn.Module): method __init__ (line 17) | def __init__(self, dim_in: int, dim_out: int): method _apply_index (line 29) | def _apply_index(self, batch): method forward (line 39) | def forward(self, batch): class GNNEdgeHead (line 47) | class GNNEdgeHead(torch.nn.Module): method __init__ (line 54) | def __init__(self, dim_in: int, dim_out: int): method _apply_index (line 91) | def _apply_index(self, batch): method forward (line 96) | def forward(self, batch): class GNNGraphHead (line 107) | class GNNGraphHead(torch.nn.Module): method __init__ (line 116) | def __init__(self, dim_in: int, dim_out: int): method _apply_index (line 123) | def _apply_index(self, batch): method forward (line 126) | def forward(self, batch): FILE: torch_geometric/graphgym/models/layer.py class LayerConfig (line 19) | class LayerConfig: function new_layer_config (line 47) | def new_layer_config( class GeneralLayer (line 86) | class GeneralLayer(torch.nn.Module): method __init__ (line 94) | def __init__(self, name, layer_config: LayerConfig, **kwargs): method forward (line 118) | def forward(self, batch): class GeneralMultiLayer (line 131) | class GeneralMultiLayer(torch.nn.Module): method __init__ (line 139) | def __init__(self, name, layer_config: LayerConfig, **kwargs): method forward (line 159) | def forward(self, batch): class Linear (line 169) | class Linear(torch.nn.Module): method __init__ (line 176) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 184) | def forward(self, batch): class BatchNorm1dNode (line 192) | class BatchNorm1dNode(torch.nn.Module): method __init__ (line 198) | def __init__(self, layer_config: LayerConfig): method forward (line 206) | def forward(self, batch): class BatchNorm1dEdge (line 211) | class BatchNorm1dEdge(torch.nn.Module): method __init__ (line 217) | def __init__(self, layer_config: LayerConfig): method forward (line 225) | def forward(self, batch): class MLP (line 231) | class MLP(torch.nn.Module): method __init__ (line 238) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 259) | def forward(self, batch): class GCNConv (line 268) | class GCNConv(torch.nn.Module): method __init__ (line 270) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 278) | def forward(self, batch): class SAGEConv (line 284) | class SAGEConv(torch.nn.Module): method __init__ (line 286) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 294) | def forward(self, batch): class GATConv (line 300) | class GATConv(torch.nn.Module): method __init__ (line 302) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 310) | def forward(self, batch): class GINConv (line 316) | class GINConv(torch.nn.Module): method __init__ (line 318) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 327) | def forward(self, batch): class SplineConv (line 333) | class SplineConv(torch.nn.Module): method __init__ (line 335) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 345) | def forward(self, batch): class GeneralConv (line 351) | class GeneralConv(torch.nn.Module): method __init__ (line 353) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 361) | def forward(self, batch): class GeneralEdgeConv (line 367) | class GeneralEdgeConv(torch.nn.Module): method __init__ (line 369) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 378) | def forward(self, batch): class GeneralSampleEdgeConv (line 385) | class GeneralSampleEdgeConv(torch.nn.Module): method __init__ (line 387) | def __init__(self, layer_config: LayerConfig, **kwargs): method forward (line 397) | def forward(self, batch): FILE: torch_geometric/graphgym/models/transform.py function create_link_label (line 6) | def create_link_label(pos_edge_index, neg_edge_index): function neg_sampling_transform (line 23) | def neg_sampling_transform(data): FILE: torch_geometric/graphgym/optim.py class OptimizerConfig (line 13) | class OptimizerConfig: function adam_optimizer (line 21) | def adam_optimizer(params: Iterator[Parameter], base_lr: float, function sgd_optimizer (line 27) | def sgd_optimizer(params: Iterator[Parameter], base_lr: float, momentum:... function create_optimizer (line 33) | def create_optimizer(params: Iterator[Parameter], cfg: Any) -> Any: class SchedulerConfig (line 43) | class SchedulerConfig: function none_scheduler (line 52) | def none_scheduler(optimizer: Optimizer, max_epoch: int) -> StepLR: function step_scheduler (line 57) | def step_scheduler(optimizer: Optimizer, steps: List[int], function cos_scheduler (line 63) | def cos_scheduler(optimizer: Optimizer, max_epoch: int) -> CosineAnneali... function create_scheduler (line 67) | def create_scheduler(optimizer: Optimizer, cfg: Any) -> Any: FILE: torch_geometric/graphgym/register.py function register_base (line 21) | def register_base(mapping: Dict[str, Any], key: str, function register_act (line 46) | def register_act(key: str, module: Any = None): function register_node_encoder (line 51) | def register_node_encoder(key: str, module: Any = None): function register_edge_encoder (line 56) | def register_edge_encoder(key: str, module: Any = None): function register_stage (line 61) | def register_stage(key: str, module: Any = None): function register_head (line 66) | def register_head(key: str, module: Any = None): function register_layer (line 71) | def register_layer(key: str, module: Any = None): function register_pooling (line 76) | def register_pooling(key: str, module: Any = None): function register_network (line 81) | def register_network(key: str, module: Any = None): function register_config (line 86) | def register_config(key: str, module: Any = None): function register_dataset (line 91) | def register_dataset(key: str, module: Any = None): function register_loader (line 96) | def register_loader(key: str, module: Any = None): function register_optimizer (line 101) | def register_optimizer(key: str, module: Any = None): function register_scheduler (line 106) | def register_scheduler(key: str, module: Any = None): function register_loss (line 111) | def register_loss(key: str, module: Any = None): function register_train (line 116) | def register_train(key: str, module: Any = None): function register_metric (line 121) | def register_metric(key: str, module: Any = None): FILE: torch_geometric/graphgym/train.py class GraphGymDataModule (line 16) | class GraphGymDataModule(LightningDataModule): method __init__ (line 24) | def __init__(self): method train_dataloader (line 28) | def train_dataloader(self) -> DataLoader: method val_dataloader (line 31) | def val_dataloader(self) -> DataLoader: method test_dataloader (line 36) | def test_dataloader(self) -> DataLoader: function train (line 40) | def train( FILE: torch_geometric/graphgym/utils/agg_runs.py function is_seed (line 23) | def is_seed(s): function is_split (line 31) | def is_split(s): function join_list (line 38) | def join_list(l1, l2): function agg_dict_list (line 46) | def agg_dict_list(dict_list): function name_to_dict (line 61) | def name_to_dict(run): function rm_keys (line 77) | def rm_keys(dict, keys): function agg_runs (line 82) | def agg_runs(dir, metric_best='auto'): function agg_batch (line 162) | def agg_batch(dir, metric_best='auto'): FILE: torch_geometric/graphgym/utils/comp_budget.py function params_count (line 7) | def params_count(model): function get_stats (line 16) | def get_stats(): function match_computation (line 21) | def match_computation(stats_baseline, key=None, mode='sqrt'): function dict_to_stats (line 59) | def dict_to_stats(cfg_dict): function match_baseline_cfg (line 69) | def match_baseline_cfg(cfg_dict, cfg_dict_baseline, verbose=True): FILE: torch_geometric/graphgym/utils/device.py function get_gpu_memory_map (line 10) | def get_gpu_memory_map(): function get_current_gpu_usage (line 20) | def get_current_gpu_usage(): function auto_select_device (line 38) | def auto_select_device(): FILE: torch_geometric/graphgym/utils/epoch.py function is_train_eval_epoch (line 4) | def is_train_eval_epoch(cur_epoch): function is_eval_epoch (line 9) | def is_eval_epoch(cur_epoch): function is_ckpt_epoch (line 15) | def is_ckpt_epoch(cur_epoch): FILE: torch_geometric/graphgym/utils/io.py function string_to_python (line 9) | def string_to_python(string): function dict_to_json (line 16) | def dict_to_json(dict, fname): function dict_list_to_json (line 28) | def dict_list_to_json(dict_list, fname): function json_to_dict_list (line 41) | def json_to_dict_list(fname): function dict_to_tb (line 55) | def dict_to_tb(dict, writer, epoch): function dict_list_to_tb (line 68) | def dict_list_to_tb(dict_list, writer): function makedirs_rm_exist (line 74) | def makedirs_rm_exist(dir): FILE: torch_geometric/graphgym/utils/plot.py function view_emb (line 4) | def view_emb(emb, dir): FILE: torch_geometric/graphgym/utils/tools.py class dummy_context (line 1) | class dummy_context(): method __enter__ (line 3) | def __enter__(self): method __exit__ (line 6) | def __exit__(self, exc_type, exc_value, traceback): FILE: torch_geometric/hash_tensor.py function implements (line 29) | def implements(torch_function: Callable) -> Callable: function as_key_tensor (line 39) | def as_key_tensor( function get_hash_map (line 67) | def get_hash_map(key: Tensor) -> Union[CPUHashMap, CUDAHashMap]: class HashTensor (line 89) | class HashTensor(Tensor): method __new__ (line 152) | def __new__( method _from_data (line 220) | def _from_data( method _key (line 263) | def _key(self) -> Tensor: method _shallow_copy (line 276) | def _shallow_copy(self) -> 'HashTensor': method _get (line 286) | def _get(self, query: Tensor) -> Tensor: method as_tensor (line 319) | def as_tensor(self) -> Tensor: method __torch_dispatch__ (line 333) | def __torch_dispatch__( # type: ignore method __tensor_flatten__ (line 352) | def __tensor_flatten__(self) -> Tuple[List[str], Tuple[Any, ...]]: method __tensor_unflatten__ (line 362) | def __tensor_unflatten__( method __repr__ (line 377) | def __repr__(self) -> str: # type: ignore method tolist (line 382) | def tolist(self) -> List[Any]: method numpy (line 386) | def numpy(self, *, force: bool = False) -> np.ndarray: method index_select (line 390) | def index_select( # type: ignore method select (line 398) | def select( # type: ignore method share_memory_ (line 406) | def share_memory_(self) -> 'HashTensor': method is_shared (line 416) | def is_shared(self) -> bool: method detach_ (line 420) | def detach_(self) -> 'HashTensor': method __getitem__ (line 426) | def __getitem__(self, indices: Any) -> Union['HashTensor', Tensor]: function _alias (line 455) | def _alias(tensor: HashTensor) -> HashTensor: function _clone (line 460) | def _clone( function _detach (line 481) | def _detach(tensor: HashTensor) -> HashTensor: function _to_copy (line 497) | def _to_copy( function _pin_memory (line 545) | def _pin_memory(tensor: HashTensor) -> HashTensor: function _unsqueeze (line 565) | def _unsqueeze(tensor: HashTensor, dim: int) -> HashTensor: function _squeeze_default (line 582) | def _squeeze_default(tensor: HashTensor) -> HashTensor: function _squeeze_dim (line 602) | def _squeeze_dim( function _slice (line 634) | def _slice( function _new_index_select (line 668) | def _new_index_select( function _index_select (line 702) | def _index_select( function _new_select (line 726) | def _new_select( function _select (line 753) | def _select( function _index (line 778) | def _index( FILE: torch_geometric/home.py function get_home_dir (line 11) | def get_home_dir() -> str: function set_home_dir (line 23) | def set_home_dir(path: str) -> None: FILE: torch_geometric/index.py function ptr2index (line 27) | def ptr2index(ptr: Tensor, output_size: Optional[int] = None) -> Tensor: function index2ptr (line 32) | def index2ptr(index: Tensor, size: Optional[int] = None) -> Tensor: class CatMetadata (line 40) | class CatMetadata(NamedTuple): function implements (line 46) | def implements(torch_function: Callable) -> Callable: function assert_valid_dtype (line 56) | def assert_valid_dtype(tensor: Tensor) -> None: function assert_one_dimensional (line 63) | def assert_one_dimensional(tensor: Tensor) -> None: function assert_contiguous (line 69) | def assert_contiguous(tensor: Tensor) -> None: function assert_sorted (line 75) | def assert_sorted(func: Callable) -> Callable: class Index (line 88) | class Index(Tensor): method __new__ (line 154) | def __new__( method validate (line 213) | def validate(self) -> 'Index': method dim_size (line 244) | def dim_size(self) -> Optional[int]: method is_sorted (line 249) | def is_sorted(self) -> bool: method dtype (line 254) | def dtype(self) -> torch.dtype: # type: ignore method get_dim_size (line 260) | def get_dim_size(self) -> int: method dim_resize_ (line 271) | def dim_resize_(self, dim_size: Optional[int]) -> 'Index': method get_indptr (line 292) | def get_indptr(self) -> Tensor: method fill_cache_ (line 302) | def fill_cache_(self) -> 'Index': method share_memory_ (line 313) | def share_memory_(self) -> 'Index': method is_shared (line 320) | def is_shared(self) -> bool: method as_tensor (line 324) | def as_tensor(self) -> Tensor: method __tensor_flatten__ (line 332) | def __tensor_flatten__(self) -> Tuple[List[str], Tuple[Any, ...]]: method __tensor_unflatten__ (line 346) | def __tensor_unflatten__( method __torch_dispatch__ (line 367) | def __torch_dispatch__( # type: ignore method __repr__ (line 393) | def __repr__(self) -> str: # type: ignore method tolist (line 414) | def tolist(self) -> List[Any]: method numpy (line 418) | def numpy(self, *, force: bool = False) -> np.ndarray: method _shallow_copy (line 424) | def _shallow_copy(self) -> 'Index': method _clear_metadata (line 432) | def _clear_metadata(self) -> 'Index': function apply_ (line 440) | def apply_( function _clone (line 471) | def _clone( function _to_copy (line 482) | def _to_copy( function _alias (line 505) | def _alias(tensor: Index) -> Index: function _pin_memory (line 510) | def _pin_memory(tensor: Index) -> Index: function _sort (line 517) | def _sort( function _sort_stable (line 539) | def _sort_stable( function _cat (line 564) | def _cat( function _flip (line 602) | def _flip( function _index_select (line 616) | def _index_select( function _slice (line 636) | def _slice( function _index (line 665) | def _index( function _add (line 696) | def _add( function add_ (line 742) | def add_( function _sub (line 775) | def _sub( function sub_ (line 810) | def sub_( FILE: torch_geometric/inspector.py class Parameter (line 11) | class Parameter(NamedTuple): class Signature (line 18) | class Signature(NamedTuple): class Inspector (line 24) | class Inspector: method __init__ (line 31) | def __init__(self, cls: Type): method _get_modules (line 36) | def _get_modules(self, cls: Type) -> List[str]: method _modules (line 48) | def _modules(self) -> List[str]: method _globals (line 52) | def _globals(self) -> Dict[str, Any]: method __repr__ (line 58) | def __repr__(self) -> str: method eval_type (line 61) | def eval_type(self, value: Any) -> Type: method type_repr (line 65) | def type_repr(self, obj: Any) -> str: method implements (line 69) | def implements(self, func_name: str) -> bool: method inspect_signature (line 83) | def inspect_signature( method get_signature (line 134) | def get_signature( method remove_signature (line 167) | def remove_signature( method get_param_dict (line 179) | def get_param_dict( method get_params (line 193) | def get_params( method get_flat_param_dict (line 207) | def get_flat_param_dict( method get_flat_params (line 255) | def get_flat_params( method get_param_names (line 270) | def get_param_names( method get_flat_param_names (line 284) | def get_flat_param_names( method collect_param_data (line 299) | def collect_param_data( method get_source (line 323) | def get_source(self, cls: Optional[Type] = None) -> str: method get_params_from_method_call (line 336) | def get_params_from_method_call( function eval_type (line 429) | def eval_type(value: Any, _globals: Dict[str, Any]) -> Type: function type_repr (line 436) | def type_repr(obj: Any, _globals: Dict[str, Any]) -> str: function find_parenthesis_content (line 484) | def find_parenthesis_content(source: str, prefix: str) -> Optional[str]: function split (line 514) | def split(content: str, sep: str) -> List[str]: function remove_comments (line 535) | def remove_comments(content: str) -> str: FILE: torch_geometric/io/fs.py function get_fs (line 19) | def get_fs(path: str) -> fsspec.AbstractFileSystem: function normpath (line 48) | def normpath(path: str) -> str: function exists (line 54) | def exists(path: str) -> bool: function makedirs (line 58) | def makedirs(path: str, exist_ok: bool = True) -> None: function isdir (line 62) | def isdir(path: str) -> bool: function isfile (line 66) | def isfile(path: str) -> bool: function isdisk (line 70) | def isdisk(path: str) -> bool: function islocal (line 74) | def islocal(path: str) -> bool: function ls (line 79) | def ls(path: str, detail: Literal[False] = False) -> List[str]: function ls (line 84) | def ls(path: str, detail: Literal[True]) -> List[Dict[str, Any]]: function ls (line 88) | def ls( function cp (line 105) | def cp( function rm (line 189) | def rm(path: str, recursive: bool = True) -> None: function mv (line 193) | def mv(path1: str, path2: str) -> None: function glob (line 200) | def glob(path: str) -> List[str]: function torch_save (line 210) | def torch_save(data: Any, path: str) -> None: function torch_load (line 217) | def torch_load(path: str, map_location: Any = None) -> Any: FILE: torch_geometric/io/npz.py function read_npz (line 11) | def read_npz(path: str, to_undirected: bool = True) -> Data: function parse_npz (line 16) | def parse_npz(f: Dict[str, Any], to_undirected: bool = True) -> Data: FILE: torch_geometric/io/obj.py function yield_file (line 8) | def yield_file(in_file: str) -> Iterator[Tuple[str, List[Union[int, floa... function read_obj (line 24) | def read_obj(in_file: str) -> Optional[Data]: FILE: torch_geometric/io/off.py function parse_off (line 12) | def parse_off(src: List[str]) -> Data: function face_to_tri (line 31) | def face_to_tri(face: List[str]) -> Tensor: function read_off (line 47) | def read_off(path: str) -> Data: function write_off (line 60) | def write_off(data: Data, path: str) -> None: FILE: torch_geometric/io/planetoid.py function read_planetoid_data (line 25) | def read_planetoid_data(folder: str, prefix: str) -> Data: function read_file (line 99) | def read_file(folder: str, prefix: str, name: str) -> Tensor: function edge_index_from_dict (line 117) | def edge_index_from_dict( FILE: torch_geometric/io/ply.py function read_ply (line 11) | def read_ply(path: str) -> Data: FILE: torch_geometric/io/sdf.py function parse_sdf (line 10) | def parse_sdf(src: str) -> Data: function read_sdf (line 30) | def read_sdf(path: str) -> Data: FILE: torch_geometric/io/tu.py function read_tu_data (line 17) | def read_tu_data( function read_file (line 92) | def read_file( function cat (line 102) | def cat(seq: List[Optional[Tensor]]) -> Optional[Tensor]: function split (line 109) | def split(data: Data, batch: Tensor) -> Tuple[Data, Dict[str, Tensor]]: FILE: torch_geometric/io/txt_array.py function parse_txt_array (line 8) | def parse_txt_array( function read_txt_array (line 23) | def read_txt_array( FILE: torch_geometric/isinstance.py function is_torch_instance (line 11) | def is_torch_instance(obj: Any, cls: Union[Type, Tuple[Type]]) -> bool: FILE: torch_geometric/lazy_loader.py class LazyLoader (line 8) | class LazyLoader(ModuleType): method __init__ (line 9) | def __init__( method _load (line 19) | def _load(self) -> Any: method __getattr__ (line 25) | def __getattr__(self, item: str) -> Any: method __dir__ (line 29) | def __dir__(self) -> List[str]: FILE: torch_geometric/llm/large_graph_indexer.py function ordered_set (line 34) | def ordered_set(values: Iterable[str]) -> List[str]: class MappedFeature (line 56) | class MappedFeature: method __eq__ (line 60) | def __eq__(self, value: "MappedFeature") -> bool: class LargeGraphIndexer (line 73) | class LargeGraphIndexer: method __init__ (line 78) | def __init__( method from_triplets (line 156) | def from_triplets( method collate (line 201) | def collate(cls, method get_unique_node_features (line 218) | def get_unique_node_features(self, method add_node_feature (line 239) | def add_node_feature( method get_node_features (line 275) | def get_node_features( method get_node_features_iter (line 304) | def get_node_features_iter( method get_unique_edge_features (line 342) | def get_unique_edge_features(self, method add_edge_feature (line 362) | def add_edge_feature( method get_edge_features (line 398) | def get_edge_features( method get_edge_features_iter (line 428) | def get_edge_features_iter( method to_triplets (line 466) | def to_triplets(self) -> Iterator[TripletLike]: method save (line 469) | def save(self, path: str) -> None: method from_disk (line 494) | def from_disk(cls, path: str) -> "LargeGraphIndexer": method to_data (line 520) | def to_data(self, node_feature_name: str, method __eq__ (line 546) | def __eq__(self, value: "LargeGraphIndexer") -> bool: function get_features_for_triplets_groups (line 570) | def get_features_for_triplets_groups( function get_features_for_triplets (line 711) | def get_features_for_triplets( FILE: torch_geometric/llm/models/g_retriever.py class GRetriever (line 10) | class GRetriever(torch.nn.Module): method __init__ (line 42) | def __init__( method encode (line 91) | def encode( method forward (line 116) | def forward( method inference (line 180) | def inference( method __repr__ (line 248) | def __repr__(self) -> str: method _align_dtype (line 254) | def _align_dtype( FILE: torch_geometric/llm/models/git_mol.py class GraphEncoder (line 13) | class GraphEncoder(torch.nn.Module): method __init__ (line 14) | def __init__( method reset_parameters (line 50) | def reset_parameters(self): method forward (line 56) | def forward( class GITFormer (line 83) | class GITFormer(torch.nn.Module): method __init__ (line 84) | def __init__( class GITMol (line 107) | class GITMol(torch.nn.Module): method __init__ (line 117) | def __init__(self) -> None: method model_freeze (line 158) | def model_freeze(self) -> None: method forward (line 165) | def forward( method _calc_xtm_loss (line 220) | def _calc_xtm_loss( method _calc_xtc_loss (line 293) | def _calc_xtc_loss( FILE: torch_geometric/llm/models/glem.py function deal_nan (line 11) | def deal_nan(x): class GLEM (line 18) | class GLEM(torch.nn.Module): method __init__ (line 46) | def __init__( method pre_train_gnn (line 102) | def pre_train_gnn(self, train_loader: NeighborLoader, method pre_train_lm (line 122) | def pre_train_lm(self, train_loader: DataLoader, method train (line 140) | def train(self, em_phase: str, train_loader: Union[DataLoader, method train_lm (line 172) | def train_lm(self, train_loader: DataLoader, method train_gnn (line 228) | def train_gnn(self, train_loader: NeighborLoader, method inference (line 284) | def inference(self, em_phase: str, data_loader: Union[NeighborLoader, method inference_lm (line 310) | def inference_lm(self, data_loader: DataLoader, verbose: bool = True): method inference_gnn (line 338) | def inference_gnn(self, data_loader: NeighborLoader, verbose: bool = T... method loss (line 365) | def loss(self, logits: torch.Tensor, labels: torch.Tensor, FILE: torch_geometric/llm/models/llm.py function get_llm_kwargs (line 25) | def get_llm_kwargs(required_memory: int, dtype=torch.dtype) -> Dict[str,... class LLM (line 51) | class LLM(torch.nn.Module): method __init__ (line 70) | def __init__( method _safe_decode (line 157) | def _safe_decode(tokenizer, tokens) -> str: method _encode_inputs (line 178) | def _encode_inputs( method _label_input_ids (line 201) | def _label_input_ids( method _input_ids (line 212) | def _input_ids( method _inputs_embeds (line 227) | def _inputs_embeds( method _append_embeds (line 243) | def _append_embeds( method _pad_embeds (line 259) | def _pad_embeds( method _get_embeds_old (line 287) | def _get_embeds_old( method _get_embeds (line 334) | def _get_embeds( method forward (line 423) | def forward( method inference (line 455) | def inference( method __repr__ (line 491) | def __repr__(self) -> str: FILE: torch_geometric/llm/models/llm_judge.py class LLMJudge (line 53) | class LLMJudge(): method __init__ (line 69) | def __init__( method _process_score (line 80) | def _process_score(self, response: str) -> float: method _average_scores (line 90) | def _average_scores(self, score0: float, score1: float): method score (line 112) | def score( FILE: torch_geometric/llm/models/molecule_gpt.py function pad_or_truncate (line 11) | def pad_or_truncate(embeddings: Tensor, max_seq_len: int, class MoleculeGPT (line 26) | class MoleculeGPT(torch.nn.Module): method __init__ (line 54) | def __init__( method encode (line 97) | def encode( method forward (line 132) | def forward( method inference (line 172) | def inference( method __repr__ (line 217) | def __repr__(self) -> str: FILE: torch_geometric/llm/models/protein_mpnn.py class PositionWiseFeedForward (line 12) | class PositionWiseFeedForward(torch.nn.Module): method __init__ (line 13) | def __init__(self, in_channels: int, hidden_channels: int) -> None: method forward (line 21) | def forward(self, x: torch.Tensor) -> torch.Tensor: class PositionalEncoding (line 25) | class PositionalEncoding(torch.nn.Module): method __init__ (line 26) | def __init__(self, hidden_channels: int, method forward (line 33) | def forward(self, offset, mask) -> torch.Tensor: class Encoder (line 40) | class Encoder(MessagePassing): method __init__ (line 41) | def __init__( method forward (line 73) | def forward( method message (line 97) | def message(self, x_i: torch.Tensor, x_j: torch.Tensor, class Decoder (line 104) | class Decoder(MessagePassing): method __init__ (line 105) | def __init__( method forward (line 128) | def forward( method message (line 147) | def message(self, x_i: torch.Tensor, x_j: torch.Tensor, class ProteinMPNN (line 158) | class ProteinMPNN(torch.nn.Module): method __init__ (line 188) | def __init__( method reset_parameters (line 225) | def reset_parameters(self): method _featurize (line 230) | def _featurize( method _rbf (line 264) | def _rbf(self, D: torch.Tensor) -> torch.Tensor: method forward (line 273) | def forward( FILE: torch_geometric/llm/models/sentence_transformer.py class PoolingStrategy (line 10) | class PoolingStrategy(Enum): class SentenceTransformer (line 17) | class SentenceTransformer(torch.nn.Module): method __init__ (line 25) | def __init__( method forward (line 55) | def forward(self, input_ids: Tensor, attention_mask: Tensor) -> Tensor: method get_input_ids (line 72) | def get_input_ids( method device (line 104) | def device(self) -> torch.device: method encode (line 108) | def encode( method __repr__ (line 171) | def __repr__(self) -> str: function mean_pooling (line 175) | def mean_pooling(emb: Tensor, attention_mask: Tensor) -> Tensor: function last_pooling (line 180) | def last_pooling(emb: Tensor, attention_mask: Tensor) -> Tensor: FILE: torch_geometric/llm/models/txt2kg.py class TXT2KG (line 19) | class TXT2KG(): method __init__ (line 54) | def __init__( method save_kg (line 83) | def save_kg(self, path: str) -> None: method _chunk_to_triples_str_local (line 94) | def _chunk_to_triples_str_local(self, txt: str) -> str: method add_doc_2_KG (line 111) | def add_doc_2_KG( method _extract_relevant_triples (line 150) | def _extract_relevant_triples( function _chunk_to_triples_str_cloud (line 214) | def _chunk_to_triples_str_cloud( function _parse_n_check_triples (line 250) | def _parse_n_check_triples(triples_str: str) -> List[Tuple[str, str, str]]: function _llm_then_python_parse (line 287) | def _llm_then_python_parse(chunks, py_fn, llm_fn, **kwargs): function _multiproc_helper (line 294) | def _multiproc_helper( function _get_num_procs (line 329) | def _get_num_procs(): function _chunk_text (line 343) | def _chunk_text(text: str, chunk_size: int = 512) -> list[str]: function _merge_triples_deterministically (line 