code: def forward(self, xs):
      outs = []
      for x, lin in zip(xs, self.lins):
          out = F.dropout(F.relu(lin(x)), p=0.5, training=self.training)
          outs.append(out)
      x = torch.cat(outs, dim=-1)
      x = self.lin(x)
      return torch.log_softmax(x, dim=-1)
  
file_path: ogb/examples/nodeproppred/products/sign.py
imports: import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.transforms import SIGN
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 3 for calling the MLP obj's underlying member method code with example usage: obj.forward(xs) and returns: torch