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import torch
import torch.nn.functional as F
from torch.nn import Linear, ModuleList
from torch_geometric.nn.dense.linear import Linear
class MLP(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers=2):
super().__init__()
self.mlp = ModuleList()
self.mlp.append(Linear(in_channels, hidden_channels))
if num_layers >= 2:
for _ in range(num_layers - 2):
self.mlp.append(Linear(hidden_channels, hidden_channels))
self.mlp.append(Linear(hidden_channels, out_channels))
def forward(self, x):
for layer in self.mlp[:-1]:
x = layer(x)
x = F.relu(x)
x = self.mlp[-1](x)
return x
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