399) | def _merge_triples_deterministically( FILE: torch_geometric/llm/models/vision_transformer.py class VisionTransformer (line 7) | class VisionTransformer(torch.nn.Module): method __init__ (line 13) | def __init__( method forward (line 26) | def forward( method device (line 34) | def device(self) -> torch.device: method __repr__ (line 37) | def __repr__(self) -> str: FILE: torch_geometric/llm/rag_loader.py class RAGFeatureStore (line 10) | class RAGFeatureStore(Protocol): method retrieve_seed_nodes (line 13) | def retrieve_seed_nodes(self, query: Any, **kwargs) -> InputNodes: method config (line 22) | def config(self) -> Dict[str, Any]: method config (line 28) | def config(self, config: Dict[str, Any]): method retrieve_seed_edges (line 33) | def retrieve_seed_edges(self, query: Any, **kwargs) -> InputEdges: method load_subgraph (line 41) | def load_subgraph( class RAGGraphStore (line 48) | class RAGGraphStore(Protocol): method sample_subgraph (line 51) | def sample_subgraph(self, seed_nodes: InputNodes, seed_edges: InputEdges, method config (line 58) | def config(self) -> Dict[str, Any]: method config (line 64) | def config(self, config: Dict[str, Any]): method register_feature_store (line 69) | def register_feature_store(self, feature_store: FeatureStore): class RAGQueryLoader (line 79) | class RAGQueryLoader: method __init__ (line 81) | def __init__(self, graph_data: Tuple[RAGFeatureStore, RAGGraphStore], method _propagate_config (line 113) | def _propagate_config(self, config: Dict[str, Any]): method config (line 119) | def config(self): method config (line 124) | def config(self, config: Dict[str, Any]): method query (line 133) | def query(self, query: Any) -> Data: FILE: torch_geometric/llm/utils/backend_utils.py function preprocess_triplet (line 48) | def preprocess_triplet(triplet: TripletLike) -> TripletLike: function retrieval_via_pcst (line 54) | def retrieval_via_pcst( function batch_knn (line 184) | def batch_knn(query_enc: Tensor, embeds: Tensor, class ConvertableGraphStore (line 196) | class ConvertableGraphStore(Protocol): method from_data (line 198) | def from_data( method from_hetero_data (line 208) | def from_hetero_data( method from_partition (line 218) | def from_partition(cls, root: str, pid: int) -> GraphStore: class ConvertableFeatureStore (line 224) | class ConvertableFeatureStore(Protocol): method from_data (line 226) | def from_data( method from_hetero_data (line 237) | def from_hetero_data( method from_partition (line 248) | def from_partition(cls, root: str, pid: int) -> FeatureStore: class RemoteDataType (line 252) | class RemoteDataType(Enum): class RemoteGraphBackendLoader (line 258) | class RemoteGraphBackendLoader: method load (line 265) | def load(self, pid: Optional[int] = None) -> RemoteGraphBackend: method __del__ (line 287) | def __del__(self) -> None: function create_graph_from_triples (line 292) | def create_graph_from_triples( function create_remote_backend_from_graph_data (line 322) | def create_remote_backend_from_graph_data( function make_pcst_filter (line 367) | def make_pcst_filter(triples: List[Tuple[str, str, FILE: torch_geometric/llm/utils/feature_store.py class KNNRAGFeatureStore (line 16) | class KNNRAGFeatureStore(LocalFeatureStore): method __init__ (line 18) | def __init__(self) -> None: method config (line 27) | def config(self) -> Dict[str, Any]: method _set_from_config (line 31) | def _set_from_config(self, config: Dict[str, Any], attr_name: str) -> ... method config (line 47) | def config(self, config: Dict[str, Any]) -> None: method x (line 66) | def x(self) -> Tensor: method edge_attr (line 71) | def edge_attr(self) -> Tensor: method retrieve_seed_nodes (line 76) | def retrieve_seed_nodes( # noqa: D417 method _retrieve_seed_nodes_batch (line 106) | def _retrieve_seed_nodes_batch( # noqa: D417 method load_subgraph (line 125) | def load_subgraph( # noqa FILE: torch_geometric/llm/utils/graph_store.py class NeighborSamplingRAGGraphStore (line 24) | class NeighborSamplingRAGGraphStore(LocalGraphStore): method __init__ (line 26) | def __init__( # type: ignore[no-untyped-def] method config (line 50) | def config(self) -> Dict[str, Any]: method _set_from_config (line 54) | def _set_from_config(self, config: Dict[str, Any], attr_name: str) -> ... method config (line 70) | def config(self, config: Dict[str, Any]) -> None: method _init_sampler (line 87) | def _init_sampler(self) -> None: method register_feature_store (line 98) | def register_feature_store(self, feature_store: FeatureStore) -> None: method put_edge_id (line 106) | def put_edge_id( # type: ignore[no-untyped-def] method edge_index (line 118) | def edge_index(self) -> _EdgeTensorType: method put_edge_index (line 125) | def put_edge_index( # type: ignore[no-untyped-def] method edge_index (line 141) | def edge_index(self, edge_index: _EdgeTensorType) -> None: method sample_subgraph (line 170) | def sample_subgraph( FILE: torch_geometric/llm/utils/vectorrag.py class VectorRetriever (line 14) | class VectorRetriever(Protocol): method query (line 17) | def query(self, query: Any, **kwargs: Optional[Dict[str, Any]]) -> Data: class DocumentRetriever (line 22) | class DocumentRetriever(VectorRetriever): method __init__ (line 24) | def __init__(self, raw_docs: List[str], method query (line 58) | def query(self, query: Union[str, Tensor]) -> List[str]: method save (line 77) | def save(self, path: str) -> None: method load (line 96) | def load(cls, path: str, model: Union[SentenceTransformer, torch.nn.Mo... FILE: torch_geometric/loader/base.py class DataLoaderIterator (line 9) | class DataLoaderIterator: method __init__ (line 25) | def __init__(self, iterator: _BaseDataLoaderIter, transform_fn: Callab... method __iter__ (line 29) | def __iter__(self) -> 'DataLoaderIterator': method _reset (line 32) | def _reset(self, loader: Any, first_iter: bool = False): method __len__ (line 35) | def __len__(self) -> int: method __next__ (line 38) | def __next__(self) -> Any: method __del__ (line 41) | def __del__(self) -> Any: FILE: torch_geometric/loader/cache.py function to_device (line 8) | def to_device(inputs: Any, device: Optional[torch.device] = None) -> Any: class CachedLoader (line 21) | class CachedLoader: method __init__ (line 33) | def __init__( method clear (line 45) | def clear(self): method __iter__ (line 49) | def __iter__(self) -> Any: method __len__ (line 66) | def __len__(self) -> int: method __repr__ (line 69) | def __repr__(self) -> str: FILE: torch_geometric/loader/cluster.py class Partition (line 22) | class Partition: class ClusterData (line 31) | class ClusterData(torch.utils.data.Dataset): method __init__ (line 57) | def __init__( method _metis (line 98) | def _metis(self, edge_index: Tensor, num_nodes: int) -> Tensor: method _partition (line 137) | def _partition(self, edge_index: Tensor, cluster: Tensor) -> Partition: method _permute_data (line 167) | def _permute_data(self, data: Data, partition: Partition) -> Data: method __len__ (line 184) | def __len__(self) -> int: method __getitem__ (line 187) | def __getitem__(self, idx: int) -> Data: method __repr__ (line 231) | def __repr__(self) -> str: class ClusterLoader (line 235) | class ClusterLoader(torch.utils.data.DataLoader): method __init__ (line 260) | def __init__(self, cluster_data, **kwargs): method _collate (line 265) | def _collate(self, batch: List[int]) -> Data: FILE: torch_geometric/loader/data_list_loader.py function collate_fn (line 9) | def collate_fn(data_list): class DataListLoader (line 13) | class DataListLoader(torch.utils.data.DataLoader): method __init__ (line 34) | def __init__(self, dataset: Union[Dataset, List[BaseData]], FILE: torch_geometric/loader/dataloader.py class Collater (line 13) | class Collater: method __init__ (line 14) | def __init__( method __call__ (line 24) | def __call__(self, batch: List[Any]) -> Any: class DataLoader (line 52) | class DataLoader(torch.utils.data.DataLoader): method __init__ (line 71) | def __init__( FILE: torch_geometric/loader/dense_data_loader.py function collate_fn (line 9) | def collate_fn(data_list: List[Data]) -> Batch: class DenseDataLoader (line 16) | class DenseDataLoader(torch.utils.data.DataLoader): method __init__ (line 39) | def __init__(self, dataset: Union[Dataset, List[Data]], FILE: torch_geometric/loader/dynamic_batch_sampler.py class DynamicBatchSampler (line 8) | class DynamicBatchSampler(torch.utils.data.sampler.Sampler): method __init__ (line 42) | def __init__( method __iter__ (line 66) | def __iter__(self) -> Iterator[List[int]]: method __len__ (line 100) | def __len__(self) -> int: FILE: torch_geometric/loader/graph_saint.py class GraphSAINTSampler (line 11) | class GraphSAINTSampler(torch.utils.data.DataLoader): method __init__ (line 46) | def __init__(self, data, batch_size: int, num_steps: int = 1, method _filename (line 88) | def _filename(self): method __len__ (line 91) | def __len__(self): method _sample_nodes (line 94) | def _sample_nodes(self, batch_size): method __getitem__ (line 97) | def __getitem__(self, idx): method _collate (line 102) | def _collate(self, data_list): method _compute_norm (line 127) | def _compute_norm(self): class GraphSAINTNodeSampler (line 166) | class GraphSAINTNodeSampler(GraphSAINTSampler): method _sample_nodes (line 170) | def _sample_nodes(self, batch_size): class GraphSAINTEdgeSampler (line 177) | class GraphSAINTEdgeSampler(GraphSAINTSampler): method _sample_nodes (line 181) | def _sample_nodes(self, batch_size): class GraphSAINTRandomWalkSampler (line 199) | class GraphSAINTRandomWalkSampler(GraphSAINTSampler): method __init__ (line 206) | def __init__(self, data, batch_size: int, walk_length: int, method _filename (line 214) | def _filename(self): method _sample_nodes (line 218) | def _sample_nodes(self, batch_size): FILE: torch_geometric/loader/hgt_loader.py class HGTLoader (line 11) | class HGTLoader(NodeLoader): method __init__ (line 104) | def __init__( FILE: torch_geometric/loader/ibmb_loader.py class OutputNodes (line 30) | class OutputNodes(NamedTuple): class _IBMBBaseLoader (line 35) | class _IBMBBaseLoader(torch.utils.data.DataLoader): method __init__ (line 36) | def __init__(self, data: Data, **kwargs): method get_output_nodes (line 50) | def get_output_nodes(self) -> List[OutputNodes]: method _cache_fn (line 53) | def _cache_fn(self, data_list: List[Data]) -> Data: method _collate_fn (line 57) | def _collate_fn(self, output_nodes: List[OutputNodes]) -> Data: method __repr__ (line 60) | def __repr__(self) -> str: function get_partitions (line 67) | def get_partitions( function get_pair_wise_distance (line 99) | def get_pair_wise_distance( function indices_complete_check (line 137) | def indices_complete_check( function get_subgraph (line 160) | def get_subgraph( function define_sampler (line 189) | def define_sampler( function create_batchwise_out_aux_pairs (line 214) | def create_batchwise_out_aux_pairs( function get_pairs (line 292) | def get_pairs(ppr_mat: Any) -> np.ndarray: function prime_orient_merge (line 311) | def prime_orient_merge( function _prime_orient_merge (line 326) | def _prime_orient_merge( function prime_post_process (line 356) | def prime_post_process(loader, merge_max_size): function topk_ppr_matrix (line 390) | def topk_ppr_matrix( class IBMBBaseLoader (line 464) | class IBMBBaseLoader(torch.utils.data.DataLoader): method __init__ (line 465) | def __init__( method create_loader (line 480) | def create_loader(self, *args, **kwargs): method prepare_cache (line 484) | def prepare_cache( method create_adj_from_edge_index (line 511) | def create_adj_from_edge_index( method collate_fn (line 537) | def collate_fn(self, data_list: List[Union[Data, Tuple]]): method __repr__ (line 551) | def __repr__(self) -> str: class IBMBBatchLoader (line 555) | class IBMBBatchLoader(IBMBBaseLoader): method __init__ (line 604) | def __init__( method create_loader (line 673) | def create_loader(self, graph: Data, adj: SparseTensor): class IBMBNodeLoader (line 702) | class IBMBNodeLoader(IBMBBaseLoader): method __init__ (line 747) | def __init__( method create_loader (line 812) | def create_loader(self, graph: Data, adj: SparseTensor): class IBMBOrderedSampler (line 858) | class IBMBOrderedSampler(torch.utils.data.Sampler[int]): method __init__ (line 866) | def __init__(self, data_source: Union[np.ndarray, torch.Tensor, method __iter__ (line 871) | def __iter__(self) -> Iterator[int]: method __len__ (line 874) | def __len__(self) -> int: class IBMBWeightedSampler (line 878) | class IBMBWeightedSampler(torch.utils.data.Sampler[int]): method __init__ (line 889) | def __init__(self, batch_kl_div: Union[np.ndarray, torch.Tensor]) -> N... method __iter__ (line 896) | def __iter__(self) -> Iterator[int]: method __len__ (line 916) | def __len__(self) -> int: FILE: torch_geometric/loader/imbalanced_sampler.py class ImbalancedSampler (line 9) | class ImbalancedSampler(torch.utils.data.WeightedRandomSampler): method __init__ (line 64) | def __init__( FILE: torch_geometric/loader/link_loader.py class LinkLoader (line 31) | class LinkLoader( method __init__ (line 130) | def __init__( method __call__ (line 197) | def __call__( method collate_fn (line 207) | def collate_fn(self, index: Union[Tensor, List[int]]) -> Any: method filter_fn (line 219) | def filter_fn( method _get_iterator (line 342) | def _get_iterator(self) -> Iterator: method __repr__ (line 349) | def __repr__(self) -> str: FILE: torch_geometric/loader/link_neighbor_loader.py class LinkNeighborLoader (line 10) | class LinkNeighborLoader(LinkLoader): method __init__ (line 219) | def __init__( FILE: torch_geometric/loader/mixin.py function get_numa_nodes_cores (line 15) | def get_numa_nodes_cores() -> Dict[str, Any]: class WorkerInitWrapper (line 66) | class WorkerInitWrapper: method __init__ (line 70) | def __init__(self, func: Callable) -> None: method __call__ (line 73) | def __call__(self, worker_id: int) -> None: class LogMemoryMixin (line 78) | class LogMemoryMixin: method _mem_init_fn (line 82) | def _mem_init_fn(self, worker_id: int) -> None: method enable_memory_log (line 91) | def enable_memory_log(self) -> None: class MultithreadingMixin (line 100) | class MultithreadingMixin: method _mt_init_fn (line 106) | def _mt_init_fn(self, worker_id: int) -> None: method enable_multithreading (line 117) | def enable_multithreading( class AffinityMixin (line 174) | class AffinityMixin: method _aff_init_fn (line 205) | def _aff_init_fn(self, worker_id: int) -> None: method enable_cpu_affinity (line 224) | def enable_cpu_affinity( FILE: torch_geometric/loader/neighbor_loader.py class NeighborLoader (line 10) | class NeighborLoader(NodeLoader): method __init__ (line 203) | def __init__( FILE: torch_geometric/loader/neighbor_sampler.py class EdgeIndex (line 9) | class EdgeIndex(NamedTuple): method to (line 14) | def to(self, *args, **kwargs): class Adj (line 20) | class Adj(NamedTuple): method to (line 25) | def to(self, *args, **kwargs): class NeighborSampler (line 31) | class NeighborSampler(torch.utils.data.DataLoader): method __init__ (line 113) | def __init__(self, edge_index: Union[Tensor, SparseTensor], method sample (line 168) | def sample(self, batch): method __repr__ (line 195) | def __repr__(self) -> str: FILE: torch_geometric/loader/node_loader.py class NodeLoader (line 30) | class NodeLoader( method __init__ (line 90) | def __init__( method __call__ (line 133) | def __call__( method collate_fn (line 143) | def collate_fn(self, index: Union[Tensor, List[int]]) -> Any: method filter_fn (line 154) | def filter_fn( method _get_iterator (line 265) | def _get_iterator(self) -> Iterator: method __repr__ (line 279) | def __repr__(self) -> str: FILE: torch_geometric/loader/prefetch.py class DeviceHelper (line 12) | class DeviceHelper: method __init__ (line 13) | def __init__(self, device: Optional[torch.device] = None): method maybe_init_stream (line 39) | def maybe_init_stream(self) -> None: method maybe_wait_stream (line 47) | def maybe_wait_stream(self) -> None: class PrefetchLoader (line 52) | class PrefetchLoader: method __init__ (line 61) | def __init__( method non_blocking_transfer (line 69) | def non_blocking_transfer(self, batch: Any) -> Any: method __iter__ (line 80) | def __iter__(self) -> Any: method __len__ (line 101) | def __len__(self) -> int: method __repr__ (line 104) | def __repr__(self) -> str: FILE: torch_geometric/loader/random_node_loader.py class RandomNodeLoader (line 11) | class RandomNodeLoader(torch.utils.data.DataLoader): method __init__ (line 31) | def __init__( method collate_fn (line 56) | def collate_fn(self, index): FILE: torch_geometric/loader/shadow.py class ShaDowKHopSampler (line 11) | class ShaDowKHopSampler(torch.utils.data.DataLoader): method __init__ (line 39) | def __init__(self, data: Data, depth: int, num_neighbors: int, method __collate__ (line 71) | def __collate__(self, n_id): FILE: torch_geometric/loader/temporal_dataloader.py class TemporalDataLoader (line 8) | class TemporalDataLoader(torch.utils.data.DataLoader): method __init__ (line 23) | def __init__( method __call__ (line 50) | def __call__(self, arange: List[int]) -> TemporalData: FILE: torch_geometric/loader/utils.py function index_select (line 32) | def index_select( function filter_node_store_ (line 85) | def filter_node_store_(store: NodeStorage, out_store: NodeStorage, function filter_edge_store_ (line 101) | def filter_edge_store_(store: EdgeStorage, out_store: EdgeStorage, row: ... function filter_data (line 159) | def filter_data(data: Data, node: Tensor, row: Tensor, col: Tensor, function filter_hetero_data (line 168) | def filter_hetero_data( function filter_custom_store (line 209) | def filter_custom_store( function filter_custom_hetero_store (line 244) | def filter_custom_hetero_store( function get_input_nodes (line 289) | def get_input_nodes( function get_edge_label_index (line 356) | def get_edge_label_index( function infer_filter_per_worker (line 409) | def infer_filter_per_worker(data: Any) -> bool: FILE: torch_geometric/loader/zip_loader.py class ZipLoader (line 12) | class ZipLoader(torch.utils.data.DataLoader): method __init__ (line 34) | def __init__( method __call__ (line 62) | def __call__( method collate_fn (line 72) | def collate_fn(self, index: List[int]) -> Tuple[Any, ...]: method filter_fn (line 78) | def filter_fn( method _get_iterator (line 85) | def _get_iterator(self) -> Iterator: method __repr__ (line 92) | def __repr__(self) -> str: FILE: torch_geometric/logging.py function init_wandb (line 7) | def init_wandb(name: str, **kwargs: Any) -> None: function log (line 26) | def log(**kwargs: Any) -> None: FILE: torch_geometric/metrics/link_pred.py class LinkPredMetricData (line 19) | class LinkPredMetricData: method __post_init__ (line 24) | def __post_init__(self) -> None: method pred_rel_mat (line 38) | def pred_rel_mat(self) -> Tensor: method label_count (line 92) | def label_count(self) -> Tensor: method label_weight_sum (line 109) | def label_weight_sum(self) -> Tensor: method edge_label_weight_pos (line 129) | def edge_label_weight_pos(self) -> Optional[Tensor]: class _LinkPredMetric (line 154) | class _LinkPredMetric(BaseMetric): method __init__ (line 164) | def __init__(self, k: int) -> None: method update (line 173) | def update( method compute (line 201) | def compute(self) -> Tensor: method reset (line 205) | def reset(self) -> None: method _reset (line 212) | def _reset(self) -> None: method __repr__ (line 215) | def __repr__(self) -> str: class LinkPredMetric (line 219) | class LinkPredMetric(_LinkPredMetric): method __init__ (line 227) | def __init__(self, k: int) -> None: method update (line 240) | def update( method _update (line 259) | def _update(self, data: LinkPredMetricData) -> None: method compute (line 265) | def compute(self) -> Tensor: method _compute (line 270) | def _compute(self, data: LinkPredMetricData) -> Tensor: method _reset (line 280) | def _reset(self) -> None: method __repr__ (line 284) | def __repr__(self) -> str: class LinkPredMetricCollection (line 289) | class LinkPredMetricCollection(torch.nn.ModuleDict): method __init__ (line 320) | def __init__( method max_k (line 344) | def max_k(self) -> int: method weighted (line 354) | def weighted(self) -> bool: method update (line 361) | def update( # type: ignore method compute (line 425) | def compute(self) -> Dict[str, Tensor]: method reset (line 432) | def reset(self) -> None: method __repr__ (line 437) | def __repr__(self) -> str: class LinkPredPrecision (line 442) | class LinkPredPrecision(LinkPredMetric): method _compute (line 456) | def _compute(self, data: LinkPredMetricData) -> Tensor: class LinkPredRecall (line 461) | class LinkPredRecall(LinkPredMetric): method __init__ (line 473) | def __init__(self, k: int, weighted: bool = False): method _compute (line 477) | def _compute(self, data: LinkPredMetricData) -> Tensor: class LinkPredF1 (line 482) | class LinkPredF1(LinkPredMetric): method _compute (line 491) | def _compute(self, data: LinkPredMetricData) -> Tensor: class LinkPredMAP (line 499) | class LinkPredMAP(LinkPredMetric): method _compute (line 513) | def _compute(self, data: LinkPredMetricData) -> Tensor: class LinkPredNDCG (line 522) | class LinkPredNDCG(LinkPredMetric): method __init__ (line 538) | def __init__(self, k: int, weighted: bool = False): method _compute (line 554) | def _compute(self, data: LinkPredMetricData) -> Tensor: class LinkPredMRR (line 585) | class LinkPredMRR(LinkPredMetric): method _compute (line 596) | def _compute(self, data: LinkPredMetricData) -> Tensor: class LinkPredHitRatio (line 603) | class LinkPredHitRatio(LinkPredMetric): method _compute (line 614) | def _compute(self, data: LinkPredMetricData) -> Tensor: class LinkPredCoverage (line 619) | class LinkPredCoverage(_LinkPredMetric): method __init__ (line 632) | def __init__(self, k: int, num_dst_nodes: int) -> None: method update (line 643) | def update( method compute (line 651) | def compute(self) -> Tensor: method _reset (line 654) | def _reset(self) -> None: method __repr__ (line 657) | def __repr__(self) -> str: class LinkPredDiversity (line 662) | class LinkPredDiversity(_LinkPredMetric): method __init__ (line 690) | def __init__(self, k: int, category: Tensor) -> None: method update (line 706) | def update( method compute (line 720) | def compute(self) -> Tensor: method _reset (line 725) | def _reset(self) -> None: class LinkPredPersonalization (line 730) | class LinkPredPersonalization(_LinkPredMetric): method __init__ (line 752) | def __init__( method update (line 772) | def update( method compute (line 791) | def compute(self) -> Tensor: method _reset (line 835) | def _reset(self) -> None: class LinkPredAveragePopularity (line 840) | class LinkPredAveragePopularity(_LinkPredMetric): method __init__ (line 853) | def __init__(self, k: int, popularity: Tensor) -> None: method update (line 869) | def update( method compute (line 881) | def compute(self) -> Tensor: method _reset (line 886) | def _reset(self) -> None: FILE: torch_geometric/nn/aggr/attention.py class AttentionalAggregation (line 11) | class AttentionalAggregation(Aggregation): method __init__ (line 37) | def __init__( method reset_parameters (line 58) | def reset_parameters(self): method forward (line 64) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 81) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/base.py class Aggregation (line 10) | class Aggregation(torch.nn.Module): method __init__ (line 62) | def __init__(self) -> None: method forward (line 69) | def forward( method reset_parameters (line 98) | def reset_parameters(self): method __call__ (line 102) | def __call__( method __repr__ (line 141) | def __repr__(self) -> str: method assert_index_present (line 146) | def assert_index_present(self, index: Optional[Tensor]): method assert_sorted_index (line 153) | def assert_sorted_index(self, index: Optional[Tensor]): method assert_two_dimensional_input (line 162) | def assert_two_dimensional_input(self, x: Tensor, dim: int): method reduce (line 173) | def reduce(self, x: Tensor, index: Optional[Tensor] = None, method to_dense_batch (line 187) | def to_dense_batch( function expand_left (line 215) | def expand_left(ptr: Tensor, dim: int, dims: int) -> Tensor: FILE: torch_geometric/nn/aggr/basic.py class SumAggregation (line 12) | class SumAggregation(Aggregation): method forward (line 19) | def forward(self, x: Tensor, index: Optional[Tensor] = None, class MeanAggregation (line 25) | class MeanAggregation(Aggregation): method forward (line 33) | def forward(self, x: Tensor, index: Optional[Tensor] = None, class MaxAggregation (line 39) | class MaxAggregation(Aggregation): method forward (line 47) | def forward(self, x: Tensor, index: Optional[Tensor] = None, class MinAggregation (line 53) | class MinAggregation(Aggregation): method forward (line 61) | def forward(self, x: Tensor, index: Optional[Tensor] = None, class MulAggregation (line 67) | class MulAggregation(Aggregation): method forward (line 75) | def forward(self, x: Tensor, index: Optional[Tensor] = None, class VarAggregation (line 83) | class VarAggregation(Aggregation): method __init__ (line 98) | def __init__(self, semi_grad: bool = False): method forward (line 102) | def forward(self, x: Tensor, index: Optional[Tensor] = None, class StdAggregation (line 114) | class StdAggregation(Aggregation): method __init__ (line 128) | def __init__(self, semi_grad: bool = False): method forward (line 132) | def forward(self, x: Tensor, index: Optional[Tensor] = None, class SoftmaxAggregation (line 142) | class SoftmaxAggregation(Aggregation): method __init__ (line 171) | def __init__(self, t: float = 1.0, learn: bool = False, method reset_parameters (line 192) | def reset_parameters(self): method forward (line 196) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 217) | def __repr__(self) -> str: class PowerMeanAggregation (line 221) | class PowerMeanAggregation(Aggregation): method __init__ (line 248) | def __init__( method reset_parameters (line 271) | def reset_parameters(self): method forward (line 275) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 295) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/deep_sets.py class DeepSetsAggregation (line 10) | class DeepSetsAggregation(Aggregation): method __init__ (line 27) | def __init__( method reset_parameters (line 48) | def reset_parameters(self): method forward (line 54) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 72) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/equilibrium.py class ResNetPotential (line 11) | class ResNetPotential(torch.nn.Module): method __init__ (line 12) | def __init__(self, in_channels: int, out_channels: int, method forward (line 29) | def forward(self, x: Tensor, y: Tensor, index: Optional[Tensor], class MomentumOptimizer (line 50) | class MomentumOptimizer(torch.nn.Module): method __init__ (line 61) | def __init__(self, learning_rate: float = 0.1, momentum: float = 0.9, method reset_parameters (line 74) | def reset_parameters(self): method learning_rate (line 79) | def learning_rate(self): method momentum (line 83) | def momentum(self): method forward (line 86) | def forward( class EquilibriumAggregation (line 107) | class EquilibriumAggregation(Aggregation): method __init__ (line 134) | def __init__(self, in_channels: int, out_channels: int, method reset_parameters (line 148) | def reset_parameters(self): method init_output (line 153) | def init_output(self, dim_size: int) -> Tensor: method reg (line 157) | def reg(self, y: Tensor) -> Tensor: method energy (line 160) | def energy(self, x: Tensor, y: Tensor, index: Optional[Tensor], method forward (line 164) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 178) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/fused.py class FusedAggregation (line 20) | class FusedAggregation(Aggregation): method __init__ (line 87) | def __init__(self, aggrs: List[Union[Aggregation, str]]): method forward (line 191) | def forward(self, x: Tensor, index: Optional[Tensor] = None, FILE: torch_geometric/nn/aggr/gmt.py