from torch_geometric.nn import GCNConv, GATConv, GINConv, global_add_pool

# GCNConv: Graph Convolutional Network layer
# GATConv: Graph Attention Network layer
# GINConv: Graph Isomorphism Network layer
# global_add_pool: Global pooling operation that sums up all node embeddings in a graph

# These are all neural network layers commonly used in graph neural networks (GNNs) for graph classification tasks. The GCNConv and GATConv layers perform message passing between neighboring nodes in the graph, while the GINConv layer uses a learnable function to aggregate node features. The global_add_pool operation aggregates node embeddings to obtain a fixed-size representation of the entire graph.

相关问题

python里sample_and_group怎么引入

`sample_and_group` 函数是 PyTorch Geometric 库中的函数，用于对图数据进行采样和分组操作。要使用该函数，需要先安装 PyTorch Geometric 库。

可以通过以下命令使用 pip 安装 PyTorch Geometric：

pip install torch-geometric

安装完成后，可以在 Python 代码中导入 `sample_and_group` 函数：

from torch_geometric.nn import fps, radius, knn, global_max_pool
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree, to_dense_batch
from torch_cluster import knn_graph, radius_graph
from torch_scatter import scatter_mean
from torch_geometric.nn import Sequential, GCNConv, BatchNorm1d, ReLU, SAGPooling
from torch_geometric.nn import global_mean_pool, global_max_pool, global_add_pool
from torch_geometric.nn import EdgeConv, NNConv
from torch.nn import Linear, Dropout, LeakyReLU
from torch_geometric.nn.pool import Pool

# 导入sample_and_group函数
from torch_geometric.nn import fps, radius, knn, global_max_pool, SampleAndGroup

导入完成后，就可以在你的代码中使用 `sample_and_group` 函数了。