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import torch
import torch.nn.functional as F
from torch_geometric.nn import GATConv
class GAT(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = GATConv(in_channels, 8, heads=8, dropout=0.6)
self.conv2 = GATConv(8 * 8, out_channels, dropout=0.6)
def forward(self, data):
x, edge_index = data.x, data.edge_index
x = F.dropout(x, p=0.6, training=self.training)
x = F.elu(self.conv1(x, edge_index))
x = F.dropout(x, p=0.6, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
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