class GraphMultisetTransformer (line 14) | class GraphMultisetTransformer(Aggregation): method __init__ (line 46) | def __init__( method reset_parameters (line 72) | def reset_parameters(self): method forward (line 79) | def forward( method __repr__ (line 101) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/gru.py class GRUAggregation (line 10) | class GRUAggregation(Aggregation): method __init__ (line 33) | def __init__(self, in_channels: int, out_channels: int, **kwargs): method reset_parameters (line 40) | def reset_parameters(self): method forward (line 44) | def forward( method __repr__ (line 59) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/lcm.py class LCMAggregation (line 12) | class LCMAggregation(Aggregation): method __init__ (line 39) | def __init__( method reset_parameters (line 62) | def reset_parameters(self): method forward (line 68) | def forward( method __repr__ (line 117) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/lstm.py class LSTMAggregation (line 10) | class LSTMAggregation(Aggregation): method __init__ (line 33) | def __init__(self, in_channels: int, out_channels: int, **kwargs): method reset_parameters (line 40) | def reset_parameters(self): method forward (line 44) | def forward( method __repr__ (line 59) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/mlp.py class MLPAggregation (line 8) | class MLPAggregation(Aggregation): method __init__ (line 35) | def __init__( method reset_parameters (line 57) | def reset_parameters(self): method forward (line 60) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 69) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/multi.py class MultiAggregation (line 14) | class MultiAggregation(Aggregation): method __init__ (line 44) | def __init__( method reset_parameters (line 135) | def reset_parameters(self): method get_out_channels (line 144) | def get_out_channels(self, in_channels: int) -> int: method forward (line 152) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method combine (line 173) | def combine(self, inputs: List[Tensor]) -> Tensor: method __repr__ (line 197) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/patch_transformer.py class PatchTransformerAggregation (line 14) | class PatchTransformerAggregation(Aggregation): method __init__ (line 38) | def __init__( method reset_parameters (line 85) | def reset_parameters(self) -> None: method forward (line 94) | def forward( method __repr__ (line 147) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/quantile.py class QuantileAggregation (line 10) | class QuantileAggregation(Aggregation): method __init__ (line 53) | def __init__(self, q: Union[float, List[float]], method forward (line 71) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 133) | def __repr__(self) -> str: class MedianAggregation (line 137) | class MedianAggregation(QuantileAggregation): method __init__ (line 157) | def __init__(self, fill_value: float = 0.0): method __repr__ (line 160) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/scaler.py class DegreeScalerAggregation (line 11) | class DegreeScalerAggregation(Aggregation): method __init__ (line 34) | def __init__( method reset_parameters (line 71) | def reset_parameters(self): method forward (line 75) | def forward(self, x: Tensor, index: Optional[Tensor] = None, FILE: torch_geometric/nn/aggr/set2set.py class Set2Set (line 10) | class Set2Set(Aggregation): method __init__ (line 32) | def __init__(self, in_channels: int, processing_steps: int, **kwargs): method reset_parameters (line 40) | def reset_parameters(self): method forward (line 43) | def forward(self, x: Tensor, index: Optional[Tensor] = None, method __repr__ (line 64) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/set_transformer.py class SetTransformerAggregation (line 14) | class SetTransformerAggregation(Aggregation): method __init__ (line 46) | def __init__( method reset_parameters (line 79) | def reset_parameters(self): method forward (line 87) | def forward( method __repr__ (line 112) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/sort.py class SortAggregation (line 10) | class SortAggregation(Aggregation): method __init__ (line 29) | def __init__(self, k: int): method forward (line 34) | def forward( method __repr__ (line 69) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/utils.py class MultiheadAttentionBlock (line 8) | class MultiheadAttentionBlock(torch.nn.Module): method __init__ (line 32) | def __init__(self, channels: int, heads: int = 1, layer_norm: bool = T... method reset_parameters (line 53) | def reset_parameters(self): method forward (line 61) | def forward(self, x: Tensor, y: Tensor, x_mask: Optional[Tensor] = None, method __repr__ (line 84) | def __repr__(self) -> str: class SetAttentionBlock (line 91) | class SetAttentionBlock(torch.nn.Module): method __init__ (line 109) | def __init__(self, channels: int, heads: int = 1, layer_norm: bool = T... method reset_parameters (line 115) | def reset_parameters(self): method forward (line 118) | def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: method __repr__ (line 121) | def __repr__(self) -> str: class InducedSetAttentionBlock (line 128) | class InducedSetAttentionBlock(torch.nn.Module): method __init__ (line 152) | def __init__(self, channels: int, num_induced_points: int, heads: int ... method reset_parameters (line 162) | def reset_parameters(self): method forward (line 167) | def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: method __repr__ (line 171) | def __repr__(self) -> str: class PoolingByMultiheadAttention (line 179) | class PoolingByMultiheadAttention(torch.nn.Module): method __init__ (line 201) | def __init__(self, channels: int, num_seed_points: int = 1, heads: int... method reset_parameters (line 210) | def reset_parameters(self): method forward (line 215) | def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: method __repr__ (line 219) | def __repr__(self) -> str: FILE: torch_geometric/nn/aggr/variance_preserving.py class VariancePreservingAggregation (line 10) | class VariancePreservingAggregation(Aggregation): method forward (line 19) | def forward(self, x: Tensor, index: Optional[Tensor] = None, FILE: torch_geometric/nn/attention/performer.py function _orthogonal_matrix (line 8) | def _orthogonal_matrix(dim: int) -> Tensor: function orthogonal_matrix (line 17) | def orthogonal_matrix(num_rows: int, num_cols: int) -> Tensor: function linear_attention (line 37) | def linear_attention(q: Tensor, k: Tensor, v: Tensor) -> Tensor: function generalized_kernel (line 53) | def generalized_kernel( class PerformerProjection (line 66) | class PerformerProjection(torch.nn.Module): method __init__ (line 79) | def __init__(self, num_cols: int, kernel: Callable = torch.nn.ReLU()): method forward (line 91) | def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: class PerformerAttention (line 98) | class PerformerAttention(torch.nn.Module): method __init__ (line 119) | def __init__( method forward (line 147) | def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: method redraw_projection_matrix (line 176) | def redraw_projection_matrix(self): method _reset_parameters (line 186) | def _reset_parameters(self): method __repr__ (line 193) | def __repr__(self) -> str: FILE: torch_geometric/nn/attention/polynormer.py class PolynormerAttention (line 8) | class PolynormerAttention(torch.nn.Module): method __init__ (line 27) | def __init__( method forward (line 54) | def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: method reset_parameters (line 95) | def reset_parameters(self) -> None: method __repr__ (line 104) | def __repr__(self) -> str: FILE: torch_geometric/nn/attention/qformer.py class QFormer (line 6) | class QFormer(torch.nn.Module): method __init__ (line 24) | def __init__( method forward (line 54) | def forward(self, x: torch.Tensor) -> torch.Tensor: method __repr__ (line 68) | def __repr__(self) -> str: FILE: torch_geometric/nn/attention/sgformer.py class SGFormerAttention (line 7) | class SGFormerAttention(torch.nn.Module): method __init__ (line 22) | def __init__( method forward (line 42) | def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: method reset_parameters (line 91) | def reset_parameters(self): method __repr__ (line 96) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/agnn_conv.py class AGNNConv (line 13) | class AGNNConv(MessagePassing): method __init__ (line 44) | def __init__(self, requires_grad: bool = True, add_self_loops: bool = ... method reset_parameters (line 59) | def reset_parameters(self): method forward (line 64) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: method message (line 78) | def message(self, x_j: Tensor, x_norm_i: Tensor, x_norm_j: Tensor, FILE: torch_geometric/nn/conv/antisymmetric_conv.py class AntiSymmetricConv (line 14) | class AntiSymmetricConv(torch.nn.Module): method __init__ (line 54) | def __init__( method reset_parameters (line 87) | def reset_parameters(self): method forward (line 93) | def forward(self, x: Tensor, edge_index: Adj, *args, **kwargs) -> Tensor: method __repr__ (line 111) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/appnp.py class APPNP (line 13) | class APPNP(MessagePassing): method __init__ (line 61) | def __init__(self, K: int, alpha: float, dropout: float = 0., method reset_parameters (line 76) | def reset_parameters(self): method forward (line 81) | def forward( method message (line 135) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 138) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 141) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/arma_conv.py class ARMAConv (line 15) | class ARMAConv(MessagePassing): method __init__ (line 61) | def __init__(self, in_channels: int, out_channels: int, method reset_parameters (line 97) | def reset_parameters(self): method forward (line 105) | def forward(self, x: Tensor, edge_index: Adj, method message (line 141) | def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: method message_and_aggregate (line 144) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method initialize_parameters (line 148) | def initialize_parameters(self, module, input): method __repr__ (line 160) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/cg_conv.py class CGConv (line 12) | class CGConv(MessagePassing): method __init__ (line 54) | def __init__(self, channels: Union[int, Tuple[int, int]], dim: int = 0, method reset_parameters (line 74) | def reset_parameters(self): method forward (line 81) | def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj, method message (line 93) | def message(self, x_i, x_j, edge_attr: OptTensor) -> Tensor: method __repr__ (line 100) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/cheb_conv.py class ChebConv (line 14) | class ChebConv(MessagePassing): method __init__ (line 73) | def __init__( method reset_parameters (line 103) | def reset_parameters(self): method __norm__ (line 109) | def __norm__( method forward (line 142) | def forward( method message (line 181) | def message(self, x_j: Tensor, norm: Tensor) -> Tensor: method __repr__ (line 184) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/cluster_gcn_conv.py class ClusterGCNConv (line 18) | class ClusterGCNConv(MessagePassing): method __init__ (line 50) | def __init__(self, in_channels: int, out_channels: int, method reset_parameters (line 68) | def reset_parameters(self): method forward (line 73) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: method message (line 126) | def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: method message_and_aggregate (line 129) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 132) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/cugraph/base.py class CuGraphModule (line 26) | class CuGraphModule(torch.nn.Module): # pragma: no cover method __init__ (line 30) | def __init__(self): method reset_parameters (line 37) | def reset_parameters(self): method get_cugraph (line 40) | def get_cugraph( method get_typed_cugraph (line 84) | def get_typed_cugraph( method forward (line 144) | def forward( FILE: torch_geometric/nn/conv/cugraph/gat_conv.py class CuGraphGATConv (line 21) | class CuGraphGATConv(CuGraphModule): # pragma: no cover method __init__ (line 32) | def __init__( method reset_parameters (line 61) | def reset_parameters(self): method forward (line 68) | def forward( method __repr__ (line 93) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/cugraph/rgcn_conv.py class CuGraphRGCNConv (line 23) | class CuGraphRGCNConv(CuGraphModule): # pragma: no cover method __init__ (line 35) | def __init__(self, in_channels: int, out_channels: int, num_relations:... method reset_parameters (line 71) | def reset_parameters(self): method forward (line 79) | def forward( method __repr__ (line 111) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/cugraph/sage_conv.py class CuGraphSAGEConv (line 22) | class CuGraphSAGEConv(CuGraphModule): # pragma: no cover method __init__ (line 33) | def __init__( method reset_parameters (line 66) | def reset_parameters(self): method forward (line 71) | def forward( method __repr__ (line 94) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/dir_gnn_conv.py class DirGNNConv (line 9) | class DirGNNConv(torch.nn.Module): method __init__ (line 26) | def __init__( method reset_parameters (line 54) | def reset_parameters(self): method forward (line 61) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: method __repr__ (line 73) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/dna_conv.py class Linear (line 15) | class Linear(torch.nn.Module): method __init__ (line 16) | def __init__(self, in_channels, out_channels, groups=1, bias=True): method reset_parameters (line 34) | def reset_parameters(self): method forward (line 38) | def forward(self, src): method __repr__ (line 57) | def __repr__(self) -> str: # pragma: no cover function restricted_softmax (line 62) | def restricted_softmax(src, dim: int = -1, margin: float = 0.): class Attention (line 69) | class Attention(torch.nn.Module): method __init__ (line 70) | def __init__(self, dropout=0): method forward (line 74) | def forward(self, query, key, value): method compute_attention (line 77) | def compute_attention(self, query, key, value): method __repr__ (line 95) | def __repr__(self) -> str: # pragma: no cover class MultiHead (line 99) | class MultiHead(Attention): method __init__ (line 100) | def __init__(self, in_channels, out_channels, heads=1, groups=1, dropo... method reset_parameters (line 120) | def reset_parameters(self): method forward (line 125) | def forward(self, query, key, value): method __repr__ (line 163) | def __repr__(self) -> str: # pragma: no cover class DNAConv (line 170) | class DNAConv(MessagePassing): method __init__ (line 233) | def __init__(self, channels: int, heads: int = 1, groups: int = 1, method reset_parameters (line 252) | def reset_parameters(self): method forward (line 258) | def forward( method message (line 303) | def message(self, x_i: Tensor, x_j: Tensor, edge_weight: Tensor) -> Te... method __repr__ (line 308) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/edge_conv.py class EdgeConv (line 17) | class EdgeConv(MessagePassing): method __init__ (line 48) | def __init__(self, nn: Callable, aggr: str = 'max', **kwargs): method reset_parameters (line 53) | def reset_parameters(self): method forward (line 57) | def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj) -> Te... method message (line 64) | def message(self, x_i: Tensor, x_j: Tensor) -> Tensor: method __repr__ (line 67) | def __repr__(self) -> str: class DynamicEdgeConv (line 71) | class DynamicEdgeConv(MessagePassing): method __init__ (line 103) | def __init__(self, nn: Callable, k: int, aggr: str = 'max', method reset_parameters (line 115) | def reset_parameters(self): method forward (line 118) | def forward( method message (line 142) | def message(self, x_i: Tensor, x_j: Tensor) -> Tensor: method __repr__ (line 145) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/eg_conv.py class EGConv (line 15) | class EGConv(MessagePassing): method __init__ (line 80) | def __init__( method reset_parameters (line 124) | def reset_parameters(self): method forward (line 132) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: method message (line 205) | def message(self, x_j: Tensor) -> Tensor: method aggregate (line 208) | def aggregate(self, inputs: Tensor, index: Tensor, method message_and_aggregate (line 232) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 258) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/fa_conv.py class FAConv (line 27) | class FAConv(MessagePassing): method __init__ (line 83) | def __init__(self, channels: int, eps: float = 0.1, dropout: float = 0.0, method reset_parameters (line 106) | def reset_parameters(self): method forward (line 114) | def forward( method forward (line 125) | def forward( # noqa: F811 method forward (line 136) | def forward( # noqa: F811 method forward (line 146) | def forward( # noqa: F811 method message (line 233) | def message(self, x_j: Tensor, alpha_j: Tensor, alpha_i: Tensor, method __repr__ (line 241) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/feast_conv.py class FeaStConv (line 15) | class FeaStConv(MessagePassing): method __init__ (line 52) | def __init__(self, in_channels: int, out_channels: int, heads: int = 1, method reset_parameters (line 75) | def reset_parameters(self): method forward (line 82) | def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj) -> Te... method message (line 103) | def message(self, x_i: Tensor, x_j: Tensor) -> Tensor: method __repr__ (line 109) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/film_conv.py class FiLMConv (line 19) | class FiLMConv(MessagePassing): method __init__ (line 71) | def __init__( method reset_parameters (line 110) | def reset_parameters(self): method forward (line 118) | def forward( method message (line 157) | def message(self, x_j: Tensor, beta_i: Tensor, gamma_i: Tensor) -> Ten... method __repr__ (line 163) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/fused_gat_conv.py class FusedGATConv (line 11) | class FusedGATConv(GATConv): # pragma: no cover method __init__ (line 29) | def __init__(self, *args, **kwargs): method to_graph_format (line 47) | def to_graph_format( method forward (line 73) | def forward( FILE: torch_geometric/nn/conv/gat_conv.py class GATConv (line 35) | class GATConv(MessagePassing): method __init__ (line 130) | def __init__( method reset_parameters (line 204) | def reset_parameters(self): method forward (line 222) | def forward( method forward (line 233) | def forward( # noqa: F811 method forward (line 244) | def forward( # noqa: F811 method forward (line 254) | def forward( # noqa: F811 method edge_update (line 387) | def edge_update(self, alpha_j: Tensor, alpha_i: OptTensor, method message (line 408) | def message(self, x_j: Tensor, alpha: Tensor) -> Tensor: method __repr__ (line 411) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/gated_graph_conv.py class GatedGraphConv (line 11) | class GatedGraphConv(MessagePassing): method __init__ (line 48) | def __init__(self, out_channels: int, num_layers: int, aggr: str = 'add', method reset_parameters (line 60) | def reset_parameters(self): method forward (line 65) | def forward(self, x: Tensor, edge_index: Adj, method message (line 84) | def message(self, x_j: Tensor, edge_weight: OptTensor): method message_and_aggregate (line 87) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 90) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/gatv2_conv.py class GATv2Conv (line 34) | class GATv2Conv(MessagePassing): method __init__ (line 133) | def __init__( method reset_parameters (line 209) | def reset_parameters(self): method forward (line 221) | def forward( method forward (line 231) | def forward( # noqa: F811 method forward (line 241) | def forward( # noqa: F811 method forward (line 250) | def forward( # noqa: F811 method edge_update (line 358) | def edge_update(self, x_j: Tensor, x_i: Tensor, edge_attr: OptTensor, method message (line 377) | def message(self, x_j: Tensor, alpha: Tensor) -> Tensor: method __repr__ (line 380) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/gcn2_conv.py class GCN2Conv (line 15) | class GCN2Conv(MessagePassing): method __init__ (line 74) | def __init__(self, channels: int, alpha: float, theta: float = None, method reset_parameters (line 104) | def reset_parameters(self): method forward (line 111) | def forward(self, x: Tensor, x_0: Tensor, edge_index: Adj, method message (line 155) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 158) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 161) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/gcn_conv.py function gcn_norm (line 30) | def gcn_norm( # noqa: F811 function gcn_norm (line 38) | def gcn_norm( # noqa: F811 function gcn_norm (line 45) | def gcn_norm( # noqa: F811 class GCNConv (line 116) | class GCNConv(MessagePassing): method __init__ (line 178) | def __init__( method reset_parameters (line 220) | def reset_parameters(self): method forward (line 227) | def forward(self, x: Tensor, edge_index: Adj, method message (line 270) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 273) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: FILE: torch_geometric/nn/conv/gen_conv.py class MLP (line 21) | class MLP(Sequential): method __init__ (line 22) | def __init__(self, channels: List[int], norm: Optional[str] = None, class GENConv (line 44) | class GENConv(MessagePassing): method __init__ (line 120) | def __init__( method reset_parameters (line 189) | def reset_parameters(self): method forward (line 203) | def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, method message (line 231) | def message(self, x_j: Tensor, edge_attr: OptTensor) -> Tensor: method __repr__ (line 241) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/general_conv.py class GeneralConv (line 21) | class GeneralConv(MessagePassing): method __init__ (line 69) | def __init__( method reset_parameters (line 133) | def reset_parameters(self): method forward (line 143) | def forward( method message_basic (line 162) | def message_basic(self, x_i: Tensor, x_j: Tensor, edge_attr: OptTensor): method message (line 171) | def message(self, x_i: Tensor, x_j: Tensor, edge_index_i: Tensor, FILE: torch_geometric/nn/conv/gin_conv.py class GINConv (line 19) | class GINConv(MessagePassing): method __init__ (line 56) | def __init__(self, nn: Callable, eps: float = 0., train_eps: bool = Fa... method reset_parameters (line 68) | def reset_parameters(self): method forward (line 73) | def forward( method message (line 92) | def message(self, x_j: Tensor) -> Tensor: method message_and_aggregate (line 95) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: method __repr__ (line 100) | def __repr__(self) -> str: class GINEConv (line 104) | class GINEConv(MessagePassing): method __init__ (line 143) | def __init__(self, nn: torch.nn.Module, eps: float = 0., method reset_parameters (line 169) | def reset_parameters(self): method forward (line 175) | def forward( method message (line 195) | def message(self, x_j: Tensor, edge_attr: Tensor) -> Tensor: method __repr__ (line 206) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/gmm_conv.py class GMMConv (line 13) | class GMMConv(MessagePassing): method __init__ (line 73) | def __init__(self, in_channels: Union[int, Tuple[int, int]], method reset_parameters (line 120) | def reset_parameters(self): method forward (line 130) | def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, method message (line 154) | def message(self, x_j: Tensor, edge_attr: Tensor) -> Tensor: method initialize_parameters (line 179) | def initialize_parameters(self, module, input): method __repr__ (line 200) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/gps_conv.py class GPSConv (line 20) | class GPSConv(torch.nn.Module): method __init__ (line 62) | def __init__( method reset_parameters (line 119) | def reset_parameters(self): method forward (line 132) | def forward( method __repr__ (line 182) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/graph_conv.py class GraphConv (line 13) | class GraphConv(MessagePassing): method __init__ (line 51) | def __init__( method reset_parameters (line 72) | def reset_parameters(self): method forward (line 77) | def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, method message (line 94) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 97) | def message_and_aggregate( FILE: torch_geometric/nn/conv/gravnet_conv.py class GravNetConv (line 19) | class GravNetConv(MessagePassing): method __init__ (line 54) | def __init__(self, in_channels: int, out_channels: int, method reset_parameters (line 81) | def reset_parameters(self): method forward (line 88) | def forward( method message (line 126) | def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: method __repr__ (line 129) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/han_conv.py function group (line 15) | def group( class HANConv (line 34) | class HANConv(MessagePassing): method __init__ (line 65) | def __init__( method reset_parameters (line 103) | def reset_parameters(self): method forward (line 111) | def forward( method message (line 170) | def message(self, x_j: Tensor, alpha_i: Tensor, alpha_j: Tensor, method __repr__ (line 181) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/heat_conv.py class HEATConv (line 14) | class HEATConv(MessagePassing): method __init__ (line 61) | def __init__(self, in_channels: int, out_channels: int, method reset_parameters (line 94) | def reset_parameters(self): method forward (line 102) | def forward(self, x: Tensor, edge_index: Adj, node_type: Tensor, method message (line 126) | def message(self, x_i: Tensor, x_j: Tensor, edge_type_emb: Tensor, method __repr__ (line 141) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/hetero_conv.py function group (line 13) | def group(xs: List[Tensor], aggr: Optional[str]) -> Optional[Tensor]: class HeteroConv (line 29) | class HeteroConv(torch.nn.Module): method __init__ (line 63) | def __init__( method reset_parameters (line 86) | def reset_parameters(self): method forward (line 91) | def forward( method __repr__ (line 171) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/hgt_conv.py class HGTConv (line 17) | class HGTConv(MessagePassing): method __init__ (line 43) | def __init__( method reset_parameters (line 98) | def reset_parameters(self): method _cat (line 107) | def _cat(self, x_dict: Dict[str, Tensor]) -> Tuple[Tensor, Dict[str, i... method _construct_src_node_feat (line 118) | def _construct_src_node_feat( method forward (line 156) | def forward( method message (line 225) | def message(self, k_j: Tensor, q_i: Tensor, v_j: Tensor, edge_attr: Te... method __repr__ (line 234) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/hypergraph_conv.py class HypergraphConv (line 15) | class HypergraphConv(MessagePassing): method __init__ (line 77) | def __init__( method reset_parameters (line 123) | def reset_parameters(self): method forward (line 131) | def forward(self, x: Tensor, hyperedge_index: Tensor, method message (line 207) | def message(self, x_j: Tensor, norm_i: Tensor, alpha: Tensor) -> Tensor: FILE: torch_geometric/nn/conv/le_conv.py class LEConv (line 10) | class LEConv(MessagePassing): method __init__ (line 47) | def __init__(self, in_channels: Union[int, Tuple[int, int]], method reset_parameters (line 64) | def reset_parameters(self): method forward (line 70) | def forward(self, x: Union[Tensor, PairTensor], edge_index: Adj, method message (line 84) | def message(self, a_j: Tensor, b_i: Tensor, method __repr__ (line 89) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/lg_conv.py class LGConv (line 9) | class LGConv(MessagePassing): method __init__ (line 32) | def __init__(self, normalize: bool = True, **kwargs): method forward (line 37) | def forward(self, x: Tensor, edge_index: Adj, method message (line 52) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 55) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: FILE: torch_geometric/nn/conv/meshcnn_conv.py class MeshCNNConv (line 13) | class MeshCNNConv(MessagePassing): method __init__ (line 272) | def __init__(self, in_channels: int, out_channels: int, method forward (line 288) | def forward(self, x: Tensor, edge_index: Tensor): method message (line 363) | def message(self, x_j: Tensor) -> Tensor: method update (line 418) | def update(self, inputs: Tensor, x: Tensor) -> Tensor: method _assert_kernels (line 433) | def _assert_kernels(self, kernels: ModuleList): FILE: torch_geometric/nn/conv/message_passing.py class MessagePassing (line 39) | class MessagePassing(torch.nn.Module): method __init__ (line 110) | def __init__( method reset_parameters (line 183) | def reset_parameters(self) -> None: method __setstate__ (line 188) | def __setstate__(self, data: Dict[str, Any]) -> None: method __repr__ (line 194) | def __repr__(self) -> str: method _check_input (line 204) | def _check_input( method _set_size (line 249) | def _set_size( method _index_select (line 263) | def _index_select(self, src: Tensor, index) -> Tensor: method _index_select_safe (line 269) | def _index_select_safe(self, src: Tensor, index: Tensor) -> Tensor: method _lift (line 292) | def _lift( method _collect (line 335) | def _collect( method forward (line 418) | def forward(self, *args: Any, **kwargs: Any) -> Any: method propagate (line 421) | def propagate( method message (line 565) | def message(self, x_j: Tensor) -> Tensor: method aggregate (line 577) | def aggregate( method message_and_aggregate (line 598) | def message_and_aggregate(self, edge_index: Adj) -> Tensor: method update (line 609) | def update(self, inputs: Tensor) -> Tensor: method edge_updater (line 620) | def edge_updater( method edge_update (line 668) | def edge_update(self) -> Tensor: method decomposed_layers (line 681) | def decomposed_layers(self) -> int: method decomposed_layers (line 685) | def decomposed_layers(self, decomposed_layers: int) -> None: method explain (line 708) | def explain(self) -> Optional[bool]: method explain (line 712) | def explain(self, explain: Optional[bool]) -> None: method explain_message (line 743) | def explain_message( method register_propagate_forward_pre_hook (line 776) | def register_propagate_forward_pre_hook( method register_propagate_forward_hook (line 800) | def register_propagate_forward_hook( method register_message_forward_pre_hook (line 825) | def register_message_forward_pre_hook( method register_message_forward_hook (line 837) | def register_message_forward_hook(self, hook: Callable) -> RemovableHa... method register_aggregate_forward_pre_hook (line 847) | def register_aggregate_forward_pre_hook( method register_aggregate_forward_hook (line 859) | def register_aggregate_forward_hook( method register_message_and_aggregate_forward_pre_hook (line 872) | def register_message_and_aggregate_forward_pre_hook( method register_message_and_aggregate_forward_hook (line 885) | def register_message_and_aggregate_forward_hook( method register_edge_update_forward_pre_hook (line 898) | def register_edge_update_forward_pre_hook( method register_edge_update_forward_hook (line 911) | def register_edge_update_forward_hook( method _set_jittable_templates (line 926) | def _set_jittable_templates(self, raise_on_error: bool = False) -> None: method _get_propagate_signature (line 1001) | def _get_propagate_signature(self) -> Signature: method _get_edge_updater_signature (line 1012) | def _get_edge_updater_signature(self) -> Signature: method jittable (line 1023) | def jittable(self, typing: Optional[str] = None) -> 'MessagePassing': FILE: torch_geometric/nn/conv/mf_conv.py class MFConv (line 13) | class MFConv(MessagePassing): method __init__ (line 46) | def __init__(self, in_channels: Union[int, Tuple[int, int]], method reset_parameters (line 70) | def reset_parameters(self): method forward (line 77) | def forward( method message (line 115) | def message(self, x_j: Tensor) -> Tensor: method message_and_aggregate (line 118) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: FILE: torch_geometric/nn/conv/mixhop_conv.py class MixHopConv (line 15) | class MixHopConv(MessagePassing): method __init__ (line 50) | def __init__( method reset_parameters (line 83) | def reset_parameters(self): method forward (line 89) | def forward(self, x: Tensor, edge_index: Adj, method message (line 116) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 119) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 122) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/nn_conv.py class NNConv (line 13) | class NNConv(MessagePassing): method __init__ (line 63) | def __init__(self, in_channels: Union[int, Tuple[int, int]], method reset_parameters (line 89) | def reset_parameters(self): method forward (line 96) | def forward( method message (line 119) | def message(self, x_j: Tensor, edge_attr: Tensor) -> Tensor: method __repr__ (line 124) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/pan_conv.py class PANConv (line 13) | class PANConv(MessagePassing): method __init__ (line 44) | def __init__(self, in_channels: int, out_channels: int, filter_size: int, method reset_parameters (line 59) | def reset_parameters(self): method forward (line 64) | def forward( method message (line 101) | def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: method message_and_aggregate (line 104) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method panentropy (line 107) | def panentropy(self, adj_t: SparseTensor, method __repr__ (line 132) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/pdn_conv.py class PDNConv (line 12) | class PDNConv(MessagePassing): method __init__ (line 47) | def __init__(self, in_channels: int, out_channels: int, edge_dim: int, method reset_parameters (line 77) | def reset_parameters(self): method forward (line 86) | def forward(self, x: Tensor, edge_index: Adj, method message (line 119) | def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: method message_and_aggregate (line 122) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 125) | def __repr__(self): FILE: torch_geometric/nn/conv/pna_conv.py class PNAConv (line 17) | class PNAConv(MessagePassing): method __init__ (line 91) | def __init__( method reset_parameters (line 148) | def reset_parameters(self): method forward (line 158) | def forward(self, x: Tensor, edge_index: Adj, method message (line 175) | def message(self, x_i: Tensor, x_j: Tensor, method __repr__ (line 190) | def __repr__(self): method get_degree_histogram (line 196) | def get_degree_histogram(loader: DataLoader) -> Tensor: FILE: torch_geometric/nn/conv/point_conv.py class PointNetConv (line 19) | class PointNetConv(MessagePassing): method __init__ (line 64) | def __init__(self, local_nn: Optional[Callable] = None, method reset_parameters (line 76) | def reset_parameters(self): method forward (line 81) | def forward( method message (line 110) | def message(self, x_j: Optional[Tensor], pos_i: Tensor, method __repr__ (line 119) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/point_gnn_conv.py class PointGNNConv (line 9) | class PointGNNConv(MessagePassing): method __init__ (line 50) | def __init__( method reset_parameters (line 66) | def reset_parameters(self): method forward (line 72) | def forward(self, x: Tensor, pos: Tensor, edge_index: Adj) -> Tensor: method message (line 78) | def message(self, pos_j: Tensor, pos_i: Tensor, x_i: Tensor, method __repr__ (line 84) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/point_transformer_conv.py class PointTransformerConv (line 18) | class PointTransformerConv(MessagePassing): method __init__ (line 77) | def __init__(self, in_channels: Union[int, Tuple[int, int]], method reset_parameters (line 102) | def reset_parameters(self): method forward (line 111) | def forward( method message (line 140) | def message(self, x_j: Tensor, pos_i: Tensor, pos_j: Tensor, method __repr__ (line 151) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/ppf_conv.py function get_angle (line 19) | def get_angle(v1: Tensor, v2: Tensor) -> Tensor: function point_pair_features (line 24) | def point_pair_features(pos_i: Tensor, pos_j: Tensor, normal_i: Tensor, class PPFConv (line 35) | class PPFConv(MessagePassing): method __init__ (line 82) | def __init__(self, local_nn: Optional[Callable] = None, method reset_parameters (line 94) | def reset_parameters(self): method forward (line 99) | def forward( method message (line 133) | def message(self, x_j: OptTensor, pos_i: Tensor, pos_j: Tensor, method __repr__ (line 142) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/res_gated_graph_conv.py class ResGatedGraphConv (line 13) | class ResGatedGraphConv(MessagePassing): method __init__ (line 57) | def __init__( method reset_parameters (line 97) | def reset_parameters(self): method forward (line 107) | def forward( method message (line 138) | def message(self, k_i: Tensor, q_j: Tensor, v_j: Tensor, FILE: torch_geometric/nn/conv/rgat_conv.py class RGATConv (line 16) | class RGATConv(MessagePassing): method __init__ (line 174) | def __init__( method reset_parameters (line 298) | def reset_parameters(self): method forward (line 316) | def forward( method message (line 373) | def message(self, x_i: Tensor, x_j: Tensor, edge_type: Tensor, method update (line 507) | def update(self, aggr_out: Tensor) -> Tensor: method __repr__ (line 531) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/rgcn_conv.py function masked_edge_index (line 23) | def masked_edge_index(edge_index: Adj, edge_mask: Tensor) -> Adj: class RGCNConv (line 29) | class RGCNConv(MessagePassing): method __init__ (line 92) | def __init__( method reset_parameters (line 157) | def reset_parameters(self): method forward (line 164) | def forward(self, x: Union[OptTensor, Tuple[OptTensor, Tensor]], method message (line 284) | def message(self, x_j: Tensor, edge_type_ptr: OptTensor) -> Tensor: method message_and_aggregate (line 292) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 297) | def __repr__(self) -> str: class FastRGCNConv (line 302) | class FastRGCNConv(RGCNConv): method forward (line 304) | def forward(self, x: Union[OptTensor, Tuple[OptTensor, Tensor]], method message (line 340) | def message(self, x_j: Tensor, edge_type: Tensor, method aggregate (line 363) | def aggregate(self, inputs: Tensor, edge_type: Tensor, index: Tensor, FILE: torch_geometric/nn/conv/sage_conv.py class SAGEConv (line 13) | class SAGEConv(MessagePassing): method __init__ (line 66) | def __init__( method reset_parameters (line 112) | def reset_parameters(self): method forward (line 120) | def forward( method message (line 146) | def message(self, x_j: Tensor) -> Tensor: method message_and_aggregate (line 149) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: method __repr__ (line 154) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/sg_conv.py class SGConv (line 12) | class SGConv(MessagePassing): method __init__ (line 56) | def __init__(self, in_channels: int, out_channels: int, K: int = 1, method reset_parameters (line 74) | def reset_parameters(self): method forward (line 79) | def forward(self, x: Tensor, edge_index: Adj, method message (line 103) | def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: method message_and_aggregate (line 106) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 109) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/signed_conv.py class SignedConv (line 12) | class SignedConv(MessagePassing): method __init__ (line 66) | def __init__(self, in_channels: int, out_channels: int, first_aggr: bool, method reset_parameters (line 89) | def reset_parameters(self): method forward (line 96) | def forward( method message (line 140) | def message(self, x_j: Tensor) -> Tensor: method message_and_aggregate (line 143) | def message_and_aggregate(self, adj_t: Adj, x: PairTensor) -> Tensor: method __repr__ (line 148) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/simple_conv.py class SimpleConv (line 19) | class SimpleConv(MessagePassing): method __init__ (line 51) | def __init__( method forward (line 64) | def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, method message (line 95) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 98) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: FILE: torch_geometric/nn/conv/spline_conv.py class SplineConv (line 21) | class SplineConv(MessagePassing): method __init__ (line 62) | def __init__( method reset_parameters (line 117) | def reset_parameters(self): method forward (line 126) | def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, method message (line 150) | def message(self, x_j: Tensor, edge_attr: Tensor) -> Tensor: method initialize_parameters (line 156) | def initialize_parameters(self, module, input): method __repr__ (line 166) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/ssg_conv.py class SSGConv (line 12) | class SSGConv(MessagePassing): method __init__ (line 64) | def __init__(self, in_channels: int, out_channels: int, alpha: float, method reset_parameters (line 83) | def reset_parameters(self): method forward (line 88) | def forward(self, x: Tensor, edge_index: Adj, method message (line 114) | def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: method message_and_aggregate (line 117) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 120) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/supergat_conv.py class SuperGATConv (line 25) | class SuperGATConv(MessagePassing): method __init__ (line 128) | def __init__(self, in_channels: int, out_channels: int, heads: int = 1, method reset_parameters (line 173) | def reset_parameters(self): method forward (line 180) | def forward( method message (line 254) | def message(self, edge_index_i: Tensor, x_i: Tensor, x_j: Tensor, method negative_sampling (line 260) | def negative_sampling(self, edge_index: Tensor, num_nodes: int, method positive_sampling (line 279) | def positive_sampling(self, edge_index: Tensor) -> Tensor: method get_attention (line 285) | def get_attention(self, edge_index_i: Tensor, x_i: Tensor, x_j: Tensor, method get_attention_loss (line 306) | def get_attention_loss(self) -> Tensor: method __repr__ (line 316) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/tag_conv.py class TAGConv (line 12) | class TAGConv(MessagePassing): method __init__ (line 45) | def __init__(self, in_channels: int, out_channels: int, K: int = 3, method reset_parameters (line 66) | def reset_parameters(self): method forward (line 72) | def forward(self, x: Tensor, edge_index: Adj, method message (line 98) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 101) | def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor: method __repr__ (line 104) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/transformer_conv.py class TransformerConv (line 26) | class TransformerConv(MessagePassing): method __init__ (line 100) | def __init__( method reset_parameters (line 155) | def reset_parameters(self): method forward (line 167) | def forward( method forward (line 177) | def forward( # noqa: F811 method forward (line 187) | def forward( # noqa: F811 method forward (line 196) | def forward( # noqa: F811 method message (line 263) | def message(self, query_i: Tensor, key_j: Tensor, value_j: Tensor, method __repr__ (line 285) | def __repr__(self) -> str: FILE: torch_geometric/nn/conv/utils/cheatsheet.py function paper_title (line 7) | def paper_title(cls: str) -> Optional[str]: function paper_link (line 13) | def paper_link(cls: str) -> Optional[str]: function supports_sparse_tensor (line 19) | def supports_sparse_tensor(cls: str) -> bool: function supports_edge_weights (line 25) | def supports_edge_weights(cls: str) -> bool: function supports_edge_features (line 31) | def supports_edge_features(cls: str) -> bool: function supports_bipartite_graphs (line 37) | def supports_bipartite_graphs(cls: str) -> bool: function supports_static_graphs (line 43) | def supports_static_graphs(cls: str) -> bool: function supports_lazy_initialization (line 48) | def supports_lazy_initialization(cls: str) -> bool: function processes_heterogeneous_graphs (line 55) | def processes_heterogeneous_graphs(cls: str) -> bool: function processes_hypergraphs (line 63) | def processes_hypergraphs(cls: str) -> bool: function processes_point_clouds (line 69) | def processes_point_clouds(cls: str) -> bool: FILE: torch_geometric/nn/conv/wl_conv.py class WLConv (line 16) | class WLConv(torch.nn.Module): method __init__ (line 34) | def __init__(self): method reset_parameters (line 38) | def reset_parameters(self): method forward (line 43) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor: method histogram (line 71) | def histogram(self, x: Tensor, batch: Optional[Tensor] = None, FILE: torch_geometric/nn/conv/wl_conv_continuous.py class WLConvContinuous (line 16) | class WLConvContinuous(MessagePassing): method __init__ (line 45) | def __init__(self, **kwargs): method forward (line 48) | def forward( method message (line 83) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 86) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: FILE: torch_geometric/nn/conv/x_conv.py class XConv (line 22) | class XConv(torch.nn.Module): method __init__ (line 70) | def __init__(self, in_channels: int, out_channels: int, dim: int, method reset_parameters (line 126) | def reset_parameters(self): method forward (line 132) | def forward(self, x: Tensor, pos: Tensor, batch: Optional[Tensor] = No... method __repr__ (line 163) | def __repr__(self) -> str: FILE: torch_geometric/nn/data_parallel.py class DataParallel (line 11) | class DataParallel(torch.nn.DataParallel): method __init__ (line 56) | def __init__(self, module, device_ids=None, output_device=None, method forward (line 70) | def forward(self, data_list): method scatter (line 95) | def scatter(self, data_list, device_ids): FILE: torch_geometric/nn/dense/dense_gat_conv.py class DenseGATConv (line 12) | class DenseGATConv(torch.nn.Module): method __init__ (line 14) | def __init__( method reset_parameters (line 50) | def reset_parameters(self): method forward (line 56) | def forward(self, x: Tensor, adj: Tensor, mask: Optional[Tensor] = None, method __repr__ (line 116) | def __repr__(self) -> str: FILE: torch_geometric/nn/dense/dense_gcn_conv.py class DenseGCNConv (line 10) | class DenseGCNConv(torch.nn.Module): method __init__ (line 12) | def __init__( method reset_parameters (line 35) | def reset_parameters(self): method forward (line 40) | def forward(self, x: Tensor, adj: Tensor, mask: OptTensor = None, method __repr__ (line 83) | def __repr__(self) -> str: FILE: torch_geometric/nn/dense/dense_gin_conv.py class DenseGINConv (line 10) | class DenseGINConv(torch.nn.Module): method __init__ (line 12) | def __init__( method reset_parameters (line 28) | def reset_parameters(self): method forward (line 33) | def forward(self, x: Tensor, adj: Tensor, mask: Optional[Tensor] = None, method __repr__ (line 68) | def __repr__(self) -> str: FILE: torch_geometric/nn/dense/dense_graph_conv.py class DenseGraphConv (line 8) | class DenseGraphConv(torch.nn.Module): method __init__ (line 10) | def __init__( method reset_parameters (line 29) | def reset_parameters(self): method forward (line 34) | def forward(self, x: Tensor, adj: Tensor, method __repr__ (line 77) | def __repr__(self) -> str: FILE: torch_geometric/nn/dense/dense_sage_conv.py class DenseSAGEConv (line 9) | class DenseSAGEConv(torch.nn.Module): method __init__ (line 19) | def __init__( method reset_parameters (line 37) | def reset_parameters(self): method forward (line 42) | def forward(self, x: Tensor, adj: Tensor, method __repr__ (line 75) | def __repr__(self) -> str: FILE: torch_geometric/nn/dense/diff_pool.py function dense_diff_pool (line 7) | def dense_diff_pool( FILE: torch_geometric/nn/dense/dmon_pool.py class DMoNPooling (line 12) | class DMoNPooling(torch.nn.Module): method __init__ (line 60) | def __init__(self, channels: Union[int, List[int]], k: int, method reset_parameters (line 74) | def reset_parameters(self): method forward (line 78) | def forward( method __repr__ (line 162) | def __repr__(self) -> str: FILE: torch_geometric/nn/dense/linear.py function is_uninitialized_parameter (line 20) | def is_uninitialized_parameter(x: Any) -> bool: function reset_weight_ (line 26) | def reset_weight_(weight: Tensor, in_channels: int, function reset_bias_ (line 45) | def reset_bias_(bias: Optional[Tensor], in_channels: int, class Linear (line 59) | class Linear(torch.nn.Module): method __init__ (line 88) | def __init__( method reset_parameters (line 116) | def reset_parameters(self): method forward (line 121) | def forward(self, x: Tensor) -> Tensor: method initialize_parameters (line 130) | def initialize_parameters(self, module, input): method _save_to_state_dict (line 138) | def _save_to_state_dict(self, destination, prefix, keep_vars): method _load_from_state_dict (line 150) | def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): method __repr__ (line 169) | def __repr__(self) -> str: class HeteroLinear (line 174) | class HeteroLinear(torch.nn.Module): method __init__ (line 207) | def __init__( method reset_parameters (line 241) | def reset_parameters(self): method forward_naive (line 248) | def forward_naive(self, x: Tensor, type_ptr: Tensor) -> Tensor: method forward_segmm (line 254) | def forward_segmm(self, x: Tensor, type_ptr: Tensor) -> Tensor: method _update_timing_cache (line 258) | def _update_timing_cache( method forward (line 287) | def forward(self, x: Tensor, type_vec: Tensor) -> Tensor: method initialize_parameters (line 332) | def initialize_parameters(self, module, input): method __repr__ (line 341) | def __repr__(self) -> str: class HeteroDictLinear (line 347) | class HeteroDictLinear(torch.nn.Module): method __init__ (line 370) | def __init__( method reset_parameters (line 414) | def reset_parameters(self): method forward (line 419) | def forward( method initialize_parameters (line 458) | def initialize_parameters(self, module, input): method __repr__ (line 468) | def __repr__(self) -> str: FILE: torch_geometric/nn/dense/mincut_pool.py function dense_mincut_pool (line 7) | def dense_mincut_pool( function _rank3_trace (line 109) | def _rank3_trace(x: Tensor) -> Tensor: function _rank3_diag (line 113) | def _rank3_diag(x: Tensor) -> Tensor: FILE: torch_geometric/nn/encoding.py class PositionalEncoding (line 13) | class PositionalEncoding(torch.nn.Module): method __init__ (line 35) | def __init__( method reset_parameters (line 58) | def reset_parameters(self): method forward (line 61) | def forward(self, x: Tensor) -> Tensor: method __repr__ (line 67) | def __repr__(self) -> str: class TemporalEncoding (line 71) | class TemporalEncoding(torch.nn.Module): method __init__ (line 91) | def __init__(self, out_channels: int, method reset_parameters (line 103) | def reset_parameters(self): method forward (line 106) | def forward(self, x: Tensor) -> Tensor: method __repr__ (line 110) | def __repr__(self) -> str: FILE: torch_geometric/nn/functional/bro.py function bro (line 6) | def bro( FILE: torch_geometric/nn/functional/gini.py function gini (line 4) | def gini(w: torch.Tensor) -> torch.Tensor: FILE: torch_geometric/nn/fx.py class Transformer (line 15) | class Transformer: method __init__ (line 68) | def __init__( method placeholder (line 81) | def placeholder(self, node: Node, target: Any, name: str): method get_attr (line 84) | def get_attr(self, node: Node, target: Any, name: str): method call_message_passing_module (line 87) | def call_message_passing_module(self, node: Node, target: Any, name: s... method call_global_pooling_module (line 90) | def call_global_pooling_module(self, node: Node, target: Any, name: str): method call_module (line 93) | def call_module(self, node: Node, target: Any, name: str): method call_method (line 96) | def call_method(self, node: Node, target: Any, name: str): method call_function (line 99) | def call_function(self, node: Node, target: Any, name: str): method output (line 102) | def output(self, node: Node, target: Any, name: str): method init_submodule (line 105) | def init_submodule(self, module: Module, target: str) -> Module: method graph (line 111) | def graph(self) -> Graph: method transform (line 114) | def transform(self) -> GraphModule: method _init_submodule (line 199) | def _init_submodule(self, module: Module, target: str) -> Module: method _is_level (line 214) | def _is_level(self, node: Node, name: str) -> bool: method _has_level_arg (line 217) | def _has_level_arg(self, node: Node, name: str) -> bool: method is_node_level (line 231) | def is_node_level(self, node: Node) -> bool: method is_edge_level (line 234) | def is_edge_level(self, node: Node) -> bool: method is_graph_level (line 237) | def is_graph_level(self, node: Node) -> bool: method has_node_level_arg (line 240) | def has_node_level_arg(self, node: Node) -> bool: method has_edge_level_arg (line 243) | def has_edge_level_arg(self, node: Node) -> bool: method has_graph_level_arg (line 246) | def has_graph_level_arg(self, node: Node) -> bool: method find_by_name (line 249) | def find_by_name(self, name: str) -> Optional[Node]: method find_by_target (line 255) | def find_by_target(self, target: Any) -> Optional[Node]: method replace_all_uses_with (line 261) | def replace_all_uses_with(self, to_replace: Node, replace_with: Node): function symbolic_trace (line 272) | def symbolic_trace( function get_submodule (line 380) | def get_submodule(module: Module, target: str) -> Module: function is_message_passing_op (line 387) | def is_message_passing_op(module: Module, op: str, target: str) -> bool: function is_global_pooling_op (line 394) | def is_global_pooling_op(module: Module, op: str, target: str) -> bool: FILE: torch_geometric/nn/glob.py class GlobalAttention (line 14) | class GlobalAttention(AttentionalAggregation): method __call__ (line 15) | def __call__(self, x, batch=None, size=None): function global_sort_pool (line 23) | def global_sort_pool(x, index, k): FILE: torch_geometric/nn/inits.py function uniform (line 8) | def uniform(size: int, value: Any): function kaiming_uniform (line 19) | def kaiming_uniform(value: Any, fan: int, a: float): function glorot (line 30) | def glorot(value: Any): function glorot_orthogonal (line 41) | def glorot_orthogonal(tensor, scale): function constant (line 48) | def constant(value: Any, fill_value: float): function zeros (line 58) | def zeros(value: Any): function ones (line 62) | def ones(tensor: Any): function normal (line 66) | def normal(value: Any, mean: float, std: float): function reset (line 76) | def reset(value: Any): FILE: torch_geometric/nn/kge/base.py class KGEModel (line 11) | class KGEModel(torch.nn.Module): method __init__ (line 21) | def __init__( method reset_parameters (line 37) | def reset_parameters(self): method forward (line 42) | def forward( method loss (line 57) | def loss( method loader (line 72) | def loader( method test (line 93) | def test( method random_sample (line 139) | def random_sample( method __repr__ (line 165) | def __repr__(self) -> str: FILE: torch_geometric/nn/kge/complex.py class ComplEx (line 9) | class ComplEx(KGEModel): method __init__ (line 35) | def __init__( method reset_parameters (line 50) | def reset_parameters(self): method forward (line 56) | def forward( method loss (line 75) | def loss( function triple_dot (line 93) | def triple_dot(x: Tensor, y: Tensor, z: Tensor) -> Tensor: FILE: torch_geometric/nn/kge/distmult.py class DistMult (line 8) | class DistMult(KGEModel): method __init__ (line 36) | def __init__( method reset_parameters (line 50) | def reset_parameters(self): method forward (line 54) | def forward( method loss (line 67) | def loss( FILE: torch_geometric/nn/kge/loader.py class KGTripletLoader (line 7) | class KGTripletLoader(torch.utils.data.DataLoader): method __init__ (line 8) | def __init__(self, head_index: Tensor, rel_type: Tensor, method sample (line 17) | def sample(self, index: List[int]) -> Tuple[Tensor, Tensor, Tensor]: FILE: torch_geometric/nn/kge/rotate.py class RotatE (line 11) | class RotatE(KGEModel): method __init__ (line 42) | def __init__( method reset_parameters (line 57) | def reset_parameters(self): method forward (line 62) | def forward( method loss (line 84) | def loss( FILE: torch_geometric/nn/kge/transe.py class TransE (line 10) | class TransE(KGEModel): method __init__ (line 44) | def __init__( method reset_parameters (line 60) | def reset_parameters(self): method forward (line 67) | def forward( method loss (line 84) | def loss( FILE: torch_geometric/nn/lr_scheduler.py class ConstantWithWarmupLR (line 9) | class ConstantWithWarmupLR(LambdaLR): method __init__ (line 20) | def __init__( method _lr_lambda (line 33) | def _lr_lambda( class LinearWithWarmupLR (line 42) | class LinearWithWarmupLR(LambdaLR): method __init__ (line 55) | def __init__( method _lr_lambda (line 70) | def _lr_lambda( class CosineWithWarmupLR (line 84) | class CosineWithWarmupLR(LambdaLR): method __init__ (line 100) | def __init__( method _lr_lambda (line 117) | def _lr_lambda( class CosineWithWarmupRestartsLR (line 134) | class CosineWithWarmupRestartsLR(LambdaLR): method __init__ (line 150) | def __init__( method _lr_lambda (line 167) | def _lr_lambda( class PolynomialWithWarmupLR (line 186) | class PolynomialWithWarmupLR(LambdaLR): method __init__ (line 202) | def __init__( method _lr_lambda (line 227) | def _lr_lambda( FILE: torch_geometric/nn/model_hub.py class PyGModelHubMixin (line 21) | class PyGModelHubMixin(ModelHubMixin): method __init__ (line 73) | def __init__(self, model_name: str, dataset_name: str, model_kwargs: D... method construct_model_card (line 87) | def construct_model_card(self, model_name: str, dataset_name: str) -> ... method _save_pretrained (line 100) | def _save_pretrained(self, save_directory: Union[Path, str]): method save_pretrained (line 105) | def save_pretrained(self, save_directory: Union[str, Path], method _from_pretrained (line 141) | def _from_pretrained( method from_pretrained (line 185) | def from_pretrained( FILE: torch_geometric/nn/models/attentive_fp.py class GATEConv (line 14) | class GATEConv(MessagePassing): method __init__ (line 15) | def __init__( method reset_parameters (line 36) | def reset_parameters(self): method forward (line 43) | def forward(self, x: Tensor, edge_index: Adj, edge_attr: Tensor) -> Te... method edge_update (line 52) | def edge_update(self, x_j: Tensor, x_i: Tensor, edge_attr: Tensor, method message (line 64) | def message(self, x_j: Tensor, alpha: Tensor) -> Tensor: class AttentiveFP (line 68) | class AttentiveFP(torch.nn.Module): method __init__ (line 86) | def __init__( method reset_parameters (line 130) | def reset_parameters(self): method forward (line 142) | def forward(self, x: Tensor, edge_index: Tensor, edge_attr: Tensor, method __repr__ (line 172) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/attract_repel.py class ARLinkPredictor (line 5) | class ARLinkPredictor(torch.nn.Module): method __init__ (line 27) | def __init__(self, in_channels, hidden_channels, out_channels=None, method reset_parameters (line 61) | def reset_parameters(self): method encode (line 68) | def encode(self, x, *args, **kwargs): method decode (line 89) | def decode(self, attract_z, repel_z, edge_index): method forward (line 116) | def forward(self, x, edge_index): method calculate_r_fraction (line 132) | def calculate_r_fraction(self, attract_z, repel_z): FILE: torch_geometric/nn/models/autoencoder.py class InnerProductDecoder (line 14) | class InnerProductDecoder(torch.nn.Module): method forward (line 24) | def forward( method forward_all (line 43) | def forward_all(self, z: Tensor, sigmoid: bool = True) -> Tensor: class GAE (line 57) | class GAE(torch.nn.Module): method __init__ (line 69) | def __init__(self, encoder: Module, decoder: Optional[Module] = None): method reset_parameters (line 75) | def reset_parameters(self): method forward (line 80) | def forward(self, *args, **kwargs) -> Tensor: # pragma: no cover method encode (line 84) | def encode(self, *args, **kwargs) -> Tensor: method decode (line 88) | def decode(self, *args, **kwargs) -> Tensor: method recon_loss (line 92) | def recon_loss(self, z: Tensor, pos_edge_index: Tensor, method test (line 116) | def test(self, z: Tensor, pos_edge_index: Tensor, class VGAE (line 145) | class VGAE(GAE): method __init__ (line 158) | def __init__(self, encoder: Module, decoder: Optional[Module] = None): method reparametrize (line 161) | def reparametrize(self, mu: Tensor, logstd: Tensor) -> Tensor: method encode (line 167) | def encode(self, *args, **kwargs) -> Tensor: method kl_loss (line 174) | def kl_loss(self, mu: Optional[Tensor] = None, class ARGA (line 194) | class ARGA(GAE): method __init__ (line 207) | def __init__( method reset_parameters (line 217) | def reset_parameters(self): method reg_loss (line 221) | def reg_loss(self, z: Tensor) -> Tensor: method discriminator_loss (line 231) | def discriminator_loss(self, z: Tensor) -> Tensor: class ARGVA (line 244) | class ARGVA(ARGA): method __init__ (line 258) | def __init__( method __mu__ (line 268) | def __mu__(self) -> Tensor: method __logstd__ (line 272) | def __logstd__(self) -> Tensor: method reparametrize (line 275) | def reparametrize(self, mu: Tensor, logstd: Tensor) -> Tensor: method encode (line 278) | def encode(self, *args, **kwargs) -> Tensor: method kl_loss (line 282) | def kl_loss( FILE: torch_geometric/nn/models/basic_gnn.py class BasicGNN (line 32) | class BasicGNN(torch.nn.Module): method __init__ (line 70) | def __init__( method init_conv (line 162) | def init_conv(self, in_channels: Union[int, Tuple[int, int]], method reset_parameters (line 166) | def reset_parameters(self): method forward (line 178) | def forward( method inference_per_layer (line 277) | def inference_per_layer( method inference (line 302) | def inference( method __repr__ (line 384) | def __repr__(self) -> str: class GCN (line 389) | class GCN(BasicGNN): method init_conv (line 429) | def init_conv(self, in_channels: int, out_channels: int, class GraphSAGE (line 434) | class GraphSAGE(BasicGNN): method init_conv (line 474) | def init_conv(self, in_channels: Union[int, Tuple[int, int]], class GIN (line 479) | class GIN(BasicGNN): method init_conv (line 516) | def init_conv(self, in_channels: int, out_channels: int, class GAT (line 528) | class GAT(BasicGNN): method init_conv (line 575) | def init_conv(self, in_channels: Union[int, Tuple[int, int]], class PNA (line 600) | class PNA(BasicGNN): method init_conv (line 638) | def init_conv(self, in_channels: int, out_channels: int, class EdgeCNN (line 643) | class EdgeCNN(BasicGNN): method init_conv (line 680) | def init_conv(self, in_channels: int, out_channels: int, FILE: torch_geometric/nn/models/captum.py function to_captum_model (line 13) | def to_captum_model( FILE: torch_geometric/nn/models/correct_and_smooth.py class CorrectAndSmooth (line 9) | class CorrectAndSmooth(torch.nn.Module): method __init__ (line 66) | def __init__(self, num_correction_layers: int, correction_alpha: float, method forward (line 76) | def forward(self, y_soft: Tensor, *args) -> Tensor: # pragma: no cover method correct (line 81) | def correct(self, y_soft: Tensor, y_true: Tensor, mask: Tensor, method smooth (line 126) | def smooth(self, y_soft: Tensor, y_true: Tensor, mask: Tensor, method __repr__ (line 153) | def __repr__(self): FILE: torch_geometric/nn/models/deep_graph_infomax.py class DeepGraphInfomax (line 13) | class DeepGraphInfomax(torch.nn.Module): method __init__ (line 26) | def __init__( method reset_parameters (line 43) | def reset_parameters(self): method forward (line 49) | def forward(self, *args, **kwargs) -> Tuple[Tensor, Tensor, Tensor]: method discriminate (line 68) | def discriminate(self, z: Tensor, summary: Tensor, method loss (line 84) | def loss(self, pos_z: Tensor, neg_z: Tensor, summary: Tensor) -> Tensor: method test (line 94) | def test( method __repr__ (line 115) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/deepgcn.py class DeepGCNLayer (line 10) | class DeepGCNLayer(torch.nn.Module): method __init__ (line 55) | def __init__( method reset_parameters (line 74) | def reset_parameters(self): method forward (line 79) | def forward(self, *args, **kwargs) -> Tensor: method __repr__ (line 119) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/dimenet.py class Envelope (line 35) | class Envelope(torch.nn.Module): method __init__ (line 36) | def __init__(self, exponent: int): method forward (line 43) | def forward(self, x: Tensor) -> Tensor: class BesselBasisLayer (line 52) | class BesselBasisLayer(torch.nn.Module): method __init__ (line 53) | def __init__(self, num_radial: int, cutoff: float = 5.0, method reset_parameters (line 63) | def reset_parameters(self): method forward (line 68) | def forward(self, dist: Tensor) -> Tensor: class SphericalBasisLayer (line 73) | class SphericalBasisLayer(torch.nn.Module): method __init__ (line 74) | def __init__( method _sph_to_tensor (line 114) | def _sph_to_tensor(sph, x: Tensor) -> Tensor: method forward (line 117) | def forward(self, dist: Tensor, angle: Tensor, idx_kj: Tensor) -> Tensor: class EmbeddingBlock (line 129) | class EmbeddingBlock(torch.nn.Module): method __init__ (line 130) | def __init__(self, num_radial: int, hidden_channels: int, act: Callable): method reset_parameters (line 140) | def reset_parameters(self): method forward (line 145) | def forward(self, x: Tensor, rbf: Tensor, i: Tensor, j: Tensor) -> Ten... class ResidualLayer (line 151) | class ResidualLayer(torch.nn.Module): method __init__ (line 152) | def __init__(self, hidden_channels: int, act: Callable): method reset_parameters (line 160) | def reset_parameters(self): method forward (line 166) | def forward(self, x: Tensor) -> Tensor: class InteractionBlock (line 170) | class InteractionBlock(torch.nn.Module): method __init__ (line 171) | def __init__( method reset_parameters (line 205) | def reset_parameters(self): method forward (line 220) | def forward(self, x: Tensor, rbf: Tensor, sbf: Tensor, idx_kj: Tensor, class InteractionPPBlock (line 241) | class InteractionPPBlock(torch.nn.Module): method __init__ (line 242) | def __init__( method reset_parameters (line 283) | def reset_parameters(self): method forward (line 304) | def forward(self, x: Tensor, rbf: Tensor, sbf: Tensor, idx_kj: Tensor, class OutputBlock (line 337) | class OutputBlock(torch.nn.Module): method __init__ (line 338) | def __init__( method reset_parameters (line 362) | def reset_parameters(self): method forward (line 372) | def forward(self, x: Tensor, rbf: Tensor, i: Tensor, class OutputPPBlock (line 381) | class OutputPPBlock(torch.nn.Module): method __init__ (line 382) | def __init__( method reset_parameters (line 410) | def reset_parameters(self): method forward (line 421) | def forward(self, x: Tensor, rbf: Tensor, i: Tensor, function triplets (line 431) | def triplets( class DimeNet (line 457) | class DimeNet(torch.nn.Module): method __init__ (line 501) | def __init__( method reset_parameters (line 558) | def reset_parameters(self): method from_qm9_pretrained (line 568) | def from_qm9_pretrained( method forward (line 672) | def forward( class DimeNetPlusPlus (line 728) | class DimeNetPlusPlus(DimeNet): method __init__ (line 768) | def __init__( method from_qm9_pretrained (line 839) | def from_qm9_pretrained( FILE: torch_geometric/nn/models/dimenet_utils.py function Jn (line 11) | def Jn(r, n): function Jn_zeros (line 15) | def Jn_zeros(n, k): function spherical_bessel_formulas (line 30) | def spherical_bessel_formulas(n): function bessel_basis (line 42) | def bessel_basis(n, k): function sph_harm_prefactor (line 66) | def sph_harm_prefactor(k, m): function associated_legendre_polynomials (line 71) | def associated_legendre_polynomials(k, zero_m_only=True): function real_sph_harm (line 104) | def real_sph_harm(k, zero_m_only=True, spherical_coordinates=True): FILE: torch_geometric/nn/models/gnnff.py class GaussianFilter (line 13) | class GaussianFilter(torch.nn.Module): method __init__ (line 14) | def __init__(self, start=0.0, stop=5.0, num_gaussians=50): method reset_parameters (line 20) | def reset_parameters(self): method forward (line 23) | def forward(self, dist: Tensor) -> Tensor: class NodeBlock (line 28) | class NodeBlock(torch.nn.Module): method __init__ (line 29) | def __init__(self, hidden_node_channels: int, hidden_edge_channels: int): method reset_parameters (line 39) | def reset_parameters(self): method forward (line 44) | def forward(self, node_emb: Tensor, edge_emb: Tensor, i: Tensor) -> Te... class EdgeBlock (line 57) | class EdgeBlock(torch.nn.Module): method __init__ (line 58) | def __init__(self, hidden_node_channels: int, hidden_edge_channels: int): method reset_parameters (line 73) | def reset_parameters(self): method forward (line 81) | def forward( class GNNFF (line 118) | class GNNFF(torch.nn.Module): method __init__ (line 138) | def __init__( method reset_parameters (line 177) | def reset_parameters(self): method forward (line 186) | def forward(self, z: Tensor, pos: Tensor, FILE: torch_geometric/nn/models/gpse.py class Linear (line 28) | class Linear(torch.nn.Module): method __init__ (line 29) | def __init__( method forward (line 38) | def forward(self, batch): class ResGatedGCNConv (line 46) | class ResGatedGCNConv(torch.nn.Module): method __init__ (line 47) | def __init__( method forward (line 62) | def forward(self, batch): class GeneralLayer (line 67) | class GeneralLayer(torch.nn.Module): method __init__ (line 68) | def __init__( method forward (line 102) | def forward(self, batch): class GeneralMultiLayer (line 115) | class GeneralMultiLayer(torch.nn.Module): method __init__ (line 116) | def __init__( method forward (line 148) | def forward(self, batch): class BatchNorm1dNode (line 154) | class BatchNorm1dNode(torch.nn.Module): method __init__ (line 155) | def __init__(self, channels: int) -> None: method forward (line 159) | def forward(self, batch): class BatchNorm1dEdge (line 164) | class BatchNorm1dEdge(torch.nn.Module): method __init__ (line 165) | def __init__(self, channels: int) -> None: method forward (line 169) | def forward(self, batch): class MLP (line 174) | class MLP(torch.nn.Module): method __init__ (line 175) | def __init__( method forward (line 209) | def forward(self, batch): class GNNStackStage (line 217) | class GNNStackStage(torch.nn.Module): method __init__ (line 218) | def __init__( method forward (line 248) | def forward(self, batch): class GNNInductiveHybridMultiHead (line 263) | class GNNInductiveHybridMultiHead(torch.nn.Module): method __init__ (line 293) | def __init__( method _pad_and_stack (line 329) | def _pad_and_stack(self, x1: torch.Tensor, x2: torch.Tensor, pad1: int, method _apply_index (line 335) | def _apply_index(self, batch, virtual_node: bool, pad_node: int, method forward (line 353) | def forward(self, batch): class IdentityHead (line 362) | class IdentityHead(torch.nn.Module): method forward (line 363) | def forward(self, batch): class GPSE (line 367) | class GPSE(torch.nn.Module): method __init__ (line 494) | def __init__( method reset_parameters (line 572) | def reset_parameters(self): method from_pretrained (line 576) | def from_pretrained(cls, name: str, root: str = 'GPSE_pretrained'): method forward (line 608) | def forward(self, batch): class GPSENodeEncoder (line 615) | class GPSENodeEncoder(torch.nn.Module): method __init__ (line 663) | def __init__(self, dim_emb: int, dim_pe_in: int, dim_pe_out: int, method forward (line 705) | def forward(self, x, pos_enc): function gpse_process (line 719) | def gpse_process( function gpse_process_batch (line 818) | def gpse_process_batch( function precompute_GPSE (line 923) | def precompute_GPSE(model: GPSE, dataset: Dataset, use_vn: bool = True, function cosim_col_sep (line 1007) | def cosim_col_sep(pred: torch.Tensor, true: torch.Tensor, function gpse_loss (line 1034) | def gpse_loss(pred: torch.Tensor, true: torch.Tensor, function process_batch_idx (line 1060) | def process_batch_idx(batch_idx, true, use_vn=True): FILE: torch_geometric/nn/models/graph_mixer.py class NodeEncoder (line 11) | class NodeEncoder(torch.nn.Module): method __init__ (line 26) | def __init__(self, time_window: int): method reset_parameters (line 30) | def reset_parameters(self): method forward (line 33) | def forward( method __repr__ (line 56) | def __repr__(self) -> str: class _MLPMixer (line 60) | class _MLPMixer(torch.nn.Module): method __init__ (line 69) | def __init__( method reset_parameters (line 91) | def reset_parameters(self): method forward (line 101) | def forward(self, x: Tensor) -> Tensor: function get_latest_k_edge_attr (line 136) | def get_latest_k_edge_attr( class LinkEncoder (line 168) | class LinkEncoder(torch.nn.Module): method __init__ (line 192) | def __init__( method reset_parameters (line 223) | def reset_parameters(self): method forward (line 228) | def forward( method __repr__ (line 270) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/graph_unet.py class GraphUNet (line 17) | class GraphUNet(torch.nn.Module): method __init__ (line 35) | def __init__( method reset_parameters (line 73) | def reset_parameters(self): method forward (line 82) | def forward( method augment_adj (line 138) | def augment_adj(self, edge_index: Tensor, edge_weight: Tensor, method __repr__ (line 150) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/jumping_knowledge.py class JumpingKnowledge (line 8) | class JumpingKnowledge(torch.nn.Module): method __init__ (line 44) | def __init__( method reset_parameters (line 70) | def reset_parameters(self) -> None: method forward (line 77) | def forward(self, xs: List[Tensor]) -> Tensor: method __repr__ (line 96) | def __repr__(self) -> str: class HeteroJumpingKnowledge (line 103) | class HeteroJumpingKnowledge(torch.nn.Module): method __init__ (line 116) | def __init__( method reset_parameters (line 133) | def reset_parameters(self) -> None: method forward (line 138) | def forward(self, xs_dict: Dict[str, List[Tensor]]) -> Dict[str, Tensor]: method __repr__ (line 147) | def __repr__(self): FILE: torch_geometric/nn/models/label_prop.py class LabelPropagation (line 12) | class LabelPropagation(MessagePassing): method __init__ (line 37) | def __init__(self, num_layers: int, alpha: float): method forward (line 43) | def forward( method message (line 93) | def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 96) | def message_and_aggregate(self, adj_t: SparseTensor, x: Tensor) -> Ten... method __repr__ (line 99) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/lightgcn.py class LightGCN (line 14) | class LightGCN(torch.nn.Module): method __init__ (line 64) | def __init__( method reset_parameters (line 92) | def reset_parameters(self): method get_embedding (line 98) | def get_embedding( method forward (line 113) | def forward( method predict_link (line 144) | def predict_link( method recommend (line 168) | def recommend( method link_pred_loss (line 212) | def link_pred_loss(self, pred: Tensor, edge_label: Tensor, method recommendation_loss (line 226) | def recommendation_loss( method __repr__ (line 261) | def __repr__(self) -> str: class BPRLoss (line 266) | class BPRLoss(_Loss): method __init__ (line 290) | def __init__(self, lambda_reg: float = 0, **kwargs): method forward (line 294) | def forward(self, positives: Tensor, negatives: Tensor, FILE: torch_geometric/nn/models/linkx.py class SparseLinear (line 14) | class SparseLinear(MessagePassing): method __init__ (line 15) | def __init__(self, in_channels: int, out_channels: int, bias: bool = T... method reset_parameters (line 28) | def reset_parameters(self): method forward (line 33) | def forward( method message (line 47) | def message(self, weight_j: Tensor, edge_weight: OptTensor) -> Tensor: method message_and_aggregate (line 53) | def message_and_aggregate(self, adj_t: Adj, weight: Tensor) -> Tensor: class LINKX (line 57) | class LINKX(torch.nn.Module): method __init__ (line 90) | def __init__( method reset_parameters (line 129) | def reset_parameters(self): method forward (line 141) | def forward( method __repr__ (line 164) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/lpformer.py class LPFormer (line 18) | class LPFormer(nn.Module): method __init__ (line 50) | def __init__( method __repr__ (line 124) | def __repr__(self) -> str: method reset_parameters (line 129) | def reset_parameters(self): method forward (line 142) | def forward( method propagate (line 190) | def propagate(self, x: Tensor, adj: Adj) -> Tensor: method calc_pairwise (line 203) | def calc_pairwise(self, batch: Tensor, X_node: Tensor, adj_mask: Tensor, method get_pos_encodings (line 244) | def get_pos_encodings( method compute_node_mask (line 285) | def compute_node_mask( method get_ppr_vals (line 363) | def get_ppr_vals( method drop_pairwise (line 409) | def drop_pairwise( method get_structure_cnts (line 443) | def get_structure_cnts( method get_num_ppr_thresh (line 483) | def get_num_ppr_thresh(self, batch: Tensor, node_mask: Tensor, method get_count (line 506) | def get_count( method get_non_1hop_ppr (line 527) | def get_non_1hop_ppr(self, batch: Tensor, adj: Tensor, method calc_sparse_ppr (line 596) | def calc_sparse_ppr(self, edge_index: Tensor, num_nodes: int, class LPAttLayer (line 615) | class LPAttLayer(MessagePassing): method __init__ (line 629) | def __init__( method __repr__ (line 674) | def __repr__(self) -> str: method reset_parameters (line 678) | def reset_parameters(self): method forward (line 685) | def forward( method message (line 722) | def message(self, x_i: Tensor, x_j: Tensor, ppr_rpes: Tensor, class MLP (line 744) | class MLP(nn.Module): method __init__ (line 746) | def __init__(self, in_channels: int, hid_channels: int, out_channels: ... method reset_parameters (line 768) | def reset_parameters(self): method forward (line 774) | def forward(self, x: Tensor) -> Tensor: FILE: torch_geometric/nn/models/mask_label.py class MaskLabel (line 5) | class MaskLabel(torch.nn.Module): method __init__ (line 29) | def __init__(self, num_classes: int, out_channels: int, method reset_parameters (line 41) | def reset_parameters(self): method forward (line 45) | def forward(self, x: Tensor, y: Tensor, mask: Tensor) -> Tensor: method ratio_mask (line 57) | def ratio_mask(mask: Tensor, ratio: float): method __repr__ (line 70) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/meta.py class MetaLayer (line 7) | class MetaLayer(torch.nn.Module): method __init__ (line 99) | def __init__( method reset_parameters (line 112) | def reset_parameters(self): method forward (line 118) | def forward( method __repr__ (line 154) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/metapath2vec.py class MetaPath2Vec (line 15) | class MetaPath2Vec(torch.nn.Module): method __init__ (line 51) | def __init__( method reset_parameters (line 129) | def reset_parameters(self): method forward (line 133) | def forward(self, node_type: str, batch: OptTensor = None) -> Tensor: method loader (line 140) | def loader(self, **kwargs): method _pos_sample (line 153) | def _pos_sample(self, batch: Tensor) -> Tensor: method _neg_sample (line 179) | def _neg_sample(self, batch: Tensor) -> Tensor: method _sample (line 198) | def _sample(self, batch: List[int]) -> Tuple[Tensor, Tensor]: method loss (line 203) | def loss(self, pos_rw: Tensor, neg_rw: Tensor) -> Tensor: method test (line 229) | def test(self, train_z: Tensor, train_y: Tensor, test_z: Tensor, method __repr__ (line 242) | def __repr__(self) -> str: function sample (line 248) | def sample(rowptr: Tensor, col: Tensor, rowcount: Tensor, subset: Tensor, FILE: torch_geometric/nn/models/mlp.py class MLP (line 18) | class MLP(torch.nn.Module): method __init__ (line 78) | def __init__( method in_channels (line 177) | def in_channels(self) -> int: method out_channels (line 182) | def out_channels(self) -> int: method num_layers (line 187) | def num_layers(self) -> int: method reset_parameters (line 191) | def reset_parameters(self): method forward (line 199) | def forward( method __repr__ (line 252) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/neural_fingerprint.py class NeuralFingerprint (line 10) | class NeuralFingerprint(torch.nn.Module): method __init__ (line 24) | def __init__( method reset_parameters (line 48) | def reset_parameters(self): method forward (line 55) | def forward( method __repr__ (line 70) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/node2vec.py class Node2Vec (line 14) | class Node2Vec(torch.nn.Module): method __init__ (line 46) | def __init__( method reset_parameters (line 95) | def reset_parameters(self): method forward (line 99) | def forward(self, batch: Optional[Tensor] = None) -> Tensor: method loader (line 104) | def loader(self, **kwargs) -> DataLoader: method pos_sample (line 109) | def pos_sample(self, batch: Tensor) -> Tensor: method neg_sample (line 123) | def neg_sample(self, batch: Tensor) -> Tensor: method sample (line 137) | def sample(self, batch: Union[List[int], Tensor]) -> Tuple[Tensor, Ten... method loss (line 143) | def loss(self, pos_rw: Tensor, neg_rw: Tensor) -> Tensor: method test (line 169) | def test( method __repr__ (line 190) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/pmlp.py class PMLP (line 11) | class PMLP(torch.nn.Module): method __init__ (line 30) | def __init__( method reset_parameters (line 68) | def reset_parameters(self): method forward (line 75) | def forward( method __repr__ (line 99) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/polynormer.py class Polynormer (line 12) | class Polynormer(torch.nn.Module): method __init__ (line 45) | def __init__( method reset_parameters (line 137) | def reset_parameters(self) -> None: method forward (line 159) | def forward( FILE: torch_geometric/nn/models/re_net.py class RENet (line 13) | class RENet(torch.nn.Module): method __init__ (line 47) | def __init__( method reset_parameters (line 78) | def reset_parameters(self): method pre_transform (line 88) | def pre_transform(seq_len: int) -> Callable: method forward (line 162) | def forward(self, data: Data) -> Tuple[Tensor, Tensor]: method test (line 210) | def test(self, logits: Tensor, y: Tensor) -> Tensor: FILE: torch_geometric/nn/models/rect.py class RECT_L (line 11) | class RECT_L(torch.nn.Module): method __init__ (line 33) | def __init__(self, in_channels: int, hidden_channels: int, method reset_parameters (line 45) | def reset_parameters(self): method forward (line 51) | def forward( method embed (line 63) | def embed( method get_semantic_labels (line 73) | def get_semantic_labels( method __repr__ (line 85) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/rev_gnn.py class InvertibleFunction (line 12) | class InvertibleFunction(torch.autograd.Function): method forward (line 32) | def forward(ctx, fn: torch.nn.Module, fn_inverse: torch.nn.Module, method backward (line 72) | def backward(ctx, *grad_outputs): class InvertibleModule (line 147) | class InvertibleModule(torch.nn.Module): method __init__ (line 158) | def __init__(self, disable: bool = False, num_bwd_passes: int = 1): method forward (line 163) | def forward(self, *args): method inverse (line 167) | def inverse(self, *args): method _forward (line 170) | def _forward(self): method _inverse (line 173) | def _inverse(self): method _fn_apply (line 176) | def _fn_apply(self, args, fn, fn_inverse): class GroupAddRev (line 196) | class GroupAddRev(InvertibleModule): method __init__ (line 236) | def __init__( method num_groups (line 263) | def num_groups(self) -> int: method reset_parameters (line 266) | def reset_parameters(self): method _forward (line 271) | def _forward(self, x: Tensor, edge_index: Adj, *args): method _inverse (line 284) | def _inverse(self, y: Tensor, edge_index: Adj, *args): method _chunk (line 301) | def _chunk(self, x: Any, channels: int) -> List[Any]: method __repr__ (line 313) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/schnet.py class SchNet (line 35) | class SchNet(torch.nn.Module): method __init__ (line 93) | def __init__( method reset_parameters (line 158) | def reset_parameters(self): method from_qm9_pretrained (line 171) | def from_qm9_pretrained( method forward (line 270) | def forward(self, z: Tensor, pos: Tensor, method __repr__ (line 319) | def __repr__(self) -> str: class RadiusInteractionGraph (line 328) | class RadiusInteractionGraph(torch.nn.Module): method __init__ (line 340) | def __init__(self, cutoff: float = 10.0, max_num_neighbors: int = 32): method forward (line 345) | def forward(self, pos: Tensor, batch: Tensor) -> Tuple[Tensor, Tensor]: class InteractionBlock (line 362) | class InteractionBlock(torch.nn.Module): method __init__ (line 363) | def __init__(self, hidden_channels: int, num_gaussians: int, method reset_parameters (line 378) | def reset_parameters(self): method forward (line 387) | def forward(self, x: Tensor, edge_index: Tensor, edge_weight: Tensor, class CFConv (line 395) | class CFConv(MessagePassing): method __init__ (line 396) | def __init__( method reset_parameters (line 412) | def reset_parameters(self): method forward (line 417) | def forward(self, x: Tensor, edge_index: Tensor, edge_weight: Tensor, method message (line 427) | def message(self, x_j: Tensor, W: Tensor) -> Tensor: class GaussianSmearing (line 431) | class GaussianSmearing(torch.nn.Module): method __init__ (line 432) | def __init__( method forward (line 443) | def forward(self, dist: Tensor) -> Tensor: class ShiftedSoftplus (line 448) | class ShiftedSoftplus(torch.nn.Module): method __init__ (line 449) | def __init__(self): method forward (line 453) | def forward(self, x: Tensor) -> Tensor: FILE: torch_geometric/nn/models/sgformer.py class GraphModule (line 12) | class GraphModule(torch.nn.Module): method __init__ (line 13) | def __init__( method reset_parameters (line 35) | def reset_parameters(self): method forward (line 43) | def forward(self, x, edge_index): class SGModule (line 59) | class SGModule(torch.nn.Module): method __init__ (line 60) | def __init__( method reset_parameters (line 83) | def reset_parameters(self): method forward (line 91) | def forward(self, x: Tensor, batch: Tensor): class SGFormer (line 123) | class SGFormer(torch.nn.Module): method __init__ (line 148) | def __init__( method reset_parameters (line 192) | def reset_parameters(self) -> None: method forward (line 197) | def forward( FILE: torch_geometric/nn/models/signed_gcn.py class SignedGCN (line 15) | class SignedGCN(torch.nn.Module): method __init__ (line 30) | def __init__( method reset_parameters (line 57) | def reset_parameters(self): method split_edges (line 64) | def split_edges( method create_spectral_features (line 84) | def create_spectral_features( method forward (line 128) | def forward( method discriminate (line 147) | def discriminate(self, z: Tensor, edge_index: Tensor) -> Tensor: method nll_loss (line 160) | def nll_loss( method pos_embedding_loss (line 190) | def pos_embedding_loss( method neg_embedding_loss (line 207) | def neg_embedding_loss(self, z: Tensor, neg_edge_index: Tensor) -> Ten... method loss (line 220) | def loss( method test (line 238) | def test( method __repr__ (line 268) | def __repr__(self) -> str: FILE: torch_geometric/nn/models/tgn.py class TGNMemory (line 15) | class TGNMemory(torch.nn.Module): method __init__ (line 38) | def __init__(self, num_nodes: int, raw_msg_dim: int, memory_dim: int, method device (line 66) | def device(self) -> torch.device: method reset_parameters (line 69) | def reset_parameters(self): method reset_state (line 81) | def reset_state(self): method detach (line 87) | def detach(self): method forward (line 91) | def forward(self, n_id: Tensor) -> Tuple[Tensor, Tensor]: method update_state (line 102) | def update_state(self, src: Tensor, dst: Tensor, t: Tensor, method _reset_message_store (line 118) | def _reset_message_store(self): method _update_memory (line 125) | def _update_memory(self, n_id: Tensor): method _get_updated_memory (line 130) | def _get_updated_memory(self, n_id: Tensor) -> Tuple[Tensor, Tensor]: method _update_msg_store (line 156) | def _update_msg_store(self, src: Tensor, dst: Tensor, t: Tensor, method _compute_msg (line 163) | def _compute_msg(self, n_id: Tensor, msg_store: TGNMessageStoreType, method train (line 181) | def train(self, mode: bool = True): class IdentityMessage (line 191) | class IdentityMessage(torch.nn.Module): method __init__ (line 192) | def __init__(self, raw_msg_dim: int, memory_dim: int, time_dim: int): method forward (line 196) | def forward(self, z_src: Tensor, z_dst: Tensor, raw_msg: Tensor, class LastAggregator (line 201) | class LastAggregator(torch.nn.Module): method forward (line 202) | def forward(self, msg: Tensor, index: Tensor, t: Tensor, dim_size: int): class MeanAggregator (line 210) | class MeanAggregator(torch.nn.Module): method forward (line 211) | def forward(self, msg: Tensor, index: Tensor, t: Tensor, dim_size: int): class TimeEncoder (line 215) | class TimeEncoder(torch.nn.Module): method __init__ (line 216) | def __init__(self, out_channels: int): method reset_parameters (line 221) | def reset_parameters(self): method forward (line 224) | def forward(self, t: Tensor) -> Tensor: class LastNeighborLoader (line 228) | class LastNeighborLoader: method __init__ (line 229) | def __init__(self, num_nodes: int, size: int, device=None): method __call__ (line 240) | def __call__(self, n_id: Tensor) -> Tuple[Tensor, Tensor, Tensor]: method insert (line 256) | def insert(self, src: Tensor, dst: Tensor): method reset_state (line 296) | def reset_state(self): FILE: torch_geometric/nn/models/visnet.py class CosineCutoff (line 13) | class CosineCutoff(torch.nn.Module): method __init__ (line 28) | def __init__(self, cutoff: float) -> None: method forward (line 32) | def forward(self, distances: Tensor) -> Tensor: class ExpNormalSmearing (line 48) | class ExpNormalSmearing(torch.nn.Module): method __init__ (line 63) | def __init__( method _initial_params (line 85) | def _initial_params(self) -> Tuple[Tensor, Tensor]: method reset_parameters (line 93) | def reset_parameters(self): method forward (line 99) | def forward(self, dist: Tensor) -> Tensor: class Sphere (line 111) | class Sphere(torch.nn.Module): method __init__ (line 124) | def __init__(self, lmax: int = 2) -> None: method forward (line 128) | def forward(self, edge_vec: Tensor) -> Tensor: method _spherical_harmonics (line 142) | def _spherical_harmonics( class VecLayerNorm (line 185) | class VecLayerNorm(torch.nn.Module): method __init__ (line 199) | def __init__( method reset_parameters (line 219) | def reset_parameters(self): method max_min_norm (line 223) | def max_min_norm(self, vec: Tensor) -> Tensor: method forward (line 254) | def forward(self, vec: Tensor) -> Tensor: class Distance (line 276) | class Distance(torch.nn.Module): method __init__ (line 292) | def __init__( method forward (line 303) | def forward( class NeighborEmbedding (line 340) | class NeighborEmbedding(MessagePassing): method __init__ (line 353) | def __init__( method reset_parameters (line 368) | def reset_parameters(self): method forward (line 376) | def forward( method message (line 411) | def message(self, x_j: Tensor, W: Tensor) -> Tensor: class EdgeEmbedding (line 415) | class EdgeEmbedding(torch.nn.Module): method __init__ (line 425) | def __init__(self, num_rbf: int, hidden_channels: int) -> None: method reset_parameters (line 430) | def reset_parameters(self): method forward (line 435) | def forward( class ViS_MP (line 456) | class ViS_MP(MessagePassing): method __init__ (line 475) | def __init__( method vector_rejection (line 528) | def vector_rejection(vec: Tensor, d_ij: Tensor) -> Tensor: method reset_parameters (line 538) | def reset_parameters(self): method forward (line 565) | def forward( method message (line 619) | def message(self, q_i: Tensor, k_j: Tensor, v_j: Tensor, vec_j: Tensor, method edge_update (line 635) | def edge_update(self, vec_i: Tensor, vec_j: Tensor, d_ij: Tensor, method aggregate (line 644) | def aggregate( class ViS_MP_Vertex (line 657) | class ViS_MP_Vertex(ViS_MP): method __init__ (line 676) | def __init__( method reset_parameters (line 695) | def reset_parameters(self): method edge_update (line 705) | def edge_update(self, vec_i: Tensor, vec_j: Tensor, d_ij: Tensor, class ViSNetBlock (line 722) | class ViSNetBlock(torch.nn.Module): method __init__ (line 753) | def __init__( method reset_parameters (line 815) | def reset_parameters(self): method forward (line 826) | def forward( class GatedEquivariantBlock (line 874) | class GatedEquivariantBlock(torch.nn.Module): method __init__ (line 891) | def __init__( method reset_parameters (line 917) | def reset_parameters(self): method forward (line 926) | def forward(self, x: Tensor, v: Tensor) -> Tuple[Tensor, Tensor]: class EquivariantScalar (line 947) | class EquivariantScalar(torch.nn.Module): method __init__ (line 955) | def __init__(self, hidden_channels: int) -> None: method reset_parameters (line 973) | def reset_parameters(self): method pre_reduce (line 978) | def pre_reduce(self, x: Tensor, v: Tensor) -> Tensor: class Atomref (line 994) | class Atomref(torch.nn.Module): method __init__ (line 1003) | def __init__( method reset_parameters (line 1023) | def reset_parameters(self): method forward (line 1027) | def forward(self, x: Tensor, z: Tensor) -> Tensor: class ViSNet (line 1037) | class ViSNet(torch.nn.Module): method __init__ (line 1079) | def __init__( method reset_parameters (line 1126) | def reset_parameters(self): method forward (line 1133) | def forward( FILE: torch_geometric/nn/module_dict.py class ModuleDict (line 13) | class ModuleDict(torch.nn.ModuleDict): method __init__ (line 16) | def __init__( method to_internal_key (line 28) | def to_internal_key(cls, key: Key) -> str: method to_external_key (line 42) | def to_external_key(cls, key: str) -> Key: method __getitem__ (line 53) | def __getitem__(self, key: Key) -> Module: method __setitem__ (line 56) | def __setitem__(self, key: Key, module: Module): method __delitem__ (line 59) | def __delitem__(self, key: Key): method __contains__ (line 62) | def __contains__(self, key: Key) -> bool: method keys (line 65) | def keys(self) -> Iterable[Key]: method items (line 68) | def items(self) -> Iterable[Tuple[Key, Module]]: FILE: torch_geometric/nn/norm/batch_norm.py class BatchNorm (line 10) | class BatchNorm(torch.nn.Module): method __init__ (line 45) | def __init__( method reset_running_stats (line 66) | def reset_running_stats(self): method reset_parameters (line 70) | def reset_parameters(self): method forward (line 74) | def forward(self, x: Tensor) -> Tensor: method __repr__ (line 93) | def __repr__(self): class HeteroBatchNorm (line 97) | class HeteroBatchNorm(torch.nn.Module): method __init__ (line 123) | def __init__( method reset_running_stats (line 168) | def reset_running_stats(self): method reset_parameters (line 175) | def reset_parameters(self): method forward (line 182) | def forward(self, x: Tensor, type_vec: Tensor) -> Tensor: method __repr__ (line 219) | def __repr__(self) -> str: FILE: torch_geometric/nn/norm/diff_group_norm.py class DiffGroupNorm (line 8) | class DiffGroupNorm(torch.nn.Module): method __init__ (line 47) | def __init__( method reset_parameters (line 70) | def reset_parameters(self): method forward (line 75) | def forward(self, x: Tensor) -> Tensor: method group_distance_ratio (line 90) | def group_distance_ratio(x: Tensor, y: Tensor, eps: float = 1e-5) -> f... method __repr__ (line 125) | def __repr__(self) -> str: FILE: torch_geometric/nn/norm/graph_norm.py class GraphNorm (line 11) | class GraphNorm(torch.nn.Module): method __init__ (line 32) | def __init__(self, in_channels: int, eps: float = 1e-5, method reset_parameters (line 47) | def reset_parameters(self): method forward (line 53) | def forward(self, x: Tensor, batch: OptTensor = None, method __repr__ (line 78) | def __repr__(self): FILE: torch_geometric/nn/norm/graph_size_norm.py class GraphSizeNorm (line 10) | class GraphSizeNorm(torch.nn.Module): method __init__ (line 19) | def __init__(self): method forward (line 22) | def forward(self, x: Tensor, batch: OptTensor = None, method __repr__ (line 41) | def __repr__(self) -> str: FILE: torch_geometric/nn/norm/instance_norm.py class InstanceNorm (line 12) | class InstanceNorm(_InstanceNorm): method __init__ (line 43) | def __init__( method reset_parameters (line 55) | def reset_parameters(self): method forward (line 59) | def forward(self, x: Tensor, batch: OptTensor = None, method __repr__ (line 121) | def __repr__(self) -> str: FILE: torch_geometric/nn/norm/layer_norm.py class LayerNorm (line 13) | class LayerNorm(torch.nn.Module): method __init__ (line 41) | def __init__( method reset_parameters (line 65) | def reset_parameters(self): method forward (line 70) | def forward(self, x: Tensor, batch: OptTensor = None, method __repr__ (line 116) | def __repr__(self): class HeteroLayerNorm (line 121) | class HeteroLayerNorm(torch.nn.Module): method __init__ (line 143) | def __init__( method reset_parameters (line 171) | def reset_parameters(self): method forward (line 177) | def forward( method __repr__ (line 216) | def __repr__(self) -> str: FILE: torch_geometric/nn/norm/mean_subtraction_norm.py class MeanSubtractionNorm (line 9) | class MeanSubtractionNorm(torch.nn.Module): method forward (line 18) | def forward(self, x: Tensor, batch: Optional[Tensor] = None, method __repr__ (line 36) | def __repr__(self) -> str: FILE: torch_geometric/nn/norm/msg_norm.py class MessageNorm (line 9) | class MessageNorm(torch.nn.Module): method __init__ (line 27) | def __init__(self, learn_scale: bool = False, method reset_parameters (line 34) | def reset_parameters(self): method forward (line 38) | def forward(self, x: Tensor, msg: Tensor, p: float = 2.0) -> Tensor: method __repr__ (line 51) | def __repr__(self) -> str: FILE: torch_geometric/nn/norm/pair_norm.py class PairNorm (line 10) | class PairNorm(torch.nn.Module): method __init__ (line 33) | def __init__(self, scale: float = 1., scale_individually: bool = False, method forward (line 41) | def forward(self, x: Tensor, batch: OptTensor = None, method __repr__ (line 74) | def __repr__(self): FILE: torch_geometric/nn/parameter_dict.py class ParameterDict (line 13) | class ParameterDict(torch.nn.ParameterDict): method __init__ (line 16) | def __init__( method to_internal_key (line 29) | def to_internal_key(cls, key: Key) -> str: method to_external_key (line 43) | def to_external_key(cls, key: str) -> Key: method __getitem__ (line 54) | def __getitem__(self, key: Key) -> Parameter: method __setitem__ (line 57) | def __setitem__(self, key: Key, parameter: Parameter): method __delitem__ (line 60) | def __delitem__(self, key: Key): method __contains__ (line 63) | def __contains__(self, key: Key) -> bool: method keys (line 66) | def keys(self) -> Iterable[Key]: method items (line 69) | def items(self) -> Iterable[Tuple[Key, Parameter]]: FILE: torch_geometric/nn/pool/__init__.py function fps (line 27) | def fps( function knn (line 67) | def knn( function knn_graph (line 121) | def knn_graph( function radius (line 179) | def radius( function radius_graph (line 239) | def radius_graph( function nearest (line 303) | def nearest( FILE: torch_geometric/nn/pool/approx_knn.py function approx_knn (line 5) | def approx_knn( function approx_knn_graph (line 73) | def approx_knn_graph( FILE: torch_geometric/nn/pool/asap.py class ASAPooling (line 21) | class ASAPooling(torch.nn.Module): method __init__ (line 49) | def __init__(self, in_channels: int, ratio: Union[float, int] = 0.5, method reset_parameters (line 75) | def reset_parameters(self): method forward (line 84) | def forward( method __repr__ (line 166) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/avg_pool.py function _avg_pool_x (line 11) | def _avg_pool_x( function avg_pool_x (line 19) | def avg_pool_x( function avg_pool (line 58) | def avg_pool( function avg_pool_neighbor_x (line 96) | def avg_pool_neighbor_x( FILE: torch_geometric/nn/pool/cluster_pool.py class UnpoolInfo (line 15) | class UnpoolInfo(NamedTuple): class ClusterPooling (line 21) | class ClusterPooling(torch.nn.Module): method __init__ (line 41) | def __init__( method reset_parameters (line 61) | def reset_parameters(self): method forward (line 65) | def forward( method _merge_edges (line 108) | def _merge_edges( method __repr__ (line 143) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/connect/base.py class ConnectOutput (line 11) | class ConnectOutput: method __init__ (line 26) | def __init__( class Connect (line 54) | class Connect(torch.nn.Module): method reset_parameters (line 65) | def reset_parameters(self): method forward (line 68) | def forward( method get_pooled_batch (line 89) | def get_pooled_batch( method __repr__ (line 108) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/connect/filter_edges.py function filter_adj (line 11) | def filter_adj( class FilterEdges (line 39) | class FilterEdges(Connect): method forward (line 48) | def forward( FILE: torch_geometric/nn/pool/consecutive.py function consecutive_cluster (line 4) | def consecutive_cluster(src): FILE: torch_geometric/nn/pool/decimation.py function decimation_indices (line 9) | def decimation_indices( FILE: torch_geometric/nn/pool/edge_pool.py class UnpoolInfo (line 10) | class UnpoolInfo(NamedTuple): class EdgePooling (line 17) | class EdgePooling(torch.nn.Module): method __init__ (line 57) | def __init__( method reset_parameters (line 76) | def reset_parameters(self): method compute_edge_score_softmax (line 81) | def compute_edge_score_softmax( method compute_edge_score_tanh (line 90) | def compute_edge_score_tanh( method compute_edge_score_sigmoid (line 99) | def compute_edge_score_sigmoid( method forward (line 107) | def forward( method _merge_edges (line 140) | def _merge_edges( method unpool (line 201) | def unpool( method __repr__ (line 226) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/glob.py function global_add_pool (line 8) | def global_add_pool(x: Tensor, batch: Optional[Tensor], function global_mean_pool (line 37) | def global_mean_pool(x: Tensor, batch: Optional[Tensor], function global_max_pool (line 66) | def global_max_pool(x: Tensor, batch: Optional[Tensor], FILE: torch_geometric/nn/pool/graclus.py function graclus (line 13) | def graclus(edge_index: Tensor, weight: Optional[Tensor] = None, FILE: torch_geometric/nn/pool/knn.py class KNNOutput (line 10) | class KNNOutput(NamedTuple): class KNNIndex (line 15) | class KNNIndex: method __init__ (line 39) | def __init__( method numel (line 57) | def numel(self) -> int: method _create_index (line 63) | def _create_index(self, channels: int): method add (line 67) | def add(self, emb: Tensor): method search (line 103) | def search( method get_emb (line 206) | def get_emb(self) -> Tensor: class L2KNNIndex (line 215) | class L2KNNIndex(KNNIndex): method __init__ (line 223) | def __init__(self, emb: Optional[Tensor] = None): method _create_index (line 226) | def _create_index(self, channels: int): class MIPSKNNIndex (line 231) | class MIPSKNNIndex(KNNIndex): method __init__ (line 239) | def __init__(self, emb: Optional[Tensor] = None): method _create_index (line 242) | def _create_index(self, channels: int): class ApproxL2KNNIndex (line 247) | class ApproxL2KNNIndex(KNNIndex): method __init__ (line 263) | def __init__( method _create_index (line 276) | def _create_index(self, channels: int): class ApproxMIPSKNNIndex (line 290) | class ApproxMIPSKNNIndex(KNNIndex): method __init__ (line 306) | def __init__( method _create_index (line 319) | def _create_index(self, channels: int): FILE: torch_geometric/nn/pool/max_pool.py function _max_pool_x (line 11) | def _max_pool_x( function max_pool_x (line 19) | def max_pool_x( function max_pool (line 60) | def max_pool( function max_pool_neighbor_x (line 100) | def max_pool_neighbor_x( FILE: torch_geometric/nn/pool/mem_pool.py class MemPooling (line 12) | class MemPooling(torch.nn.Module): method __init__ (line 40) | def __init__(self, in_channels: int, out_channels: int, heads: int, method reset_parameters (line 55) | def reset_parameters(self): method kl_loss (line 62) | def kl_loss(S: Tensor) -> Tensor: method forward (line 81) | def forward( method __repr__ (line 134) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/pan_pool.py class PANPooling (line 13) | class PANPooling(torch.nn.Module): method __init__ (line 42) | def __init__( method reset_parameters (line 64) | def reset_parameters(self): method forward (line 70) | def forward( method __repr__ (line 112) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/pool.py function pool_edge (line 8) | def pool_edge( function pool_batch (line 23) | def pool_batch(perm, batch): function pool_pos (line 27) | def pool_pos(cluster, pos): FILE: torch_geometric/nn/pool/sag_pool.py class SAGPooling (line 12) | class SAGPooling(torch.nn.Module): method __init__ (line 72) | def __init__( method reset_parameters (line 95) | def reset_parameters(self): method forward (line 100) | def forward( method __repr__ (line 143) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/select/base.py class SelectOutput (line 9) | class SelectOutput: method __init__ (line 28) | def __init__( class Select (line 68) | class Select(torch.nn.Module): method reset_parameters (line 78) | def reset_parameters(self): method forward (line 81) | def forward(self, *args, **kwargs) -> SelectOutput: method __repr__ (line 84) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/select/topk.py function topk (line 13) | def topk( class SelectTopK (line 51) | class SelectTopK(Select): method __init__ (line 92) | def __init__( method reset_parameters (line 115) | def reset_parameters(self): method forward (line 118) | def forward( method __repr__ (line 145) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/topk_pool.py class TopKPooling (line 11) | class TopKPooling(torch.nn.Module): method __init__ (line 65) | def __init__( method reset_parameters (line 85) | def reset_parameters(self): method forward (line 89) | def forward( method __repr__ (line 129) | def __repr__(self) -> str: FILE: torch_geometric/nn/pool/voxel_grid.py function voxel_grid (line 15) | def voxel_grid( FILE: torch_geometric/nn/reshape.py class Reshape (line 5) | class Reshape(torch.nn.Module): method __init__ (line 6) | def __init__(self, *shape): method forward (line 10) | def forward(self, x: Tensor) -> Tensor: method __repr__ (line 15) | def __repr__(self) -> str: FILE: torch_geometric/nn/resolver.py function swish (line 26) | def swish(x: Tensor) -> Tensor: function activation_resolver (line 30) | def activation_resolver(query: Union[Any, str] = 'relu', *args, **kwargs): function normalization_resolver (line 48) | def normalization_resolver(query: Union[Any, str], *args, **kwargs): function aggregation_resolver (line 64) | def aggregation_resolver(query: Union[Any, str], *args, **kwargs): function optimizer_resolver (line 83) | def optimizer_resolver(query: Union[Any, str], *args, **kwargs): function lr_scheduler_resolver (line 95) | def lr_scheduler_resolver( FILE: torch_geometric/nn/sequential.py class Child (line 24) | class Child(NamedTuple): class Sequential (line 30) | class Sequential(torch.nn.Module): method __init__ (line 87) | def __init__( method reset_parameters (line 185) | def reset_parameters(self) -> None: method __len__ (line 192) | def __len__(self) -> int: method __getitem__ (line 195) | def __getitem__(self, idx: int) -> torch.nn.Module: method __setstate__ (line 198) | def __setstate__(self, data: Dict[str, Any]) -> None: method __repr__ (line 202) | def __repr__(self) -> str: method forward (line 215) | def forward(self, *args: Any, **kwargs: Any) -> Any: method _set_jittable_template (line 240) | def _set_jittable_template(self, raise_on_error: bool = False) -> None: method __prepare_scriptable__ (line 269) | def __prepare_scriptable__(self) -> 'Sequential': FILE: torch_geometric/nn/summary.py function summary (line 13) | def summary( function get_shape (line 128) | def get_shape(inputs: Any) -> str: function postprocess (line 141) | def postprocess(info_list: List[dict]) -> List[dict]: function make_table (line 160) | def make_table(info_list: List[dict], max_depth: int) -> str: FILE: torch_geometric/nn/to_fixed_size_transformer.py function to_fixed_size (line 13) | def to_fixed_size(module: Module, batch_size: int, class ToFixedSizeTransformer (line 28) | class ToFixedSizeTransformer(Transformer): method __init__ (line 29) | def __init__(self, module: Module, batch_size: int, debug: bool = False): method call_global_pooling_module (line 33) | def call_global_pooling_module(self, node: Node, target: Any, name: str): FILE: torch_geometric/nn/to_hetero_module.py class ToHeteroLinear (line 15) | class ToHeteroLinear(torch.nn.Module): method __init__ (line 16) | def __init__( method fused_forward (line 49) | def fused_forward(self, x: Tensor, type_vec: Tensor) -> Tensor: method dict_forward (line 52) | def dict_forward( method forward (line 73) | def forward( class ToHeteroMessagePassing (line 89) | class ToHeteroMessagePassing(torch.nn.Module): method __init__ (line 90) | def __init__( method fused_forward (line 120) | def fused_forward(self, x: Tensor, edge_index: Tensor, node_type: Tensor, method dict_forward (line 150) | def dict_forward( method forward (line 158) | def forward( FILE: torch_geometric/nn/to_hetero_transformer.py function get_dict (line 23) | def get_dict(mapping: Optional[Dict[str, Any]]) -> Dict[str, Any]: function to_hetero (line 27) | def to_hetero(module: Module, metadata: Metadata, aggr: str = "sum", class ToHeteroTransformer (line 123) | class ToHeteroTransformer(Transformer): method __init__ (line 135) | def __init__( method validate (line 153) | def validate(self): method placeholder (line 171) | def placeholder(self, node: Node, target: Any, name: str): method get_attr (line 190) | def get_attr(self, node: Node, target: Any, name: str): method call_message_passing_module (line 193) | def call_message_passing_module(self, node: Node, target: Any, name: s... method call_global_pooling_module (line 252) | def call_global_pooling_module(self, node: Node, target: Any, name: str): method call_module (line 287) | def call_module(self, node: Node, target: Any, name: str): method call_method (line 301) | def call_method(self, node: Node, target: Any, name: str): method call_function (line 314) | def call_function(self, node: Node, target: Any, name: str): method output (line 327) | def output(self, node: Node, target: Any, name: str): method init_submodule (line 358) | def init_submodule(self, module: Module, target: str) -> Module: method map_args_kwargs (line 388) | def map_args_kwargs(self, node: Node, function key2str (line 423) | def key2str(key: Union[NodeType, EdgeType]) -> str: FILE: torch_geometric/nn/to_hetero_with_bases_transformer.py function to_hetero_with_bases (line 21) | def to_hetero_with_bases(module: Module, metadata: Metadata, num_bases: ... class ToHeteroWithBasesTransformer (line 137) | class ToHeteroWithBasesTransformer(Transformer): method __init__ (line 138) | def __init__( method validate (line 161) | def validate(self): method transform (line 179) | def transform(self) -> GraphModule: method placeholder (line 189) | def placeholder(self, node: Node, target: Any, name: str): method call_message_passing_module (line 258) | def call_message_passing_module(self, node: Node, target: Any, name: s... method output (line 264) | def output(self, node: Node, target: Any, name: str): method init_submodule (line 300) | def init_submodule(self, module: Module, target: str) -> Module: function hook (line 315) | def hook(module, inputs, output): class HeteroBasisConv (line 329) | class HeteroBasisConv(torch.nn.Module): method __init__ (line 332) | def __init__(self, module: MessagePassing, num_relations: int, method reset_parameters (line 356) | def reset_parameters(self): method forward (line 367) | def forward(self, edge_type: Tensor, *args, **kwargs) -> Tensor: method __repr__ (line 377) | def __repr__(self) -> str: class LinearAlign (line 382) | class LinearAlign(torch.nn.Module): method __init__ (line 386) | def __init__(self, keys: List[Union[NodeType, EdgeType]], method forward (line 394) | def forward( method __repr__ (line 399) | def __repr__(self) -> str: function get_node_offset_dict (line 411) | def get_node_offset_dict( function get_edge_offset_dict (line 423) | def get_edge_offset_dict( function get_edge_type (line 447) | def get_edge_type( function group_node_placeholder (line 474) | def group_node_placeholder(input_dict: Dict[NodeType, Tensor], function group_edge_placeholder (line 481) | def group_edge_placeholder( function split_output (line 541) | def split_output( function key2str (line 555) | def key2str(key: Union[NodeType, EdgeType]) -> str: FILE: torch_geometric/nn/unpool/knn_interpolate.py function knn_interpolate (line 8) | def knn_interpolate(x: torch.Tensor, pos_x: torch.Tensor, pos_y: torch.T... FILE: torch_geometric/profile/benchmark.py function require_grad (line 10) | def require_grad(x: Any, requires_grad: bool = True) -> Any: function benchmark (line 23) | def benchmark( function get_func_name (line 135) | def get_func_name(func: Callable) -> str: FILE: torch_geometric/profile/nvtx.py function begin_cuda_profile (line 9) | def begin_cuda_profile(): function end_cuda_profile (line 18) | def end_cuda_profile(prev_state: bool): function nvtxit (line 25) | def nvtxit(name: Optional[str] = None, n_warmups: int = 0, FILE: torch_geometric/profile/profile.py class GPUStats (line 20) | class GPUStats: class CUDAStats (line 28) | class CUDAStats(GPUStats): class GPUStatsSummary (line 34) | class GPUStatsSummary: class CUDAStatsSummary (line 43) | class CUDAStatsSummary(GPUStatsSummary): function profileit (line 48) | def profileit(device: str): # pragma: no cover class timeit (line 144) | class timeit(ContextDecorator): method __init__ (line 166) | def __init__(self, log: bool = True, avg_time_divisor: int = 0): method __enter__ (line 170) | def __enter__(self): method __exit__ (line 176) | def __exit__(self, *args): method reset (line 186) | def reset(self): function get_stats_summary (line 195) | def get_stats_summary( function read_from_memlab (line 231) | def read_from_memlab(line_profiler: Any) -> List[float]: # pragma: no c... function trace_handler (line 248) | def trace_handler(p): function print_time_total (line 255) | def print_time_total(p): function rename_profile_file (line 264) | def rename_profile_file(*args): function torch_profile (line 274) | def torch_profile(export_chrome_trace=True, csv_data=None, write_csv=None): function xpu_profile (line 308) | def xpu_profile(export_chrome_trace=True): function format_prof_time (line 316) | def format_prof_time(time): function save_profile_data (line 321) | def save_profile_data(csv_data, events, use_cuda): FILE: torch_geometric/profile/profiler.py class Profiler (line 27) | class Profiler: method __init__ (line 43) | def __init__( method __enter__ (line 63) | def __enter__(self): method __exit__ (line 75) | def __exit__(self, exc_type, exc_val, exc_tb): method get_trace (line 82) | def get_trace(self): method __repr__ (line 85) | def __repr__(self) -> str: method __call__ (line 88) | def __call__(self, *args, **kwargs): method _hook_trace (line 91) | def _hook_trace(self, trace): method _remove_hook_trace (line 136) | def _remove_hook_trace(self, trace): function _layer_trace (line 149) | def _layer_trace( function _flatten_tree (line 320) | def _flatten_tree(t, depth=0): function _build_measure_tuple (line 329) | def _build_measure_tuple(events: List, occurrences: List) -> NamedTuple: function _format_measure_tuple (line 382) | def _format_measure_tuple(measure: NamedTuple) -> NamedTuple: function _group_by (line 411) | def _group_by(events, keyfn): function _walk_modules (line 421) | def _walk_modules(module, name: str = "", path=()): function format_time (line 443) | def format_time(time_us: int) -> str: function format_memory (line 454) | def format_memory(nbytes: int) -> str: FILE: torch_geometric/profile/utils.py function count_parameters (line 18) | def count_parameters(model: torch.nn.Module) -> int: function get_model_size (line 27) | def get_model_size(model: torch.nn.Module) -> int: function get_data_size (line 40) | def get_data_size(data: BaseData) -> int: function get_cpu_memory_from_gc (line 69) | def get_cpu_memory_from_gc() -> int: function get_gpu_memory_from_gc (line 85) | def get_gpu_memory_from_gc(device: int = 0) -> int: # pragma: no cover function get_gpu_memory_from_nvidia_smi (line 104) | def get_gpu_memory_from_nvidia_smi( # pragma: no cover function get_gpu_memory_from_ipex (line 153) | def get_gpu_memory_from_ipex( function byte_to_megabyte (line 178) | def byte_to_megabyte(value: int, digits: int = 2) -> float: function medibyte_to_megabyte (line 182) | def medibyte_to_megabyte(value: int, digits: int = 2) -> float: FILE: torch_geometric/resolver.py function normalize_string (line 5) | def normalize_string(s: str) -> str: function resolver (line 9) | def resolver( FILE: torch_geometric/sampler/base.py class DataType (line 24) | class DataType(Enum): method from_data (line 31) | def from_data(cls, data: Any): class SubgraphType (line 46) | class SubgraphType(Enum): class NodeSamplerInput (line 54) | class NodeSamplerInput(CastMixin): method __init__ (line 72) | def __init__( method __getitem__ (line 90) | def __getitem__(self, index: Union[Tensor, Any]) -> 'NodeSamplerInput': class EdgeSamplerInput (line 103) | class EdgeSamplerInput(CastMixin): method __init__ (line 128) | def __init__( method __getitem__ (line 153) | def __getitem__(self, index: Union[Tensor, Any]) -> 'EdgeSamplerInput': class SamplerOutput (line 168) | class SamplerOutput(CastMixin): method global_row (line 217) | def global_row(self) -> Tensor: method global_col (line 221) | def global_col(self) -> Tensor: method seed_node (line 225) | def seed_node(self) -> Tensor: method seed_node (line 234) | def seed_node(self, value: Tensor): method global_orig_row (line 239) | def global_orig_row(self) -> Tensor: method global_orig_col (line 244) | def global_orig_col(self) -> Tensor: method to_bidirectional (line 248) | def to_bidirectional( method collate (line 279) | def collate(cls, outputs: List['SamplerOutput'], method merge_with (line 313) | def merge_with(self, other: 'SamplerOutput', class HeteroSamplerOutput (line 504) | class HeteroSamplerOutput(CastMixin): method global_row (line 560) | def global_row(self) -> Dict[EdgeType, Tensor]: method global_col (line 567) | def global_col(self) -> Dict[EdgeType, Tensor]: method seed_node (line 574) | def seed_node(self) -> Optional[Dict[NodeType, Tensor]]: method global_orig_row (line 581) | def global_orig_row(self) -> Optional[Dict[EdgeType, Tensor]]: method global_orig_col (line 589) | def global_orig_col(self) -> Optional[Dict[EdgeType, Tensor]]: method to_bidirectional (line 596) | def to_bidirectional( method collate (line 679) | def collate(cls, outputs: List['HeteroSamplerOutput'], method merge_with (line 689) | def merge_with(self, other: 'HeteroSamplerOutput', class NumNeighbors (line 699) | class NumNeighbors: method __init__ (line 716) | def __init__( method _get_values (line 733) | def _get_values( method get_values (line 782) | def get_values( method get_mapped_values (line 800) | def get_mapped_values( method num_hops (line 821) | def num_hops(self) -> int: method __len__ (line 835) | def __len__(self) -> int: class NegativeSamplingMode (line 840) | class NegativeSamplingMode(Enum): class NegativeSampling (line 849) | class NegativeSampling(CastMixin): method __init__ (line 877) | def __init__( method is_binary (line 902) | def is_binary(self) -> bool: method is_triplet (line 905) | def is_triplet(self) -> bool: method sample (line 908) | def sample( class BaseSampler (line 932) | class BaseSampler(ABC): method sample_from_nodes (line 945) | def sample_from_nodes( method sample_from_edges (line 966) | def sample_from_edges( method edge_permutation (line 990) | def edge_permutation(self) -> Union[OptTensor, Dict[EdgeType, OptTenso... FILE: torch_geometric/sampler/hgt_sampler.py class HGTSampler (line 23) | class HGTSampler(BaseSampler): method __init__ (line 27) | def __init__( method sample_from_nodes (line 60) | def sample_from_nodes( method sample_from_edges (line 82) | def sample_from_edges( method edge_permutation (line 90) | def edge_permutation(self) -> Union[OptTensor, Dict[EdgeType, OptTenso... FILE: torch_geometric/sampler/neighbor_sampler.py class NeighborSampler (line 40) | class NeighborSampler(BaseSampler): method __init__ (line 44) | def __init__( method num_neighbors (line 341) | def num_neighbors(self) -> NumNeighbors: method num_neighbors (line 349) | def num_neighbors(self, num_neighbors: NumNeighborsType): method is_hetero (line 376) | def is_hetero(self) -> bool: method is_temporal (line 386) | def is_temporal(self) -> bool: method disjoint (line 390) | def disjoint(self) -> bool: method disjoint (line 394) | def disjoint(self, disjoint: bool): method sample_from_nodes (line 399) | def sample_from_nodes( method sample_from_edges (line 410) | def sample_from_edges( method edge_permutation (line 424) | def edge_permutation(self) -> Union[OptTensor, Dict[EdgeType, OptTenso... method _sample (line 429) | def _sample( class BidirectionalNeighborSampler (line 623) | class BidirectionalNeighborSampler(NeighborSampler): method __init__ (line 625) | def __init__( method num_neighbors (line 662) | def num_neighbors(self) -> NumNeighbors: method num_neighbors (line 666) | def num_neighbors(self, num_neighbors: NumNeighborsType): method is_hetero (line 675) | def is_hetero(self) -> bool: method is_temporal (line 679) | def is_temporal(self) -> bool: method disjoint (line 683) | def disjoint(self) -> bool: method disjoint (line 687) | def disjoint(self, disjoint: bool): method sample_from_nodes (line 691) | def sample_from_nodes( method sample_from_edges (line 697) | def sample_from_edges( method edge_permutation (line 712) | def edge_permutation(self) -> Union[OptTensor, Dict[EdgeType, OptTenso... method _sample (line 715) | def _sample( function node_sample (line 798) | def node_sample( function edge_sample (line 821) | def edge_sample( function neg_sample (line 1051) | def neg_sample( FILE: torch_geometric/sampler/utils.py function reverse_edge_type (line 13) | def reverse_edge_type(edge_type: EdgeType) -> EdgeType: function sort_csc (line 24) | def sort_csc( function to_csc (line 46) | def to_csc( function to_hetero_csc (line 114) | def to_hetero_csc( function to_bidirectional (line 143) | def to_bidirectional( function remap_keys (line 179) | def remap_keys( function local_to_global_node_idx (line 191) | def local_to_global_node_idx(node_values: Tensor, function global_to_local_node_idx (line 207) | def global_to_local_node_idx(node_values: Tensor, function unique_unsorted (line 236) | def unique_unsorted(tensor: Tensor) -> Tensor: FILE: torch_geometric/seed.py function seed_everything (line 7) | def seed_everything(seed: int) -> None: FILE: torch_geometric/template.py function module_from_template (line 10) | def module_from_template( FILE: torch_geometric/testing/asserts.py function assert_module (line 16) | def assert_module( FILE: torch_geometric/testing/data.py function get_random_edge_index (line 11) | def get_random_edge_index( function get_random_tensor_frame (line 31) | def get_random_tensor_frame( class FakeHeteroDataset (line 55) | class FakeHeteroDataset(InMemoryDataset): method __init__ (line 56) | def __init__(self, transform: Optional[Callable] = None): FILE: torch_geometric/testing/decorators.py function is_full_test (line 18) | def is_full_test() -> bool: function onlyFullTest (line 23) | def onlyFullTest(func: Callable) -> Callable: function is_distributed_test (line 34) | def is_distributed_test() -> bool: function onlyDistributedTest (line 40) | def onlyDistributedTest(func: Callable) -> Callable: function onlyLinux (line 51) | def onlyLinux(func: Callable) -> Callable: function noWindows (line 62) | def noWindows(func: Callable) -> Callable: function noMac (line 73) | def noMac(func: Callable) -> Callable: function minPython (line 84) | def minPython(version: str) -> Callable: function onlyCUDA (line 106) | def onlyCUDA(func: Callable) -> Callable: function onlyXPU (line 115) | def onlyXPU(func: Callable) -> Callable: function onlyOnline (line 124) | def onlyOnline(func: Callable) -> Callable: function onlyGraphviz (line 147) | def onlyGraphviz(func: Callable) -> Callable: function onlyNeighborSampler (line 158) | def onlyNeighborSampler(func: Callable) -> Callable: function has_package (line 169) | def has_package(package: str) -> bool: function withPackage (line 189) | def withPackage(*args: str) -> Callable: function withCUDA (line 207) | def withCUDA(func: Callable) -> Callable: function withDevice (line 218) | def withDevice(func: Callable) -> Callable: function withMETIS (line 248) | def withMETIS(func: Callable) -> Callable: function withHashTensor (line 270) | def withHashTensor(func: Callable) -> Callable: function disableExtensions (line 281) | def disableExtensions(func: Callable) -> Callable: function withoutExtensions (line 291) | def withoutExtensions(func: Callable) -> Callable: FILE: torch_geometric/testing/distributed.py class ProcArgs (line 13) | class ProcArgs: class MPCaptOutput (line 18) | class MPCaptOutput: method __enter__ (line 19) | def __enter__(self) -> Self: method __exit__ (line 30) | def __exit__(self, *args: Any) -> None: method stdout_str (line 35) | def stdout_str(self) -> str: method stderr_str (line 39) | def stderr_str(self) -> str: function ps_std_capture (line 43) | def ps_std_capture( function assert_run_mproc (line 59) | def assert_run_mproc( FILE: torch_geometric/testing/feature_store.py class MyFeatureStore (line 12) | class MyFeatureStore(FeatureStore): method __init__ (line 13) | def __init__(self) -> None: method key (line 18) | def key(attr: TensorAttr) -> KeyType: method _put_tensor (line 21) | def _put_tensor(self, tensor: FeatureTensorType, attr: TensorAttr) -> ... method _get_tensor (line 35) | def _get_tensor(self, attr: TensorAttr) -> Optional[Tensor]: method _remove_tensor (line 60) | def _remove_tensor(self, attr: TensorAttr) -> bool: method _get_tensor_size (line 63) | def _get_tensor_size(self, attr: TensorAttr) -> Optional[Tuple[int, ..... method get_all_tensor_attrs (line 67) | def get_all_tensor_attrs(self) -> List[TensorAttr]: FILE: torch_geometric/testing/graph_store.py class MyGraphStore (line 9) | class MyGraphStore(GraphStore): method __init__ (line 10) | def __init__(self) -> None: method key (line 15) | def key(attr: EdgeAttr) -> Tuple: method _put_edge_index (line 18) | def _put_edge_index( method _get_edge_index (line 26) | def _get_edge_index(self, edge_attr: EdgeAttr) -> Optional[EdgeTensorT... method _remove_edge_index (line 29) | def _remove_edge_index(self, edge_attr: EdgeAttr) -> bool: method get_all_edge_attrs (line 32) | def get_all_edge_attrs(self) -> List[EdgeAttr]: FILE: torch_geometric/transforms/add_gpse.py class AddGPSE (line 11) | class AddGPSE(BaseTransform): method __init__ (line 27) | def __init__( method forward (line 38) | def forward(self, data: Data) -> Any: method __call__ (line 41) | def __call__(self, data: Data) -> Data: FILE: torch_geometric/transforms/add_metapaths.py class AddMetaPaths (line 16) | class AddMetaPaths(BaseTransform): method __init__ (line 100) | def __init__( method forward (line 135) | def forward(self, data: HeteroData) -> HeteroData: method _edge_index (line 173) | def _edge_index( method _sample (line 195) | def _sample( class AddRandomMetaPaths (line 215) | class AddRandomMetaPaths(BaseTransform): method __init__ (line 241) | def __init__( method forward (line 267) | def forward(self, data: HeteroData) -> HeteroData: method sample (line 301) | def sample(edge_index: EdgeIndex, subset: Tensor) -> Tuple[Tensor, Ten... method __repr__ (line 320) | def __repr__(self) -> str: function postprocess (line 326) | def postprocess( FILE: torch_geometric/transforms/add_positional_encoding.py function add_node_attr (line 23) | def add_node_attr( class AddLaplacianEigenvectorPE (line 42) | class AddLaplacianEigenvectorPE(BaseTransform): method __init__ (line 65) | def __init__( method forward (line 77) | def forward(self, data: Data) -> Data: class AddRandomWalkPE (line 118) | class AddRandomWalkPE(BaseTransform): method __init__ (line 131) | def __init__( method forward (line 139) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/add_remaining_self_loops.py class AddRemainingSelfLoops (line 12) | class AddRemainingSelfLoops(BaseTransform): method __init__ (line 30) | def __init__( method forward (line 38) | def forward( FILE: torch_geometric/transforms/add_self_loops.py class AddSelfLoops (line 12) | class AddSelfLoops(BaseTransform): method __init__ (line 30) | def __init__( method forward (line 38) | def forward( FILE: torch_geometric/transforms/base_transform.py class BaseTransform (line 6) | class BaseTransform(ABC): method __call__ (line 30) | def __call__(self, data: Any) -> Any: method forward (line 35) | def forward(self, data: Any) -> Any: method __repr__ (line 38) | def __repr__(self) -> str: FILE: torch_geometric/transforms/cartesian.py class Cartesian (line 11) | class Cartesian(BaseTransform): method __init__ (line 27) | def __init__( method forward (line 39) | def forward(self, data: Data) -> Data: method __repr__ (line 62) | def __repr__(self) -> str: FILE: torch_geometric/transforms/center.py class Center (line 9) | class Center(BaseTransform): method forward (line 13) | def forward( FILE: torch_geometric/transforms/compose.py class Compose (line 7) | class Compose(BaseTransform): method __init__ (line 13) | def __init__(self, transforms: List[Callable]): method forward (line 16) | def forward( method __repr__ (line 27) | def __repr__(self) -> str: class ComposeFilters (line 32) | class ComposeFilters: method __init__ (line 38) | def __init__(self, filters: List[Callable]): method __call__ (line 41) | def __call__( method __repr__ (line 53) | def __repr__(self) -> str: FILE: torch_geometric/transforms/constant.py class Constant (line 11) | class Constant(BaseTransform): method __init__ (line 24) | def __init__( method forward (line 37) | def forward( method __repr__ (line 56) | def __repr__(self) -> str: FILE: torch_geometric/transforms/delaunay.py class _QhullTransform (line 10) | class _QhullTransform(BaseTransform): method forward (line 12) | def forward(self, data: Data) -> Data: class _ShullTransform (line 24) | class _ShullTransform(BaseTransform): method forward (line 26) | def forward(self, data: Data) -> Data: class _SequentialTransform (line 35) | class _SequentialTransform(BaseTransform): method __init__ (line 40) | def __init__(self, transforms: List[BaseTransform]) -> None: method forward (line 44) | def forward(self, data: Data) -> Data: class Delaunay (line 55) | class Delaunay(BaseTransform): method __init__ (line 64) | def __init__(self) -> None: method forward (line 70) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/distance.py class Distance (line 11) | class Distance(BaseTransform): method __init__ (line 27) | def __init__( method forward (line 39) | def forward(self, data: Data) -> Data: method __repr__ (line 60) | def __repr__(self) -> str: FILE: torch_geometric/transforms/face_to_edge.py class FaceToEdge (line 10) | class FaceToEdge(BaseTransform): method __init__ (line 25) | def __init__(self, remove_faces: bool = True) -> None: method forward (line 28) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/feature_propagation.py class FeaturePropagation (line 11) | class FeaturePropagation(BaseTransform): method __init__ (line 39) | def __init__(self, missing_mask: Tensor, num_iterations: int = 40) -> ... method forward (line 43) | def forward(self, data: Data) -> Data: method __repr__ (line 79) | def __repr__(self) -> str: FILE: torch_geometric/transforms/fixed_points.py class FixedPoints (line 14) | class FixedPoints(BaseTransform): method __init__ (line 31) | def __init__( method forward (line 41) | def forward(self, data: Data) -> Data: method __repr__ (line 67) | def __repr__(self) -> str: FILE: torch_geometric/transforms/gcn_norm.py class GCNNorm (line 8) | class GCNNorm(BaseTransform): method __init__ (line 19) | def __init__(self, add_self_loops: bool = True): method forward (line 22) | def forward(self, data: Data) -> Data: method __repr__ (line 36) | def __repr__(self) -> str: FILE: torch_geometric/transforms/gdc.py class GDC (line 22) | class GDC(BaseTransform): method __init__ (line 76) | def __init__( method forward (line 98) | def forward(self, data: Data) -> Data: method transition_matrix (line 142) | def transition_matrix( method diffusion_matrix_exact (line 195) | def diffusion_matrix_exact( # noqa: D417 method diffusion_matrix_approx (line 268) | def diffusion_matrix_approx( # noqa: D417 method sparsify_dense (line 348) | def sparsify_dense( # noqa: D417 method sparsify_sparse (line 417) | def sparsify_sparse( # noqa: D417 method __expm__ (line 465) | def __expm__(self, matrix: Tensor, symmetric: bool) -> Tensor: method __calculate_eps__ (line 484) | def __calculate_eps__( FILE: torch_geometric/transforms/generate_mesh_normals.py class GenerateMeshNormals (line 10) | class GenerateMeshNormals(BaseTransform): method forward (line 14) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/grid_sampling.py class GridSampling (line 15) | class GridSampling(BaseTransform): method __init__ (line 32) | def __init__( method forward (line 42) | def forward(self, data: Data) -> Data: method __repr__ (line 66) | def __repr__(self) -> str: FILE: torch_geometric/transforms/half_hop.py class HalfHop (line 9) | class HalfHop(BaseTransform): method __init__ (line 36) | def __init__(self, alpha: float = 0.5, p: float = 1.0) -> None: method forward (line 47) | def forward(self, data: Data) -> Data: method __repr__ (line 99) | def __repr__(self) -> str: FILE: torch_geometric/transforms/knn_graph.py class KNNGraph (line 9) | class KNNGraph(BaseTransform): method __init__ (line 32) | def __init__( method forward (line 48) | def forward(self, data: Data) -> Data: method __repr__ (line 69) | def __repr__(self) -> str: FILE: torch_geometric/transforms/laplacian_lambda_max.py class LaplacianLambdaMax (line 10) | class LaplacianLambdaMax(BaseTransform): method __init__ (line 32) | def __init__( method forward (line 41) | def forward(self, data: Data) -> Data: method __repr__ (line 69) | def __repr__(self) -> str: FILE: torch_geometric/transforms/largest_connected_components.py class LargestConnectedComponents (line 10) | class LargestConnectedComponents(BaseTransform): method __init__ (line 26) | def __init__( method forward (line 35) | def forward(self, data: Data) -> Data: method __repr__ (line 56) | def __repr__(self) -> str: FILE: torch_geometric/transforms/line_graph.py class LineGraph (line 11) | class LineGraph(BaseTransform): method __init__ (line 35) | def __init__(self, force_directed: bool = False) -> None: method forward (line 38) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/linear_transformation.py class LinearTransformation (line 12) | class LinearTransformation(BaseTransform): method __init__ (line 20) | def __init__(self, matrix: Tensor): method forward (line 32) | def forward( method __repr__ (line 51) | def __repr__(self) -> str: FILE: torch_geometric/transforms/local_cartesian.py class LocalCartesian (line 12) | class LocalCartesian(BaseTransform): method __init__ (line 26) | def __init__( method forward (line 36) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/local_degree_profile.py class LocalDegreeProfile (line 10) | class LocalDegreeProfile(BaseTransform): method __init__ (line 23) | def __init__(self) -> None: method forward (line 27) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/mask.py function get_attrs_with_suffix (line 12) | def get_attrs_with_suffix( function get_mask_size (line 22) | def get_mask_size( class IndexToMask (line 33) | class IndexToMask(BaseTransform): method __init__ (line 49) | def __init__( method forward (line 59) | def forward( method __repr__ (line 92) | def __repr__(self) -> str: class MaskToIndex (line 98) | class MaskToIndex(BaseTransform): method __init__ (line 109) | def __init__( method forward (line 117) | def forward( method __repr__ (line 134) | def __repr__(self) -> str: FILE: torch_geometric/transforms/node_property_split.py class NodePropertySplit (line 13) | class NodePropertySplit(BaseTransform): method __init__ (line 60) | def __init__( method forward (line 82) | def forward(self, data: Data) -> Data: method _compute_popularity_property (line 93) | def _compute_popularity_property(G: Any, ascending: bool = True) -> Te... method _compute_locality_property (line 101) | def _compute_locality_property(G: Any, ascending: bool = True) -> Tensor: method _compute_density_property (line 116) | def _compute_density_property(G: Any, ascending: bool = True) -> Tensor: method _mask_nodes_by_property (line 124) | def _mask_nodes_by_property( method __repr__ (line 154) | def __repr__(self) -> str: FILE: torch_geometric/transforms/normalize_features.py class NormalizeFeatures (line 9) | class NormalizeFeatures(BaseTransform): method __init__ (line 17) | def __init__(self, attrs: Optional[List[str]] = None) -> None: method forward (line 20) | def forward( FILE: torch_geometric/transforms/normalize_rotation.py class NormalizeRotation (line 10) | class NormalizeRotation(BaseTransform): method __init__ (line 23) | def __init__(self, max_points: int = -1, sort: bool = False) -> None: method forward (line 27) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/normalize_scale.py class NormalizeScale (line 7) | class NormalizeScale(BaseTransform): method __init__ (line 11) | def __init__(self) -> None: method forward (line 14) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/one_hot_degree.py class OneHotDegree (line 10) | class OneHotDegree(BaseTransform): method __init__ (line 22) | def __init__( method forward (line 32) | def forward(self, data: Data) -> Data: method __repr__ (line 46) | def __repr__(self) -> str: FILE: torch_geometric/transforms/pad.py class Padding (line 15) | class Padding(ABC): method get_value (line 18) | def get_value( class UniformPadding (line 27) | class UniformPadding(Padding): method __init__ (line 36) | def __init__(self, value: Union[int, float] = 0.0): method get_value (line 43) | def get_value( class MappingPadding (line 52) | class MappingPadding(Padding): method __init__ (line 57) | def __init__( method validate_key_value (line 75) | def validate_key_value(self, key: Any, value: Any) -> None: class AttrNamePadding (line 79) | class AttrNamePadding(MappingPadding): method validate_key_value (line 88) | def validate_key_value(self, key: Any, value: Any) -> None: method get_value (line 97) | def get_value( class NodeTypePadding (line 106) | class NodeTypePadding(MappingPadding): method validate_key_value (line 114) | def validate_key_value(self, key: Any, value: Any) -> None: method get_value (line 124) | def get_value( class EdgeTypePadding (line 133) | class EdgeTypePadding(MappingPadding): method validate_key_value (line 141) | def validate_key_value(self, key: Any, value: Any) -> None: method get_value (line 155) | def get_value( class _NumNodes (line 164) | class _NumNodes: method __init__ (line 165) | def __init__( method get_value (line 171) | def get_value(self, key: Optional[NodeType] = None) -> Optional[int]: class _NumEdges (line 178) | class _NumEdges: method __init__ (line 179) | def __init__( method get_value (line 194) | def get_value(self, key: Optional[EdgeType] = None) -> Optional[int]: class Pad (line 209) | class Pad(BaseTransform): method __init__ (line 317) | def __init__( method __should_pad_node_attr (line 350) | def __should_pad_node_attr(self, attr_name: str) -> bool: method __should_pad_edge_attr (line 357) | def __should_pad_edge_attr(self, attr_name: str) -> bool: method __get_node_padding (line 366) | def __get_node_padding( method __get_edge_padding (line 375) | def __get_edge_padding( method forward (line 382) | def forward( method __pad_node_store (line 428) | def __pad_node_store( method __pad_edge_store (line 461) | def __pad_edge_store( method _pad_tensor_dim (line 506) | def _pad_tensor_dim(input: torch.Tensor, dim: int, length: int, method _pad_edge_index (line 516) | def _pad_edge_index(input: torch.Tensor, length: int, src_pad_value: f... method __repr__ (line 527) | def __repr__(self) -> str: FILE: torch_geometric/transforms/point_pair_features.py class PointPairFeatures (line 10) | class PointPairFeatures(BaseTransform): method __init__ (line 28) | def __init__(self, cat: bool = True): method forward (line 31) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/polar.py class Polar (line 12) | class Polar(BaseTransform): method __init__ (line 26) | def __init__( method forward (line 36) | def forward(self, data: Data) -> Data: method __repr__ (line 63) | def __repr__(self) -> str: FILE: torch_geometric/transforms/radius_graph.py class RadiusGraph (line 8) | class RadiusGraph(BaseTransform): method __init__ (line 26) | def __init__( method forward (line 40) | def forward(self, data: Data) -> Data: method __repr__ (line 56) | def __repr__(self) -> str: FILE: torch_geometric/transforms/random_flip.py class RandomFlip (line 9) | class RandomFlip(BaseTransform): method __init__ (line 18) | def __init__(self, axis: int, p: float = 0.5) -> None: method forward (line 22) | def forward(self, data: Data) -> Data: method __repr__ (line 31) | def __repr__(self) -> str: FILE: torch_geometric/transforms/random_jitter.py class RandomJitter (line 10) | class RandomJitter(BaseTransform): method __init__ (line 23) | def __init__( method forward (line 29) | def forward(self, data: Data) -> Data: method __repr__ (line 48) | def __repr__(self) -> str: FILE: torch_geometric/transforms/random_link_split.py class RandomLinkSplit (line 17) | class RandomLinkSplit(BaseTransform): method __init__ (line 88) | def __init__( method forward (line 122) | def forward( method _split (line 277) | def _split( method _create_label (line 313) | def _create_label( method __repr__ (line 357) | def __repr__(self) -> str: FILE: torch_geometric/transforms/random_node_split.py class RandomNodeSplit (line 13) | class RandomNodeSplit(BaseTransform): method __init__ (line 55) | def __init__( method forward (line 72) | def forward( method _split (line 89) | def _split(self, store: NodeStorage) -> Tuple[Tensor, Tensor, Tensor]: method __repr__ (line 134) | def __repr__(self) -> str: FILE: torch_geometric/transforms/random_rotate.py class RandomRotate (line 13) | class RandomRotate(BaseTransform): method __init__ (line 24) | def __init__( method forward (line 35) | def forward(self, data: Data) -> Data: method __repr__ (line 53) | def __repr__(self) -> str: FILE: torch_geometric/transforms/random_scale.py class RandomScale (line 10) | class RandomScale(BaseTransform): method __init__ (line 29) | def __init__(self, scales: Tuple[float, float]) -> None: method forward (line 33) | def forward(self, data: Data) -> Data: method __repr__ (line 40) | def __repr__(self) -> str: FILE: torch_geometric/transforms/random_shear.py class RandomShear (line 11) | class RandomShear(BaseTransform): method __init__ (line 29) | def __init__(self, shear: Union[float, int]) -> None: method forward (line 32) | def forward(self, data: Data) -> Data: method __repr__ (line 43) | def __repr__(self) -> str: FILE: torch_geometric/transforms/remove_duplicated_edges.py class RemoveDuplicatedEdges (line 10) | class RemoveDuplicatedEdges(BaseTransform): method __init__ (line 23) | def __init__( method forward (line 36) | def forward( FILE: torch_geometric/transforms/remove_isolated_nodes.py class RemoveIsolatedNodes (line 13) | class RemoveIsolatedNodes(BaseTransform): method forward (line 17) | def forward( FILE: torch_geometric/transforms/remove_self_loops.py class RemoveSelfLoops (line 10) | class RemoveSelfLoops(BaseTransform): method __init__ (line 20) | def __init__(self, attr: str = 'edge_weight') -> None: method forward (line 23) | def forward( FILE: torch_geometric/transforms/remove_training_classes.py class RemoveTrainingClasses (line 9) | class RemoveTrainingClasses(BaseTransform): method __init__ (line 17) | def __init__(self, classes: List[int]): method forward (line 20) | def forward(self, data: Data) -> Data: method __repr__ (line 26) | def __repr__(self) -> str: FILE: torch_geometric/transforms/rooted_subgraph.py class RootedSubgraphData (line 13) | class RootedSubgraphData(Data): method __inc__ (line 31) | def __inc__(self, key: str, value: Any, *args: Any, **kwargs: Any) -> ... method map_data (line 43) | def map_data(self) -> Data: class RootedSubgraph (line 67) | class RootedSubgraph(BaseTransform, ABC): method extract (line 70) | def extract( method map (line 79) | def map( method forward (line 102) | def forward(self, data: Data) -> RootedSubgraphData: class RootedEgoNets (line 109) | class RootedEgoNets(RootedSubgraph): method __init__ (line 117) | def __init__(self, num_hops: int) -> None: method extract (line 121) | def extract( method __repr__ (line 137) | def __repr__(self) -> str: class RootedRWSubgraph (line 141) | class RootedRWSubgraph(RootedSubgraph): method __init__ (line 151) | def __init__(self, walk_length: int, repeat: int = 1): method extract (line 156) | def extract( method __repr__ (line 178) | def __repr__(self) -> str: FILE: torch_geometric/transforms/sample_points.py class SamplePoints (line 9) | class SamplePoints(BaseTransform): method __init__ (line 20) | def __init__( method forward (line 30) | def forward(self, data: Data) -> Data: method __repr__ (line 73) | def __repr__(self) -> str: FILE: torch_geometric/transforms/sign.py class SIGN (line 11) | class SIGN(BaseTransform): method __init__ (line 35) | def __init__(self, K: int) -> None: method forward (line 38) | def forward(self, data: Data) -> Data: method __repr__ (line 65) | def __repr__(self) -> str: FILE: torch_geometric/transforms/spherical.py class Spherical (line 12) | class Spherical(BaseTransform): method __init__ (line 26) | def __init__( method forward (line 36) | def forward(self, data: Data) -> Data: method __repr__ (line 66) | def __repr__(self) -> str: FILE: torch_geometric/transforms/svd_feature_reduction.py class SVDFeatureReduction (line 9) | class SVDFeatureReduction(BaseTransform): method __init__ (line 17) | def __init__(self, out_channels: int): method forward (line 20) | def forward(self, data: Data) -> Data: method __repr__ (line 29) | def __repr__(self) -> str: FILE: torch_geometric/transforms/target_indegree.py class TargetIndegree (line 12) | class TargetIndegree(BaseTransform): method __init__ (line 26) | def __init__( method forward (line 36) | def forward(self, data: Data) -> Data: method __repr__ (line 56) | def __repr__(self) -> str: FILE: torch_geometric/transforms/to_dense.py class ToDense (line 12) | class ToDense(BaseTransform): method __init__ (line 21) | def __init__(self, num_nodes: Optional[int] = None) -> None: method forward (line 24) | def forward(self, data: Data) -> Data: method __repr__ (line 65) | def __repr__(self) -> str: FILE: torch_geometric/transforms/to_device.py class ToDevice (line 9) | class ToDevice(BaseTransform): method __init__ (line 22) | def __init__( method forward (line 32) | def forward( method __repr__ (line 39) | def __repr__(self) -> str: FILE: torch_geometric/transforms/to_sparse_tensor.py class ToSparseTensor (line 19) | class ToSparseTensor(BaseTransform): method __init__ (line 55) | def __init__( method forward (line 71) | def forward( method __repr__ (line 144) | def __repr__(self) -> str: FILE: torch_geometric/transforms/to_superpixels.py class ToSLIC (line 13) | class ToSLIC(BaseTransform): method __init__ (line 41) | def __init__( method forward (line 51) | def forward(self, img: Tensor) -> Data: FILE: torch_geometric/transforms/to_undirected.py class ToUndirected (line 13) | class ToUndirected(BaseTransform): method __init__ (line 33) | def __init__(self, reduce: str = "add", merge: bool = True): method forward (line 37) | def forward( FILE: torch_geometric/transforms/two_hop.py class TwoHop (line 11) | class TwoHop(BaseTransform): method forward (line 15) | def forward(self, data: Data) -> Data: FILE: torch_geometric/transforms/virtual_node.py class VirtualNode (line 12) | class VirtualNode(BaseTransform): method forward (line 27) | def forward(self, data: Data) -> Data: FILE: torch_geometric/typing.py class CPUHashMap (line 105) | class CPUHashMap: # type: ignore method __init__ (line 106) | def __init__(self, key: Tensor) -> None: method get (line 109) | def get(self, query: Tensor) -> Tensor: class CUDAHashMap (line 117) | class CUDAHashMap: # type: ignore method __init__ (line 118) | def __init__(self, key: Tensor) -> None: method get (line 121) | def get(self, query: Tensor) -> Tensor: class TorchCluster (line 148) | class TorchCluster: method __getattr__ (line 149) | def __getattr__(self, key: str) -> Any: class SparseStorage (line 173) | class SparseStorage: # type: ignore method __init__ (line 174) | def __init__( method value (line 191) | def value(self) -> Optional[Tensor]: method rowcount (line 194) | def rowcount(self) -> Tensor: class SparseTensor (line 197) | class SparseTensor: # type: ignore method __init__ (line 198) | def __init__( method from_edge_index (line 211) | def from_edge_index( method storage (line 222) | def storage(self) -> SparseStorage: method from_dense (line 226) | def from_dense(self, mat: Tensor, method size (line 230) | def size(self, dim: int) -> int: method nnz (line 233) | def nnz(self) -> int: method is_cuda (line 236) | def is_cuda(self) -> bool: method has_value (line 239) | def has_value(self) -> bool: method set_value (line 242) | def set_value(self, value: Optional[Tensor], method fill_value (line 246) | def fill_value(self, fill_value: float, method coo (line 250) | def coo(self) -> Tuple[Tensor, Tensor, Optional[Tensor]]: method csr (line 253) | def csr(self) -> Tuple[Tensor, Tensor, Optional[Tensor]]: method requires_grad (line 256) | def requires_grad(self) -> bool: method to_torch_sparse_csr_tensor (line 259) | def to_torch_sparse_csr_tensor( class torch_sparse (line 265) | class torch_sparse: # type: ignore method matmul (line 267) | def matmul(src: SparseTensor, other: Tensor, method sum (line 272) | def sum(src: SparseTensor, dim: Optional[int] = None) -> Tensor: method mul (line 276) | def mul(src: SparseTensor, other: Tensor) -> SparseTensor: method set_diag (line 280) | def set_diag(src: SparseTensor, values: Optional[Tensor] = None, method fill_diag (line 285) | def fill_diag(src: SparseTensor, fill_value: float, method masked_select_nnz (line 290) | def masked_select_nnz(src: SparseTensor, mask: Tensor, class TensorFrame (line 303) | class TensorFrame: # type: ignore class MockTorchCSCTensor (line 314) | class MockTorchCSCTensor: method __init__ (line 315) | def __init__( method t (line 325) | def t(self) -> Tensor: # Only support accessing its transpose: class EdgeTypeStr (line 350) | class EdgeTypeStr(str): method __new__ (line 356) | def __new__(cls, *args: Any) -> 'EdgeTypeStr': method to_tuple (line 385) | def to_tuple(self) -> EdgeType: method __reduce__ (line 392) | def __reduce__(self) -> tuple[Any, Any]: FILE: torch_geometric/utils/_assortativity.py function assortativity (line 9) | def assortativity(edge_index: Adj) -> float: FILE: torch_geometric/utils/_coalesce.py function coalesce (line 23) | def coalesce( function coalesce (line 35) | def coalesce( # noqa: F811 function coalesce (line 47) | def coalesce( # noqa: F811 function coalesce (line 59) | def coalesce( # noqa: F811 function coalesce (line 70) | def coalesce( # noqa: F811 FILE: torch_geometric/utils/_degree.py function degree (line 9) | def degree(index: Tensor, num_nodes: Optional[int] = None, FILE: torch_geometric/utils/_grid.py function grid (line 9) | def grid( function grid_index (line 45) | def grid_index( function grid_pos (line 72) | def grid_pos( FILE: torch_geometric/utils/_homophily.py function homophily (line 11) | def homophily( function homophily (line 21) | def homophily( function homophily (line 30) | def homophily( FILE: torch_geometric/utils/_index_sort.py function index_sort (line 10) | def index_sort( FILE: torch_geometric/utils/_lexsort.py function lexsort (line 6) | def lexsort( FILE: torch_geometric/utils/_negative_sampling.py function negative_sampling (line 12) | def negative_sampling( function batched_negative_sampling (line 125) | def batched_negative_sampling( function structured_negative_sampling (line 224) | def structured_negative_sampling( function structured_negative_sampling_feasible (line 274) | def structured_negative_sampling_feasible( function sample (line 321) | def sample( function edge_index_to_vector (line 332) | def edge_index_to_vector( function vector_to_edge_index (line 372) | def vector_to_edge_index( FILE: torch_geometric/utils/_normalize_edge_index.py function normalize_edge_index (line 10) | def normalize_edge_index( FILE: torch_geometric/utils/_normalized_cut.py function normalized_cut (line 8) | def normalized_cut( FILE: torch_geometric/utils/_one_hot.py function one_hot (line 7) | def one_hot( FILE: torch_geometric/utils/_scatter.py function scatter (line 14) | def scatter( function broadcast (line 141) | def broadcast(src: Tensor, ref: Tensor, dim: int) -> Tensor: function scatter_argmax (line 147) | def scatter_argmax( function group_argsort (line 187) | def group_argsort( function group_cat (line 251) | def group_cat( FILE: torch_geometric/utils/_segment.py function segment (line 11) | def segment(src: Tensor, ptr: Tensor, reduce: str = 'sum') -> Tensor: function _torch_segment (line 37) | def _torch_segment(src: Tensor, ptr: Tensor, reduce: str = 'sum') -> Ten... function segment_logsumexp (line 53) | def segment_logsumexp( FILE: torch_geometric/utils/_select.py function select (line 11) | def select( function narrow (line 45) | def narrow(src: Union[Tensor, List[Any]], dim: int, start: int, FILE: torch_geometric/utils/_softmax.py function softmax (line 12) | def softmax( FILE: torch_geometric/utils/_sort_edge_index.py function sort_edge_index (line 23) | def sort_edge_index( function sort_edge_index (line 33) | def sort_edge_index( # noqa: F811 function sort_edge_index (line 43) | def sort_edge_index( # noqa: F811 function sort_edge_index (line 53) | def sort_edge_index( # noqa: F811 function sort_edge_index (line 62) | def sort_edge_index( # noqa: F811 FILE: torch_geometric/utils/_spmm.py function spmm (line 12) | def spmm( FILE: torch_geometric/utils/_subgraph.py function get_num_hops (line 13) | def get_num_hops(model: torch.nn.Module) -> int: function subgraph (line 49) | def subgraph( function subgraph (line 60) | def subgraph( function subgraph (line 73) | def subgraph( function subgraph (line 85) | def subgraph( function bipartite_subgraph (line 162) | def bipartite_subgraph( function k_hop_subgraph (line 249) | def k_hop_subgraph( function hyper_subgraph (line 379) | def hyper_subgraph( function hyper_subgraph (line 390) | def hyper_subgraph( function hyper_subgraph (line 403) | def hyper_subgraph( function hyper_subgraph (line 415) | def hyper_subgraph( FILE: torch_geometric/utils/_to_dense_adj.py function to_dense_adj (line 10) | def to_dense_adj( FILE: torch_geometric/utils/_to_dense_batch.py function to_dense_batch (line 14) | def to_dense_batch( FILE: torch_geometric/utils/_train_test_split_edges.py function train_test_split_edges (line 11) | def train_test_split_edges( FILE: torch_geometric/utils/_tree_decomposition.py function tree_decomposition (line 15) | def tree_decomposition(mol: Any) -> Tuple[Tensor, Tensor, int]: function tree_decomposition (line 20) | def tree_decomposition( function tree_decomposition (line 28) | def tree_decomposition( function tree_decomposition (line 35) | def tree_decomposition( FILE: torch_geometric/utils/_trim_to_layer.py function trim_to_layer (line 20) | def trim_to_layer( function trim_to_layer (line 32) | def trim_to_layer( function trim_to_layer (line 44) | def trim_to_layer( class TrimToLayer (line 130) | class TrimToLayer(torch.nn.Module): method forward (line 132) | def forward( function trim_feat (line 167) | def trim_feat(x: Tensor, layer: int, num_samples_per_hop: List[int]) -> ... function trim_adj (line 178) | def trim_adj( function trim_sparse_tensor (line 217) | def trim_sparse_tensor(src: SparseTensor, size: Tuple[int, int], FILE: torch_geometric/utils/_unbatch.py function unbatch (line 9) | def unbatch( function unbatch_edge_index (line 39) | def unbatch_edge_index( FILE: torch_geometric/utils/augmentation.py function shuffle_node (line 9) | def shuffle_node( function mask_feature (line 71) | def mask_feature( function add_random_edge (line 156) | def add_random_edge( FILE: torch_geometric/utils/convert.py function to_scipy_sparse_matrix (line 12) | def to_scipy_sparse_matrix( function from_scipy_sparse_matrix (line 52) | def from_scipy_sparse_matrix(A: Any) -> Tuple[Tensor, Tensor]: function to_networkx (line 78) | def to_networkx( function from_networkx (line 193) | def from_networkx( function to_networkit (line 309) | def to_networkit( function from_networkit (line 351) | def from_networkit(g: Any) -> Tuple[Tensor, Optional[Tensor]]: function to_trimesh (line 377) | def to_trimesh(data: 'torch_geometric.data.Data') -> Any: function from_trimesh (line 405) | def from_trimesh(mesh: Any) -> 'torch_geometric.data.Data': function to_cugraph (line 430) | def to_cugraph( function from_cugraph (line 477) | def from_cugraph(g: Any) -> Tuple[Tensor, Optional[Tensor]]: function to_dgl (line 497) | def to_dgl( function from_dgl (line 582) | def from_dgl( FILE: torch_geometric/utils/cross_entropy.py class SparseCrossEntropy (line 9) | class SparseCrossEntropy(torch.autograd.Function): method forward (line 13) | def forward( method backward (line 56) | def backward(ctx: Any, grad_out: Tensor) -> Tuple[Tensor, None, None]: function sparse_cross_entropy (line 78) | def sparse_cross_entropy( FILE: torch_geometric/utils/dropout.py function filter_adj (line 14) | def filter_adj(row: Tensor, col: Tensor, edge_attr: OptTensor, function dropout_adj (line 20) | def dropout_adj( function dropout_node (line 94) | def dropout_node( function dropout_edge (line 157) | def dropout_edge(edge_index: Tensor, p: float = 0.5, function dropout_path (line 221) | def dropout_path(edge_index: Tensor, p: float = 0.2, walks_per_node: int... FILE: torch_geometric/utils/embedding.py function get_embeddings (line 10) | def get_embeddings( function get_embeddings_hetero (line 60) | def get_embeddings_hetero( FILE: torch_geometric/utils/functions.py function cumsum (line 5) | def cumsum(x: Tensor, dim: int = 0) -> Tensor: FILE: torch_geometric/utils/geodesic.py function geodesic_distance (line 10) | def geodesic_distance( # noqa: D417 FILE: torch_geometric/utils/hetero.py function group_hetero_graph (line 12) | def group_hetero_graph( function get_unused_node_types (line 64) | def get_unused_node_types(node_types: List[NodeType], function check_add_self_loops (line 70) | def check_add_self_loops( function construct_bipartite_edge_index (line 82) | def construct_bipartite_edge_index( FILE: torch_geometric/utils/influence.py function k_hop_subsets_rough (line 12) | def k_hop_subsets_rough( function k_hop_subsets_exact (line 60) | def k_hop_subsets_exact( function jacobian_l1 (line 90) | def jacobian_l1( function jacobian_l1_agg_per_hop (line 142) | def jacobian_l1_agg_per_hop( function avg_total_influence (line 166) | def avg_total_influence( function influence_weighted_receptive_field (line 177) | def influence_weighted_receptive_field(T: Tensor) -> float: function total_influence (line 193) | def total_influence( FILE: torch_geometric/utils/isolated.py function contains_isolated_nodes (line 10) | def contains_isolated_nodes( function remove_isolated_nodes (line 38) | def remove_isolated_nodes( FILE: torch_geometric/utils/laplacian.py function get_laplacian (line 11) | def get_laplacian( FILE: torch_geometric/utils/loop.py function contains_self_loops (line 23) | def contains_self_loops(edge_index: Tensor) -> bool: function remove_self_loops (line 48) | def remove_self_loops( function remove_self_loops (line 56) | def remove_self_loops( # noqa: F811 function remove_self_loops (line 64) | def remove_self_loops( # noqa: F811 function remove_self_loops (line 71) | def remove_self_loops( # noqa: F811 function segregate_self_loops (line 135) | def segregate_self_loops( function segregate_self_loops (line 143) | def segregate_self_loops( # noqa: F811 function segregate_self_loops (line 151) | def segregate_self_loops( # noqa: F811 function segregate_self_loops (line 158) | def segregate_self_loops( # noqa: F811 function add_self_loops (line 203) | def add_self_loops( function add_self_loops (line 213) | def add_self_loops( # noqa: F811 function add_self_loops (line 223) | def add_self_loops( # noqa: F811 function add_self_loops (line 233) | def add_self_loops( # noqa: F811 function add_self_loops (line 243) | def add_self_loops( # noqa: F811 function add_self_loops (line 253) | def add_self_loops( # noqa: F811 function add_self_loops (line 263) | def add_self_loops( # noqa: F811 function add_self_loops (line 273) | def add_self_loops( # noqa: F811 function add_self_loops (line 283) | def add_self_loops( # noqa: F811 function add_self_loops (line 293) | def add_self_loops( # noqa: F811 function add_self_loops (line 303) | def add_self_loops( # noqa: F811 function add_self_loops (line 313) | def add_self_loops( # noqa: F811 function add_self_loops (line 323) | def add_self_loops( # noqa: F811 function add_self_loops (line 333) | def add_self_loops( # noqa: F811 function add_self_loops (line 343) | def add_self_loops( # noqa: F811 function add_self_loops (line 353) | def add_self_loops( # noqa: F811 function add_self_loops (line 363) | def add_self_loops( # noqa: F811 function add_self_loops (line 373) | def add_self_loops( # noqa: F811 function add_self_loops (line 382) | def add_self_loops( # noqa: F811 function add_remaining_self_loops (line 496) | def add_remaining_self_loops( function add_remaining_self_loops (line 506) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 516) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 526) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 536) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 546) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 556) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 566) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 576) | def add_remaining_self_loops( # noqa: F811 function add_remaining_self_loops (line 585) | def add_remaining_self_loops( # noqa: F811 function get_self_loop_attr (line 660) | def get_self_loop_attr( function compute_loop_attr (line 710) | def compute_loop_attr( function compute_loop_attr (line 721) | def compute_loop_attr( # noqa: F811 function compute_loop_attr (line 732) | def compute_loop_attr( # noqa: F811 function compute_loop_attr (line 742) | def compute_loop_attr( # noqa: F811 FILE: torch_geometric/utils/map.py function map_index (line 13) | def map_index( FILE: torch_geometric/utils/mask.py function mask_select (line 9) | def mask_select(src: Tensor, dim: int, mask: Tensor) -> Tensor: function index_to_mask (line 41) | def index_to_mask(index: Tensor, size: Optional[int] = None) -> Tensor: function mask_to_index (line 64) | def mask_to_index(mask: Tensor) -> Tensor: FILE: torch_geometric/utils/mesh_laplacian.py function get_mesh_laplacian (line 9) | def get_mesh_laplacian( FILE: torch_geometric/utils/mixin.py class CastMixin (line 6) | class CastMixin: method cast (line 8) | def cast(cls: T, *args: Any, **kwargs: Any) -> T: method __iter__ (line 21) | def __iter__(self) -> Iterator: FILE: torch_geometric/utils/nested.py function to_nested_tensor (line 9) | def to_nested_tensor( function from_nested_tensor (line 50) | def from_nested_tensor( FILE: torch_geometric/utils/noise_scheduler.py function get_smld_sigma_schedule (line 8) | def get_smld_sigma_schedule( function get_diffusion_beta_schedule (line 46) | def get_diffusion_beta_schedule( FILE: torch_geometric/utils/num_nodes.py function maybe_num_nodes (line 12) | def maybe_num_nodes( function maybe_num_nodes_dict (line 43) | def maybe_num_nodes_dict( FILE: torch_geometric/utils/ppr.py function _get_ppr (line 18) | def _get_ppr( # pragma: no cover function get_ppr (line 77) | def get_ppr( FILE: torch_geometric/utils/random.py function erdos_renyi_graph (line 10) | def erdos_renyi_graph( function stochastic_blockmodel_graph (line 55) | def stochastic_blockmodel_graph( function barabasi_albert_graph (line 120) | def barabasi_albert_graph(num_nodes: int, num_edges: int) -> torch.Tensor: FILE: torch_geometric/utils/repeat.py function repeat (line 9) | def repeat(src: Any, length: int) -> Any: FILE: torch_geometric/utils/smiles.py function from_rdmol (line 80) | def from_rdmol(mol: Any) -> 'torch_geometric.data.Data': function from_smiles (line 134) | def from_smiles( function to_rdmol (line 167) | def to_rdmol( function to_smiles (line 234) | def to_smiles( FILE: torch_geometric/utils/sparse.py function dense_to_sparse (line 13) | def dense_to_sparse( function is_torch_sparse_tensor (line 115) | def is_torch_sparse_tensor(src: Any) -> bool: function is_sparse (line 132) | def is_sparse(src: Any) -> bool: function to_torch_coo_tensor (line 143) | def to_torch_coo_tensor( function to_torch_csr_tensor (line 219) | def to_torch_csr_tensor( function to_torch_csc_tensor (line 287) | def to_torch_csc_tensor( function to_torch_sparse_tensor (line 356) | def to_torch_sparse_tensor( function to_edge_index (line 394) | def to_edge_index(adj: Union[Tensor, SparseTensor]) -> Tuple[Tensor, Ten... function get_sparse_diag (line 438) | def get_sparse_diag( function set_sparse_value (line 453) | def set_sparse_value(adj: Tensor, value: Tensor) -> Tensor: function cat_coo (line 488) | def cat_coo(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> ... function cat_csr (line 555) | def cat_csr(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> ... function cat_csc (line 622) | def cat_csc(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> ... function cat (line 689) | def cat(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> Tensor: FILE: torch_geometric/utils/undirected.py function is_undirected (line 20) | def is_undirected( function is_undirected (line 29) | def is_undirected( # noqa: F811 function is_undirected (line 37) | def is_undirected( # noqa: F811 function to_undirected (line 104) | def to_undirected( function to_undirected (line 114) | def to_undirected( # noqa: F811 function to_undirected (line 124) | def to_undirected( # noqa: F811 function to_undirected (line 134) | def to_undirected( # noqa: F811 function to_undirected (line 143) | def to_undirected( # noqa: F811 FILE: torch_geometric/visualization/graph.py function has_graphviz (line 10) | def has_graphviz() -> bool: function visualize_graph (line 24) | def visualize_graph( function _visualize_graph_via_graphviz (line 74) | def _visualize_graph_via_graphviz( function _visualize_graph_via_networkx (line 106) | def _visualize_graph_via_networkx( function visualize_hetero_graph (line 155) | def visualize_hetero_graph( function _visualize_hetero_graph_via_networkx (line 227) | def _visualize_hetero_graph_via_networkx( FILE: torch_geometric/visualization/influence.py function influence (line 8) | def influence(model: torch.nn.Module, src: Tensor, *args: Any) -> Tensor: FILE: torch_geometric/warnings.py function warn (line 7) | def warn(message: str, stacklevel: int = 5) -> None: function filterwarnings (line 14) | def filterwarnings( class WarningCache (line 24) | class WarningCache(set): method warn (line 26) | def warn(self, message: str, stacklevel: int = 5) -> None: